From af3219706788586323b8fa5eb6f904f54f71c7ca Mon Sep 17 00:00:00 2001 From: Gregor Adams <1148334+pixelass@users.noreply.github.com> Date: Wed, 9 Aug 2023 13:03:30 +0200 Subject: [PATCH 01/63] feat(extensions): Allow hiding link connectors Thank you for adding this feature (linksRenderMode) to core. I would like to add the "Hidden" option (invalid number 3 will just hide the connector lines), so that I can remove that extension from my extension pack to prevent conflicts https://github.com/failfa-st/failfast-comfyui-extensions --- web/extensions/core/linkRenderMode.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/web/extensions/core/linkRenderMode.js b/web/extensions/core/linkRenderMode.js index 1e9091ec..fb4df423 100644 --- a/web/extensions/core/linkRenderMode.js +++ b/web/extensions/core/linkRenderMode.js @@ -9,7 +9,7 @@ const ext = { name: "Link Render Mode", defaultValue: 2, type: "combo", - options: LiteGraph.LINK_RENDER_MODES.map((m, i) => ({ + options: [...LiteGraph.LINK_RENDER_MODES, "Hidden"].map((m, i) => ({ value: i, text: m, selected: i == app.canvas.links_render_mode, From ec96f6d03a0a21051811f5dbd7f90405f18c319a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 24 Aug 2023 22:20:30 -0400 Subject: [PATCH 02/63] Move text_projection to base clip model. --- comfy/sd.py | 3 --- comfy/sd1_clip.py | 8 +++++++- comfy/sd2_clip_config.json | 2 +- comfy/sdxl_clip.py | 6 ------ 4 files changed, 8 insertions(+), 11 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 3568a2aa..20d00952 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -564,9 +564,6 @@ class CLIP: n.layer_idx = self.layer_idx return n - def load_from_state_dict(self, sd): - self.cond_stage_model.load_sd(sd) - def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): return self.patcher.add_patches(patches, strength_patch, strength_model) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 4616ca4e..477d5c30 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -66,7 +66,9 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder): self.layer = layer self.layer_idx = None self.empty_tokens = [[49406] + [49407] * 76] - self.text_projection = None + self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1])) + self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) + self.layer_norm_hidden_state = True if layer == "hidden": assert layer_idx is not None @@ -163,6 +165,10 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder): return self(tokens) def load_sd(self, sd): + if "text_projection" in sd: + self.text_projection[:] = sd.pop("text_projection") + if "text_projection.weight" in sd: + self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1) return self.transformer.load_state_dict(sd, strict=False) def parse_parentheses(string): diff --git a/comfy/sd2_clip_config.json b/comfy/sd2_clip_config.json index ace6ef00..85cec832 100644 --- a/comfy/sd2_clip_config.json +++ b/comfy/sd2_clip_config.json @@ -17,7 +17,7 @@ "num_attention_heads": 16, "num_hidden_layers": 24, "pad_token_id": 1, - "projection_dim": 512, + "projection_dim": 1024, "torch_dtype": "float32", "vocab_size": 49408 } diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py index d05c0a9b..e3ac2ee0 100644 --- a/comfy/sdxl_clip.py +++ b/comfy/sdxl_clip.py @@ -11,15 +11,9 @@ class SDXLClipG(sd1_clip.SD1ClipModel): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype) self.empty_tokens = [[49406] + [49407] + [0] * 75] - self.text_projection = torch.nn.Parameter(torch.empty(1280, 1280)) - self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) self.layer_norm_hidden_state = False def load_sd(self, sd): - if "text_projection" in sd: - self.text_projection[:] = sd.pop("text_projection") - if "text_projection.weight" in sd: - self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1) return super().load_sd(sd) class SDXLClipGTokenizer(sd1_clip.SD1Tokenizer): From 15a7716fa6f040615da5fb2e93ba034dd695bf06 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 25 Aug 2023 17:11:51 -0400 Subject: [PATCH 03/63] Move lora code to comfy/lora.py --- comfy/lora.py | 186 ++++++++++++++++++++++++++++++++++++++++++++++++ comfy/sd.py | 191 ++------------------------------------------------ 2 files changed, 191 insertions(+), 186 deletions(-) create mode 100644 comfy/lora.py diff --git a/comfy/lora.py b/comfy/lora.py new file mode 100644 index 00000000..d685a455 --- /dev/null +++ b/comfy/lora.py @@ -0,0 +1,186 @@ +import comfy.utils + +LORA_CLIP_MAP = { + "mlp.fc1": "mlp_fc1", + "mlp.fc2": "mlp_fc2", + "self_attn.k_proj": "self_attn_k_proj", + "self_attn.q_proj": "self_attn_q_proj", + "self_attn.v_proj": "self_attn_v_proj", + "self_attn.out_proj": "self_attn_out_proj", +} + + +def load_lora(lora, to_load): + patch_dict = {} + loaded_keys = set() + for x in to_load: + alpha_name = "{}.alpha".format(x) + alpha = None + if alpha_name in lora.keys(): + alpha = lora[alpha_name].item() + loaded_keys.add(alpha_name) + + regular_lora = "{}.lora_up.weight".format(x) + diffusers_lora = "{}_lora.up.weight".format(x) + transformers_lora = "{}.lora_linear_layer.up.weight".format(x) + A_name = None + + if regular_lora in lora.keys(): + A_name = regular_lora + B_name = "{}.lora_down.weight".format(x) + mid_name = "{}.lora_mid.weight".format(x) + elif diffusers_lora in lora.keys(): + A_name = diffusers_lora + B_name = "{}_lora.down.weight".format(x) + mid_name = None + elif transformers_lora in lora.keys(): + A_name = transformers_lora + B_name ="{}.lora_linear_layer.down.weight".format(x) + mid_name = None + + if A_name is not None: + mid = None + if mid_name is not None and mid_name in lora.keys(): + mid = lora[mid_name] + loaded_keys.add(mid_name) + patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid) + loaded_keys.add(A_name) + loaded_keys.add(B_name) + + + ######## loha + hada_w1_a_name = "{}.hada_w1_a".format(x) + hada_w1_b_name = "{}.hada_w1_b".format(x) + hada_w2_a_name = "{}.hada_w2_a".format(x) + hada_w2_b_name = "{}.hada_w2_b".format(x) + hada_t1_name = "{}.hada_t1".format(x) + hada_t2_name = "{}.hada_t2".format(x) + if hada_w1_a_name in lora.keys(): + hada_t1 = None + hada_t2 = None + if hada_t1_name in lora.keys(): + hada_t1 = lora[hada_t1_name] + hada_t2 = lora[hada_t2_name] + loaded_keys.add(hada_t1_name) + loaded_keys.add(hada_t2_name) + + patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2) + loaded_keys.add(hada_w1_a_name) + loaded_keys.add(hada_w1_b_name) + loaded_keys.add(hada_w2_a_name) + loaded_keys.add(hada_w2_b_name) + + + ######## lokr + lokr_w1_name = "{}.lokr_w1".format(x) + lokr_w2_name = "{}.lokr_w2".format(x) + lokr_w1_a_name = "{}.lokr_w1_a".format(x) + lokr_w1_b_name = "{}.lokr_w1_b".format(x) + lokr_t2_name = "{}.lokr_t2".format(x) + lokr_w2_a_name = "{}.lokr_w2_a".format(x) + lokr_w2_b_name = "{}.lokr_w2_b".format(x) + + lokr_w1 = None + if lokr_w1_name in lora.keys(): + lokr_w1 = lora[lokr_w1_name] + loaded_keys.add(lokr_w1_name) + + lokr_w2 = None + if lokr_w2_name in lora.keys(): + lokr_w2 = lora[lokr_w2_name] + loaded_keys.add(lokr_w2_name) + + lokr_w1_a = None + if lokr_w1_a_name in lora.keys(): + lokr_w1_a = lora[lokr_w1_a_name] + loaded_keys.add(lokr_w1_a_name) + + lokr_w1_b = None + if lokr_w1_b_name in lora.keys(): + lokr_w1_b = lora[lokr_w1_b_name] + loaded_keys.add(lokr_w1_b_name) + + lokr_w2_a = None + if lokr_w2_a_name in lora.keys(): + lokr_w2_a = lora[lokr_w2_a_name] + loaded_keys.add(lokr_w2_a_name) + + lokr_w2_b = None + if lokr_w2_b_name in lora.keys(): + lokr_w2_b = lora[lokr_w2_b_name] + loaded_keys.add(lokr_w2_b_name) + + lokr_t2 = None + if lokr_t2_name in lora.keys(): + lokr_t2 = lora[lokr_t2_name] + loaded_keys.add(lokr_t2_name) + + if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): + patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2) + + for x in lora.keys(): + if x not in loaded_keys: + print("lora key not loaded", x) + return patch_dict + +def model_lora_keys_clip(model, key_map={}): + sdk = model.state_dict().keys() + + text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" + clip_l_present = False + for b in range(32): + for c in LORA_CLIP_MAP: + k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base + key_map[lora_key] = k + clip_l_present = True + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + if clip_l_present: + lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base + key_map[lora_key] = k + lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + else: + lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + return key_map + +def model_lora_keys_unet(model, key_map={}): + sdk = model.state_dict().keys() + + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = k + + diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config) + for k in diffusers_keys: + if k.endswith(".weight"): + unet_key = "diffusion_model.{}".format(diffusers_keys[k]) + key_lora = k[:-len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = unet_key + + diffusers_lora_prefix = ["", "unet."] + for p in diffusers_lora_prefix: + diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) + if diffusers_lora_key.endswith(".to_out.0"): + diffusers_lora_key = diffusers_lora_key[:-2] + key_map[diffusers_lora_key] = unet_key + return key_map diff --git a/comfy/sd.py b/comfy/sd.py index 20d00952..e42d4cdc 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -22,6 +22,8 @@ from . import sd1_clip from . import sd2_clip from . import sdxl_clip +import comfy.lora + def load_model_weights(model, sd): m, u = model.load_state_dict(sd, strict=False) m = set(m) @@ -51,190 +53,7 @@ def load_clip_weights(model, sd): sd = utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) return load_model_weights(model, sd) -LORA_CLIP_MAP = { - "mlp.fc1": "mlp_fc1", - "mlp.fc2": "mlp_fc2", - "self_attn.k_proj": "self_attn_k_proj", - "self_attn.q_proj": "self_attn_q_proj", - "self_attn.v_proj": "self_attn_v_proj", - "self_attn.out_proj": "self_attn_out_proj", -} - - -def load_lora(lora, to_load): - patch_dict = {} - loaded_keys = set() - for x in to_load: - alpha_name = "{}.alpha".format(x) - alpha = None - if alpha_name in lora.keys(): - alpha = lora[alpha_name].item() - loaded_keys.add(alpha_name) - - regular_lora = "{}.lora_up.weight".format(x) - diffusers_lora = "{}_lora.up.weight".format(x) - transformers_lora = "{}.lora_linear_layer.up.weight".format(x) - A_name = None - - if regular_lora in lora.keys(): - A_name = regular_lora - B_name = "{}.lora_down.weight".format(x) - mid_name = "{}.lora_mid.weight".format(x) - elif diffusers_lora in lora.keys(): - A_name = diffusers_lora - B_name = "{}_lora.down.weight".format(x) - mid_name = None - elif transformers_lora in lora.keys(): - A_name = transformers_lora - B_name ="{}.lora_linear_layer.down.weight".format(x) - mid_name = None - - if A_name is not None: - mid = None - if mid_name is not None and mid_name in lora.keys(): - mid = lora[mid_name] - loaded_keys.add(mid_name) - patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid) - loaded_keys.add(A_name) - loaded_keys.add(B_name) - - - ######## loha - hada_w1_a_name = "{}.hada_w1_a".format(x) - hada_w1_b_name = "{}.hada_w1_b".format(x) - hada_w2_a_name = "{}.hada_w2_a".format(x) - hada_w2_b_name = "{}.hada_w2_b".format(x) - hada_t1_name = "{}.hada_t1".format(x) - hada_t2_name = "{}.hada_t2".format(x) - if hada_w1_a_name in lora.keys(): - hada_t1 = None - hada_t2 = None - if hada_t1_name in lora.keys(): - hada_t1 = lora[hada_t1_name] - hada_t2 = lora[hada_t2_name] - loaded_keys.add(hada_t1_name) - loaded_keys.add(hada_t2_name) - - patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2) - loaded_keys.add(hada_w1_a_name) - loaded_keys.add(hada_w1_b_name) - loaded_keys.add(hada_w2_a_name) - loaded_keys.add(hada_w2_b_name) - - - ######## lokr - lokr_w1_name = "{}.lokr_w1".format(x) - lokr_w2_name = "{}.lokr_w2".format(x) - lokr_w1_a_name = "{}.lokr_w1_a".format(x) - lokr_w1_b_name = "{}.lokr_w1_b".format(x) - lokr_t2_name = "{}.lokr_t2".format(x) - lokr_w2_a_name = "{}.lokr_w2_a".format(x) - lokr_w2_b_name = "{}.lokr_w2_b".format(x) - - lokr_w1 = None - if lokr_w1_name in lora.keys(): - lokr_w1 = lora[lokr_w1_name] - loaded_keys.add(lokr_w1_name) - - lokr_w2 = None - if lokr_w2_name in lora.keys(): - lokr_w2 = lora[lokr_w2_name] - loaded_keys.add(lokr_w2_name) - - lokr_w1_a = None - if lokr_w1_a_name in lora.keys(): - lokr_w1_a = lora[lokr_w1_a_name] - loaded_keys.add(lokr_w1_a_name) - - lokr_w1_b = None - if lokr_w1_b_name in lora.keys(): - lokr_w1_b = lora[lokr_w1_b_name] - loaded_keys.add(lokr_w1_b_name) - - lokr_w2_a = None - if lokr_w2_a_name in lora.keys(): - lokr_w2_a = lora[lokr_w2_a_name] - loaded_keys.add(lokr_w2_a_name) - - lokr_w2_b = None - if lokr_w2_b_name in lora.keys(): - lokr_w2_b = lora[lokr_w2_b_name] - loaded_keys.add(lokr_w2_b_name) - - lokr_t2 = None - if lokr_t2_name in lora.keys(): - lokr_t2 = lora[lokr_t2_name] - loaded_keys.add(lokr_t2_name) - - if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): - patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2) - - for x in lora.keys(): - if x not in loaded_keys: - print("lora key not loaded", x) - return patch_dict - -def model_lora_keys_clip(model, key_map={}): - sdk = model.state_dict().keys() - - text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" - clip_l_present = False - for b in range(32): - for c in LORA_CLIP_MAP: - k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) - if k in sdk: - lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) - key_map[lora_key] = k - lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) - key_map[lora_key] = k - lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora - key_map[lora_key] = k - - k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) - if k in sdk: - lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base - key_map[lora_key] = k - clip_l_present = True - lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora - key_map[lora_key] = k - - k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) - if k in sdk: - if clip_l_present: - lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base - key_map[lora_key] = k - lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora - key_map[lora_key] = k - else: - lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner - key_map[lora_key] = k - lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora - key_map[lora_key] = k - - return key_map -def model_lora_keys_unet(model, key_map={}): - sdk = model.state_dict().keys() - - for k in sdk: - if k.startswith("diffusion_model.") and k.endswith(".weight"): - key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") - key_map["lora_unet_{}".format(key_lora)] = k - - diffusers_keys = utils.unet_to_diffusers(model.model_config.unet_config) - for k in diffusers_keys: - if k.endswith(".weight"): - unet_key = "diffusion_model.{}".format(diffusers_keys[k]) - key_lora = k[:-len(".weight")].replace(".", "_") - key_map["lora_unet_{}".format(key_lora)] = unet_key - - diffusers_lora_prefix = ["", "unet."] - for p in diffusers_lora_prefix: - diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) - if diffusers_lora_key.endswith(".to_out.0"): - diffusers_lora_key = diffusers_lora_key[:-2] - key_map[diffusers_lora_key] = unet_key - return key_map def set_attr(obj, attr, value): attrs = attr.split(".") @@ -518,9 +337,9 @@ class ModelPatcher: def load_lora_for_models(model, clip, lora, strength_model, strength_clip): - key_map = model_lora_keys_unet(model.model) - key_map = model_lora_keys_clip(clip.cond_stage_model, key_map) - loaded = load_lora(lora, key_map) + key_map = comfy.lora.model_lora_keys_unet(model.model) + key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) + loaded = comfy.lora.load_lora(lora, key_map) new_modelpatcher = model.clone() k = new_modelpatcher.add_patches(loaded, strength_model) new_clip = clip.clone() From c77f02e1c6d49fdd9fa52414e4b97848b159a883 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 25 Aug 2023 17:25:39 -0400 Subject: [PATCH 04/63] Move controlnet code to comfy/controlnet.py --- comfy/controlnet.py | 483 ++++++++++++++++++++++++++++++++++++++ comfy/sd.py | 558 +++----------------------------------------- comfy/utils.py | 14 ++ nodes.py | 5 +- 4 files changed, 533 insertions(+), 527 deletions(-) create mode 100644 comfy/controlnet.py diff --git a/comfy/controlnet.py b/comfy/controlnet.py new file mode 100644 index 00000000..5279307c --- /dev/null +++ b/comfy/controlnet.py @@ -0,0 +1,483 @@ +import torch +import math +import comfy.utils +import comfy.sd +import comfy.model_management +import comfy.model_detection + +import comfy.cldm.cldm +import comfy.t2i_adapter.adapter + + +def broadcast_image_to(tensor, target_batch_size, batched_number): + current_batch_size = tensor.shape[0] + #print(current_batch_size, target_batch_size) + if current_batch_size == 1: + return tensor + + per_batch = target_batch_size // batched_number + tensor = tensor[:per_batch] + + if per_batch > tensor.shape[0]: + tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0) + + current_batch_size = tensor.shape[0] + if current_batch_size == target_batch_size: + return tensor + else: + return torch.cat([tensor] * batched_number, dim=0) + +class ControlBase: + def __init__(self, device=None): + self.cond_hint_original = None + self.cond_hint = None + self.strength = 1.0 + self.timestep_percent_range = (1.0, 0.0) + self.timestep_range = None + + if device is None: + device = comfy.model_management.get_torch_device() + self.device = device + self.previous_controlnet = None + self.global_average_pooling = False + + def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(1.0, 0.0)): + self.cond_hint_original = cond_hint + self.strength = strength + self.timestep_percent_range = timestep_percent_range + return self + + def pre_run(self, model, percent_to_timestep_function): + self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1])) + if self.previous_controlnet is not None: + self.previous_controlnet.pre_run(model, percent_to_timestep_function) + + def set_previous_controlnet(self, controlnet): + self.previous_controlnet = controlnet + return self + + def cleanup(self): + if self.previous_controlnet is not None: + self.previous_controlnet.cleanup() + if self.cond_hint is not None: + del self.cond_hint + self.cond_hint = None + self.timestep_range = None + + def get_models(self): + out = [] + if self.previous_controlnet is not None: + out += self.previous_controlnet.get_models() + return out + + def copy_to(self, c): + c.cond_hint_original = self.cond_hint_original + c.strength = self.strength + c.timestep_percent_range = self.timestep_percent_range + + def inference_memory_requirements(self, dtype): + if self.previous_controlnet is not None: + return self.previous_controlnet.inference_memory_requirements(dtype) + return 0 + + def control_merge(self, control_input, control_output, control_prev, output_dtype): + out = {'input':[], 'middle':[], 'output': []} + + if control_input is not None: + for i in range(len(control_input)): + key = 'input' + x = control_input[i] + if x is not None: + x *= self.strength + if x.dtype != output_dtype: + x = x.to(output_dtype) + out[key].insert(0, x) + + if control_output is not None: + for i in range(len(control_output)): + if i == (len(control_output) - 1): + key = 'middle' + index = 0 + else: + key = 'output' + index = i + x = control_output[i] + if x is not None: + if self.global_average_pooling: + x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) + + x *= self.strength + if x.dtype != output_dtype: + x = x.to(output_dtype) + + out[key].append(x) + if control_prev is not None: + for x in ['input', 'middle', 'output']: + o = out[x] + for i in range(len(control_prev[x])): + prev_val = control_prev[x][i] + if i >= len(o): + o.append(prev_val) + elif prev_val is not None: + if o[i] is None: + o[i] = prev_val + else: + o[i] += prev_val + return out + +class ControlNet(ControlBase): + def __init__(self, control_model, global_average_pooling=False, device=None): + super().__init__(device) + self.control_model = control_model + self.control_model_wrapped = comfy.sd.ModelPatcher(self.control_model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device()) + self.global_average_pooling = global_average_pooling + + def get_control(self, x_noisy, t, cond, batched_number): + control_prev = None + if self.previous_controlnet is not None: + control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) + + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + if control_prev is not None: + return control_prev + else: + return {} + + output_dtype = x_noisy.dtype + if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: + if self.cond_hint is not None: + del self.cond_hint + self.cond_hint = None + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device) + if x_noisy.shape[0] != self.cond_hint.shape[0]: + self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) + + + context = torch.cat(cond['c_crossattn'], 1) + y = cond.get('c_adm', None) + if y is not None: + y = y.to(self.control_model.dtype) + control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=t, context=context.to(self.control_model.dtype), y=y) + return self.control_merge(None, control, control_prev, output_dtype) + + def copy(self): + c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling) + self.copy_to(c) + return c + + def get_models(self): + out = super().get_models() + out.append(self.control_model_wrapped) + return out + +class ControlLoraOps: + class Linear(torch.nn.Module): + def __init__(self, in_features: int, out_features: int, bias: bool = True, + device=None, dtype=None) -> None: + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.weight = None + self.up = None + self.down = None + self.bias = None + + def forward(self, input): + if self.up is not None: + return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias) + else: + return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias) + + class Conv2d(torch.nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + padding_mode='zeros', + device=None, + dtype=None + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.transposed = False + self.output_padding = 0 + self.groups = groups + self.padding_mode = padding_mode + + self.weight = None + self.bias = None + self.up = None + self.down = None + + + def forward(self, input): + if self.up is not None: + return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups) + else: + return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups) + + def conv_nd(self, dims, *args, **kwargs): + if dims == 2: + return self.Conv2d(*args, **kwargs) + else: + raise ValueError(f"unsupported dimensions: {dims}") + + +class ControlLora(ControlNet): + def __init__(self, control_weights, global_average_pooling=False, device=None): + ControlBase.__init__(self, device) + self.control_weights = control_weights + self.global_average_pooling = global_average_pooling + + def pre_run(self, model, percent_to_timestep_function): + super().pre_run(model, percent_to_timestep_function) + controlnet_config = model.model_config.unet_config.copy() + controlnet_config.pop("out_channels") + controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] + controlnet_config["operations"] = ControlLoraOps() + self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) + dtype = model.get_dtype() + self.control_model.to(dtype) + self.control_model.to(comfy.model_management.get_torch_device()) + diffusion_model = model.diffusion_model + sd = diffusion_model.state_dict() + cm = self.control_model.state_dict() + + for k in sd: + weight = sd[k] + if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break. + key_split = k.split('.') # I have no idea why they don't just leave the weight there instead of using the meta device. + op = comfy.utils.get_attr(diffusion_model, '.'.join(key_split[:-1])) + weight = op._hf_hook.weights_map[key_split[-1]] + + try: + comfy.utils.set_attr(self.control_model, k, weight) + except: + pass + + for k in self.control_weights: + if k not in {"lora_controlnet"}: + comfy.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) + + def copy(self): + c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) + self.copy_to(c) + return c + + def cleanup(self): + del self.control_model + self.control_model = None + super().cleanup() + + def get_models(self): + out = ControlBase.get_models(self) + return out + + def inference_memory_requirements(self, dtype): + return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) + +def load_controlnet(ckpt_path, model=None): + controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) + if "lora_controlnet" in controlnet_data: + return ControlLora(controlnet_data) + + controlnet_config = None + if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format + use_fp16 = comfy.model_management.should_use_fp16() + controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16) + diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config) + diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" + diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" + + count = 0 + loop = True + while loop: + suffix = [".weight", ".bias"] + for s in suffix: + k_in = "controlnet_down_blocks.{}{}".format(count, s) + k_out = "zero_convs.{}.0{}".format(count, s) + if k_in not in controlnet_data: + loop = False + break + diffusers_keys[k_in] = k_out + count += 1 + + count = 0 + loop = True + while loop: + suffix = [".weight", ".bias"] + for s in suffix: + if count == 0: + k_in = "controlnet_cond_embedding.conv_in{}".format(s) + else: + k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s) + k_out = "input_hint_block.{}{}".format(count * 2, s) + if k_in not in controlnet_data: + k_in = "controlnet_cond_embedding.conv_out{}".format(s) + loop = False + diffusers_keys[k_in] = k_out + count += 1 + + new_sd = {} + for k in diffusers_keys: + if k in controlnet_data: + new_sd[diffusers_keys[k]] = controlnet_data.pop(k) + + leftover_keys = controlnet_data.keys() + if len(leftover_keys) > 0: + print("leftover keys:", leftover_keys) + controlnet_data = new_sd + + pth_key = 'control_model.zero_convs.0.0.weight' + pth = False + key = 'zero_convs.0.0.weight' + if pth_key in controlnet_data: + pth = True + key = pth_key + prefix = "control_model." + elif key in controlnet_data: + prefix = "" + else: + net = load_t2i_adapter(controlnet_data) + if net is None: + print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path) + return net + + if controlnet_config is None: + use_fp16 = comfy.model_management.should_use_fp16() + controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16).unet_config + controlnet_config.pop("out_channels") + controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] + control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) + + if pth: + if 'difference' in controlnet_data: + if model is not None: + comfy.model_management.load_models_gpu([model]) + model_sd = model.model_state_dict() + for x in controlnet_data: + c_m = "control_model." + if x.startswith(c_m): + sd_key = "diffusion_model.{}".format(x[len(c_m):]) + if sd_key in model_sd: + cd = controlnet_data[x] + cd += model_sd[sd_key].type(cd.dtype).to(cd.device) + else: + print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") + + class WeightsLoader(torch.nn.Module): + pass + w = WeightsLoader() + w.control_model = control_model + missing, unexpected = w.load_state_dict(controlnet_data, strict=False) + else: + missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) + print(missing, unexpected) + + if use_fp16: + control_model = control_model.half() + + global_average_pooling = False + if ckpt_path.endswith("_shuffle.pth") or ckpt_path.endswith("_shuffle.safetensors") or ckpt_path.endswith("_shuffle_fp16.safetensors"): #TODO: smarter way of enabling global_average_pooling + global_average_pooling = True + + control = ControlNet(control_model, global_average_pooling=global_average_pooling) + return control + +class T2IAdapter(ControlBase): + def __init__(self, t2i_model, channels_in, device=None): + super().__init__(device) + self.t2i_model = t2i_model + self.channels_in = channels_in + self.control_input = None + + def scale_image_to(self, width, height): + unshuffle_amount = self.t2i_model.unshuffle_amount + width = math.ceil(width / unshuffle_amount) * unshuffle_amount + height = math.ceil(height / unshuffle_amount) * unshuffle_amount + return width, height + + def get_control(self, x_noisy, t, cond, batched_number): + control_prev = None + if self.previous_controlnet is not None: + control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) + + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + if control_prev is not None: + return control_prev + else: + return {} + + if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: + if self.cond_hint is not None: + del self.cond_hint + self.control_input = None + self.cond_hint = None + width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8) + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device) + if self.channels_in == 1 and self.cond_hint.shape[1] > 1: + self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) + if x_noisy.shape[0] != self.cond_hint.shape[0]: + self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) + if self.control_input is None: + self.t2i_model.to(x_noisy.dtype) + self.t2i_model.to(self.device) + self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) + self.t2i_model.cpu() + + control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input)) + mid = None + if self.t2i_model.xl == True: + mid = control_input[-1:] + control_input = control_input[:-1] + return self.control_merge(control_input, mid, control_prev, x_noisy.dtype) + + def copy(self): + c = T2IAdapter(self.t2i_model, self.channels_in) + self.copy_to(c) + return c + +def load_t2i_adapter(t2i_data): + keys = t2i_data.keys() + if 'adapter' in keys: + t2i_data = t2i_data['adapter'] + keys = t2i_data.keys() + if "body.0.in_conv.weight" in keys: + cin = t2i_data['body.0.in_conv.weight'].shape[1] + model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) + elif 'conv_in.weight' in keys: + cin = t2i_data['conv_in.weight'].shape[1] + channel = t2i_data['conv_in.weight'].shape[0] + ksize = t2i_data['body.0.block2.weight'].shape[2] + use_conv = False + down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) + if len(down_opts) > 0: + use_conv = True + xl = False + if cin == 256: + xl = True + model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) + else: + return None + missing, unexpected = model_ad.load_state_dict(t2i_data) + if len(missing) > 0: + print("t2i missing", missing) + + if len(unexpected) > 0: + print("t2i unexpected", unexpected) + + return T2IAdapter(model_ad, model_ad.input_channels) diff --git a/comfy/sd.py b/comfy/sd.py index e42d4cdc..7462c79e 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -8,10 +8,9 @@ from comfy import model_management from .ldm.util import instantiate_from_config from .ldm.models.autoencoder import AutoencoderKL import yaml -from .cldm import cldm -from .t2i_adapter import adapter -from . import utils +import comfy.utils + from . import clip_vision from . import gligen from . import diffusers_convert @@ -23,6 +22,7 @@ from . import sd2_clip from . import sdxl_clip import comfy.lora +import comfy.t2i_adapter.adapter def load_model_weights(model, sd): m, u = model.load_state_dict(sd, strict=False) @@ -50,26 +50,9 @@ def load_clip_weights(model, sd): if ids.dtype == torch.float32: sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() - sd = utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) + sd = comfy.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) return load_model_weights(model, sd) - - -def set_attr(obj, attr, value): - attrs = attr.split(".") - for name in attrs[:-1]: - obj = getattr(obj, name) - prev = getattr(obj, attrs[-1]) - setattr(obj, attrs[-1], torch.nn.Parameter(value)) - del prev - -def get_attr(obj, attr): - attrs = attr.split(".") - for name in attrs: - obj = getattr(obj, name) - return obj - - class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0, current_device=None): self.size = size @@ -224,7 +207,7 @@ class ModelPatcher: else: temp_weight = weight.to(torch.float32, copy=True) out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) - set_attr(self.model, key, out_weight) + comfy.utils.set_attr(self.model, key, out_weight) del temp_weight if device_to is not None: @@ -327,7 +310,7 @@ class ModelPatcher: keys = list(self.backup.keys()) for k in keys: - set_attr(self.model, k, self.backup[k]) + comfy.utils.set_attr(self.model, k, self.backup[k]) self.backup = {} @@ -431,7 +414,7 @@ class VAE: self.first_stage_model = AutoencoderKL(**(config['params'])) self.first_stage_model = self.first_stage_model.eval() if ckpt_path is not None: - sd = utils.load_torch_file(ckpt_path) + sd = comfy.utils.load_torch_file(ckpt_path) if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) self.first_stage_model.load_state_dict(sd, strict=False) @@ -444,29 +427,29 @@ class VAE: self.first_stage_model.to(self.vae_dtype) def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): - steps = samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) - steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) - steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) - pbar = utils.ProgressBar(steps) + steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) + steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = comfy.utils.ProgressBar(steps) decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float() output = torch.clamp(( - (utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) + - utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) + - utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar)) + (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) + + comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) + + comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar)) / 3.0) / 2.0, min=0.0, max=1.0) return output def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): - steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) - steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) - steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) - pbar = utils.ProgressBar(steps) + steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) + steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = comfy.utils.ProgressBar(steps) encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.vae_dtype).to(self.device) - 1.).sample().float() - samples = utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) - samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) - samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) + samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) + samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) + samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) samples /= 3.0 return samples @@ -528,481 +511,6 @@ class VAE: def get_sd(self): return self.first_stage_model.state_dict() - -def broadcast_image_to(tensor, target_batch_size, batched_number): - current_batch_size = tensor.shape[0] - #print(current_batch_size, target_batch_size) - if current_batch_size == 1: - return tensor - - per_batch = target_batch_size // batched_number - tensor = tensor[:per_batch] - - if per_batch > tensor.shape[0]: - tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0) - - current_batch_size = tensor.shape[0] - if current_batch_size == target_batch_size: - return tensor - else: - return torch.cat([tensor] * batched_number, dim=0) - -class ControlBase: - def __init__(self, device=None): - self.cond_hint_original = None - self.cond_hint = None - self.strength = 1.0 - self.timestep_percent_range = (1.0, 0.0) - self.timestep_range = None - - if device is None: - device = model_management.get_torch_device() - self.device = device - self.previous_controlnet = None - self.global_average_pooling = False - - def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(1.0, 0.0)): - self.cond_hint_original = cond_hint - self.strength = strength - self.timestep_percent_range = timestep_percent_range - return self - - def pre_run(self, model, percent_to_timestep_function): - self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1])) - if self.previous_controlnet is not None: - self.previous_controlnet.pre_run(model, percent_to_timestep_function) - - def set_previous_controlnet(self, controlnet): - self.previous_controlnet = controlnet - return self - - def cleanup(self): - if self.previous_controlnet is not None: - self.previous_controlnet.cleanup() - if self.cond_hint is not None: - del self.cond_hint - self.cond_hint = None - self.timestep_range = None - - def get_models(self): - out = [] - if self.previous_controlnet is not None: - out += self.previous_controlnet.get_models() - return out - - def copy_to(self, c): - c.cond_hint_original = self.cond_hint_original - c.strength = self.strength - c.timestep_percent_range = self.timestep_percent_range - - def inference_memory_requirements(self, dtype): - if self.previous_controlnet is not None: - return self.previous_controlnet.inference_memory_requirements(dtype) - return 0 - - def control_merge(self, control_input, control_output, control_prev, output_dtype): - out = {'input':[], 'middle':[], 'output': []} - - if control_input is not None: - for i in range(len(control_input)): - key = 'input' - x = control_input[i] - if x is not None: - x *= self.strength - if x.dtype != output_dtype: - x = x.to(output_dtype) - out[key].insert(0, x) - - if control_output is not None: - for i in range(len(control_output)): - if i == (len(control_output) - 1): - key = 'middle' - index = 0 - else: - key = 'output' - index = i - x = control_output[i] - if x is not None: - if self.global_average_pooling: - x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) - - x *= self.strength - if x.dtype != output_dtype: - x = x.to(output_dtype) - - out[key].append(x) - if control_prev is not None: - for x in ['input', 'middle', 'output']: - o = out[x] - for i in range(len(control_prev[x])): - prev_val = control_prev[x][i] - if i >= len(o): - o.append(prev_val) - elif prev_val is not None: - if o[i] is None: - o[i] = prev_val - else: - o[i] += prev_val - return out - -class ControlNet(ControlBase): - def __init__(self, control_model, global_average_pooling=False, device=None): - super().__init__(device) - self.control_model = control_model - self.control_model_wrapped = ModelPatcher(self.control_model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) - self.global_average_pooling = global_average_pooling - - def get_control(self, x_noisy, t, cond, batched_number): - control_prev = None - if self.previous_controlnet is not None: - control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) - - if self.timestep_range is not None: - if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: - if control_prev is not None: - return control_prev - else: - return {} - - output_dtype = x_noisy.dtype - if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: - if self.cond_hint is not None: - del self.cond_hint - self.cond_hint = None - self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device) - if x_noisy.shape[0] != self.cond_hint.shape[0]: - self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) - - - context = torch.cat(cond['c_crossattn'], 1) - y = cond.get('c_adm', None) - if y is not None: - y = y.to(self.control_model.dtype) - control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=t, context=context.to(self.control_model.dtype), y=y) - return self.control_merge(None, control, control_prev, output_dtype) - - def copy(self): - c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling) - self.copy_to(c) - return c - - def get_models(self): - out = super().get_models() - out.append(self.control_model_wrapped) - return out - -class ControlLoraOps: - class Linear(torch.nn.Module): - def __init__(self, in_features: int, out_features: int, bias: bool = True, - device=None, dtype=None) -> None: - factory_kwargs = {'device': device, 'dtype': dtype} - super().__init__() - self.in_features = in_features - self.out_features = out_features - self.weight = None - self.up = None - self.down = None - self.bias = None - - def forward(self, input): - if self.up is not None: - return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias) - else: - return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias) - - class Conv2d(torch.nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=True, - padding_mode='zeros', - device=None, - dtype=None - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.dilation = dilation - self.transposed = False - self.output_padding = 0 - self.groups = groups - self.padding_mode = padding_mode - - self.weight = None - self.bias = None - self.up = None - self.down = None - - - def forward(self, input): - if self.up is not None: - return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups) - else: - return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups) - - def conv_nd(self, dims, *args, **kwargs): - if dims == 2: - return self.Conv2d(*args, **kwargs) - else: - raise ValueError(f"unsupported dimensions: {dims}") - - -class ControlLora(ControlNet): - def __init__(self, control_weights, global_average_pooling=False, device=None): - ControlBase.__init__(self, device) - self.control_weights = control_weights - self.global_average_pooling = global_average_pooling - - def pre_run(self, model, percent_to_timestep_function): - super().pre_run(model, percent_to_timestep_function) - controlnet_config = model.model_config.unet_config.copy() - controlnet_config.pop("out_channels") - controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] - controlnet_config["operations"] = ControlLoraOps() - self.control_model = cldm.ControlNet(**controlnet_config) - dtype = model.get_dtype() - self.control_model.to(dtype) - self.control_model.to(model_management.get_torch_device()) - diffusion_model = model.diffusion_model - sd = diffusion_model.state_dict() - cm = self.control_model.state_dict() - - for k in sd: - weight = sd[k] - if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break. - key_split = k.split('.') # I have no idea why they don't just leave the weight there instead of using the meta device. - op = get_attr(diffusion_model, '.'.join(key_split[:-1])) - weight = op._hf_hook.weights_map[key_split[-1]] - - try: - set_attr(self.control_model, k, weight) - except: - pass - - for k in self.control_weights: - if k not in {"lora_controlnet"}: - set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(model_management.get_torch_device())) - - def copy(self): - c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) - self.copy_to(c) - return c - - def cleanup(self): - del self.control_model - self.control_model = None - super().cleanup() - - def get_models(self): - out = ControlBase.get_models(self) - return out - - def inference_memory_requirements(self, dtype): - return utils.calculate_parameters(self.control_weights) * model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) - -def load_controlnet(ckpt_path, model=None): - controlnet_data = utils.load_torch_file(ckpt_path, safe_load=True) - if "lora_controlnet" in controlnet_data: - return ControlLora(controlnet_data) - - controlnet_config = None - if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format - use_fp16 = model_management.should_use_fp16() - controlnet_config = model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16) - diffusers_keys = utils.unet_to_diffusers(controlnet_config) - diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" - diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" - - count = 0 - loop = True - while loop: - suffix = [".weight", ".bias"] - for s in suffix: - k_in = "controlnet_down_blocks.{}{}".format(count, s) - k_out = "zero_convs.{}.0{}".format(count, s) - if k_in not in controlnet_data: - loop = False - break - diffusers_keys[k_in] = k_out - count += 1 - - count = 0 - loop = True - while loop: - suffix = [".weight", ".bias"] - for s in suffix: - if count == 0: - k_in = "controlnet_cond_embedding.conv_in{}".format(s) - else: - k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s) - k_out = "input_hint_block.{}{}".format(count * 2, s) - if k_in not in controlnet_data: - k_in = "controlnet_cond_embedding.conv_out{}".format(s) - loop = False - diffusers_keys[k_in] = k_out - count += 1 - - new_sd = {} - for k in diffusers_keys: - if k in controlnet_data: - new_sd[diffusers_keys[k]] = controlnet_data.pop(k) - - leftover_keys = controlnet_data.keys() - if len(leftover_keys) > 0: - print("leftover keys:", leftover_keys) - controlnet_data = new_sd - - pth_key = 'control_model.zero_convs.0.0.weight' - pth = False - key = 'zero_convs.0.0.weight' - if pth_key in controlnet_data: - pth = True - key = pth_key - prefix = "control_model." - elif key in controlnet_data: - prefix = "" - else: - net = load_t2i_adapter(controlnet_data) - if net is None: - print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path) - return net - - if controlnet_config is None: - use_fp16 = model_management.should_use_fp16() - controlnet_config = model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16).unet_config - controlnet_config.pop("out_channels") - controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] - control_model = cldm.ControlNet(**controlnet_config) - - if pth: - if 'difference' in controlnet_data: - if model is not None: - model_management.load_models_gpu([model]) - model_sd = model.model_state_dict() - for x in controlnet_data: - c_m = "control_model." - if x.startswith(c_m): - sd_key = "diffusion_model.{}".format(x[len(c_m):]) - if sd_key in model_sd: - cd = controlnet_data[x] - cd += model_sd[sd_key].type(cd.dtype).to(cd.device) - else: - print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") - - class WeightsLoader(torch.nn.Module): - pass - w = WeightsLoader() - w.control_model = control_model - missing, unexpected = w.load_state_dict(controlnet_data, strict=False) - else: - missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) - print(missing, unexpected) - - if use_fp16: - control_model = control_model.half() - - global_average_pooling = False - if ckpt_path.endswith("_shuffle.pth") or ckpt_path.endswith("_shuffle.safetensors") or ckpt_path.endswith("_shuffle_fp16.safetensors"): #TODO: smarter way of enabling global_average_pooling - global_average_pooling = True - - control = ControlNet(control_model, global_average_pooling=global_average_pooling) - return control - -class T2IAdapter(ControlBase): - def __init__(self, t2i_model, channels_in, device=None): - super().__init__(device) - self.t2i_model = t2i_model - self.channels_in = channels_in - self.control_input = None - - def scale_image_to(self, width, height): - unshuffle_amount = self.t2i_model.unshuffle_amount - width = math.ceil(width / unshuffle_amount) * unshuffle_amount - height = math.ceil(height / unshuffle_amount) * unshuffle_amount - return width, height - - def get_control(self, x_noisy, t, cond, batched_number): - control_prev = None - if self.previous_controlnet is not None: - control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) - - if self.timestep_range is not None: - if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: - if control_prev is not None: - return control_prev - else: - return {} - - if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: - if self.cond_hint is not None: - del self.cond_hint - self.control_input = None - self.cond_hint = None - width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8) - self.cond_hint = utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device) - if self.channels_in == 1 and self.cond_hint.shape[1] > 1: - self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) - if x_noisy.shape[0] != self.cond_hint.shape[0]: - self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) - if self.control_input is None: - self.t2i_model.to(x_noisy.dtype) - self.t2i_model.to(self.device) - self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) - self.t2i_model.cpu() - - control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input)) - mid = None - if self.t2i_model.xl == True: - mid = control_input[-1:] - control_input = control_input[:-1] - return self.control_merge(control_input, mid, control_prev, x_noisy.dtype) - - def copy(self): - c = T2IAdapter(self.t2i_model, self.channels_in) - self.copy_to(c) - return c - -def load_t2i_adapter(t2i_data): - keys = t2i_data.keys() - if 'adapter' in keys: - t2i_data = t2i_data['adapter'] - keys = t2i_data.keys() - if "body.0.in_conv.weight" in keys: - cin = t2i_data['body.0.in_conv.weight'].shape[1] - model_ad = adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) - elif 'conv_in.weight' in keys: - cin = t2i_data['conv_in.weight'].shape[1] - channel = t2i_data['conv_in.weight'].shape[0] - ksize = t2i_data['body.0.block2.weight'].shape[2] - use_conv = False - down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) - if len(down_opts) > 0: - use_conv = True - xl = False - if cin == 256: - xl = True - model_ad = adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) - else: - return None - missing, unexpected = model_ad.load_state_dict(t2i_data) - if len(missing) > 0: - print("t2i missing", missing) - - if len(unexpected) > 0: - print("t2i unexpected", unexpected) - - return T2IAdapter(model_ad, model_ad.input_channels) - - class StyleModel: def __init__(self, model, device="cpu"): self.model = model @@ -1012,10 +520,10 @@ class StyleModel: def load_style_model(ckpt_path): - model_data = utils.load_torch_file(ckpt_path, safe_load=True) + model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) keys = model_data.keys() if "style_embedding" in keys: - model = adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) + model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) else: raise Exception("invalid style model {}".format(ckpt_path)) model.load_state_dict(model_data) @@ -1025,14 +533,14 @@ def load_style_model(ckpt_path): def load_clip(ckpt_paths, embedding_directory=None): clip_data = [] for p in ckpt_paths: - clip_data.append(utils.load_torch_file(p, safe_load=True)) + clip_data.append(comfy.utils.load_torch_file(p, safe_load=True)) class EmptyClass: pass for i in range(len(clip_data)): if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: - clip_data[i] = utils.transformers_convert(clip_data[i], "", "text_model.", 32) + clip_data[i] = comfy.utils.transformers_convert(clip_data[i], "", "text_model.", 32) clip_target = EmptyClass() clip_target.params = {} @@ -1061,7 +569,7 @@ def load_clip(ckpt_paths, embedding_directory=None): return clip def load_gligen(ckpt_path): - data = utils.load_torch_file(ckpt_path, safe_load=True) + data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) model = gligen.load_gligen(data) if model_management.should_use_fp16(): model = model.half() @@ -1101,7 +609,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl pass if state_dict is None: - state_dict = utils.load_torch_file(ckpt_path) + state_dict = comfy.utils.load_torch_file(ckpt_path) class EmptyClass: pass @@ -1148,7 +656,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None): - sd = utils.load_torch_file(ckpt_path) + sd = comfy.utils.load_torch_file(ckpt_path) sd_keys = sd.keys() clip = None clipvision = None @@ -1156,7 +664,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o model = None clip_target = None - parameters = utils.calculate_parameters(sd, "model.diffusion_model.") + parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.") fp16 = model_management.should_use_fp16(model_params=parameters) class WeightsLoader(torch.nn.Module): @@ -1206,8 +714,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o def load_unet(unet_path): #load unet in diffusers format - sd = utils.load_torch_file(unet_path) - parameters = utils.calculate_parameters(sd) + sd = comfy.utils.load_torch_file(unet_path) + parameters = comfy.utils.calculate_parameters(sd) fp16 = model_management.should_use_fp16(model_params=parameters) model_config = model_detection.model_config_from_diffusers_unet(sd, fp16) @@ -1215,7 +723,7 @@ def load_unet(unet_path): #load unet in diffusers format print("ERROR UNSUPPORTED UNET", unet_path) return None - diffusers_keys = utils.unet_to_diffusers(model_config.unet_config) + diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config) new_sd = {} for k in diffusers_keys: @@ -1232,4 +740,4 @@ def load_unet(unet_path): #load unet in diffusers format def save_checkpoint(output_path, model, clip, vae, metadata=None): model_management.load_models_gpu([model, clip.load_model()]) sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd()) - utils.save_torch_file(sd, output_path, metadata=metadata) + comfy.utils.save_torch_file(sd, output_path, metadata=metadata) diff --git a/comfy/utils.py b/comfy/utils.py index e69125ab..693e2612 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -237,6 +237,20 @@ def safetensors_header(safetensors_path, max_size=100*1024*1024): return None return f.read(length_of_header) +def set_attr(obj, attr, value): + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + setattr(obj, attrs[-1], torch.nn.Parameter(value)) + del prev + +def get_attr(obj, attr): + attrs = attr.split(".") + for name in attrs: + obj = getattr(obj, name) + return obj + def bislerp(samples, width, height): def slerp(b1, b2, r): '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' diff --git a/nodes.py b/nodes.py index b2f224ea..233bc8d4 100644 --- a/nodes.py +++ b/nodes.py @@ -22,6 +22,7 @@ import comfy.samplers import comfy.sample import comfy.sd import comfy.utils +import comfy.controlnet import comfy.clip_vision @@ -569,7 +570,7 @@ class ControlNetLoader: def load_controlnet(self, control_net_name): controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) - controlnet = comfy.sd.load_controlnet(controlnet_path) + controlnet = comfy.controlnet.load_controlnet(controlnet_path) return (controlnet,) class DiffControlNetLoader: @@ -585,7 +586,7 @@ class DiffControlNetLoader: def load_controlnet(self, model, control_net_name): controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) - controlnet = comfy.sd.load_controlnet(controlnet_path, model) + controlnet = comfy.controlnet.load_controlnet(controlnet_path, model) return (controlnet,) From f72780a7e3634de1400bc3dd13207c463884dcb9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 25 Aug 2023 18:02:15 -0400 Subject: [PATCH 05/63] The new smart memory management makes this unnecessary. --- comfy/model_management.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 0e86df41..01643449 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -111,9 +111,6 @@ if not args.normalvram and not args.cpu: if lowvram_available and total_vram <= 4096: print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram") set_vram_to = VRAMState.LOW_VRAM - elif total_vram > total_ram * 1.1 and total_vram > 14336: - print("Enabling highvram mode because your GPU has more vram than your computer has ram. If you don't want this use: --normalvram") - vram_state = VRAMState.HIGH_VRAM try: OOM_EXCEPTION = torch.cuda.OutOfMemoryError From a57b0c797b0670e6fd48ff3cf1f37dbf0d0a67b5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 26 Aug 2023 11:52:07 -0400 Subject: [PATCH 06/63] Fix lowvram model merging. --- comfy/controlnet.py | 7 +------ comfy/model_base.py | 7 ++++++- comfy/model_management.py | 8 ++++++++ 3 files changed, 15 insertions(+), 7 deletions(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 5279307c..4e20100c 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -257,12 +257,7 @@ class ControlLora(ControlNet): cm = self.control_model.state_dict() for k in sd: - weight = sd[k] - if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break. - key_split = k.split('.') # I have no idea why they don't just leave the weight there instead of using the meta device. - op = comfy.utils.get_attr(diffusion_model, '.'.join(key_split[:-1])) - weight = op._hf_hook.weights_map[key_split[-1]] - + weight = comfy.model_management.resolve_lowvram_weight(sd[k], diffusion_model, k) try: comfy.utils.set_attr(self.control_model, k, weight) except: diff --git a/comfy/model_base.py b/comfy/model_base.py index 979e2c65..d654f56f 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -3,6 +3,7 @@ from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule from comfy.ldm.modules.diffusionmodules.openaimodel import Timestep +import comfy.model_management import numpy as np from enum import Enum from . import utils @@ -93,7 +94,11 @@ class BaseModel(torch.nn.Module): def state_dict_for_saving(self, clip_state_dict, vae_state_dict): clip_state_dict = self.model_config.process_clip_state_dict_for_saving(clip_state_dict) - unet_state_dict = self.diffusion_model.state_dict() + unet_sd = self.diffusion_model.state_dict() + unet_state_dict = {} + for k in unet_sd: + unet_state_dict[k] = comfy.model_management.resolve_lowvram_weight(unet_sd[k], self.diffusion_model, k) + unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) vae_state_dict = self.model_config.process_vae_state_dict_for_saving(vae_state_dict) if self.get_dtype() == torch.float16: diff --git a/comfy/model_management.py b/comfy/model_management.py index 01643449..e5c80bf6 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1,6 +1,7 @@ import psutil from enum import Enum from comfy.cli_args import args +import comfy.utils import torch import sys @@ -637,6 +638,13 @@ def soft_empty_cache(): torch.cuda.empty_cache() torch.cuda.ipc_collect() +def resolve_lowvram_weight(weight, model, key): + if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break. + key_split = key.split('.') # I have no idea why they don't just leave the weight there instead of using the meta device. + op = comfy.utils.get_attr(model, '.'.join(key_split[:-1])) + weight = op._hf_hook.weights_map[key_split[-1]] + return weight + #TODO: might be cleaner to put this somewhere else import threading From 0b6cf7a5589351f677f76a6504850d5702c40d08 Mon Sep 17 00:00:00 2001 From: ramyma Date: Sat, 26 Aug 2023 19:48:20 +0300 Subject: [PATCH 07/63] Increase client_max_size to allow bigger request bodies --- server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server.py b/server.py index d1295342..87abcc67 100644 --- a/server.py +++ b/server.py @@ -79,7 +79,7 @@ class PromptServer(): if args.enable_cors_header: middlewares.append(create_cors_middleware(args.enable_cors_header)) - self.app = web.Application(client_max_size=20971520, middlewares=middlewares) + self.app = web.Application(client_max_size=104857600, middlewares=middlewares) self.sockets = dict() self.web_root = os.path.join(os.path.dirname( os.path.realpath(__file__)), "web") From d9f4922993af48182e0e48844ca890db1529eb95 Mon Sep 17 00:00:00 2001 From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com> Date: Mon, 28 Aug 2023 01:34:24 +0900 Subject: [PATCH 08/63] fix: cannot disable dynamicPrompts (#1327) * fix: cannot disable dynamicPrompts * indent fix --------- Co-authored-by: Lt.Dr.Data --- web/scripts/widgets.js | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/web/scripts/widgets.js b/web/scripts/widgets.js index adf5f26f..5a4644b1 100644 --- a/web/scripts/widgets.js +++ b/web/scripts/widgets.js @@ -299,11 +299,17 @@ export const ComfyWidgets = { const defaultVal = inputData[1].default || ""; const multiline = !!inputData[1].multiline; + let res; if (multiline) { - return addMultilineWidget(node, inputName, { defaultVal, ...inputData[1] }, app); + res = addMultilineWidget(node, inputName, { defaultVal, ...inputData[1] }, app); } else { - return { widget: node.addWidget("text", inputName, defaultVal, () => {}, {}) }; + res = { widget: node.addWidget("text", inputName, defaultVal, () => {}, {}) }; } + + if(inputData[1].dynamicPrompts != undefined) + res.widget.dynamicPrompts = inputData[1].dynamicPrompts; + + return res; }, COMBO(node, inputName, inputData) { const type = inputData[0]; From d935ba50c4e66055d5d9f72620d46417fadbce26 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 27 Aug 2023 21:33:53 -0400 Subject: [PATCH 09/63] Make --bf16-vae work on torch 2.0 --- comfy/ldm/modules/diffusionmodules/model.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py index b596408d..9c2ea66b 100644 --- a/comfy/ldm/modules/diffusionmodules/model.py +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -56,7 +56,18 @@ class Upsample(nn.Module): padding=1) def forward(self, x): - x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + try: + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + except: #operation not implemented for bf16 + b, c, h, w = x.shape + out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device) + split = 8 + l = out.shape[1] // split + for i in range(0, out.shape[1], l): + out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype) + del x + x = out + if self.with_conv: x = self.conv(x) return x From 1c794a21613d2d99f1cac13aee3c8e6fee16def3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 27 Aug 2023 22:24:42 -0400 Subject: [PATCH 10/63] Fallback to slice attention if xformers doesn't support the operation. --- comfy/ldm/modules/diffusionmodules/model.py | 22 +++++++-------------- 1 file changed, 7 insertions(+), 15 deletions(-) diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py index 9c2ea66b..4c1f4ece 100644 --- a/comfy/ldm/modules/diffusionmodules/model.py +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -286,25 +286,17 @@ class MemoryEfficientAttnBlock(nn.Module): # compute attention B, C, H, W = q.shape - q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v)) - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(B, t.shape[1], 1, C) - .permute(0, 2, 1, 3) - .reshape(B * 1, t.shape[1], C) - .contiguous(), + lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), (q, k, v), ) - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) - out = ( - out.unsqueeze(0) - .reshape(B, 1, out.shape[1], C) - .permute(0, 2, 1, 3) - .reshape(B, out.shape[1], C) - ) - out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C) + try: + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) + out = out.transpose(1, 2).reshape(B, C, H, W) + except NotImplementedError as e: + out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) + out = self.proj_out(out) return x+out From b8c7c770d3259543af35acfc45608449b3bc6caa Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 27 Aug 2023 23:06:19 -0400 Subject: [PATCH 11/63] Enable bf16-vae by default on ampere and up. --- comfy/cli_args.py | 3 ++- comfy/model_management.py | 35 ++++++++++++++++++++++------------- 2 files changed, 24 insertions(+), 14 deletions(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index b4f22f31..fda24543 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -54,7 +54,8 @@ fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.") fpvae_group = parser.add_mutually_exclusive_group() fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.") -fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16, might lower quality.") +fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.") +fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.") parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") diff --git a/comfy/model_management.py b/comfy/model_management.py index e5c80bf6..aca8af99 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -148,15 +148,27 @@ def is_nvidia(): return True ENABLE_PYTORCH_ATTENTION = args.use_pytorch_cross_attention +VAE_DTYPE = torch.float32 -if ENABLE_PYTORCH_ATTENTION == False and XFORMERS_IS_AVAILABLE == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: - try: - if is_nvidia(): - torch_version = torch.version.__version__ - if int(torch_version[0]) >= 2: + +try: + if is_nvidia(): + torch_version = torch.version.__version__ + if int(torch_version[0]) >= 2: + if ENABLE_PYTORCH_ATTENTION == False and XFORMERS_IS_AVAILABLE == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: ENABLE_PYTORCH_ATTENTION = True - except: - pass + if torch.cuda.is_bf16_supported(): + VAE_DTYPE = torch.bfloat16 +except: + pass + +if args.fp16_vae: + VAE_DTYPE = torch.float16 +elif args.bf16_vae: + VAE_DTYPE = torch.bfloat16 +elif args.fp32_vae: + VAE_DTYPE = torch.float32 + if ENABLE_PYTORCH_ATTENTION: torch.backends.cuda.enable_math_sdp(True) @@ -228,6 +240,7 @@ try: except: print("Could not pick default device.") +print("VAE dtype:", VAE_DTYPE) current_loaded_models = [] @@ -448,12 +461,8 @@ def vae_offload_device(): return torch.device("cpu") def vae_dtype(): - if args.fp16_vae: - return torch.float16 - elif args.bf16_vae: - return torch.bfloat16 - else: - return torch.float32 + global VAE_DTYPE + return VAE_DTYPE def get_autocast_device(dev): if hasattr(dev, 'type'): From 0faee1186f49d7e1166401fd694d4ab3fa949a8d Mon Sep 17 00:00:00 2001 From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com> Date: Mon, 28 Aug 2023 13:52:22 +0900 Subject: [PATCH 12/63] support on prompt event handler (#765) Co-authored-by: Lt.Dr.Data --- server.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/server.py b/server.py index 87abcc67..538f9075 100644 --- a/server.py +++ b/server.py @@ -1,6 +1,8 @@ import os import sys import asyncio +import traceback + import nodes import folder_paths import execution @@ -88,6 +90,8 @@ class PromptServer(): self.last_node_id = None self.client_id = None + self.on_prompt_handlers = [] + @routes.get('/ws') async def websocket_handler(request): ws = web.WebSocketResponse() @@ -438,6 +442,7 @@ class PromptServer(): resp_code = 200 out_string = "" json_data = await request.json() + json_data = self.trigger_on_prompt(json_data) if "number" in json_data: number = float(json_data['number']) @@ -606,3 +611,15 @@ class PromptServer(): if call_on_start is not None: call_on_start(address, port) + def add_on_prompt_handler(self, handler): + self.on_prompt_handlers.append(handler) + + def trigger_on_prompt(self, json_data): + for handler in self.on_prompt_handlers: + try: + json_data = handler(json_data) + except Exception as e: + print(f"[ERROR] An error occurred during the on_prompt_handler processing") + traceback.print_exc() + + return json_data From 9196588088f69bcdd90b3b2e571f120409c43bcd Mon Sep 17 00:00:00 2001 From: BVH <82035780+bvhari@users.noreply.github.com> Date: Mon, 28 Aug 2023 19:57:22 +0530 Subject: [PATCH 13/63] Make tile size in Tiled VAE encode/decode user configurable --- nodes.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/nodes.py b/nodes.py index 233bc8d4..e07b988b 100644 --- a/nodes.py +++ b/nodes.py @@ -244,14 +244,16 @@ class VAEDecode: class VAEDecodeTiled: @classmethod def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} + return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}) + }} RETURN_TYPES = ("IMAGE",) FUNCTION = "decode" CATEGORY = "_for_testing" - def decode(self, vae, samples): - return (vae.decode_tiled(samples["samples"]), ) + def decode(self, vae, samples, tile_size): + return (vae.decode_tiled(samples["samples"], tile_x=tile_size, tile_y=tile_size, ), ) class VAEEncode: @classmethod @@ -280,15 +282,17 @@ class VAEEncode: class VAEEncodeTiled: @classmethod def INPUT_TYPES(s): - return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} + return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 512, "max": 4096, "step": 64}) + }} RETURN_TYPES = ("LATENT",) FUNCTION = "encode" CATEGORY = "_for_testing" - def encode(self, vae, pixels): + def encode(self, vae, pixels, tile_size): pixels = VAEEncode.vae_encode_crop_pixels(pixels) - t = vae.encode_tiled(pixels[:,:,:,:3]) + t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, ) return ({"samples":t}, ) class VAEEncodeForInpaint: From 4798cf5a629ef349dc9a44c38ba5221f0db25092 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 28 Aug 2023 11:20:06 -0400 Subject: [PATCH 14/63] Implement loras with norm keys. --- comfy/lora.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/comfy/lora.py b/comfy/lora.py index d685a455..3009a1c9 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -118,6 +118,19 @@ def load_lora(lora, to_load): if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2) + + w_norm_name = "{}.w_norm".format(x) + b_norm_name = "{}.b_norm".format(x) + w_norm = lora.get(w_norm_name, None) + b_norm = lora.get(b_norm_name, None) + + if w_norm is not None: + loaded_keys.add(w_norm_name) + patch_dict[to_load[x]] = (w_norm,) + if b_norm is not None: + loaded_keys.add(b_norm_name) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = (b_norm,) + for x in lora.keys(): if x not in loaded_keys: print("lora key not loaded", x) From d86b222fe95c5af6a15d6b7dcb17c1100285fdbd Mon Sep 17 00:00:00 2001 From: BVH <82035780+bvhari@users.noreply.github.com> Date: Mon, 28 Aug 2023 22:39:09 +0530 Subject: [PATCH 15/63] Reduce min tile size for encode --- nodes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index e07b988b..81eeda04 100644 --- a/nodes.py +++ b/nodes.py @@ -283,7 +283,7 @@ class VAEEncodeTiled: @classmethod def INPUT_TYPES(s): return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), - "tile_size": ("INT", {"default": 512, "min": 512, "max": 4096, "step": 64}) + "tile_size": ("INT", {"default": 512, "min": 192, "max": 4096, "step": 64}) }} RETURN_TYPES = ("LATENT",) FUNCTION = "encode" From f92074b84f44af6127e8f4ed1245d6c04dc0b750 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 28 Aug 2023 14:49:18 -0400 Subject: [PATCH 16/63] Move ModelPatcher to model_patcher.py --- comfy/controlnet.py | 4 +- comfy/model_patcher.py | 270 +++++++++++++++++++++++++++++++++++++++ comfy/sd.py | 278 +---------------------------------------- 3 files changed, 278 insertions(+), 274 deletions(-) create mode 100644 comfy/model_patcher.py diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 4e20100c..b18ab967 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -1,9 +1,9 @@ import torch import math import comfy.utils -import comfy.sd import comfy.model_management import comfy.model_detection +import comfy.model_patcher import comfy.cldm.cldm import comfy.t2i_adapter.adapter @@ -129,7 +129,7 @@ class ControlNet(ControlBase): def __init__(self, control_model, global_average_pooling=False, device=None): super().__init__(device) self.control_model = control_model - self.control_model_wrapped = comfy.sd.ModelPatcher(self.control_model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device()) + self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device()) self.global_average_pooling = global_average_pooling def get_control(self, x_noisy, t, cond, batched_number): diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py new file mode 100644 index 00000000..2f087a60 --- /dev/null +++ b/comfy/model_patcher.py @@ -0,0 +1,270 @@ +import torch +import copy +import inspect + +import comfy.utils + +class ModelPatcher: + def __init__(self, model, load_device, offload_device, size=0, current_device=None): + self.size = size + self.model = model + self.patches = {} + self.backup = {} + self.model_options = {"transformer_options":{}} + self.model_size() + self.load_device = load_device + self.offload_device = offload_device + if current_device is None: + self.current_device = self.offload_device + else: + self.current_device = current_device + + def model_size(self): + if self.size > 0: + return self.size + model_sd = self.model.state_dict() + size = 0 + for k in model_sd: + t = model_sd[k] + size += t.nelement() * t.element_size() + self.size = size + self.model_keys = set(model_sd.keys()) + return size + + def clone(self): + n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device) + n.patches = {} + for k in self.patches: + n.patches[k] = self.patches[k][:] + + n.model_options = copy.deepcopy(self.model_options) + n.model_keys = self.model_keys + return n + + def is_clone(self, other): + if hasattr(other, 'model') and self.model is other.model: + return True + return False + + def set_model_sampler_cfg_function(self, sampler_cfg_function): + if len(inspect.signature(sampler_cfg_function).parameters) == 3: + self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way + else: + self.model_options["sampler_cfg_function"] = sampler_cfg_function + + def set_model_unet_function_wrapper(self, unet_wrapper_function): + self.model_options["model_function_wrapper"] = unet_wrapper_function + + def set_model_patch(self, patch, name): + to = self.model_options["transformer_options"] + if "patches" not in to: + to["patches"] = {} + to["patches"][name] = to["patches"].get(name, []) + [patch] + + def set_model_patch_replace(self, patch, name, block_name, number): + to = self.model_options["transformer_options"] + if "patches_replace" not in to: + to["patches_replace"] = {} + if name not in to["patches_replace"]: + to["patches_replace"][name] = {} + to["patches_replace"][name][(block_name, number)] = patch + + def set_model_attn1_patch(self, patch): + self.set_model_patch(patch, "attn1_patch") + + def set_model_attn2_patch(self, patch): + self.set_model_patch(patch, "attn2_patch") + + def set_model_attn1_replace(self, patch, block_name, number): + self.set_model_patch_replace(patch, "attn1", block_name, number) + + def set_model_attn2_replace(self, patch, block_name, number): + self.set_model_patch_replace(patch, "attn2", block_name, number) + + def set_model_attn1_output_patch(self, patch): + self.set_model_patch(patch, "attn1_output_patch") + + def set_model_attn2_output_patch(self, patch): + self.set_model_patch(patch, "attn2_output_patch") + + def model_patches_to(self, device): + to = self.model_options["transformer_options"] + if "patches" in to: + patches = to["patches"] + for name in patches: + patch_list = patches[name] + for i in range(len(patch_list)): + if hasattr(patch_list[i], "to"): + patch_list[i] = patch_list[i].to(device) + if "patches_replace" in to: + patches = to["patches_replace"] + for name in patches: + patch_list = patches[name] + for k in patch_list: + if hasattr(patch_list[k], "to"): + patch_list[k] = patch_list[k].to(device) + + def model_dtype(self): + if hasattr(self.model, "get_dtype"): + return self.model.get_dtype() + + def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): + p = set() + for k in patches: + if k in self.model_keys: + p.add(k) + current_patches = self.patches.get(k, []) + current_patches.append((strength_patch, patches[k], strength_model)) + self.patches[k] = current_patches + + return list(p) + + def get_key_patches(self, filter_prefix=None): + model_sd = self.model_state_dict() + p = {} + for k in model_sd: + if filter_prefix is not None: + if not k.startswith(filter_prefix): + continue + if k in self.patches: + p[k] = [model_sd[k]] + self.patches[k] + else: + p[k] = (model_sd[k],) + return p + + def model_state_dict(self, filter_prefix=None): + sd = self.model.state_dict() + keys = list(sd.keys()) + if filter_prefix is not None: + for k in keys: + if not k.startswith(filter_prefix): + sd.pop(k) + return sd + + def patch_model(self, device_to=None): + model_sd = self.model_state_dict() + for key in self.patches: + if key not in model_sd: + print("could not patch. key doesn't exist in model:", k) + continue + + weight = model_sd[key] + + if key not in self.backup: + self.backup[key] = weight.to(self.offload_device) + + if device_to is not None: + temp_weight = weight.float().to(device_to, copy=True) + else: + temp_weight = weight.to(torch.float32, copy=True) + out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) + comfy.utils.set_attr(self.model, key, out_weight) + del temp_weight + + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to + + return self.model + + def calculate_weight(self, patches, weight, key): + for p in patches: + alpha = p[0] + v = p[1] + strength_model = p[2] + + if strength_model != 1.0: + weight *= strength_model + + if isinstance(v, list): + v = (self.calculate_weight(v[1:], v[0].clone(), key), ) + + if len(v) == 1: + w1 = v[0] + if alpha != 0.0: + if w1.shape != weight.shape: + print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) + else: + weight += alpha * w1.type(weight.dtype).to(weight.device) + elif len(v) == 4: #lora/locon + mat1 = v[0].float().to(weight.device) + mat2 = v[1].float().to(weight.device) + if v[2] is not None: + alpha *= v[2] / mat2.shape[0] + if v[3] is not None: + #locon mid weights, hopefully the math is fine because I didn't properly test it + mat3 = v[3].float().to(weight.device) + final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] + mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) + try: + weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif len(v) == 8: #lokr + w1 = v[0] + w2 = v[1] + w1_a = v[3] + w1_b = v[4] + w2_a = v[5] + w2_b = v[6] + t2 = v[7] + dim = None + + if w1 is None: + dim = w1_b.shape[0] + w1 = torch.mm(w1_a.float(), w1_b.float()) + else: + w1 = w1.float().to(weight.device) + + if w2 is None: + dim = w2_b.shape[0] + if t2 is None: + w2 = torch.mm(w2_a.float().to(weight.device), w2_b.float().to(weight.device)) + else: + w2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float().to(weight.device), w2_b.float().to(weight.device), w2_a.float().to(weight.device)) + else: + w2 = w2.float().to(weight.device) + + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + if v[2] is not None and dim is not None: + alpha *= v[2] / dim + + try: + weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + else: #loha + w1a = v[0] + w1b = v[1] + if v[2] is not None: + alpha *= v[2] / w1b.shape[0] + w2a = v[3] + w2b = v[4] + if v[5] is not None: #cp decomposition + t1 = v[5] + t2 = v[6] + m1 = torch.einsum('i j k l, j r, i p -> p r k l', t1.float().to(weight.device), w1b.float().to(weight.device), w1a.float().to(weight.device)) + m2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float().to(weight.device), w2b.float().to(weight.device), w2a.float().to(weight.device)) + else: + m1 = torch.mm(w1a.float().to(weight.device), w1b.float().to(weight.device)) + m2 = torch.mm(w2a.float().to(weight.device), w2b.float().to(weight.device)) + + try: + weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + + return weight + + def unpatch_model(self, device_to=None): + keys = list(self.backup.keys()) + + for k in keys: + comfy.utils.set_attr(self.model, k, self.backup[k]) + + self.backup = {} + + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to diff --git a/comfy/sd.py b/comfy/sd.py index 7462c79e..1edc5fd2 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -1,7 +1,5 @@ import torch import contextlib -import copy -import inspect import math from comfy import model_management @@ -21,6 +19,7 @@ from . import sd1_clip from . import sd2_clip from . import sdxl_clip +import comfy.model_patcher import comfy.lora import comfy.t2i_adapter.adapter @@ -53,271 +52,6 @@ def load_clip_weights(model, sd): sd = comfy.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) return load_model_weights(model, sd) -class ModelPatcher: - def __init__(self, model, load_device, offload_device, size=0, current_device=None): - self.size = size - self.model = model - self.patches = {} - self.backup = {} - self.model_options = {"transformer_options":{}} - self.model_size() - self.load_device = load_device - self.offload_device = offload_device - if current_device is None: - self.current_device = self.offload_device - else: - self.current_device = current_device - - def model_size(self): - if self.size > 0: - return self.size - model_sd = self.model.state_dict() - size = 0 - for k in model_sd: - t = model_sd[k] - size += t.nelement() * t.element_size() - self.size = size - self.model_keys = set(model_sd.keys()) - return size - - def clone(self): - n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device) - n.patches = {} - for k in self.patches: - n.patches[k] = self.patches[k][:] - - n.model_options = copy.deepcopy(self.model_options) - n.model_keys = self.model_keys - return n - - def is_clone(self, other): - if hasattr(other, 'model') and self.model is other.model: - return True - return False - - def set_model_sampler_cfg_function(self, sampler_cfg_function): - if len(inspect.signature(sampler_cfg_function).parameters) == 3: - self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way - else: - self.model_options["sampler_cfg_function"] = sampler_cfg_function - - def set_model_unet_function_wrapper(self, unet_wrapper_function): - self.model_options["model_function_wrapper"] = unet_wrapper_function - - def set_model_patch(self, patch, name): - to = self.model_options["transformer_options"] - if "patches" not in to: - to["patches"] = {} - to["patches"][name] = to["patches"].get(name, []) + [patch] - - def set_model_patch_replace(self, patch, name, block_name, number): - to = self.model_options["transformer_options"] - if "patches_replace" not in to: - to["patches_replace"] = {} - if name not in to["patches_replace"]: - to["patches_replace"][name] = {} - to["patches_replace"][name][(block_name, number)] = patch - - def set_model_attn1_patch(self, patch): - self.set_model_patch(patch, "attn1_patch") - - def set_model_attn2_patch(self, patch): - self.set_model_patch(patch, "attn2_patch") - - def set_model_attn1_replace(self, patch, block_name, number): - self.set_model_patch_replace(patch, "attn1", block_name, number) - - def set_model_attn2_replace(self, patch, block_name, number): - self.set_model_patch_replace(patch, "attn2", block_name, number) - - def set_model_attn1_output_patch(self, patch): - self.set_model_patch(patch, "attn1_output_patch") - - def set_model_attn2_output_patch(self, patch): - self.set_model_patch(patch, "attn2_output_patch") - - def model_patches_to(self, device): - to = self.model_options["transformer_options"] - if "patches" in to: - patches = to["patches"] - for name in patches: - patch_list = patches[name] - for i in range(len(patch_list)): - if hasattr(patch_list[i], "to"): - patch_list[i] = patch_list[i].to(device) - if "patches_replace" in to: - patches = to["patches_replace"] - for name in patches: - patch_list = patches[name] - for k in patch_list: - if hasattr(patch_list[k], "to"): - patch_list[k] = patch_list[k].to(device) - - def model_dtype(self): - if hasattr(self.model, "get_dtype"): - return self.model.get_dtype() - - def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): - p = set() - for k in patches: - if k in self.model_keys: - p.add(k) - current_patches = self.patches.get(k, []) - current_patches.append((strength_patch, patches[k], strength_model)) - self.patches[k] = current_patches - - return list(p) - - def get_key_patches(self, filter_prefix=None): - model_sd = self.model_state_dict() - p = {} - for k in model_sd: - if filter_prefix is not None: - if not k.startswith(filter_prefix): - continue - if k in self.patches: - p[k] = [model_sd[k]] + self.patches[k] - else: - p[k] = (model_sd[k],) - return p - - def model_state_dict(self, filter_prefix=None): - sd = self.model.state_dict() - keys = list(sd.keys()) - if filter_prefix is not None: - for k in keys: - if not k.startswith(filter_prefix): - sd.pop(k) - return sd - - def patch_model(self, device_to=None): - model_sd = self.model_state_dict() - for key in self.patches: - if key not in model_sd: - print("could not patch. key doesn't exist in model:", k) - continue - - weight = model_sd[key] - - if key not in self.backup: - self.backup[key] = weight.to(self.offload_device) - - if device_to is not None: - temp_weight = weight.float().to(device_to, copy=True) - else: - temp_weight = weight.to(torch.float32, copy=True) - out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) - comfy.utils.set_attr(self.model, key, out_weight) - del temp_weight - - if device_to is not None: - self.model.to(device_to) - self.current_device = device_to - - return self.model - - def calculate_weight(self, patches, weight, key): - for p in patches: - alpha = p[0] - v = p[1] - strength_model = p[2] - - if strength_model != 1.0: - weight *= strength_model - - if isinstance(v, list): - v = (self.calculate_weight(v[1:], v[0].clone(), key), ) - - if len(v) == 1: - w1 = v[0] - if alpha != 0.0: - if w1.shape != weight.shape: - print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) - else: - weight += alpha * w1.type(weight.dtype).to(weight.device) - elif len(v) == 4: #lora/locon - mat1 = v[0].float().to(weight.device) - mat2 = v[1].float().to(weight.device) - if v[2] is not None: - alpha *= v[2] / mat2.shape[0] - if v[3] is not None: - #locon mid weights, hopefully the math is fine because I didn't properly test it - mat3 = v[3].float().to(weight.device) - final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] - mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) - try: - weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) - except Exception as e: - print("ERROR", key, e) - elif len(v) == 8: #lokr - w1 = v[0] - w2 = v[1] - w1_a = v[3] - w1_b = v[4] - w2_a = v[5] - w2_b = v[6] - t2 = v[7] - dim = None - - if w1 is None: - dim = w1_b.shape[0] - w1 = torch.mm(w1_a.float(), w1_b.float()) - else: - w1 = w1.float().to(weight.device) - - if w2 is None: - dim = w2_b.shape[0] - if t2 is None: - w2 = torch.mm(w2_a.float().to(weight.device), w2_b.float().to(weight.device)) - else: - w2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float().to(weight.device), w2_b.float().to(weight.device), w2_a.float().to(weight.device)) - else: - w2 = w2.float().to(weight.device) - - if len(w2.shape) == 4: - w1 = w1.unsqueeze(2).unsqueeze(2) - if v[2] is not None and dim is not None: - alpha *= v[2] / dim - - try: - weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) - except Exception as e: - print("ERROR", key, e) - else: #loha - w1a = v[0] - w1b = v[1] - if v[2] is not None: - alpha *= v[2] / w1b.shape[0] - w2a = v[3] - w2b = v[4] - if v[5] is not None: #cp decomposition - t1 = v[5] - t2 = v[6] - m1 = torch.einsum('i j k l, j r, i p -> p r k l', t1.float().to(weight.device), w1b.float().to(weight.device), w1a.float().to(weight.device)) - m2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float().to(weight.device), w2b.float().to(weight.device), w2a.float().to(weight.device)) - else: - m1 = torch.mm(w1a.float().to(weight.device), w1b.float().to(weight.device)) - m2 = torch.mm(w2a.float().to(weight.device), w2b.float().to(weight.device)) - - try: - weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) - except Exception as e: - print("ERROR", key, e) - - return weight - - def unpatch_model(self, device_to=None): - keys = list(self.backup.keys()) - - for k in keys: - comfy.utils.set_attr(self.model, k, self.backup[k]) - - self.backup = {} - - if device_to is not None: - self.model.to(device_to) - self.current_device = device_to - def load_lora_for_models(model, clip, lora, strength_model, strength_clip): key_map = comfy.lora.model_lora_keys_unet(model.model) @@ -355,7 +89,7 @@ class CLIP: self.cond_stage_model = clip(**(params)) self.tokenizer = tokenizer(embedding_directory=embedding_directory) - self.patcher = ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) + self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) self.layer_idx = None def clone(self): @@ -573,7 +307,7 @@ def load_gligen(ckpt_path): model = gligen.load_gligen(data) if model_management.should_use_fp16(): model = model.half() - return ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) + return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): #TODO: this function is a mess and should be removed eventually @@ -653,7 +387,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl w.cond_stage_model = clip.cond_stage_model load_clip_weights(w, state_dict) - return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) + return (comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None): sd = comfy.utils.load_torch_file(ckpt_path) @@ -705,7 +439,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o if len(left_over) > 0: print("left over keys:", left_over) - model_patcher = ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device(), current_device=inital_load_device) + model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device(), current_device=inital_load_device) if inital_load_device != torch.device("cpu"): print("loaded straight to GPU") model_management.load_model_gpu(model_patcher) @@ -735,7 +469,7 @@ def load_unet(unet_path): #load unet in diffusers format model = model_config.get_model(new_sd, "") model = model.to(offload_device) model.load_model_weights(new_sd, "") - return ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device) + return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device) def save_checkpoint(output_path, model, clip, vae, metadata=None): model_management.load_models_gpu([model, clip.load_model()]) From 1300a1bb4c87246b846c6209ab65ef0553004290 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 28 Aug 2023 15:08:45 -0400 Subject: [PATCH 17/63] Text encoder should initially load on the offload_device not the regular. --- comfy/sd.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/sd.py b/comfy/sd.py index 1edc5fd2..a63a0d1d 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -80,7 +80,7 @@ class CLIP: load_device = model_management.text_encoder_device() offload_device = model_management.text_encoder_offload_device() - params['device'] = load_device + params['device'] = offload_device if model_management.should_use_fp16(load_device, prioritize_performance=False): params['dtype'] = torch.float16 else: From a094b45c93e29193ba2695e834aefe7bf4635c06 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 28 Aug 2023 15:26:29 -0400 Subject: [PATCH 18/63] Load clipvision model to GPU for faster performance. --- comfy/clip_vision.py | 26 ++++++++++++++++++++++++-- 1 file changed, 24 insertions(+), 2 deletions(-) diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py index a887e51b..8635e577 100644 --- a/comfy/clip_vision.py +++ b/comfy/clip_vision.py @@ -2,14 +2,27 @@ from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPIm from .utils import load_torch_file, transformers_convert import os import torch +import contextlib + import comfy.ops +import comfy.model_patcher +import comfy.model_management class ClipVisionModel(): def __init__(self, json_config): config = CLIPVisionConfig.from_json_file(json_config) - with comfy.ops.use_comfy_ops(): + self.load_device = comfy.model_management.text_encoder_device() + offload_device = comfy.model_management.text_encoder_offload_device() + self.dtype = torch.float32 + if comfy.model_management.should_use_fp16(self.load_device, prioritize_performance=False): + self.dtype = torch.float16 + + with comfy.ops.use_comfy_ops(offload_device, self.dtype): with modeling_utils.no_init_weights(): self.model = CLIPVisionModelWithProjection(config) + self.model.to(self.dtype) + + self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) self.processor = CLIPImageProcessor(crop_size=224, do_center_crop=True, do_convert_rgb=True, @@ -27,7 +40,16 @@ class ClipVisionModel(): img = torch.clip((255. * image), 0, 255).round().int() img = list(map(lambda a: a, img)) inputs = self.processor(images=img, return_tensors="pt") - outputs = self.model(**inputs) + comfy.model_management.load_model_gpu(self.patcher) + pixel_values = inputs['pixel_values'].to(self.load_device) + + if self.dtype != torch.float32: + precision_scope = torch.autocast + else: + precision_scope = lambda a, b: contextlib.nullcontext(a) + + with precision_scope(comfy.model_management.get_autocast_device(self.load_device), torch.float32): + outputs = self.model(pixel_values=pixel_values) return outputs def convert_to_transformers(sd, prefix): From 4e89b2c25a2ee7346b4892120176fadb0e7f931a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 28 Aug 2023 16:26:11 -0400 Subject: [PATCH 19/63] Put clip vision outputs on the CPU. --- comfy/clip_vision.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py index 8635e577..daaa2f2b 100644 --- a/comfy/clip_vision.py +++ b/comfy/clip_vision.py @@ -50,6 +50,11 @@ class ClipVisionModel(): with precision_scope(comfy.model_management.get_autocast_device(self.load_device), torch.float32): outputs = self.model(pixel_values=pixel_values) + + for k in outputs: + t = outputs[k] + if t is not None: + outputs[k] = t.cpu() return outputs def convert_to_transformers(sd, prefix): From 65cae62c7149048951fc70d601fc9c0d5434f69c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 28 Aug 2023 16:49:06 -0400 Subject: [PATCH 20/63] No need to check filename extensions to detect shuffle controlnet. --- comfy/controlnet.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index b18ab967..7098186f 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -1,5 +1,6 @@ import torch import math +import os import comfy.utils import comfy.model_management import comfy.model_detection @@ -386,7 +387,8 @@ def load_controlnet(ckpt_path, model=None): control_model = control_model.half() global_average_pooling = False - if ckpt_path.endswith("_shuffle.pth") or ckpt_path.endswith("_shuffle.safetensors") or ckpt_path.endswith("_shuffle_fp16.safetensors"): #TODO: smarter way of enabling global_average_pooling + filename = os.path.splitext(ckpt_path)[0] + if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling global_average_pooling = True control = ControlNet(control_model, global_average_pooling=global_average_pooling) From 8ddd081b093f2774982f261ecb7d23d616328323 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 29 Aug 2023 01:51:35 -0400 Subject: [PATCH 21/63] Use the same units for tile size in VAEDecodeTiled and VAEEncodeTiled. --- nodes.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/nodes.py b/nodes.py index 81eeda04..3e4d5240 100644 --- a/nodes.py +++ b/nodes.py @@ -245,7 +245,7 @@ class VAEDecodeTiled: @classmethod def INPUT_TYPES(s): return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), - "tile_size": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}) + "tile_size": ("INT", {"default": 512, "min": 192, "max": 4096, "step": 64}) }} RETURN_TYPES = ("IMAGE",) FUNCTION = "decode" @@ -253,7 +253,7 @@ class VAEDecodeTiled: CATEGORY = "_for_testing" def decode(self, vae, samples, tile_size): - return (vae.decode_tiled(samples["samples"], tile_x=tile_size, tile_y=tile_size, ), ) + return (vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), ) class VAEEncode: @classmethod @@ -283,7 +283,7 @@ class VAEEncodeTiled: @classmethod def INPUT_TYPES(s): return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), - "tile_size": ("INT", {"default": 512, "min": 192, "max": 4096, "step": 64}) + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) }} RETURN_TYPES = ("LATENT",) FUNCTION = "encode" From e30d546e38522dfdfbefa33570624cac78047acc Mon Sep 17 00:00:00 2001 From: Michael Poutre Date: Mon, 28 Aug 2023 23:36:54 -0700 Subject: [PATCH 22/63] feat(ui): Add node mode toggles to group context menu --- web/extensions/core/groupOptions.js | 136 ++++++++++++++++++++++++++++ 1 file changed, 136 insertions(+) create mode 100644 web/extensions/core/groupOptions.js diff --git a/web/extensions/core/groupOptions.js b/web/extensions/core/groupOptions.js new file mode 100644 index 00000000..fe9577c2 --- /dev/null +++ b/web/extensions/core/groupOptions.js @@ -0,0 +1,136 @@ +import {app} from "../../scripts/app.js"; + +function setNodeMode(node, mode) { + node.mode = mode; + node.graph.change(); +} + +app.registerExtension({ + name: "Comfy.GroupOptions", + setup() { + const orig = LGraphCanvas.prototype.getCanvasMenuOptions; + // graph_mouse + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + const options = orig.apply(this, arguments); + const group = this.graph.getGroupOnPos(this.graph_mouse[0], this.graph_mouse[1]); + if (!group) { + return options; + } + + // Group nodes aren't recomputed until the group is moved, this ensures the nodes are up-to-date + group.recomputeInsideNodes(); + const nodesInGroup = group._nodes; + + // No nodes in group, return default options + if (nodesInGroup.length === 0) { + return options; + } else { + // Add a separator between the default options and the group options + options.push(null); + } + + // Check if all nodes are the same mode + let allNodesAreSameMode = true; + for (let i = 1; i < nodesInGroup.length; i++) { + if (nodesInGroup[i].mode !== nodesInGroup[0].mode) { + allNodesAreSameMode = false; + break; + } + } + + // Modes + // 0: Always + // 2: Never + // 4: Bypass + // If all nodes are the same mode, add a menu option to change the mode + if (allNodesAreSameMode) { + const mode = nodesInGroup[0].mode; + // All nodes are always, option to disable, and bypass + if (mode === 0) { + options.push({ + content: "Disable Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 2); + } + } + }); + options.push({ + content: "Bypass Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 4); + } + } + }) + } + // All nodes are never, option to enable, and bypass + if (mode === 2) { + options.push({ + content: "Enable Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 0); + } + } + }); + options.push({ + content: "Bypass Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 4); + } + } + }) + } + // All nodes are bypass, option to enable, and disable + if (mode === 4) { + options.push({ + content: "Enable Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 0); + } + } + }); + options.push({ + content: "Disable Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 2); + } + } + }) + } + } else { + // Nodes are not all the same mode, add a menu option to change the mode to always, never, or bypass + options.push({ + content: "Enable Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 0); + } + } + }); + options.push({ + content: "Disable Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 2); + } + } + }); + options.push({ + content: "Bypass Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 4); + } + } + }); + } + + return options + } + } +}); From 6944288aff3d1419304433cb7b563ced6f809a2b Mon Sep 17 00:00:00 2001 From: Michael Poutre Date: Tue, 29 Aug 2023 00:24:31 -0700 Subject: [PATCH 23/63] refactor(ui): Switch statement, and handle other modes in group actions --- web/extensions/core/groupOptions.js | 135 +++++++++++++++++----------- 1 file changed, 83 insertions(+), 52 deletions(-) diff --git a/web/extensions/core/groupOptions.js b/web/extensions/core/groupOptions.js index fe9577c2..1d935e90 100644 --- a/web/extensions/core/groupOptions.js +++ b/web/extensions/core/groupOptions.js @@ -40,72 +40,103 @@ app.registerExtension({ // Modes // 0: Always + // 1: On Event // 2: Never + // 3: On Trigger // 4: Bypass // If all nodes are the same mode, add a menu option to change the mode if (allNodesAreSameMode) { const mode = nodesInGroup[0].mode; - // All nodes are always, option to disable, and bypass - if (mode === 0) { - options.push({ - content: "Disable Group Nodes", - callback: () => { - for (const node of nodesInGroup) { - setNodeMode(node, 2); + switch (mode) { + case 0: + // All nodes are always, option to disable, and bypass + options.push({ + content: "Set Group Nodes to Never", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 2); + } } - } - }); - options.push({ - content: "Bypass Group Nodes", - callback: () => { - for (const node of nodesInGroup) { - setNodeMode(node, 4); + }); + options.push({ + content: "Bypass Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 4); + } } - } - }) - } - // All nodes are never, option to enable, and bypass - if (mode === 2) { - options.push({ - content: "Enable Group Nodes", - callback: () => { - for (const node of nodesInGroup) { - setNodeMode(node, 0); + }); + break; + case 2: + // All nodes are never, option to enable, and bypass + options.push({ + content: "Set Group Nodes to Always", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 0); + } } - } - }); - options.push({ - content: "Bypass Group Nodes", - callback: () => { - for (const node of nodesInGroup) { - setNodeMode(node, 4); + }); + options.push({ + content: "Bypass Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 4); + } } - } - }) - } - // All nodes are bypass, option to enable, and disable - if (mode === 4) { - options.push({ - content: "Enable Group Nodes", - callback: () => { - for (const node of nodesInGroup) { - setNodeMode(node, 0); + }); + break; + case 4: + // All nodes are bypass, option to enable, and disable + options.push({ + content: "Set Group Nodes to Always", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 0); + } } - } - }); - options.push({ - content: "Disable Group Nodes", - callback: () => { - for (const node of nodesInGroup) { - setNodeMode(node, 2); + }); + options.push({ + content: "Set Group Nodes to Never", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 2); + } } - } - }) + }); + break; + default: + // All nodes are On Trigger or On Event(Or other?), option to disable, set to always, or bypass + options.push({ + content: "Set Group Nodes to Always", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 0); + } + } + }); + options.push({ + content: "Set Group Nodes to Never", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 2); + } + } + }); + options.push({ + content: "Bypass Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 4); + } + } + }); + break; } } else { // Nodes are not all the same mode, add a menu option to change the mode to always, never, or bypass options.push({ - content: "Enable Group Nodes", + content: "Set Group Nodes to Always", callback: () => { for (const node of nodesInGroup) { setNodeMode(node, 0); @@ -113,7 +144,7 @@ app.registerExtension({ } }); options.push({ - content: "Disable Group Nodes", + content: "Set Group Nodes to Never", callback: () => { for (const node of nodesInGroup) { setNodeMode(node, 2); From edcff9ab8a370dbe4fd4814398a2b7e38459934d Mon Sep 17 00:00:00 2001 From: Chris Date: Tue, 29 Aug 2023 18:34:43 +1000 Subject: [PATCH 24/63] copy metadata into modified image --- server.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/server.py b/server.py index 538f9075..1cac3281 100644 --- a/server.py +++ b/server.py @@ -12,6 +12,7 @@ import json import glob import struct from PIL import Image, ImageOps +from PIL.PngImagePlugin import PngInfo from io import BytesIO try: @@ -233,13 +234,16 @@ class PromptServer(): if os.path.isfile(file): with Image.open(file) as original_pil: + metadata = PngInfo() + for key in original_pil.text: + metadata.add_text(key, original_pil.text[key]) original_pil = original_pil.convert('RGBA') mask_pil = Image.open(image.file).convert('RGBA') # alpha copy new_alpha = mask_pil.getchannel('A') original_pil.putalpha(new_alpha) - original_pil.save(filepath, compress_level=4) + original_pil.save(filepath, compress_level=4, pnginfo=metadata) return image_upload(post, image_save_function) From 18379dea366e97105cd01685d629df83c995c150 Mon Sep 17 00:00:00 2001 From: Chris Date: Tue, 29 Aug 2023 18:47:17 +1000 Subject: [PATCH 25/63] check for text attr and save --- server.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/server.py b/server.py index 1cac3281..0d7d28a0 100644 --- a/server.py +++ b/server.py @@ -235,8 +235,9 @@ class PromptServer(): if os.path.isfile(file): with Image.open(file) as original_pil: metadata = PngInfo() - for key in original_pil.text: - metadata.add_text(key, original_pil.text[key]) + if hasattr(original_pil,'text'): + for key in original_pil.text: + metadata.add_text(key, original_pil.text[key]) original_pil = original_pil.convert('RGBA') mask_pil = Image.open(image.file).convert('RGBA') From bed116a1f916f23b65c7b68cf60d09b2401fcf29 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 29 Aug 2023 11:20:17 -0400 Subject: [PATCH 26/63] Remove optimization that caused border. --- comfy/ldm/modules/diffusionmodules/model.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py index 4c1f4ece..43154848 100644 --- a/comfy/ldm/modules/diffusionmodules/model.py +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -85,11 +85,10 @@ class Downsample(nn.Module): stride=2, padding=0) - def forward(self, x, already_padded=False): + def forward(self, x): if self.with_conv: - if not already_padded: - pad = (0,1,0,1) - x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) x = self.conv(x) else: x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) @@ -606,9 +605,6 @@ class Encoder(nn.Module): def forward(self, x): # timestep embedding temb = None - pad = (0,1,0,1) - x = torch.nn.functional.pad(x, pad, mode="constant", value=0) - already_padded = True # downsampling h = self.conv_in(x) for i_level in range(self.num_resolutions): @@ -617,8 +613,7 @@ class Encoder(nn.Module): if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) if i_level != self.num_resolutions-1: - h = self.down[i_level].downsample(h, already_padded) - already_padded = False + h = self.down[i_level].downsample(h) # middle h = self.mid.block_1(h, temb) From 15adc3699f74e0f3fcd3c29e62f8256825098e88 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 29 Aug 2023 14:22:53 -0400 Subject: [PATCH 27/63] Move beta_schedule to model_config and allow disabling unet creation. --- comfy/model_base.py | 5 +++-- comfy/supported_models_base.py | 1 + 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index d654f56f..acd4169a 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -19,8 +19,9 @@ class BaseModel(torch.nn.Module): unet_config = model_config.unet_config self.latent_format = model_config.latent_format self.model_config = model_config - self.register_schedule(given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3) - self.diffusion_model = UNetModel(**unet_config, device=device) + self.register_schedule(given_betas=None, beta_schedule=model_config.beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3) + if not unet_config.get("disable_unet_model_creation", False): + self.diffusion_model = UNetModel(**unet_config, device=device) self.model_type = model_type self.adm_channels = unet_config.get("adm_in_channels", None) if self.adm_channels is None: diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py index d0088bbd..c7283800 100644 --- a/comfy/supported_models_base.py +++ b/comfy/supported_models_base.py @@ -33,6 +33,7 @@ class BASE: clip_prefix = [] clip_vision_prefix = None noise_aug_config = None + beta_schedule = "linear" @classmethod def matches(s, unet_config): From f2f5e5dcbb9a39d15514a00de008b50bb4cba8e0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 29 Aug 2023 16:44:57 -0400 Subject: [PATCH 28/63] Support SDXL t2i adapters with 3 channel input. --- comfy/controlnet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 7098186f..83e1be05 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -465,7 +465,7 @@ def load_t2i_adapter(t2i_data): if len(down_opts) > 0: use_conv = True xl = False - if cin == 256: + if cin == 256 or cin == 768: xl = True model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) else: From 81d9200e1851f160dacf9ad30e55444a2e42241e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 29 Aug 2023 17:55:42 -0400 Subject: [PATCH 29/63] Add node to convert a specific colour in an image to a mask. --- comfy_extras/nodes_mask.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py index 5adb468a..43f623a6 100644 --- a/comfy_extras/nodes_mask.py +++ b/comfy_extras/nodes_mask.py @@ -125,6 +125,27 @@ class ImageToMask: mask = image[0, :, :, channels.index(channel)] return (mask,) +class ImageColorToMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, image, color): + temp = (torch.clamp(image[0], 0, 1.0) * 255.0).round().to(torch.int) + temp = torch.bitwise_left_shift(temp[:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,1], 8) + temp[:,:,2] + mask = torch.where(temp == color, 255, 0).float() + return (mask,) + class SolidMask: @classmethod def INPUT_TYPES(cls): @@ -315,6 +336,7 @@ NODE_CLASS_MAPPINGS = { "ImageCompositeMasked": ImageCompositeMasked, "MaskToImage": MaskToImage, "ImageToMask": ImageToMask, + "ImageColorToMask": ImageColorToMask, "SolidMask": SolidMask, "InvertMask": InvertMask, "CropMask": CropMask, From d70b0bc43c2d1d0a2212e9493926ddf231c80a41 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 29 Aug 2023 17:58:40 -0400 Subject: [PATCH 30/63] Use the GPU for the canny preprocessor when available. --- comfy_extras/nodes_canny.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py index d7c3f132..94d453f2 100644 --- a/comfy_extras/nodes_canny.py +++ b/comfy_extras/nodes_canny.py @@ -3,7 +3,7 @@ import math import torch import torch.nn.functional as F - +import comfy.model_management def get_canny_nms_kernel(device=None, dtype=None): """Utility function that returns 3x3 kernels for the Canny Non-maximal suppression.""" @@ -290,8 +290,8 @@ class Canny: CATEGORY = "image/preprocessors" def detect_edge(self, image, low_threshold, high_threshold): - output = canny(image.movedim(-1, 1), low_threshold, high_threshold) - img_out = output[1].repeat(1, 3, 1, 1).movedim(1, -1) + output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) + img_out = output[1].cpu().repeat(1, 3, 1, 1).movedim(1, -1) return (img_out,) NODE_CLASS_MAPPINGS = { From fe4c07400c792ff8c5247cc4697235e33d96fcc9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 29 Aug 2023 23:58:32 -0400 Subject: [PATCH 31/63] Fix "Load Checkpoint with config" node. --- comfy/sd.py | 6 ++++-- comfy/supported_models_base.py | 2 ++ 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index a63a0d1d..e98dabe8 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -22,6 +22,7 @@ from . import sdxl_clip import comfy.model_patcher import comfy.lora import comfy.t2i_adapter.adapter +import comfy.supported_models_base def load_model_weights(model, sd): m, u = model.load_state_dict(sd, strict=False) @@ -348,10 +349,11 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl class EmptyClass: pass - model_config = EmptyClass() - model_config.unet_config = unet_config + model_config = comfy.supported_models_base.BASE({}) + from . import latent_formats model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor) + model_config.unet_config = unet_config if config['model']["target"].endswith("LatentInpaintDiffusion"): model = model_base.SDInpaint(model_config, model_type=model_type) diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py index c7283800..c9cd54d0 100644 --- a/comfy/supported_models_base.py +++ b/comfy/supported_models_base.py @@ -1,6 +1,7 @@ import torch from . import model_base from . import utils +from . import latent_formats def state_dict_key_replace(state_dict, keys_to_replace): @@ -34,6 +35,7 @@ class BASE: clip_vision_prefix = None noise_aug_config = None beta_schedule = "linear" + latent_format = latent_formats.LatentFormat @classmethod def matches(s, unet_config): From 18617967e5be09d4d24ff0bb337ab4468dd80e6c Mon Sep 17 00:00:00 2001 From: Simon Lui <502929+simonlui@users.noreply.github.com> Date: Wed, 30 Aug 2023 00:25:04 -0700 Subject: [PATCH 32/63] Fix error message in model_patcher.py Found while tinkering. --- comfy/model_patcher.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 2f087a60..a6ee0bae 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -145,7 +145,7 @@ class ModelPatcher: model_sd = self.model_state_dict() for key in self.patches: if key not in model_sd: - print("could not patch. key doesn't exist in model:", k) + print("could not patch. key doesn't exist in model:", key) continue weight = model_sd[key] From 7e941f9f247f9b013a33c2e7d117466108414e99 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 30 Aug 2023 12:55:07 -0400 Subject: [PATCH 33/63] Clean up DiffusersLoader node. --- comfy/diffusers_load.py | 101 ++++++++++------------------------------ nodes.py | 2 +- 2 files changed, 26 insertions(+), 77 deletions(-) diff --git a/comfy/diffusers_load.py b/comfy/diffusers_load.py index 11d94c34..a52e0102 100644 --- a/comfy/diffusers_load.py +++ b/comfy/diffusers_load.py @@ -1,87 +1,36 @@ import json import os -import yaml -import folder_paths -from comfy.sd import load_checkpoint -import os.path as osp -import re -import torch -from safetensors.torch import load_file, save_file -from . import diffusers_convert +import comfy.sd +def first_file(path, filenames): + for f in filenames: + p = os.path.join(path, f) + if os.path.exists(p): + return p + return None -def load_diffusers(model_path, fp16=True, output_vae=True, output_clip=True, embedding_directory=None): - diffusers_unet_conf = json.load(open(osp.join(model_path, "unet/config.json"))) - diffusers_scheduler_conf = json.load(open(osp.join(model_path, "scheduler/scheduler_config.json"))) +def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None): + diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"] + unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names) + vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names) - # magic - v2 = diffusers_unet_conf["sample_size"] == 96 - if 'prediction_type' in diffusers_scheduler_conf: - v_pred = diffusers_scheduler_conf['prediction_type'] == 'v_prediction' + text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"] + text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names) + text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names) - if v2: - if v_pred: - config_path = folder_paths.get_full_path("configs", 'v2-inference-v.yaml') - else: - config_path = folder_paths.get_full_path("configs", 'v2-inference.yaml') - else: - config_path = folder_paths.get_full_path("configs", 'v1-inference.yaml') + text_encoder_paths = [text_encoder1_path] + if text_encoder2_path is not None: + text_encoder_paths.append(text_encoder2_path) - with open(config_path, 'r') as stream: - config = yaml.safe_load(stream) + unet = comfy.sd.load_unet(unet_path) - model_config_params = config['model']['params'] - clip_config = model_config_params['cond_stage_config'] - scale_factor = model_config_params['scale_factor'] - vae_config = model_config_params['first_stage_config'] - vae_config['scale_factor'] = scale_factor - model_config_params["unet_config"]["params"]["use_fp16"] = fp16 + clip = None + if output_clip: + clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) - unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors") - vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors") - text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors") + vae = None + if output_vae: + vae = comfy.sd.VAE(ckpt_path=vae_path) - # Load models from safetensors if it exists, if it doesn't pytorch - if osp.exists(unet_path): - unet_state_dict = load_file(unet_path, device="cpu") - else: - unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin") - unet_state_dict = torch.load(unet_path, map_location="cpu") - - if osp.exists(vae_path): - vae_state_dict = load_file(vae_path, device="cpu") - else: - vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin") - vae_state_dict = torch.load(vae_path, map_location="cpu") - - if osp.exists(text_enc_path): - text_enc_dict = load_file(text_enc_path, device="cpu") - else: - text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin") - text_enc_dict = torch.load(text_enc_path, map_location="cpu") - - # Convert the UNet model - unet_state_dict = diffusers_convert.convert_unet_state_dict(unet_state_dict) - unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} - - # Convert the VAE model - vae_state_dict = diffusers_convert.convert_vae_state_dict(vae_state_dict) - vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} - - # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper - is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict - - if is_v20_model: - # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm - text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()} - text_enc_dict = diffusers_convert.convert_text_enc_state_dict_v20(text_enc_dict) - text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()} - else: - text_enc_dict = diffusers_convert.convert_text_enc_state_dict(text_enc_dict) - text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} - - # Put together new checkpoint - sd = {**unet_state_dict, **vae_state_dict, **text_enc_dict} - - return load_checkpoint(embedding_directory=embedding_directory, state_dict=sd, config=config) + return (unet, clip, vae) diff --git a/nodes.py b/nodes.py index 3e4d5240..5e755f14 100644 --- a/nodes.py +++ b/nodes.py @@ -475,7 +475,7 @@ class DiffusersLoader: model_path = path break - return comfy.diffusers_load.load_diffusers(model_path, fp16=comfy.model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return comfy.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) class unCLIPCheckpointLoader: From 2cd3980199ea1769ea3007009c516683b472337b Mon Sep 17 00:00:00 2001 From: Ridan Vandenbergh Date: Wed, 30 Aug 2023 20:46:53 +0200 Subject: [PATCH 34/63] Remove forced lowercase on embeddings endpoint --- server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server.py b/server.py index 0d7d28a0..57d5a65d 100644 --- a/server.py +++ b/server.py @@ -127,7 +127,7 @@ class PromptServer(): @routes.get("/embeddings") def get_embeddings(self): embeddings = folder_paths.get_filename_list("embeddings") - return web.json_response(list(map(lambda a: os.path.splitext(a)[0].lower(), embeddings))) + return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings))) @routes.get("/extensions") async def get_extensions(request): From 5f101f4da14e0b4a360ca1d0c380fab174d301bf Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 31 Aug 2023 02:25:21 -0400 Subject: [PATCH 35/63] Update litegraph with upstream: middle mouse dragging. --- web/lib/litegraph.core.js | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/web/lib/litegraph.core.js b/web/lib/litegraph.core.js index 356c71ac..4bb2f0d9 100644 --- a/web/lib/litegraph.core.js +++ b/web/lib/litegraph.core.js @@ -6233,11 +6233,17 @@ LGraphNode.prototype.executeAction = function(action) ,posAdd:[!mClikSlot_isOut?-30:30, -alphaPosY*130] //-alphaPosY*30] ,posSizeFix:[!mClikSlot_isOut?-1:0, 0] //-alphaPosY*2*/ }); - + skip_action = true; } } } } + + if (!skip_action && this.allow_dragcanvas) { + //console.log("pointerevents: dragging_canvas start from middle button"); + this.dragging_canvas = true; + } + } else if (e.which == 3 || this.pointer_is_double) { From 1c012d69afa8bd92a007a3e468e2a1f874365d39 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 31 Aug 2023 13:25:00 -0400 Subject: [PATCH 36/63] It doesn't make sense for c_crossattn and c_concat to be lists. --- comfy/model_base.py | 4 ++-- comfy/samplers.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index acd4169a..677a23de 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -50,10 +50,10 @@ class BaseModel(torch.nn.Module): def apply_model(self, x, t, c_concat=None, c_crossattn=None, c_adm=None, control=None, transformer_options={}): if c_concat is not None: - xc = torch.cat([x] + c_concat, dim=1) + xc = torch.cat([x] + [c_concat], dim=1) else: xc = x - context = torch.cat(c_crossattn, 1) + context = c_crossattn dtype = self.get_dtype() xc = xc.to(dtype) t = t.to(dtype) diff --git a/comfy/samplers.py b/comfy/samplers.py index 134336de..103ac33f 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -165,9 +165,9 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con c_crossattn_out.append(c) if len(c_crossattn_out) > 0: - out['c_crossattn'] = [torch.cat(c_crossattn_out)] + out['c_crossattn'] = torch.cat(c_crossattn_out) if len(c_concat) > 0: - out['c_concat'] = [torch.cat(c_concat)] + out['c_concat'] = torch.cat(c_concat) if len(c_adm) > 0: out['c_adm'] = torch.cat(c_adm) return out From 57beace324b49f4b6b45291e3940b99c84387e89 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 31 Aug 2023 14:26:16 -0400 Subject: [PATCH 37/63] Fix VAEDecodeTiled minimum. --- nodes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index 5e755f14..38d947d6 100644 --- a/nodes.py +++ b/nodes.py @@ -245,7 +245,7 @@ class VAEDecodeTiled: @classmethod def INPUT_TYPES(s): return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), - "tile_size": ("INT", {"default": 512, "min": 192, "max": 4096, "step": 64}) + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) }} RETURN_TYPES = ("IMAGE",) FUNCTION = "decode" From cfe1c54de88e7525ec7e4189a8a3294dfc3cd4c0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 31 Aug 2023 15:16:58 -0400 Subject: [PATCH 38/63] Fix controlnet issue. --- comfy/controlnet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 83e1be05..f62dd9c8 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -155,7 +155,7 @@ class ControlNet(ControlBase): self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) - context = torch.cat(cond['c_crossattn'], 1) + context = cond['c_crossattn'] y = cond.get('c_adm', None) if y is not None: y = y.to(self.control_model.dtype) From 9a7a52f8b5321e9a67fab18d4f256d2cd6bc338f Mon Sep 17 00:00:00 2001 From: Michael Poutre Date: Thu, 3 Aug 2023 19:49:52 -0700 Subject: [PATCH 39/63] refactor/fix: Treat forceInput widgets as standard widgets --- web/extensions/core/widgetInputs.js | 23 ++++++++++++++++++++--- web/scripts/app.js | 29 ++++++++++++++--------------- 2 files changed, 34 insertions(+), 18 deletions(-) diff --git a/web/extensions/core/widgetInputs.js b/web/extensions/core/widgetInputs.js index d9eaf8a0..a6b1a1dc 100644 --- a/web/extensions/core/widgetInputs.js +++ b/web/extensions/core/widgetInputs.js @@ -5,7 +5,7 @@ const CONVERTED_TYPE = "converted-widget"; const VALID_TYPES = ["STRING", "combo", "number", "BOOLEAN"]; function isConvertableWidget(widget, config) { - return VALID_TYPES.includes(widget.type) || VALID_TYPES.includes(config[0]); + return (VALID_TYPES.includes(widget.type) || VALID_TYPES.includes(config[0])) && !widget.options?.forceInput; } function hideWidget(node, widget, suffix = "") { @@ -103,6 +103,9 @@ app.registerExtension({ let toInput = []; let toWidget = []; for (const w of this.widgets) { + if (w.options?.forceInput) { + continue; + } if (w.type === CONVERTED_TYPE) { toWidget.push({ content: `Convert ${w.name} to widget`, @@ -130,6 +133,20 @@ app.registerExtension({ return r; }; + const origOnNodeCreated = nodeType.prototype.onNodeCreated + nodeType.prototype.onNodeCreated = function () { + const r = origOnNodeCreated ? origOnNodeCreated.apply(this) : undefined; + if (this.widgets) { + for (const w of this.widgets) { + if (w?.options?.forceInput) { + const config = nodeData?.input?.required[w.name] || nodeData?.input?.optional?.[w.name] || [w.type, w.options || {}]; + convertToInput(this, w, config); + } + } + } + return r; + } + // On initial configure of nodes hide all converted widgets const origOnConfigure = nodeType.prototype.onConfigure; nodeType.prototype.onConfigure = function () { @@ -137,7 +154,7 @@ app.registerExtension({ if (this.inputs) { for (const input of this.inputs) { - if (input.widget) { + if (input.widget && !input.widget.config[1]?.forceInput) { const w = this.widgets.find((w) => w.name === input.widget.name); if (w) { hideWidget(this, w); @@ -374,7 +391,7 @@ app.registerExtension({ } for (const k in config1[1]) { - if (k !== "default") { + if (k !== "default" && k !== 'forceInput') { if (config1[1][k] !== config2[1][k]) { return false; } diff --git a/web/scripts/app.js b/web/scripts/app.js index 6a2c6329..42adfde8 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -1151,22 +1151,21 @@ export class ComfyApp { const inputData = inputs[inputName]; const type = inputData[0]; - if(inputData[1]?.forceInput) { - this.addInput(inputName, type); + if (Array.isArray(type)) { + // Enums + Object.assign(config, widgets.COMBO(this, inputName, inputData, app) || {}); + } else if (`${type}:${inputName}` in widgets) { + // Support custom widgets by Type:Name + Object.assign(config, widgets[`${type}:${inputName}`](this, inputName, inputData, app) || {}); + } else if (type in widgets) { + // Standard type widgets + Object.assign(config, widgets[type](this, inputName, inputData, app) || {}); } else { - if (Array.isArray(type)) { - // Enums - Object.assign(config, widgets.COMBO(this, inputName, inputData, app) || {}); - } else if (`${type}:${inputName}` in widgets) { - // Support custom widgets by Type:Name - Object.assign(config, widgets[`${type}:${inputName}`](this, inputName, inputData, app) || {}); - } else if (type in widgets) { - // Standard type widgets - Object.assign(config, widgets[type](this, inputName, inputData, app) || {}); - } else { - // Node connection inputs - this.addInput(inputName, type); - } + // Node connection inputs + this.addInput(inputName, type); + } + if(inputData[1]?.forceInput && config?.widget) { + config.widget.options.forceInput = inputData[1].forceInput; } } From 69c5e6de85cdb46e0869704abff51b483caea967 Mon Sep 17 00:00:00 2001 From: Michael Poutre Date: Thu, 31 Aug 2023 17:55:24 -0700 Subject: [PATCH 40/63] fix(widgets): Add options object if not present when forceInput: true --- web/scripts/app.js | 1 + 1 file changed, 1 insertion(+) diff --git a/web/scripts/app.js b/web/scripts/app.js index 42adfde8..3b7483cd 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -1165,6 +1165,7 @@ export class ComfyApp { this.addInput(inputName, type); } if(inputData[1]?.forceInput && config?.widget) { + if (!config.widget.options) config.widget.options = {}; config.widget.options.forceInput = inputData[1].forceInput; } } From 5c363a9d86827d194e3a8e5dd6085a67f65c7ee6 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 1 Sep 2023 02:01:08 -0400 Subject: [PATCH 41/63] Fix controlnet bug. --- comfy/controlnet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index f62dd9c8..490be6bb 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -143,7 +143,7 @@ class ControlNet(ControlBase): if control_prev is not None: return control_prev else: - return {} + return None output_dtype = x_noisy.dtype if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: From 0e3b64117218c50a554b492269f5f35779839695 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 1 Sep 2023 02:12:03 -0400 Subject: [PATCH 42/63] Remove xformers related print. --- comfy/ldm/modules/attention.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 973619bf..9fdfbd21 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -402,8 +402,6 @@ class MemoryEfficientCrossAttention(nn.Module): # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=comfy.ops): super().__init__() - print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " - f"{heads} heads.") inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) From 7931ff0fd95c1842b0c8e7f5cc3a2ce5d3b88b3b Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 1 Sep 2023 15:18:25 -0400 Subject: [PATCH 43/63] Support SDXL inpaint models. --- comfy/model_base.py | 9 +++------ comfy/model_detection.py | 6 +++++- comfy/sd.py | 7 ++++--- comfy/supported_models.py | 5 ++++- comfy/supported_models_base.py | 11 ++++++----- 5 files changed, 22 insertions(+), 16 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 677a23de..ca154dba 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -111,6 +111,9 @@ class BaseModel(torch.nn.Module): return {**unet_state_dict, **vae_state_dict, **clip_state_dict} + def set_inpaint(self): + self.concat_keys = ("mask", "masked_image") + def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0): adm_inputs = [] weights = [] @@ -148,12 +151,6 @@ class SD21UNCLIP(BaseModel): else: return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05)) - -class SDInpaint(BaseModel): - def __init__(self, model_config, model_type=ModelType.EPS, device=None): - super().__init__(model_config, model_type, device=device) - self.concat_keys = ("mask", "masked_image") - def sdxl_pooled(args, noise_augmentor): if "unclip_conditioning" in args: return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor)[:,:1280] diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 0edc4f18..372d5a2d 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -183,8 +183,12 @@ def unet_config_from_diffusers_unet(state_dict, use_fp16): 'num_res_blocks': 2, 'attention_resolutions': [], 'transformer_depth': [0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0, 'use_linear_in_transformer': True, "num_head_channels": 64, 'context_dim': 1} + SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 9, 'model_channels': 320, + 'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64} - supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet] + supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint] for unet_config in supported_models: matches = True diff --git a/comfy/sd.py b/comfy/sd.py index e98dabe8..8be0bcbc 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -355,13 +355,14 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor) model_config.unet_config = unet_config - if config['model']["target"].endswith("LatentInpaintDiffusion"): - model = model_base.SDInpaint(model_config, model_type=model_type) - elif config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"): + if config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"): model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], model_type=model_type) else: model = model_base.BaseModel(model_config, model_type=model_type) + if config['model']["target"].endswith("LatentInpaintDiffusion"): + model.set_inpaint() + if fp16: model = model.half() diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 95fc8f3f..0b3e4bcb 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -153,7 +153,10 @@ class SDXL(supported_models_base.BASE): return model_base.ModelType.EPS def get_model(self, state_dict, prefix="", device=None): - return model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) + out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) + if self.inpaint_model(): + out.set_inpaint() + return out def process_clip_state_dict(self, state_dict): keys_to_replace = {} diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py index c9cd54d0..395a90ab 100644 --- a/comfy/supported_models_base.py +++ b/comfy/supported_models_base.py @@ -57,12 +57,13 @@ class BASE: self.unet_config[x] = self.unet_extra_config[x] def get_model(self, state_dict, prefix="", device=None): - if self.inpaint_model(): - return model_base.SDInpaint(self, model_type=self.model_type(state_dict, prefix), device=device) - elif self.noise_aug_config is not None: - return model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) + if self.noise_aug_config is not None: + out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) else: - return model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) + out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) + if self.inpaint_model(): + out.set_inpaint() + return out def process_clip_state_dict(self, state_dict): return state_dict From 7891d13329c13943c1d90a5a0973262a85da97d0 Mon Sep 17 00:00:00 2001 From: Muhammed Yusuf <32941435+myusf01@users.noreply.github.com> Date: Sat, 2 Sep 2023 09:58:23 +0300 Subject: [PATCH 44/63] Added label for autoQueueCheckbox. (#1295) * Added label for autoQueueCheckbox. * Menu gets behind of some custom nodes. * Edited extraOptions. Options divided in to different divs to manage them with ease. --- web/scripts/ui.js | 18 +++++++++++++++--- web/style.css | 2 +- 2 files changed, 16 insertions(+), 4 deletions(-) diff --git a/web/scripts/ui.js b/web/scripts/ui.js index 86e2a1c4..8611c248 100644 --- a/web/scripts/ui.js +++ b/web/scripts/ui.js @@ -617,7 +617,10 @@ export class ComfyUI { ]), ]), $el("div", {id: "extraOptions", style: {width: "100%", display: "none"}}, [ - $el("label", {innerHTML: "Batch count"}, [ + $el("div",[ + + $el("label", {innerHTML: "Batch count"}), + $el("input", { id: "batchCountInputNumber", type: "number", @@ -639,14 +642,23 @@ export class ComfyUI { this.batchCount = i.srcElement.value; document.getElementById("batchCountInputNumber").value = i.srcElement.value; }, + }), + ]), + + $el("div",[ + $el("label",{ + for:"autoQueueCheckbox", + innerHTML: "Auto Queue" + // textContent: "Auto Queue" }), $el("input", { id: "autoQueueCheckbox", type: "checkbox", checked: false, - title: "automatically queue prompt when the queue size hits 0", + title: "Automatically queue prompt when the queue size hits 0", + }), - ]), + ]) ]), $el("div.comfy-menu-btns", [ $el("button", { diff --git a/web/style.css b/web/style.css index 5b6b9ec5..692fa31d 100644 --- a/web/style.css +++ b/web/style.css @@ -88,7 +88,7 @@ body { top: 50%; right: 0; text-align: center; - z-index: 100; + z-index: 999; width: 170px; display: flex; flex-direction: column; From 36ea8784a875bde21c88f84dfb99475b6e8187e8 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Sep 2023 03:34:57 -0400 Subject: [PATCH 45/63] Only return tuple of 3 args in CheckpointLoaderSimple. --- nodes.py | 2 +- web/scripts/ui.js | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/nodes.py b/nodes.py index 38d947d6..fa26e593 100644 --- a/nodes.py +++ b/nodes.py @@ -449,7 +449,7 @@ class CheckpointLoaderSimple: def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) - return out + return out[:3] class DiffusersLoader: @classmethod diff --git a/web/scripts/ui.js b/web/scripts/ui.js index 8611c248..ce3f4fce 100644 --- a/web/scripts/ui.js +++ b/web/scripts/ui.js @@ -620,7 +620,6 @@ export class ComfyUI { $el("div",[ $el("label", {innerHTML: "Batch count"}), - $el("input", { id: "batchCountInputNumber", type: "number", From 77a176f9e0f4777363a414fbb006cb133d31e034 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Sep 2023 03:42:49 -0400 Subject: [PATCH 46/63] Use common function to reshape batch to. --- comfy/sample.py | 8 +++----- comfy/utils.py | 7 +++++++ 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/comfy/sample.py b/comfy/sample.py index 79ea37e0..e4730b18 100644 --- a/comfy/sample.py +++ b/comfy/sample.py @@ -1,6 +1,7 @@ import torch import comfy.model_management import comfy.samplers +import comfy.utils import math import numpy as np @@ -28,8 +29,7 @@ def prepare_mask(noise_mask, shape, device): noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear") noise_mask = noise_mask.round() noise_mask = torch.cat([noise_mask] * shape[1], dim=1) - if noise_mask.shape[0] < shape[0]: - noise_mask = noise_mask.repeat(math.ceil(shape[0] / noise_mask.shape[0]), 1, 1, 1)[:shape[0]] + noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0]) noise_mask = noise_mask.to(device) return noise_mask @@ -37,9 +37,7 @@ def broadcast_cond(cond, batch, device): """broadcasts conditioning to the batch size""" copy = [] for p in cond: - t = p[0] - if t.shape[0] < batch: - t = torch.cat([t] * batch) + t = comfy.utils.repeat_to_batch_size(p[0], batch) t = t.to(device) copy += [[t] + p[1:]] return copy diff --git a/comfy/utils.py b/comfy/utils.py index 693e2612..47f4b970 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -223,6 +223,13 @@ def unet_to_diffusers(unet_config): return diffusers_unet_map +def repeat_to_batch_size(tensor, batch_size): + if tensor.shape[0] > batch_size: + return tensor[:batch_size] + elif tensor.shape[0] < batch_size: + return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size] + return tensor + def convert_sd_to(state_dict, dtype): keys = list(state_dict.keys()) for k in keys: From 7291e303f662f35bc545a8aaa0020558e82a8ca9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Sep 2023 11:48:44 -0400 Subject: [PATCH 47/63] Fix issue with some workflows not getting serialized. --- web/extensions/core/widgetInputs.js | 3 +++ 1 file changed, 3 insertions(+) diff --git a/web/extensions/core/widgetInputs.js b/web/extensions/core/widgetInputs.js index a6b1a1dc..34c656de 100644 --- a/web/extensions/core/widgetInputs.js +++ b/web/extensions/core/widgetInputs.js @@ -16,6 +16,9 @@ function hideWidget(node, widget, suffix = "") { widget.type = CONVERTED_TYPE + suffix; widget.serializeValue = () => { // Prevent serializing the widget if we have no input linked + if (!node.inputs) { + return undefined; + } const { link } = node.inputs.find((i) => i.widget?.name === widget.name); if (link == null) { return undefined; From 6962cb46a99ed0a9895dc06ca293b9e48e3eabc8 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Sep 2023 12:17:30 -0400 Subject: [PATCH 48/63] Fix issue when node_input is undefined. --- web/extensions/core/widgetInputs.js | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/web/extensions/core/widgetInputs.js b/web/extensions/core/widgetInputs.js index 34c656de..f9a5b727 100644 --- a/web/extensions/core/widgetInputs.js +++ b/web/extensions/core/widgetInputs.js @@ -19,8 +19,9 @@ function hideWidget(node, widget, suffix = "") { if (!node.inputs) { return undefined; } - const { link } = node.inputs.find((i) => i.widget?.name === widget.name); - if (link == null) { + let node_input = node.inputs.find((i) => i.widget?.name === widget.name); + + if (!node_input || !node_input.link) { return undefined; } return widget.origSerializeValue ? widget.origSerializeValue() : widget.value; From 62efc78a4b13b87ef0df51323fe1bd71b433fa11 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Sep 2023 15:36:45 -0400 Subject: [PATCH 49/63] Display history in reverse order to make it easier to load last gen. --- web/scripts/ui.js | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/web/scripts/ui.js b/web/scripts/ui.js index ce3f4fce..f39939bf 100644 --- a/web/scripts/ui.js +++ b/web/scripts/ui.js @@ -431,10 +431,12 @@ class ComfySettingsDialog extends ComfyDialog { class ComfyList { #type; #text; + #reverse; - constructor(text, type) { + constructor(text, type, reverse) { this.#text = text; this.#type = type || text.toLowerCase(); + this.#reverse = reverse || false; this.element = $el("div.comfy-list"); this.element.style.display = "none"; } @@ -451,7 +453,7 @@ class ComfyList { textContent: section, }), $el("div.comfy-list-items", [ - ...items[section].map((item) => { + ...(this.#reverse ? items[section].reverse() : items[section]).map((item) => { // Allow items to specify a custom remove action (e.g. for interrupt current prompt) const removeAction = item.remove || { name: "Delete", @@ -529,7 +531,7 @@ export class ComfyUI { this.batchCount = 1; this.lastQueueSize = 0; this.queue = new ComfyList("Queue"); - this.history = new ComfyList("History"); + this.history = new ComfyList("History", "history", true); api.addEventListener("status", () => { this.queue.update(); From dfd6489c9622fdf48728e336fc263df283c84903 Mon Sep 17 00:00:00 2001 From: Chris Date: Sun, 3 Sep 2023 07:53:02 +1000 Subject: [PATCH 50/63] onExecutionStart --- web/scripts/app.js | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/web/scripts/app.js b/web/scripts/app.js index 3b7483cd..ce5e27d0 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -994,6 +994,10 @@ export class ComfyApp { api.addEventListener("execution_start", ({ detail }) => { this.runningNodeId = null; this.lastExecutionError = null + this.graph._nodes.forEach((node) => { + if (node.onExecutionStart) + node.onExecutionStart() + }) }); api.addEventListener("execution_error", ({ detail }) => { From 4a0c4ce4ef3c1e0f2b777dcd20a8864be1420f19 Mon Sep 17 00:00:00 2001 From: Simon Lui <502929+simonlui@users.noreply.github.com> Date: Sat, 2 Sep 2023 18:22:10 -0700 Subject: [PATCH 51/63] Some fixes to generalize CUDA specific functionality to Intel or other GPUs. --- comfy/ldm/modules/attention.py | 3 +- comfy/ldm/modules/diffusionmodules/util.py | 24 ++++++++++---- comfy/model_management.py | 37 ++++++++++++---------- 3 files changed, 38 insertions(+), 26 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 9fdfbd21..8f953d33 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -323,8 +323,7 @@ class CrossAttentionDoggettx(nn.Module): break except model_management.OOM_EXCEPTION as e: if first_op_done == False: - torch.cuda.empty_cache() - torch.cuda.ipc_collect() + model_management.soft_empty_cache() if cleared_cache == False: cleared_cache = True print("out of memory error, emptying cache and trying again") diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py index d890c804..9d07d935 100644 --- a/comfy/ldm/modules/diffusionmodules/util.py +++ b/comfy/ldm/modules/diffusionmodules/util.py @@ -15,6 +15,7 @@ import torch.nn as nn import numpy as np from einops import repeat +from comfy import model_management from comfy.ldm.util import instantiate_from_config import comfy.ops @@ -139,13 +140,22 @@ class CheckpointFunction(torch.autograd.Function): @staticmethod def backward(ctx, *output_grads): ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] - with torch.enable_grad(), \ - torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): - # Fixes a bug where the first op in run_function modifies the - # Tensor storage in place, which is not allowed for detach()'d - # Tensors. - shallow_copies = [x.view_as(x) for x in ctx.input_tensors] - output_tensors = ctx.run_function(*shallow_copies) + if model_management.is_nvidia(): + with torch.enable_grad(), \ + torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + elif model_management.is_intel_xpu(): + with torch.enable_grad(), \ + torch.xpu.amp.autocast(**ctx.gpu_autocast_kwargs): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) input_grads = torch.autograd.grad( output_tensors, ctx.input_tensors + ctx.input_params, diff --git a/comfy/model_management.py b/comfy/model_management.py index aca8af99..bdbbbd84 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -58,8 +58,15 @@ except: if args.cpu: cpu_state = CPUState.CPU -def get_torch_device(): +def is_intel_xpu(): + global cpu_state global xpu_available + if cpu_state == CPUState.GPU: + if xpu_available: + return True + return False + +def get_torch_device(): global directml_enabled global cpu_state if directml_enabled: @@ -70,13 +77,12 @@ def get_torch_device(): if cpu_state == CPUState.CPU: return torch.device("cpu") else: - if xpu_available: + if is_intel_xpu(): return torch.device("xpu") else: return torch.device(torch.cuda.current_device()) def get_total_memory(dev=None, torch_total_too=False): - global xpu_available global directml_enabled if dev is None: dev = get_torch_device() @@ -88,7 +94,7 @@ def get_total_memory(dev=None, torch_total_too=False): if directml_enabled: mem_total = 1024 * 1024 * 1024 #TODO mem_total_torch = mem_total - elif xpu_available: + elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] mem_total = torch.xpu.get_device_properties(dev).total_memory @@ -146,11 +152,11 @@ def is_nvidia(): if cpu_state == CPUState.GPU: if torch.version.cuda: return True + return False ENABLE_PYTORCH_ATTENTION = args.use_pytorch_cross_attention VAE_DTYPE = torch.float32 - try: if is_nvidia(): torch_version = torch.version.__version__ @@ -162,6 +168,9 @@ try: except: pass +if is_intel_xpu(): + VAE_DTYPE = torch.bfloat16 + if args.fp16_vae: VAE_DTYPE = torch.float16 elif args.bf16_vae: @@ -220,7 +229,6 @@ if DISABLE_SMART_MEMORY: print("Disabling smart memory management") def get_torch_device_name(device): - global xpu_available if hasattr(device, 'type'): if device.type == "cuda": try: @@ -230,7 +238,7 @@ def get_torch_device_name(device): return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend) else: return "{}".format(device.type) - elif xpu_available: + elif is_intel_xpu(): return "{} {}".format(device, torch.xpu.get_device_name(device)) else: return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) @@ -260,7 +268,6 @@ class LoadedModel: return self.model_memory() def model_load(self, lowvram_model_memory=0): - global xpu_available patch_model_to = None if lowvram_model_memory == 0: patch_model_to = self.device @@ -281,7 +288,7 @@ class LoadedModel: accelerate.dispatch_model(self.real_model, device_map=device_map, main_device=self.device) self.model_accelerated = True - if xpu_available and not args.disable_ipex_optimize: + if is_intel_xpu() and not args.disable_ipex_optimize: self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True) return self.real_model @@ -471,12 +478,11 @@ def get_autocast_device(dev): def xformers_enabled(): - global xpu_available global directml_enabled global cpu_state if cpu_state != CPUState.GPU: return False - if xpu_available: + if is_intel_xpu(): return False if directml_enabled: return False @@ -503,7 +509,6 @@ def pytorch_attention_flash_attention(): return False def get_free_memory(dev=None, torch_free_too=False): - global xpu_available global directml_enabled if dev is None: dev = get_torch_device() @@ -515,7 +520,7 @@ def get_free_memory(dev=None, torch_free_too=False): if directml_enabled: mem_free_total = 1024 * 1024 * 1024 #TODO mem_free_torch = mem_free_total - elif xpu_available: + elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_active = stats['active_bytes.all.current'] mem_allocated = stats['allocated_bytes.all.current'] @@ -577,7 +582,6 @@ def is_device_mps(device): return False def should_use_fp16(device=None, model_params=0, prioritize_performance=True): - global xpu_available global directml_enabled if device is not None: @@ -600,7 +604,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True): if cpu_mode() or mps_mode(): return False #TODO ? - if xpu_available: + if is_intel_xpu(): return True if torch.cuda.is_bf16_supported(): @@ -636,11 +640,10 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True): return True def soft_empty_cache(): - global xpu_available global cpu_state if cpu_state == CPUState.MPS: torch.mps.empty_cache() - elif xpu_available: + elif is_intel_xpu(): torch.xpu.empty_cache() elif torch.cuda.is_available(): if is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda From 766c7b3815c0203d98200772fd7fe1b908cfaa0c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Sep 2023 22:25:12 -0400 Subject: [PATCH 52/63] Update upscale model code to latest Chainner model code. Don't add SRFormer because the code license is incompatible with the GPL. Remove MAT because it's unused and the license is incompatible with GPL. --- .../chainner_models/architecture/DAT.py | 1182 ++++++++++++ .../chainner_models/architecture/LICENSE-DAT | 201 ++ .../architecture/LICENSE-SCUNet | 201 ++ .../chainner_models/architecture/LICENSE-mat | 161 -- .../chainner_models/architecture/MAT.py | 1636 ----------------- .../architecture/OmniSR/OmniSR.py | 12 +- .../chainner_models/architecture/SCUNet.py | 455 +++++ .../chainner_models/architecture/SPSR.py | 1 - .../chainner_models/architecture/SwinIR.py | 1 + .../chainner_models/architecture/mat/utils.py | 698 ------- comfy_extras/chainner_models/model_loading.py | 25 +- comfy_extras/chainner_models/types.py | 22 +- 12 files changed, 2084 insertions(+), 2511 deletions(-) create mode 100644 comfy_extras/chainner_models/architecture/DAT.py create mode 100644 comfy_extras/chainner_models/architecture/LICENSE-DAT create mode 100644 comfy_extras/chainner_models/architecture/LICENSE-SCUNet delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-mat delete mode 100644 comfy_extras/chainner_models/architecture/MAT.py create mode 100644 comfy_extras/chainner_models/architecture/SCUNet.py delete mode 100644 comfy_extras/chainner_models/architecture/mat/utils.py diff --git a/comfy_extras/chainner_models/architecture/DAT.py b/comfy_extras/chainner_models/architecture/DAT.py new file mode 100644 index 00000000..0bcc26ef --- /dev/null +++ b/comfy_extras/chainner_models/architecture/DAT.py @@ -0,0 +1,1182 @@ +# pylint: skip-file +import math +import re + +import numpy as np +import torch +import torch.nn as nn +import torch.utils.checkpoint as checkpoint +from einops import rearrange +from einops.layers.torch import Rearrange +from torch import Tensor +from torch.nn import functional as F + +from .timm.drop import DropPath +from .timm.weight_init import trunc_normal_ + + +def img2windows(img, H_sp, W_sp): + """ + Input: Image (B, C, H, W) + Output: Window Partition (B', N, C) + """ + B, C, H, W = img.shape + img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp) + img_perm = ( + img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp * W_sp, C) + ) + return img_perm + + +def windows2img(img_splits_hw, H_sp, W_sp, H, W): + """ + Input: Window Partition (B', N, C) + Output: Image (B, H, W, C) + """ + B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp)) + + img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1) + img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return img + + +class SpatialGate(nn.Module): + """Spatial-Gate. + Args: + dim (int): Half of input channels. + """ + + def __init__(self, dim): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.conv = nn.Conv2d( + dim, dim, kernel_size=3, stride=1, padding=1, groups=dim + ) # DW Conv + + def forward(self, x, H, W): + # Split + x1, x2 = x.chunk(2, dim=-1) + B, N, C = x.shape + x2 = ( + self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C // 2, H, W)) + .flatten(2) + .transpose(-1, -2) + .contiguous() + ) + + return x1 * x2 + + +class SGFN(nn.Module): + """Spatial-Gate Feed-Forward Network. + Args: + in_features (int): Number of input channels. + hidden_features (int | None): Number of hidden channels. Default: None + out_features (int | None): Number of output channels. Default: None + act_layer (nn.Module): Activation layer. Default: nn.GELU + drop (float): Dropout rate. Default: 0.0 + """ + + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.sg = SpatialGate(hidden_features // 2) + self.fc2 = nn.Linear(hidden_features // 2, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + + x = self.sg(x, H, W) + x = self.drop(x) + + x = self.fc2(x) + x = self.drop(x) + return x + + +class DynamicPosBias(nn.Module): + # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py + """Dynamic Relative Position Bias. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + residual (bool): If True, use residual strage to connect conv. + """ + + def __init__(self, dim, num_heads, residual): + super().__init__() + self.residual = residual + self.num_heads = num_heads + self.pos_dim = dim // 4 + self.pos_proj = nn.Linear(2, self.pos_dim) + self.pos1 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.pos_dim), + ) + self.pos2 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.pos_dim), + ) + self.pos3 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.num_heads), + ) + + def forward(self, biases): + if self.residual: + pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads + pos = pos + self.pos1(pos) + pos = pos + self.pos2(pos) + pos = self.pos3(pos) + else: + pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases)))) + return pos + + +class Spatial_Attention(nn.Module): + """Spatial Window Self-Attention. + It supports rectangle window (containing square window). + Args: + dim (int): Number of input channels. + idx (int): The indentix of window. (0/1) + split_size (tuple(int)): Height and Width of spatial window. + dim_out (int | None): The dimension of the attention output. Default: None + num_heads (int): Number of attention heads. Default: 6 + attn_drop (float): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float): Dropout ratio of output. Default: 0.0 + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set + position_bias (bool): The dynamic relative position bias. Default: True + """ + + def __init__( + self, + dim, + idx, + split_size=[8, 8], + dim_out=None, + num_heads=6, + attn_drop=0.0, + proj_drop=0.0, + qk_scale=None, + position_bias=True, + ): + super().__init__() + self.dim = dim + self.dim_out = dim_out or dim + self.split_size = split_size + self.num_heads = num_heads + self.idx = idx + self.position_bias = position_bias + + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + if idx == 0: + H_sp, W_sp = self.split_size[0], self.split_size[1] + elif idx == 1: + W_sp, H_sp = self.split_size[0], self.split_size[1] + else: + print("ERROR MODE", idx) + exit(0) + self.H_sp = H_sp + self.W_sp = W_sp + + if self.position_bias: + self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False) + # generate mother-set + position_bias_h = torch.arange(1 - self.H_sp, self.H_sp) + position_bias_w = torch.arange(1 - self.W_sp, self.W_sp) + biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w])) + biases = biases.flatten(1).transpose(0, 1).contiguous().float() + self.register_buffer("rpe_biases", biases) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.H_sp) + coords_w = torch.arange(self.W_sp) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) + coords_flatten = torch.flatten(coords, 1) + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] + relative_coords = relative_coords.permute(1, 2, 0).contiguous() + relative_coords[:, :, 0] += self.H_sp - 1 + relative_coords[:, :, 1] += self.W_sp - 1 + relative_coords[:, :, 0] *= 2 * self.W_sp - 1 + relative_position_index = relative_coords.sum(-1) + self.register_buffer("relative_position_index", relative_position_index) + + self.attn_drop = nn.Dropout(attn_drop) + + def im2win(self, x, H, W): + B, N, C = x.shape + x = x.transpose(-2, -1).contiguous().view(B, C, H, W) + x = img2windows(x, self.H_sp, self.W_sp) + x = ( + x.reshape(-1, self.H_sp * self.W_sp, self.num_heads, C // self.num_heads) + .permute(0, 2, 1, 3) + .contiguous() + ) + return x + + def forward(self, qkv, H, W, mask=None): + """ + Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size + Output: x (B, H, W, C) + """ + q, k, v = qkv[0], qkv[1], qkv[2] + + B, L, C = q.shape + assert L == H * W, "flatten img_tokens has wrong size" + + # partition the q,k,v, image to window + q = self.im2win(q, H, W) + k = self.im2win(k, H, W) + v = self.im2win(v, H, W) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) # B head N C @ B head C N --> B head N N + + # calculate drpe + if self.position_bias: + pos = self.pos(self.rpe_biases) + # select position bias + relative_position_bias = pos[self.relative_position_index.view(-1)].view( + self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1 + ) + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() + attn = attn + relative_position_bias.unsqueeze(0) + + N = attn.shape[3] + + # use mask for shift window + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze( + 0 + ) + attn = attn.view(-1, self.num_heads, N, N) + + attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype) + attn = self.attn_drop(attn) + + x = attn @ v + x = x.transpose(1, 2).reshape( + -1, self.H_sp * self.W_sp, C + ) # B head N N @ B head N C + + # merge the window, window to image + x = windows2img(x, self.H_sp, self.W_sp, H, W) # B H' W' C + + return x + + +class Adaptive_Spatial_Attention(nn.Module): + # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT + """Adaptive Spatial Self-Attention + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. Default: 6 + split_size (tuple(int)): Height and Width of spatial window. + shift_size (tuple(int)): Shift size for spatial window. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. + drop (float): Dropout rate. Default: 0.0 + attn_drop (float): Attention dropout rate. Default: 0.0 + rg_idx (int): The indentix of Residual Group (RG) + b_idx (int): The indentix of Block in each RG + """ + + def __init__( + self, + dim, + num_heads, + reso=64, + split_size=[8, 8], + shift_size=[1, 2], + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + rg_idx=0, + b_idx=0, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.split_size = split_size + self.shift_size = shift_size + self.b_idx = b_idx + self.rg_idx = rg_idx + self.patches_resolution = reso + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + + assert ( + 0 <= self.shift_size[0] < self.split_size[0] + ), "shift_size must in 0-split_size0" + assert ( + 0 <= self.shift_size[1] < self.split_size[1] + ), "shift_size must in 0-split_size1" + + self.branch_num = 2 + + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(drop) + + self.attns = nn.ModuleList( + [ + Spatial_Attention( + dim // 2, + idx=i, + split_size=split_size, + num_heads=num_heads // 2, + dim_out=dim // 2, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + position_bias=True, + ) + for i in range(self.branch_num) + ] + ) + + if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( + self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 + ): + attn_mask = self.calculate_mask( + self.patches_resolution, self.patches_resolution + ) + self.register_buffer("attn_mask_0", attn_mask[0]) + self.register_buffer("attn_mask_1", attn_mask[1]) + else: + attn_mask = None + self.register_buffer("attn_mask_0", None) + self.register_buffer("attn_mask_1", None) + + self.dwconv = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), + nn.BatchNorm2d(dim), + nn.GELU(), + ) + self.channel_interaction = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(dim, dim // 8, kernel_size=1), + nn.BatchNorm2d(dim // 8), + nn.GELU(), + nn.Conv2d(dim // 8, dim, kernel_size=1), + ) + self.spatial_interaction = nn.Sequential( + nn.Conv2d(dim, dim // 16, kernel_size=1), + nn.BatchNorm2d(dim // 16), + nn.GELU(), + nn.Conv2d(dim // 16, 1, kernel_size=1), + ) + + def calculate_mask(self, H, W): + # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py + # calculate attention mask for shift window + img_mask_0 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=0 + img_mask_1 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=1 + h_slices_0 = ( + slice(0, -self.split_size[0]), + slice(-self.split_size[0], -self.shift_size[0]), + slice(-self.shift_size[0], None), + ) + w_slices_0 = ( + slice(0, -self.split_size[1]), + slice(-self.split_size[1], -self.shift_size[1]), + slice(-self.shift_size[1], None), + ) + + h_slices_1 = ( + slice(0, -self.split_size[1]), + slice(-self.split_size[1], -self.shift_size[1]), + slice(-self.shift_size[1], None), + ) + w_slices_1 = ( + slice(0, -self.split_size[0]), + slice(-self.split_size[0], -self.shift_size[0]), + slice(-self.shift_size[0], None), + ) + cnt = 0 + for h in h_slices_0: + for w in w_slices_0: + img_mask_0[:, h, w, :] = cnt + cnt += 1 + cnt = 0 + for h in h_slices_1: + for w in w_slices_1: + img_mask_1[:, h, w, :] = cnt + cnt += 1 + + # calculate mask for window-0 + img_mask_0 = img_mask_0.view( + 1, + H // self.split_size[0], + self.split_size[0], + W // self.split_size[1], + self.split_size[1], + 1, + ) + img_mask_0 = ( + img_mask_0.permute(0, 1, 3, 2, 4, 5) + .contiguous() + .view(-1, self.split_size[0], self.split_size[1], 1) + ) # nW, sw[0], sw[1], 1 + mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1]) + attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2) + attn_mask_0 = attn_mask_0.masked_fill( + attn_mask_0 != 0, float(-100.0) + ).masked_fill(attn_mask_0 == 0, float(0.0)) + + # calculate mask for window-1 + img_mask_1 = img_mask_1.view( + 1, + H // self.split_size[1], + self.split_size[1], + W // self.split_size[0], + self.split_size[0], + 1, + ) + img_mask_1 = ( + img_mask_1.permute(0, 1, 3, 2, 4, 5) + .contiguous() + .view(-1, self.split_size[1], self.split_size[0], 1) + ) # nW, sw[1], sw[0], 1 + mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0]) + attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2) + attn_mask_1 = attn_mask_1.masked_fill( + attn_mask_1 != 0, float(-100.0) + ).masked_fill(attn_mask_1 == 0, float(0.0)) + + return attn_mask_0, attn_mask_1 + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + B, L, C = x.shape + assert L == H * W, "flatten img_tokens has wrong size" + + qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C + # V without partition + v = qkv[2].transpose(-2, -1).contiguous().view(B, C, H, W) + + # image padding + max_split_size = max(self.split_size[0], self.split_size[1]) + pad_l = pad_t = 0 + pad_r = (max_split_size - W % max_split_size) % max_split_size + pad_b = (max_split_size - H % max_split_size) % max_split_size + + qkv = qkv.reshape(3 * B, H, W, C).permute(0, 3, 1, 2) # 3B C H W + qkv = ( + F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)) + .reshape(3, B, C, -1) + .transpose(-2, -1) + ) # l r t b + _H = pad_b + H + _W = pad_r + W + _L = _H * _W + + # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged + # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ... + if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( + self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 + ): + qkv = qkv.view(3, B, _H, _W, C) + qkv_0 = torch.roll( + qkv[:, :, :, :, : C // 2], + shifts=(-self.shift_size[0], -self.shift_size[1]), + dims=(2, 3), + ) + qkv_0 = qkv_0.view(3, B, _L, C // 2) + qkv_1 = torch.roll( + qkv[:, :, :, :, C // 2 :], + shifts=(-self.shift_size[1], -self.shift_size[0]), + dims=(2, 3), + ) + qkv_1 = qkv_1.view(3, B, _L, C // 2) + + if self.patches_resolution != _H or self.patches_resolution != _W: + mask_tmp = self.calculate_mask(_H, _W) + x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device)) + x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device)) + else: + x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0) + x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1) + + x1 = torch.roll( + x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2) + ) + x2 = torch.roll( + x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2) + ) + x1 = x1[:, :H, :W, :].reshape(B, L, C // 2) + x2 = x2[:, :H, :W, :].reshape(B, L, C // 2) + # attention output + attened_x = torch.cat([x1, x2], dim=2) + + else: + x1 = self.attns[0](qkv[:, :, :, : C // 2], _H, _W)[:, :H, :W, :].reshape( + B, L, C // 2 + ) + x2 = self.attns[1](qkv[:, :, :, C // 2 :], _H, _W)[:, :H, :W, :].reshape( + B, L, C // 2 + ) + # attention output + attened_x = torch.cat([x1, x2], dim=2) + + # convolution output + conv_x = self.dwconv(v) + + # Adaptive Interaction Module (AIM) + # C-Map (before sigmoid) + channel_map = ( + self.channel_interaction(conv_x) + .permute(0, 2, 3, 1) + .contiguous() + .view(B, 1, C) + ) + # S-Map (before sigmoid) + attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) + spatial_map = self.spatial_interaction(attention_reshape) + + # C-I + attened_x = attened_x * torch.sigmoid(channel_map) + # S-I + conv_x = torch.sigmoid(spatial_map) * conv_x + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C) + + x = attened_x + conv_x + + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class Adaptive_Channel_Attention(nn.Module): + # The implementation builds on XCiT code https://github.com/facebookresearch/xcit + """Adaptive Channel Self-Attention + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. Default: 6 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. + attn_drop (float): Attention dropout rate. Default: 0.0 + drop_path (float): Stochastic depth rate. Default: 0.0 + """ + + def __init__( + self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.num_heads = num_heads + self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.dwconv = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), + nn.BatchNorm2d(dim), + nn.GELU(), + ) + self.channel_interaction = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(dim, dim // 8, kernel_size=1), + nn.BatchNorm2d(dim // 8), + nn.GELU(), + nn.Conv2d(dim // 8, dim, kernel_size=1), + ) + self.spatial_interaction = nn.Sequential( + nn.Conv2d(dim, dim // 16, kernel_size=1), + nn.BatchNorm2d(dim // 16), + nn.GELU(), + nn.Conv2d(dim // 16, 1, kernel_size=1), + ) + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + qkv = qkv.permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + q = q.transpose(-2, -1) + k = k.transpose(-2, -1) + v = v.transpose(-2, -1) + + v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W) + + q = torch.nn.functional.normalize(q, dim=-1) + k = torch.nn.functional.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + # attention output + attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C) + + # convolution output + conv_x = self.dwconv(v_) + + # Adaptive Interaction Module (AIM) + # C-Map (before sigmoid) + attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) + channel_map = self.channel_interaction(attention_reshape) + # S-Map (before sigmoid) + spatial_map = ( + self.spatial_interaction(conv_x) + .permute(0, 2, 3, 1) + .contiguous() + .view(B, N, 1) + ) + + # S-I + attened_x = attened_x * torch.sigmoid(spatial_map) + # C-I + conv_x = conv_x * torch.sigmoid(channel_map) + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C) + + x = attened_x + conv_x + + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class DATB(nn.Module): + def __init__( + self, + dim, + num_heads, + reso=64, + split_size=[2, 4], + shift_size=[1, 2], + expansion_factor=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + rg_idx=0, + b_idx=0, + ): + super().__init__() + + self.norm1 = norm_layer(dim) + + if b_idx % 2 == 0: + # DSTB + self.attn = Adaptive_Spatial_Attention( + dim, + num_heads=num_heads, + reso=reso, + split_size=split_size, + shift_size=shift_size, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + rg_idx=rg_idx, + b_idx=b_idx, + ) + else: + # DCTB + self.attn = Adaptive_Channel_Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + ffn_hidden_dim = int(dim * expansion_factor) + self.ffn = SGFN( + in_features=dim, + hidden_features=ffn_hidden_dim, + out_features=dim, + act_layer=act_layer, + ) + self.norm2 = norm_layer(dim) + + def forward(self, x, x_size): + """ + Input: x: (B, H*W, C), x_size: (H, W) + Output: x: (B, H*W, C) + """ + H, W = x_size + x = x + self.drop_path(self.attn(self.norm1(x), H, W)) + x = x + self.drop_path(self.ffn(self.norm2(x), H, W)) + + return x + + +class ResidualGroup(nn.Module): + """ResidualGroup + Args: + dim (int): Number of input channels. + reso (int): Input resolution. + num_heads (int): Number of attention heads. + split_size (tuple(int)): Height and Width of spatial window. + expansion_factor (float): Ratio of ffn hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop (float): Dropout rate. Default: 0 + attn_drop(float): Attention dropout rate. Default: 0 + drop_paths (float | None): Stochastic depth rate. + act_layer (nn.Module): Activation layer. Default: nn.GELU + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm + depth (int): Number of dual aggregation Transformer blocks in residual group. + use_chk (bool): Whether to use checkpointing to save memory. + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + dim, + reso, + num_heads, + split_size=[2, 4], + expansion_factor=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_paths=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + depth=2, + use_chk=False, + resi_connection="1conv", + rg_idx=0, + ): + super().__init__() + self.use_chk = use_chk + self.reso = reso + + self.blocks = nn.ModuleList( + [ + DATB( + dim=dim, + num_heads=num_heads, + reso=reso, + split_size=split_size, + shift_size=[split_size[0] // 2, split_size[1] // 2], + expansion_factor=expansion_factor, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_paths[i], + act_layer=act_layer, + norm_layer=norm_layer, + rg_idx=rg_idx, + b_idx=i, + ) + for i in range(depth) + ] + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + def forward(self, x, x_size): + """ + Input: x: (B, H*W, C), x_size: (H, W) + Output: x: (B, H*W, C) + """ + H, W = x_size + res = x + for blk in self.blocks: + if self.use_chk: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) + x = self.conv(x) + x = rearrange(x, "b c h w -> b (h w) c") + x = res + x + + return x + + +class Upsample(nn.Sequential): + """Upsample module. + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + h, w = self.input_resolution + flops = h * w * self.num_feat * 3 * 9 + return flops + + +class DAT(nn.Module): + """Dual Aggregation Transformer + Args: + img_size (int): Input image size. Default: 64 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 180 + depths (tuple(int)): Depth of each residual group (number of DATB in each RG). + split_size (tuple(int)): Height and Width of spatial window. + num_heads (tuple(int)): Number of attention heads in different residual groups. + expansion_factor (float): Ratio of ffn hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + act_layer (nn.Module): Activation layer. Default: nn.GELU + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm + use_chk (bool): Whether to use checkpointing to save memory. + upscale: Upscale factor. 2/3/4 for image SR + img_range: Image range. 1. or 255. + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__(self, state_dict): + super().__init__() + + # defaults + img_size = 64 + in_chans = 3 + embed_dim = 180 + split_size = [2, 4] + depth = [2, 2, 2, 2] + num_heads = [2, 2, 2, 2] + expansion_factor = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + act_layer = nn.GELU + norm_layer = nn.LayerNorm + use_chk = False + upscale = 2 + img_range = 1.0 + resi_connection = "1conv" + upsampler = "pixelshuffle" + + self.model_arch = "DAT" + self.sub_type = "SR" + self.state = state_dict + + state_keys = state_dict.keys() + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + state_dict.get("conv_before_upsample.0.weight", None).shape[1] + if state_dict.get("conv_before_upsample.weight", None) + else 64 + ) + + num_in_ch = state_dict["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = state_dict["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = state_dict[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(state_dict["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match(r"layers.(\d*).blocks.(\d*).norm1.weight", key) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depth = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if "layers.0.blocks.1.attn.temperature" in state_keys: + num_heads_num = state_dict["layers.0.blocks.1.attn.temperature"].shape[0] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depth + + embed_dim = state_dict["conv_first.weight"].shape[0] + expansion_factor = float( + state_dict["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + if "layers.0.blocks.2.attn.attn_mask_0" in state_keys: + attn_mask_0_x, attn_mask_0_y, attn_mask_0_z = state_dict[ + "layers.0.blocks.2.attn.attn_mask_0" + ].shape + + img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y)) + + if "layers.0.blocks.0.attn.attns.0.rpe_biases" in state_keys: + split_sizes = ( + state_dict["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1 + ) + split_size = [int(x) for x in split_sizes] + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depth = depth + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.expansion_factor = expansion_factor + self.resi_connection = resi_connection + self.split_size = split_size + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + + # ------------------------- 1, Shallow Feature Extraction ------------------------- # + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + # ------------------------- 2, Deep Feature Extraction ------------------------- # + self.num_layers = len(depth) + self.use_chk = use_chk + self.num_features = ( + self.embed_dim + ) = embed_dim # num_features for consistency with other models + heads = num_heads + + self.before_RG = nn.Sequential( + Rearrange("b c h w -> b (h w) c"), nn.LayerNorm(embed_dim) + ) + + curr_dim = embed_dim + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth)) + ] # stochastic depth decay rule + + self.layers = nn.ModuleList() + for i in range(self.num_layers): + layer = ResidualGroup( + dim=embed_dim, + num_heads=heads[i], + reso=img_size, + split_size=split_size, + expansion_factor=expansion_factor, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_paths=dpr[sum(depth[:i]) : sum(depth[: i + 1])], + act_layer=act_layer, + norm_layer=norm_layer, + depth=depth[i], + use_chk=use_chk, + resi_connection=resi_connection, + rg_idx=i, + ) + self.layers.append(layer) + + self.norm = norm_layer(curr_dim) + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + # ------------------------- 3, Reconstruction ------------------------- # + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, embed_dim, num_out_ch, (img_size, img_size) + ) + + self.apply(self._init_weights) + self.load_state_dict(state_dict, strict=True) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance( + m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d) + ): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward_features(self, x): + _, _, H, W = x.shape + x_size = [H, W] + x = self.before_RG(x) + for layer in self.layers: + x = layer(x, x_size) + x = self.norm(x) + x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) + + return x + + def forward(self, x): + """ + Input: x: (B, C, H, W) + """ + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == "pixelshuffle": + # for image SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + + x = x / self.img_range + self.mean + return x diff --git a/comfy_extras/chainner_models/architecture/LICENSE-DAT b/comfy_extras/chainner_models/architecture/LICENSE-DAT new file mode 100644 index 00000000..261eeb9e --- /dev/null +++ b/comfy_extras/chainner_models/architecture/LICENSE-DAT @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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For the avoidance of doubt, this paragraph does not form part of the public licenses. -> -> Creative Commons may be contacted at creativecommons.org diff --git a/comfy_extras/chainner_models/architecture/MAT.py b/comfy_extras/chainner_models/architecture/MAT.py deleted file mode 100644 index 8fe17026..00000000 --- a/comfy_extras/chainner_models/architecture/MAT.py +++ /dev/null @@ -1,1636 +0,0 @@ -# pylint: skip-file -"""Original MAT project is copyright of fenglingwb: https://github.com/fenglinglwb/MAT -Code used for this implementation of MAT is modified from lama-cleaner, -copyright of Sanster: https://github.com/fenglinglwb/MAT""" - -import random - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as checkpoint - -from .mat.utils import ( - Conv2dLayer, - FullyConnectedLayer, - activation_funcs, - bias_act, - conv2d_resample, - normalize_2nd_moment, - setup_filter, - to_2tuple, - upsample2d, -) - - -class ModulatedConv2d(nn.Module): - def __init__( - self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - kernel_size, # Width and height of the convolution kernel. - style_dim, # dimension of the style code - demodulate=True, # perfrom demodulation - up=1, # Integer upsampling factor. - down=1, # Integer downsampling factor. - resample_filter=[ - 1, - 3, - 3, - 1, - ], # Low-pass filter to apply when resampling activations. - conv_clamp=None, # Clamp the output to +-X, None = disable clamping. - ): - super().__init__() - self.demodulate = demodulate - - self.weight = torch.nn.Parameter( - torch.randn([1, out_channels, in_channels, kernel_size, kernel_size]) - ) - self.out_channels = out_channels - self.kernel_size = kernel_size - self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2)) - self.padding = self.kernel_size // 2 - self.up = up - self.down = down - self.register_buffer("resample_filter", setup_filter(resample_filter)) - self.conv_clamp = conv_clamp - - self.affine = FullyConnectedLayer(style_dim, in_channels, bias_init=1) - - def forward(self, x, style): - batch, in_channels, height, width = x.shape - style = self.affine(style).view(batch, 1, in_channels, 1, 1).to(x.device) - weight = self.weight.to(x.device) * self.weight_gain * style - - if self.demodulate: - decoefs = (weight.pow(2).sum(dim=[2, 3, 4]) + 1e-8).rsqrt() - weight = weight * decoefs.view(batch, self.out_channels, 1, 1, 1) - - weight = weight.view( - batch * self.out_channels, in_channels, self.kernel_size, self.kernel_size - ) - x = x.view(1, batch * in_channels, height, width) - x = conv2d_resample( - x=x, - w=weight, - f=self.resample_filter, - up=self.up, - down=self.down, - padding=self.padding, - groups=batch, - ) - out = x.view(batch, self.out_channels, *x.shape[2:]) - - return out - - -class StyleConv(torch.nn.Module): - def __init__( - self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - style_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this layer. - kernel_size=3, # Convolution kernel size. - up=1, # Integer upsampling factor. - use_noise=False, # Enable noise input? - activation="lrelu", # Activation function: 'relu', 'lrelu', etc. - resample_filter=[ - 1, - 3, - 3, - 1, - ], # Low-pass filter to apply when resampling activations. - conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. - demodulate=True, # perform demodulation - ): - super().__init__() - - self.conv = ModulatedConv2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - style_dim=style_dim, - demodulate=demodulate, - up=up, - resample_filter=resample_filter, - conv_clamp=conv_clamp, - ) - - self.use_noise = use_noise - self.resolution = resolution - if use_noise: - self.register_buffer("noise_const", torch.randn([resolution, resolution])) - self.noise_strength = torch.nn.Parameter(torch.zeros([])) - - self.bias = torch.nn.Parameter(torch.zeros([out_channels])) - self.activation = activation - self.act_gain = activation_funcs[activation].def_gain - self.conv_clamp = conv_clamp - - def forward(self, x, style, noise_mode="random", gain=1): - x = self.conv(x, style) - - assert noise_mode in ["random", "const", "none"] - - if self.use_noise: - if noise_mode == "random": - xh, xw = x.size()[-2:] - noise = ( - torch.randn([x.shape[0], 1, xh, xw], device=x.device) - * self.noise_strength - ) - if noise_mode == "const": - noise = self.noise_const * self.noise_strength - x = x + noise - - act_gain = self.act_gain * gain - act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - out = bias_act( - x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp - ) - - return out - - -class ToRGB(torch.nn.Module): - def __init__( - self, - in_channels, - out_channels, - style_dim, - kernel_size=1, - resample_filter=[1, 3, 3, 1], - conv_clamp=None, - demodulate=False, - ): - super().__init__() - - self.conv = ModulatedConv2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - style_dim=style_dim, - demodulate=demodulate, - resample_filter=resample_filter, - conv_clamp=conv_clamp, - ) - self.bias = torch.nn.Parameter(torch.zeros([out_channels])) - self.register_buffer("resample_filter", setup_filter(resample_filter)) - self.conv_clamp = conv_clamp - - def forward(self, x, style, skip=None): - x = self.conv(x, style) - out = bias_act(x, self.bias, clamp=self.conv_clamp) - - if skip is not None: - if skip.shape != out.shape: - skip = upsample2d(skip, self.resample_filter) - out = out + skip - - return out - - -def get_style_code(a, b): - return torch.cat([a, b.to(a.device)], dim=1) - - -class DecBlockFirst(nn.Module): - def __init__( - self, - in_channels, - out_channels, - activation, - style_dim, - use_noise, - demodulate, - img_channels, - ): - super().__init__() - self.fc = FullyConnectedLayer( - in_features=in_channels * 2, - out_features=in_channels * 4**2, - activation=activation, - ) - self.conv = StyleConv( - in_channels=in_channels, - out_channels=out_channels, - style_dim=style_dim, - resolution=4, - kernel_size=3, - use_noise=use_noise, - activation=activation, - demodulate=demodulate, - ) - self.toRGB = ToRGB( - in_channels=out_channels, - out_channels=img_channels, - style_dim=style_dim, - kernel_size=1, - demodulate=False, - ) - - def forward(self, x, ws, gs, E_features, noise_mode="random"): - x = self.fc(x).view(x.shape[0], -1, 4, 4) - x = x + E_features[2] - style = get_style_code(ws[:, 0], gs) - x = self.conv(x, style, noise_mode=noise_mode) - style = get_style_code(ws[:, 1], gs) - img = self.toRGB(x, style, skip=None) - - return x, img - - -class MappingNet(torch.nn.Module): - def __init__( - self, - z_dim, # Input latent (Z) dimensionality, 0 = no latent. - c_dim, # Conditioning label (C) dimensionality, 0 = no label. - w_dim, # Intermediate latent (W) dimensionality. - num_ws, # Number of intermediate latents to output, None = do not broadcast. - num_layers=8, # Number of mapping layers. - embed_features=None, # Label embedding dimensionality, None = same as w_dim. - layer_features=None, # Number of intermediate features in the mapping layers, None = same as w_dim. - activation="lrelu", # Activation function: 'relu', 'lrelu', etc. - lr_multiplier=0.01, # Learning rate multiplier for the mapping layers. - w_avg_beta=0.995, # Decay for tracking the moving average of W during training, None = do not track. - ): - super().__init__() - self.z_dim = z_dim - self.c_dim = c_dim - self.w_dim = w_dim - self.num_ws = num_ws - self.num_layers = num_layers - self.w_avg_beta = w_avg_beta - - if embed_features is None: - embed_features = w_dim - if c_dim == 0: - embed_features = 0 - if layer_features is None: - layer_features = w_dim - features_list = ( - [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] - ) - - if c_dim > 0: - self.embed = FullyConnectedLayer(c_dim, embed_features) - for idx in range(num_layers): - in_features = features_list[idx] - out_features = features_list[idx + 1] - layer = FullyConnectedLayer( - in_features, - out_features, - activation=activation, - lr_multiplier=lr_multiplier, - ) - setattr(self, f"fc{idx}", layer) - - if num_ws is not None and w_avg_beta is not None: - self.register_buffer("w_avg", torch.zeros([w_dim])) - - def forward( - self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False - ): - # Embed, normalize, and concat inputs. - x = None - with torch.autograd.profiler.record_function("input"): - if self.z_dim > 0: - x = normalize_2nd_moment(z.to(torch.float32)) - if self.c_dim > 0: - y = normalize_2nd_moment(self.embed(c.to(torch.float32))) - x = torch.cat([x, y], dim=1) if x is not None else y - - # Main layers. - for idx in range(self.num_layers): - layer = getattr(self, f"fc{idx}") - x = layer(x) - - # Update moving average of W. - if self.w_avg_beta is not None and self.training and not skip_w_avg_update: - with torch.autograd.profiler.record_function("update_w_avg"): - self.w_avg.copy_( - x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta) - ) - - # Broadcast. - if self.num_ws is not None: - with torch.autograd.profiler.record_function("broadcast"): - x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) - - # Apply truncation. - if truncation_psi != 1: - with torch.autograd.profiler.record_function("truncate"): - assert self.w_avg_beta is not None - if self.num_ws is None or truncation_cutoff is None: - x = self.w_avg.lerp(x, truncation_psi) - else: - x[:, :truncation_cutoff] = self.w_avg.lerp( - x[:, :truncation_cutoff], truncation_psi - ) - - return x - - -class DisFromRGB(nn.Module): - def __init__( - self, in_channels, out_channels, activation - ): # res = 2, ..., resolution_log2 - super().__init__() - self.conv = Conv2dLayer( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=1, - activation=activation, - ) - - def forward(self, x): - return self.conv(x) - - -class DisBlock(nn.Module): - def __init__( - self, in_channels, out_channels, activation - ): # res = 2, ..., resolution_log2 - super().__init__() - self.conv0 = Conv2dLayer( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=3, - activation=activation, - ) - self.conv1 = Conv2dLayer( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=3, - down=2, - activation=activation, - ) - self.skip = Conv2dLayer( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=1, - down=2, - bias=False, - ) - - def forward(self, x): - skip = self.skip(x, gain=np.sqrt(0.5)) - x = self.conv0(x) - x = self.conv1(x, gain=np.sqrt(0.5)) - out = skip + x - - return out - - -def nf(stage, channel_base=32768, channel_decay=1.0, channel_max=512): - NF = {512: 64, 256: 128, 128: 256, 64: 512, 32: 512, 16: 512, 8: 512, 4: 512} - return NF[2**stage] - - -class Mlp(nn.Module): - def __init__( - self, - in_features, - hidden_features=None, - out_features=None, - act_layer=nn.GELU, - drop=0.0, - ): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = FullyConnectedLayer( - in_features=in_features, out_features=hidden_features, activation="lrelu" - ) - self.fc2 = FullyConnectedLayer( - in_features=hidden_features, out_features=out_features - ) - - def forward(self, x): - x = self.fc1(x) - x = self.fc2(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = ( - x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - ) - return windows - - -def window_reverse(windows, window_size: int, H: int, W: int): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - # B = windows.shape[0] / (H * W / window_size / window_size) - x = windows.view( - B, H // window_size, W // window_size, window_size, window_size, -1 - ) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class Conv2dLayerPartial(nn.Module): - def __init__( - self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - kernel_size, # Width and height of the convolution kernel. - bias=True, # Apply additive bias before the activation function? - activation="linear", # Activation function: 'relu', 'lrelu', etc. - up=1, # Integer upsampling factor. - down=1, # Integer downsampling factor. - resample_filter=[ - 1, - 3, - 3, - 1, - ], # Low-pass filter to apply when resampling activations. - conv_clamp=None, # Clamp the output to +-X, None = disable clamping. - trainable=True, # Update the weights of this layer during training? - ): - super().__init__() - self.conv = Conv2dLayer( - in_channels, - out_channels, - kernel_size, - bias, - activation, - up, - down, - resample_filter, - conv_clamp, - trainable, - ) - - self.weight_maskUpdater = torch.ones(1, 1, kernel_size, kernel_size) - self.slide_winsize = kernel_size**2 - self.stride = down - self.padding = kernel_size // 2 if kernel_size % 2 == 1 else 0 - - def forward(self, x, mask=None): - if mask is not None: - with torch.no_grad(): - if self.weight_maskUpdater.type() != x.type(): - self.weight_maskUpdater = self.weight_maskUpdater.to(x) - update_mask = F.conv2d( - mask, - self.weight_maskUpdater, - bias=None, - stride=self.stride, - padding=self.padding, - ) - mask_ratio = self.slide_winsize / (update_mask + 1e-8) - update_mask = torch.clamp(update_mask, 0, 1) # 0 or 1 - mask_ratio = torch.mul(mask_ratio, update_mask) - x = self.conv(x) - x = torch.mul(x, mask_ratio) - return x, update_mask - else: - x = self.conv(x) - return x, None - - -class WindowAttention(nn.Module): - r"""Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__( - self, - dim, - window_size, - num_heads, - down_ratio=1, - qkv_bias=True, - qk_scale=None, - attn_drop=0.0, - proj_drop=0.0, - ): - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - - self.q = FullyConnectedLayer(in_features=dim, out_features=dim) - self.k = FullyConnectedLayer(in_features=dim, out_features=dim) - self.v = FullyConnectedLayer(in_features=dim, out_features=dim) - self.proj = FullyConnectedLayer(in_features=dim, out_features=dim) - - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask_windows=None, mask=None): - """ - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - norm_x = F.normalize(x, p=2.0, dim=-1) - q = ( - self.q(norm_x) - .reshape(B_, N, self.num_heads, C // self.num_heads) - .permute(0, 2, 1, 3) - ) - k = ( - self.k(norm_x) - .view(B_, -1, self.num_heads, C // self.num_heads) - .permute(0, 2, 3, 1) - ) - v = ( - self.v(x) - .view(B_, -1, self.num_heads, C // self.num_heads) - .permute(0, 2, 1, 3) - ) - - attn = (q @ k) * self.scale - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( - 1 - ).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - - if mask_windows is not None: - attn_mask_windows = mask_windows.squeeze(-1).unsqueeze(1).unsqueeze(1) - attn = attn + attn_mask_windows.masked_fill( - attn_mask_windows == 0, float(-100.0) - ).masked_fill(attn_mask_windows == 1, float(0.0)) - with torch.no_grad(): - mask_windows = torch.clamp( - torch.sum(mask_windows, dim=1, keepdim=True), 0, 1 - ).repeat(1, N, 1) - - attn = self.softmax(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - return x, mask_windows - - -class SwinTransformerBlock(nn.Module): - r"""Swin Transformer Block. - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resulotion. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__( - self, - dim, - input_resolution, - num_heads, - down_ratio=1, - window_size=7, - shift_size=0, - mlp_ratio=4.0, - qkv_bias=True, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - act_layer=nn.GELU, - norm_layer=nn.LayerNorm, - ): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert ( - 0 <= self.shift_size < self.window_size - ), "shift_size must in 0-window_size" - - if self.shift_size > 0: - down_ratio = 1 - self.attn = WindowAttention( - dim, - window_size=to_2tuple(self.window_size), - num_heads=num_heads, - down_ratio=down_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - attn_drop=attn_drop, - proj_drop=drop, - ) - - self.fuse = FullyConnectedLayer( - in_features=dim * 2, out_features=dim, activation="lrelu" - ) - - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp( - in_features=dim, - hidden_features=mlp_hidden_dim, - act_layer=act_layer, - drop=drop, - ) - - if self.shift_size > 0: - attn_mask = self.calculate_mask(self.input_resolution) - else: - attn_mask = None - - self.register_buffer("attn_mask", attn_mask) - - def calculate_mask(self, x_size): - # calculate attention mask for SW-MSA - H, W = x_size - img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 - h_slices = ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None), - ) - w_slices = ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None), - ) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition( - img_mask, self.window_size - ) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( - attn_mask == 0, float(0.0) - ) - - return attn_mask - - def forward(self, x, x_size, mask=None): - # H, W = self.input_resolution - H, W = x_size - B, _, C = x.shape - # assert L == H * W, "input feature has wrong size" - - shortcut = x - x = x.view(B, H, W, C) - if mask is not None: - mask = mask.view(B, H, W, 1) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll( - x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) - ) - if mask is not None: - shifted_mask = torch.roll( - mask, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) - ) - else: - shifted_x = x - if mask is not None: - shifted_mask = mask - - # partition windows - x_windows = window_partition( - shifted_x, self.window_size - ) # nW*B, window_size, window_size, C - x_windows = x_windows.view( - -1, self.window_size * self.window_size, C - ) # nW*B, window_size*window_size, C - if mask is not None: - mask_windows = window_partition(shifted_mask, self.window_size) - mask_windows = mask_windows.view(-1, self.window_size * self.window_size, 1) - else: - mask_windows = None - - # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size - if self.input_resolution == x_size: - attn_windows, mask_windows = self.attn( - x_windows, mask_windows, mask=self.attn_mask - ) # nW*B, window_size*window_size, C - else: - attn_windows, mask_windows = self.attn( - x_windows, mask_windows, mask=self.calculate_mask(x_size).to(x.device) - ) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C - if mask is not None: - mask_windows = mask_windows.view(-1, self.window_size, self.window_size, 1) - shifted_mask = window_reverse(mask_windows, self.window_size, H, W) - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll( - shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) - ) - if mask is not None: - mask = torch.roll( - shifted_mask, shifts=(self.shift_size, self.shift_size), dims=(1, 2) - ) - else: - x = shifted_x - if mask is not None: - mask = shifted_mask - x = x.view(B, H * W, C) - if mask is not None: - mask = mask.view(B, H * W, 1) - - # FFN - x = self.fuse(torch.cat([shortcut, x], dim=-1)) - x = self.mlp(x) - - return x, mask - - -class PatchMerging(nn.Module): - def __init__(self, in_channels, out_channels, down=2): - super().__init__() - self.conv = Conv2dLayerPartial( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=3, - activation="lrelu", - down=down, - ) - self.down = down - - def forward(self, x, x_size, mask=None): - x = token2feature(x, x_size) - if mask is not None: - mask = token2feature(mask, x_size) - x, mask = self.conv(x, mask) - if self.down != 1: - ratio = 1 / self.down - x_size = (int(x_size[0] * ratio), int(x_size[1] * ratio)) - x = feature2token(x) - if mask is not None: - mask = feature2token(mask) - return x, x_size, mask - - -class PatchUpsampling(nn.Module): - def __init__(self, in_channels, out_channels, up=2): - super().__init__() - self.conv = Conv2dLayerPartial( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=3, - activation="lrelu", - up=up, - ) - self.up = up - - def forward(self, x, x_size, mask=None): - x = token2feature(x, x_size) - if mask is not None: - mask = token2feature(mask, x_size) - x, mask = self.conv(x, mask) - if self.up != 1: - x_size = (int(x_size[0] * self.up), int(x_size[1] * self.up)) - x = feature2token(x) - if mask is not None: - mask = feature2token(mask) - return x, x_size, mask - - -class BasicLayer(nn.Module): - """A basic Swin Transformer layer for one stage. - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__( - self, - dim, - input_resolution, - depth, - num_heads, - window_size, - down_ratio=1, - mlp_ratio=2.0, - qkv_bias=True, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False, - ): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - self.use_checkpoint = use_checkpoint - - # patch merging layer - if downsample is not None: - # self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) - self.downsample = downsample - else: - self.downsample = None - - # build blocks - self.blocks = nn.ModuleList( - [ - SwinTransformerBlock( - dim=dim, - input_resolution=input_resolution, - num_heads=num_heads, - down_ratio=down_ratio, - window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path[i] - if isinstance(drop_path, list) - else drop_path, - norm_layer=norm_layer, - ) - for i in range(depth) - ] - ) - - self.conv = Conv2dLayerPartial( - in_channels=dim, out_channels=dim, kernel_size=3, activation="lrelu" - ) - - def forward(self, x, x_size, mask=None): - if self.downsample is not None: - x, x_size, mask = self.downsample(x, x_size, mask) - identity = x - for blk in self.blocks: - if self.use_checkpoint: - x, mask = checkpoint.checkpoint(blk, x, x_size, mask) - else: - x, mask = blk(x, x_size, mask) - if mask is not None: - mask = token2feature(mask, x_size) - x, mask = self.conv(token2feature(x, x_size), mask) - x = feature2token(x) + identity - if mask is not None: - mask = feature2token(mask) - return x, x_size, mask - - -class ToToken(nn.Module): - def __init__(self, in_channels=3, dim=128, kernel_size=5, stride=1): - super().__init__() - - self.proj = Conv2dLayerPartial( - in_channels=in_channels, - out_channels=dim, - kernel_size=kernel_size, - activation="lrelu", - ) - - def forward(self, x, mask): - x, mask = self.proj(x, mask) - - return x, mask - - -class EncFromRGB(nn.Module): - def __init__( - self, in_channels, out_channels, activation - ): # res = 2, ..., resolution_log2 - super().__init__() - self.conv0 = Conv2dLayer( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=1, - activation=activation, - ) - self.conv1 = Conv2dLayer( - in_channels=out_channels, - out_channels=out_channels, - kernel_size=3, - activation=activation, - ) - - def forward(self, x): - x = self.conv0(x) - x = self.conv1(x) - - return x - - -class ConvBlockDown(nn.Module): - def __init__( - self, in_channels, out_channels, activation - ): # res = 2, ..., resolution_log - super().__init__() - - self.conv0 = Conv2dLayer( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=3, - activation=activation, - down=2, - ) - self.conv1 = Conv2dLayer( - in_channels=out_channels, - out_channels=out_channels, - kernel_size=3, - activation=activation, - ) - - def forward(self, x): - x = self.conv0(x) - x = self.conv1(x) - - return x - - -def token2feature(x, x_size): - B, _, C = x.shape - h, w = x_size - x = x.permute(0, 2, 1).reshape(B, C, h, w) - return x - - -def feature2token(x): - B, C, _, _ = x.shape - x = x.view(B, C, -1).transpose(1, 2) - return x - - -class Encoder(nn.Module): - def __init__( - self, - res_log2, - img_channels, - activation, - patch_size=5, - channels=16, - drop_path_rate=0.1, - ): - super().__init__() - - self.resolution = [] - - for i in range(res_log2, 3, -1): # from input size to 16x16 - res = 2**i - self.resolution.append(res) - if i == res_log2: - block = EncFromRGB(img_channels * 2 + 1, nf(i), activation) - else: - block = ConvBlockDown(nf(i + 1), nf(i), activation) - setattr(self, "EncConv_Block_%dx%d" % (res, res), block) - - def forward(self, x): - out = {} - for res in self.resolution: - res_log2 = int(np.log2(res)) - x = getattr(self, "EncConv_Block_%dx%d" % (res, res))(x) - out[res_log2] = x - - return out - - -class ToStyle(nn.Module): - def __init__(self, in_channels, out_channels, activation, drop_rate): - super().__init__() - self.conv = nn.Sequential( - Conv2dLayer( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=3, - activation=activation, - down=2, - ), - Conv2dLayer( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=3, - activation=activation, - down=2, - ), - Conv2dLayer( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=3, - activation=activation, - down=2, - ), - ) - - self.pool = nn.AdaptiveAvgPool2d(1) - self.fc = FullyConnectedLayer( - in_features=in_channels, out_features=out_channels, activation=activation - ) - # self.dropout = nn.Dropout(drop_rate) - - def forward(self, x): - x = self.conv(x) - x = self.pool(x) - x = self.fc(x.flatten(start_dim=1)) - # x = self.dropout(x) - - return x - - -class DecBlockFirstV2(nn.Module): - def __init__( - self, - res, - in_channels, - out_channels, - activation, - style_dim, - use_noise, - demodulate, - img_channels, - ): - super().__init__() - self.res = res - - self.conv0 = Conv2dLayer( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=3, - activation=activation, - ) - self.conv1 = StyleConv( - in_channels=in_channels, - out_channels=out_channels, - style_dim=style_dim, - resolution=2**res, - kernel_size=3, - use_noise=use_noise, - activation=activation, - demodulate=demodulate, - ) - self.toRGB = ToRGB( - in_channels=out_channels, - out_channels=img_channels, - style_dim=style_dim, - kernel_size=1, - demodulate=False, - ) - - def forward(self, x, ws, gs, E_features, noise_mode="random"): - # x = self.fc(x).view(x.shape[0], -1, 4, 4) - x = self.conv0(x) - x = x + E_features[self.res] - style = get_style_code(ws[:, 0], gs) - x = self.conv1(x, style, noise_mode=noise_mode) - style = get_style_code(ws[:, 1], gs) - img = self.toRGB(x, style, skip=None) - - return x, img - - -class DecBlock(nn.Module): - def __init__( - self, - res, - in_channels, - out_channels, - activation, - style_dim, - use_noise, - demodulate, - img_channels, - ): # res = 4, ..., resolution_log2 - super().__init__() - self.res = res - - self.conv0 = StyleConv( - in_channels=in_channels, - out_channels=out_channels, - style_dim=style_dim, - resolution=2**res, - kernel_size=3, - up=2, - use_noise=use_noise, - activation=activation, - demodulate=demodulate, - ) - self.conv1 = StyleConv( - in_channels=out_channels, - out_channels=out_channels, - style_dim=style_dim, - resolution=2**res, - kernel_size=3, - use_noise=use_noise, - activation=activation, - demodulate=demodulate, - ) - self.toRGB = ToRGB( - in_channels=out_channels, - out_channels=img_channels, - style_dim=style_dim, - kernel_size=1, - demodulate=False, - ) - - def forward(self, x, img, ws, gs, E_features, noise_mode="random"): - style = get_style_code(ws[:, self.res * 2 - 9], gs) - x = self.conv0(x, style, noise_mode=noise_mode) - x = x + E_features[self.res] - style = get_style_code(ws[:, self.res * 2 - 8], gs) - x = self.conv1(x, style, noise_mode=noise_mode) - style = get_style_code(ws[:, self.res * 2 - 7], gs) - img = self.toRGB(x, style, skip=img) - - return x, img - - -class Decoder(nn.Module): - def __init__( - self, res_log2, activation, style_dim, use_noise, demodulate, img_channels - ): - super().__init__() - self.Dec_16x16 = DecBlockFirstV2( - 4, nf(4), nf(4), activation, style_dim, use_noise, demodulate, img_channels - ) - for res in range(5, res_log2 + 1): - setattr( - self, - "Dec_%dx%d" % (2**res, 2**res), - DecBlock( - res, - nf(res - 1), - nf(res), - activation, - style_dim, - use_noise, - demodulate, - img_channels, - ), - ) - self.res_log2 = res_log2 - - def forward(self, x, ws, gs, E_features, noise_mode="random"): - x, img = self.Dec_16x16(x, ws, gs, E_features, noise_mode=noise_mode) - for res in range(5, self.res_log2 + 1): - block = getattr(self, "Dec_%dx%d" % (2**res, 2**res)) - x, img = block(x, img, ws, gs, E_features, noise_mode=noise_mode) - - return img - - -class DecStyleBlock(nn.Module): - def __init__( - self, - res, - in_channels, - out_channels, - activation, - style_dim, - use_noise, - demodulate, - img_channels, - ): - super().__init__() - self.res = res - - self.conv0 = StyleConv( - in_channels=in_channels, - out_channels=out_channels, - style_dim=style_dim, - resolution=2**res, - kernel_size=3, - up=2, - use_noise=use_noise, - activation=activation, - demodulate=demodulate, - ) - self.conv1 = StyleConv( - in_channels=out_channels, - out_channels=out_channels, - style_dim=style_dim, - resolution=2**res, - kernel_size=3, - use_noise=use_noise, - activation=activation, - demodulate=demodulate, - ) - self.toRGB = ToRGB( - in_channels=out_channels, - out_channels=img_channels, - style_dim=style_dim, - kernel_size=1, - demodulate=False, - ) - - def forward(self, x, img, style, skip, noise_mode="random"): - x = self.conv0(x, style, noise_mode=noise_mode) - x = x + skip - x = self.conv1(x, style, noise_mode=noise_mode) - img = self.toRGB(x, style, skip=img) - - return x, img - - -class FirstStage(nn.Module): - def __init__( - self, - img_channels, - img_resolution=256, - dim=180, - w_dim=512, - use_noise=False, - demodulate=True, - activation="lrelu", - ): - super().__init__() - res = 64 - - self.conv_first = Conv2dLayerPartial( - in_channels=img_channels + 1, - out_channels=dim, - kernel_size=3, - activation=activation, - ) - self.enc_conv = nn.ModuleList() - down_time = int(np.log2(img_resolution // res)) - # 根据图片尺寸构建 swim transformer 的层数 - for i in range(down_time): # from input size to 64 - self.enc_conv.append( - Conv2dLayerPartial( - in_channels=dim, - out_channels=dim, - kernel_size=3, - down=2, - activation=activation, - ) - ) - - # from 64 -> 16 -> 64 - depths = [2, 3, 4, 3, 2] - ratios = [1, 1 / 2, 1 / 2, 2, 2] - num_heads = 6 - window_sizes = [8, 16, 16, 16, 8] - drop_path_rate = 0.1 - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] - - self.tran = nn.ModuleList() - for i, depth in enumerate(depths): - res = int(res * ratios[i]) - if ratios[i] < 1: - merge = PatchMerging(dim, dim, down=int(1 / ratios[i])) - elif ratios[i] > 1: - merge = PatchUpsampling(dim, dim, up=ratios[i]) - else: - merge = None - self.tran.append( - BasicLayer( - dim=dim, - input_resolution=[res, res], - depth=depth, - num_heads=num_heads, - window_size=window_sizes[i], - drop_path=dpr[sum(depths[:i]) : sum(depths[: i + 1])], - downsample=merge, - ) - ) - - # global style - down_conv = [] - for i in range(int(np.log2(16))): - down_conv.append( - Conv2dLayer( - in_channels=dim, - out_channels=dim, - kernel_size=3, - down=2, - activation=activation, - ) - ) - down_conv.append(nn.AdaptiveAvgPool2d((1, 1))) - self.down_conv = nn.Sequential(*down_conv) - self.to_style = FullyConnectedLayer( - in_features=dim, out_features=dim * 2, activation=activation - ) - self.ws_style = FullyConnectedLayer( - in_features=w_dim, out_features=dim, activation=activation - ) - self.to_square = FullyConnectedLayer( - in_features=dim, out_features=16 * 16, activation=activation - ) - - style_dim = dim * 3 - self.dec_conv = nn.ModuleList() - for i in range(down_time): # from 64 to input size - res = res * 2 - self.dec_conv.append( - DecStyleBlock( - res, - dim, - dim, - activation, - style_dim, - use_noise, - demodulate, - img_channels, - ) - ) - - def forward(self, images_in, masks_in, ws, noise_mode="random"): - x = torch.cat([masks_in - 0.5, images_in * masks_in], dim=1) - - skips = [] - x, mask = self.conv_first(x, masks_in) # input size - skips.append(x) - for i, block in enumerate(self.enc_conv): # input size to 64 - x, mask = block(x, mask) - if i != len(self.enc_conv) - 1: - skips.append(x) - - x_size = x.size()[-2:] - x = feature2token(x) - mask = feature2token(mask) - mid = len(self.tran) // 2 - for i, block in enumerate(self.tran): # 64 to 16 - if i < mid: - x, x_size, mask = block(x, x_size, mask) - skips.append(x) - elif i > mid: - x, x_size, mask = block(x, x_size, None) - x = x + skips[mid - i] - else: - x, x_size, mask = block(x, x_size, None) - - mul_map = torch.ones_like(x) * 0.5 - mul_map = F.dropout(mul_map, training=True).to(x.device) - ws = self.ws_style(ws[:, -1]).to(x.device) - add_n = self.to_square(ws).unsqueeze(1).to(x.device) - add_n = ( - F.interpolate( - add_n, size=x.size(1), mode="linear", align_corners=False - ) - .squeeze(1) - .unsqueeze(-1) - ).to(x.device) - x = x * mul_map + add_n * (1 - mul_map) - gs = self.to_style( - self.down_conv(token2feature(x, x_size)).flatten(start_dim=1) - ).to(x.device) - style = torch.cat([gs, ws], dim=1) - - x = token2feature(x, x_size).contiguous() - img = None - for i, block in enumerate(self.dec_conv): - x, img = block( - x, img, style, skips[len(self.dec_conv) - i - 1], noise_mode=noise_mode - ) - - # ensemble - img = img * (1 - masks_in) + images_in * masks_in - - return img - - -class SynthesisNet(nn.Module): - def __init__( - self, - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output image resolution. - img_channels=3, # Number of color channels. - channel_base=32768, # Overall multiplier for the number of channels. - channel_decay=1.0, - channel_max=512, # Maximum number of channels in any layer. - activation="lrelu", # Activation function: 'relu', 'lrelu', etc. - drop_rate=0.5, - use_noise=False, - demodulate=True, - ): - super().__init__() - resolution_log2 = int(np.log2(img_resolution)) - assert img_resolution == 2**resolution_log2 and img_resolution >= 4 - - self.num_layers = resolution_log2 * 2 - 3 * 2 - self.img_resolution = img_resolution - self.resolution_log2 = resolution_log2 - - # first stage - self.first_stage = FirstStage( - img_channels, - img_resolution=img_resolution, - w_dim=w_dim, - use_noise=False, - demodulate=demodulate, - ) - - # second stage - self.enc = Encoder( - resolution_log2, img_channels, activation, patch_size=5, channels=16 - ) - self.to_square = FullyConnectedLayer( - in_features=w_dim, out_features=16 * 16, activation=activation - ) - self.to_style = ToStyle( - in_channels=nf(4), - out_channels=nf(2) * 2, - activation=activation, - drop_rate=drop_rate, - ) - style_dim = w_dim + nf(2) * 2 - self.dec = Decoder( - resolution_log2, activation, style_dim, use_noise, demodulate, img_channels - ) - - def forward(self, images_in, masks_in, ws, noise_mode="random", return_stg1=False): - out_stg1 = self.first_stage(images_in, masks_in, ws, noise_mode=noise_mode) - - # encoder - x = images_in * masks_in + out_stg1 * (1 - masks_in) - x = torch.cat([masks_in - 0.5, x, images_in * masks_in], dim=1) - E_features = self.enc(x) - - fea_16 = E_features[4].to(x.device) - mul_map = torch.ones_like(fea_16) * 0.5 - mul_map = F.dropout(mul_map, training=True).to(x.device) - add_n = self.to_square(ws[:, 0]).view(-1, 16, 16).unsqueeze(1) - add_n = F.interpolate( - add_n, size=fea_16.size()[-2:], mode="bilinear", align_corners=False - ).to(x.device) - fea_16 = fea_16 * mul_map + add_n * (1 - mul_map) - E_features[4] = fea_16 - - # style - gs = self.to_style(fea_16).to(x.device) - - # decoder - img = self.dec(fea_16, ws, gs, E_features, noise_mode=noise_mode).to(x.device) - - # ensemble - img = img * (1 - masks_in) + images_in * masks_in - - if not return_stg1: - return img - else: - return img, out_stg1 - - -class Generator(nn.Module): - def __init__( - self, - z_dim, # Input latent (Z) dimensionality, 0 = no latent. - c_dim, # Conditioning label (C) dimensionality, 0 = no label. - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # resolution of generated image - img_channels, # Number of input color channels. - synthesis_kwargs={}, # Arguments for SynthesisNetwork. - mapping_kwargs={}, # Arguments for MappingNetwork. - ): - super().__init__() - self.z_dim = z_dim - self.c_dim = c_dim - self.w_dim = w_dim - self.img_resolution = img_resolution - self.img_channels = img_channels - - self.synthesis = SynthesisNet( - w_dim=w_dim, - img_resolution=img_resolution, - img_channels=img_channels, - **synthesis_kwargs, - ) - self.mapping = MappingNet( - z_dim=z_dim, - c_dim=c_dim, - w_dim=w_dim, - num_ws=self.synthesis.num_layers, - **mapping_kwargs, - ) - - def forward( - self, - images_in, - masks_in, - z, - c, - truncation_psi=1, - truncation_cutoff=None, - skip_w_avg_update=False, - noise_mode="none", - return_stg1=False, - ): - ws = self.mapping( - z, - c, - truncation_psi=truncation_psi, - truncation_cutoff=truncation_cutoff, - skip_w_avg_update=skip_w_avg_update, - ) - img = self.synthesis(images_in, masks_in, ws, noise_mode=noise_mode) - return img - - -class MAT(nn.Module): - def __init__(self, state_dict): - super(MAT, self).__init__() - self.model_arch = "MAT" - self.sub_type = "Inpaint" - self.in_nc = 3 - self.out_nc = 3 - self.scale = 1 - - self.supports_fp16 = False - self.supports_bf16 = True - - self.min_size = 512 - self.pad_mod = 512 - self.pad_to_square = True - - seed = 240 # pick up a random number - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - - self.model = Generator( - z_dim=512, c_dim=0, w_dim=512, img_resolution=512, img_channels=3 - ) - self.z = torch.from_numpy(np.random.randn(1, self.model.z_dim)) # [1., 512] - self.label = torch.zeros([1, self.model.c_dim]) - self.state = { - k.replace("synthesis", "model.synthesis").replace( - "mapping", "model.mapping" - ): v - for k, v in state_dict.items() - } - self.load_state_dict(self.state, strict=False) - - def forward(self, image, mask): - """Input images and output images have same size - images: [H, W, C] RGB - masks: [H, W] mask area == 255 - return: BGR IMAGE - """ - - image = image * 2 - 1 # [0, 1] -> [-1, 1] - mask = 1 - mask - - output = self.model( - image, mask, self.z, self.label, truncation_psi=1, noise_mode="none" - ) - - return output * 0.5 + 0.5 diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py b/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py index dec16952..1e1c3f35 100644 --- a/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py +++ b/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py @@ -56,7 +56,17 @@ class OmniSR(nn.Module): residual_layer = [] self.res_num = res_num - self.window_size = 8 # we can just assume this for now, but there's probably a way to calculate it (just need to get the sqrt of the right layer) + if ( + "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" + in state_dict.keys() + ): + rel_pos_bias_weight = state_dict[ + "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" + ].shape[0] + self.window_size = int((math.sqrt(rel_pos_bias_weight) + 1) / 2) + else: + self.window_size = 8 + self.up_scale = up_scale for _ in range(res_num): diff --git a/comfy_extras/chainner_models/architecture/SCUNet.py b/comfy_extras/chainner_models/architecture/SCUNet.py new file mode 100644 index 00000000..b8354a87 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/SCUNet.py @@ -0,0 +1,455 @@ +# pylint: skip-file +# ----------------------------------------------------------------------------------- +# SCUNet: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, https://arxiv.org/abs/2203.13278 +# Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Timofte, Radu and Van Gool, Luc +# ----------------------------------------------------------------------------------- + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from einops.layers.torch import Rearrange + +from .timm.drop import DropPath +from .timm.weight_init import trunc_normal_ + + +# Borrowed from https://github.com/cszn/SCUNet/blob/main/models/network_scunet.py +class WMSA(nn.Module): + """Self-attention module in Swin Transformer""" + + def __init__(self, input_dim, output_dim, head_dim, window_size, type): + super(WMSA, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.head_dim = head_dim + self.scale = self.head_dim**-0.5 + self.n_heads = input_dim // head_dim + self.window_size = window_size + self.type = type + self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True) + + self.relative_position_params = nn.Parameter( + torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) + ) + # TODO recover + # self.relative_position_params = nn.Parameter(torch.zeros(self.n_heads, 2 * window_size - 1, 2 * window_size -1)) + self.relative_position_params = nn.Parameter( + torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) + ) + + self.linear = nn.Linear(self.input_dim, self.output_dim) + + trunc_normal_(self.relative_position_params, std=0.02) + self.relative_position_params = torch.nn.Parameter( + self.relative_position_params.view( + 2 * window_size - 1, 2 * window_size - 1, self.n_heads + ) + .transpose(1, 2) + .transpose(0, 1) + ) + + def generate_mask(self, h, w, p, shift): + """generating the mask of SW-MSA + Args: + shift: shift parameters in CyclicShift. + Returns: + attn_mask: should be (1 1 w p p), + """ + # supporting square. + attn_mask = torch.zeros( + h, + w, + p, + p, + p, + p, + dtype=torch.bool, + device=self.relative_position_params.device, + ) + if self.type == "W": + return attn_mask + + s = p - shift + attn_mask[-1, :, :s, :, s:, :] = True + attn_mask[-1, :, s:, :, :s, :] = True + attn_mask[:, -1, :, :s, :, s:] = True + attn_mask[:, -1, :, s:, :, :s] = True + attn_mask = rearrange( + attn_mask, "w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)" + ) + return attn_mask + + def forward(self, x): + """Forward pass of Window Multi-head Self-attention module. + Args: + x: input tensor with shape of [b h w c]; + attn_mask: attention mask, fill -inf where the value is True; + Returns: + output: tensor shape [b h w c] + """ + if self.type != "W": + x = torch.roll( + x, + shifts=(-(self.window_size // 2), -(self.window_size // 2)), + dims=(1, 2), + ) + + x = rearrange( + x, + "b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c", + p1=self.window_size, + p2=self.window_size, + ) + h_windows = x.size(1) + w_windows = x.size(2) + # square validation + # assert h_windows == w_windows + + x = rearrange( + x, + "b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c", + p1=self.window_size, + p2=self.window_size, + ) + qkv = self.embedding_layer(x) + q, k, v = rearrange( + qkv, "b nw np (threeh c) -> threeh b nw np c", c=self.head_dim + ).chunk(3, dim=0) + sim = torch.einsum("hbwpc,hbwqc->hbwpq", q, k) * self.scale + # Adding learnable relative embedding + sim = sim + rearrange(self.relative_embedding(), "h p q -> h 1 1 p q") + # Using Attn Mask to distinguish different subwindows. + if self.type != "W": + attn_mask = self.generate_mask( + h_windows, w_windows, self.window_size, shift=self.window_size // 2 + ) + sim = sim.masked_fill_(attn_mask, float("-inf")) + + probs = nn.functional.softmax(sim, dim=-1) + output = torch.einsum("hbwij,hbwjc->hbwic", probs, v) + output = rearrange(output, "h b w p c -> b w p (h c)") + output = self.linear(output) + output = rearrange( + output, + "b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c", + w1=h_windows, + p1=self.window_size, + ) + + if self.type != "W": + output = torch.roll( + output, + shifts=(self.window_size // 2, self.window_size // 2), + dims=(1, 2), + ) + + return output + + def relative_embedding(self): + cord = torch.tensor( + np.array( + [ + [i, j] + for i in range(self.window_size) + for j in range(self.window_size) + ] + ) + ) + relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1 + # negative is allowed + return self.relative_position_params[ + :, relation[:, :, 0].long(), relation[:, :, 1].long() + ] + + +class Block(nn.Module): + def __init__( + self, + input_dim, + output_dim, + head_dim, + window_size, + drop_path, + type="W", + input_resolution=None, + ): + """SwinTransformer Block""" + super(Block, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + assert type in ["W", "SW"] + self.type = type + if input_resolution <= window_size: + self.type = "W" + + self.ln1 = nn.LayerNorm(input_dim) + self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.ln2 = nn.LayerNorm(input_dim) + self.mlp = nn.Sequential( + nn.Linear(input_dim, 4 * input_dim), + nn.GELU(), + nn.Linear(4 * input_dim, output_dim), + ) + + def forward(self, x): + x = x + self.drop_path(self.msa(self.ln1(x))) + x = x + self.drop_path(self.mlp(self.ln2(x))) + return x + + +class ConvTransBlock(nn.Module): + def __init__( + self, + conv_dim, + trans_dim, + head_dim, + window_size, + drop_path, + type="W", + input_resolution=None, + ): + """SwinTransformer and Conv Block""" + super(ConvTransBlock, self).__init__() + self.conv_dim = conv_dim + self.trans_dim = trans_dim + self.head_dim = head_dim + self.window_size = window_size + self.drop_path = drop_path + self.type = type + self.input_resolution = input_resolution + + assert self.type in ["W", "SW"] + if self.input_resolution <= self.window_size: + self.type = "W" + + self.trans_block = Block( + self.trans_dim, + self.trans_dim, + self.head_dim, + self.window_size, + self.drop_path, + self.type, + self.input_resolution, + ) + self.conv1_1 = nn.Conv2d( + self.conv_dim + self.trans_dim, + self.conv_dim + self.trans_dim, + 1, + 1, + 0, + bias=True, + ) + self.conv1_2 = nn.Conv2d( + self.conv_dim + self.trans_dim, + self.conv_dim + self.trans_dim, + 1, + 1, + 0, + bias=True, + ) + + self.conv_block = nn.Sequential( + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), + nn.ReLU(True), + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), + ) + + def forward(self, x): + conv_x, trans_x = torch.split( + self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1 + ) + conv_x = self.conv_block(conv_x) + conv_x + trans_x = Rearrange("b c h w -> b h w c")(trans_x) + trans_x = self.trans_block(trans_x) + trans_x = Rearrange("b h w c -> b c h w")(trans_x) + res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1)) + x = x + res + + return x + + +class SCUNet(nn.Module): + def __init__( + self, + state_dict, + in_nc=3, + config=[4, 4, 4, 4, 4, 4, 4], + dim=64, + drop_path_rate=0.0, + input_resolution=256, + ): + super(SCUNet, self).__init__() + self.model_arch = "SCUNet" + self.sub_type = "SR" + + self.num_filters: int = 0 + + self.state = state_dict + self.config = config + self.dim = dim + self.head_dim = 32 + self.window_size = 8 + + self.in_nc = in_nc + self.out_nc = self.in_nc + self.scale = 1 + self.supports_fp16 = True + + # drop path rate for each layer + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))] + + self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)] + + begin = 0 + self.m_down1 = [ + ConvTransBlock( + dim // 2, + dim // 2, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution, + ) + for i in range(config[0]) + ] + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)] + + begin += config[0] + self.m_down2 = [ + ConvTransBlock( + dim, + dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 2, + ) + for i in range(config[1]) + ] + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)] + + begin += config[1] + self.m_down3 = [ + ConvTransBlock( + 2 * dim, + 2 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 4, + ) + for i in range(config[2]) + ] + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)] + + begin += config[2] + self.m_body = [ + ConvTransBlock( + 4 * dim, + 4 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 8, + ) + for i in range(config[3]) + ] + + begin += config[3] + self.m_up3 = [ + nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + 2 * dim, + 2 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 4, + ) + for i in range(config[4]) + ] + + begin += config[4] + self.m_up2 = [ + nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + dim, + dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 2, + ) + for i in range(config[5]) + ] + + begin += config[5] + self.m_up1 = [ + nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + dim // 2, + dim // 2, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution, + ) + for i in range(config[6]) + ] + + self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)] + + self.m_head = nn.Sequential(*self.m_head) + self.m_down1 = nn.Sequential(*self.m_down1) + self.m_down2 = nn.Sequential(*self.m_down2) + self.m_down3 = nn.Sequential(*self.m_down3) + self.m_body = nn.Sequential(*self.m_body) + self.m_up3 = nn.Sequential(*self.m_up3) + self.m_up2 = nn.Sequential(*self.m_up2) + self.m_up1 = nn.Sequential(*self.m_up1) + self.m_tail = nn.Sequential(*self.m_tail) + # self.apply(self._init_weights) + self.load_state_dict(state_dict, strict=True) + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (64 - h % 64) % 64 + mod_pad_w = (64 - w % 64) % 64 + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward(self, x0): + h, w = x0.size()[-2:] + x0 = self.check_image_size(x0) + + x1 = self.m_head(x0) + x2 = self.m_down1(x1) + x3 = self.m_down2(x2) + x4 = self.m_down3(x3) + x = self.m_body(x4) + x = self.m_up3(x + x4) + x = self.m_up2(x + x3) + x = self.m_up1(x + x2) + x = self.m_tail(x + x1) + + x = x[:, :, :h, :w] + return x + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) diff --git a/comfy_extras/chainner_models/architecture/SPSR.py b/comfy_extras/chainner_models/architecture/SPSR.py index 6f5ac458..c3cefff1 100644 --- a/comfy_extras/chainner_models/architecture/SPSR.py +++ b/comfy_extras/chainner_models/architecture/SPSR.py @@ -60,7 +60,6 @@ class SPSRNet(nn.Module): self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0] self.scale = self.get_scale(4) - print(self.scale) self.num_filters: int = self.state["model.0.weight"].shape[0] self.supports_fp16 = True diff --git a/comfy_extras/chainner_models/architecture/SwinIR.py b/comfy_extras/chainner_models/architecture/SwinIR.py index 8cce2d0e..1abf450b 100644 --- a/comfy_extras/chainner_models/architecture/SwinIR.py +++ b/comfy_extras/chainner_models/architecture/SwinIR.py @@ -972,6 +972,7 @@ class SwinIR(nn.Module): self.upsampler = upsampler self.img_size = img_size self.img_range = img_range + self.resi_connection = resi_connection self.supports_fp16 = False # Too much weirdness to support this at the moment self.supports_bfp16 = True diff --git a/comfy_extras/chainner_models/architecture/mat/utils.py b/comfy_extras/chainner_models/architecture/mat/utils.py deleted file mode 100644 index 1e9445a2..00000000 --- a/comfy_extras/chainner_models/architecture/mat/utils.py +++ /dev/null @@ -1,698 +0,0 @@ -"""Code used for this implementation of the MAT helper utils is modified from -lama-cleaner, copyright of Sanster: https://github.com/fenglinglwb/MAT""" - -import collections -from itertools import repeat -from typing import Any - -import numpy as np -import torch -from torch import conv2d, conv_transpose2d - - -def normalize_2nd_moment(x, dim=1, eps=1e-8): - return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() - - -class EasyDict(dict): - """Convenience class that behaves like a dict but allows access with the attribute syntax.""" - - def __getattr__(self, name: str) -> Any: - try: - return self[name] - except KeyError: - raise AttributeError(name) - - def __setattr__(self, name: str, value: Any) -> None: - self[name] = value - - def __delattr__(self, name: str) -> None: - del self[name] - - -activation_funcs = { - "linear": EasyDict( - func=lambda x, **_: x, - def_alpha=0, - def_gain=1, - cuda_idx=1, - ref="", - has_2nd_grad=False, - ), - "relu": EasyDict( - func=lambda x, **_: torch.nn.functional.relu(x), - def_alpha=0, - def_gain=np.sqrt(2), - cuda_idx=2, - ref="y", - has_2nd_grad=False, - ), - "lrelu": EasyDict( - func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), - def_alpha=0.2, - def_gain=np.sqrt(2), - cuda_idx=3, - ref="y", - has_2nd_grad=False, - ), - "tanh": EasyDict( - func=lambda x, **_: torch.tanh(x), - def_alpha=0, - def_gain=1, - cuda_idx=4, - ref="y", - has_2nd_grad=True, - ), - "sigmoid": EasyDict( - func=lambda x, **_: torch.sigmoid(x), - def_alpha=0, - def_gain=1, - cuda_idx=5, - ref="y", - has_2nd_grad=True, - ), - "elu": EasyDict( - func=lambda x, **_: torch.nn.functional.elu(x), - def_alpha=0, - def_gain=1, - cuda_idx=6, - ref="y", - has_2nd_grad=True, - ), - "selu": EasyDict( - func=lambda x, **_: torch.nn.functional.selu(x), - def_alpha=0, - def_gain=1, - cuda_idx=7, - ref="y", - has_2nd_grad=True, - ), - "softplus": EasyDict( - func=lambda x, **_: torch.nn.functional.softplus(x), - def_alpha=0, - def_gain=1, - cuda_idx=8, - ref="y", - has_2nd_grad=True, - ), - "swish": EasyDict( - func=lambda x, **_: torch.sigmoid(x) * x, - def_alpha=0, - def_gain=np.sqrt(2), - cuda_idx=9, - ref="x", - has_2nd_grad=True, - ), -} - - -def _bias_act_ref(x, b=None, dim=1, act="linear", alpha=None, gain=None, clamp=None): - """Slow reference implementation of `bias_act()` using standard TensorFlow ops.""" - assert isinstance(x, torch.Tensor) - assert clamp is None or clamp >= 0 - spec = activation_funcs[act] - alpha = float(alpha if alpha is not None else spec.def_alpha) - gain = float(gain if gain is not None else spec.def_gain) - clamp = float(clamp if clamp is not None else -1) - - # Add bias. - if b is not None: - assert isinstance(b, torch.Tensor) and b.ndim == 1 - assert 0 <= dim < x.ndim - assert b.shape[0] == x.shape[dim] - x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)]).to(x.device) - - # Evaluate activation function. - alpha = float(alpha) - x = spec.func(x, alpha=alpha) - - # Scale by gain. - gain = float(gain) - if gain != 1: - x = x * gain - - # Clamp. - if clamp >= 0: - x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type - return x - - -def bias_act( - x, b=None, dim=1, act="linear", alpha=None, gain=None, clamp=None, impl="ref" -): - r"""Fused bias and activation function. - Adds bias `b` to activation tensor `x`, evaluates activation function `act`, - and scales the result by `gain`. Each of the steps is optional. In most cases, - the fused op is considerably more efficient than performing the same calculation - using standard PyTorch ops. It supports first and second order gradients, - but not third order gradients. - Args: - x: Input activation tensor. Can be of any shape. - b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type - as `x`. The shape must be known, and it must match the dimension of `x` - corresponding to `dim`. - dim: The dimension in `x` corresponding to the elements of `b`. - The value of `dim` is ignored if `b` is not specified. - act: Name of the activation function to evaluate, or `"linear"` to disable. - Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. - See `activation_funcs` for a full list. `None` is not allowed. - alpha: Shape parameter for the activation function, or `None` to use the default. - gain: Scaling factor for the output tensor, or `None` to use default. - See `activation_funcs` for the default scaling of each activation function. - If unsure, consider specifying 1. - clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable - the clamping (default). - impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). - Returns: - Tensor of the same shape and datatype as `x`. - """ - assert isinstance(x, torch.Tensor) - assert impl in ["ref", "cuda"] - return _bias_act_ref( - x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp - ) - - -def setup_filter( - f, - device=torch.device("cpu"), - normalize=True, - flip_filter=False, - gain=1, - separable=None, -): - r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`. - Args: - f: Torch tensor, numpy array, or python list of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), - `[]` (impulse), or - `None` (identity). - device: Result device (default: cpu). - normalize: Normalize the filter so that it retains the magnitude - for constant input signal (DC)? (default: True). - flip_filter: Flip the filter? (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - separable: Return a separable filter? (default: select automatically). - Returns: - Float32 tensor of the shape - `[filter_height, filter_width]` (non-separable) or - `[filter_taps]` (separable). - """ - # Validate. - if f is None: - f = 1 - f = torch.as_tensor(f, dtype=torch.float32) - assert f.ndim in [0, 1, 2] - assert f.numel() > 0 - if f.ndim == 0: - f = f[np.newaxis] - - # Separable? - if separable is None: - separable = f.ndim == 1 and f.numel() >= 8 - if f.ndim == 1 and not separable: - f = f.ger(f) - assert f.ndim == (1 if separable else 2) - - # Apply normalize, flip, gain, and device. - if normalize: - f /= f.sum() - if flip_filter: - f = f.flip(list(range(f.ndim))) - f = f * (gain ** (f.ndim / 2)) - f = f.to(device=device) - return f - - -def _get_filter_size(f): - if f is None: - return 1, 1 - - assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] - fw = f.shape[-1] - fh = f.shape[0] - - fw = int(fw) - fh = int(fh) - assert fw >= 1 and fh >= 1 - return fw, fh - - -def _get_weight_shape(w): - shape = [int(sz) for sz in w.shape] - return shape - - -def _parse_scaling(scaling): - if isinstance(scaling, int): - scaling = [scaling, scaling] - assert isinstance(scaling, (list, tuple)) - assert all(isinstance(x, int) for x in scaling) - sx, sy = scaling - assert sx >= 1 and sy >= 1 - return sx, sy - - -def _parse_padding(padding): - if isinstance(padding, int): - padding = [padding, padding] - assert isinstance(padding, (list, tuple)) - assert all(isinstance(x, int) for x in padding) - if len(padding) == 2: - padx, pady = padding - padding = [padx, padx, pady, pady] - padx0, padx1, pady0, pady1 = padding - return padx0, padx1, pady0, pady1 - - -def _ntuple(n): - def parse(x): - if isinstance(x, collections.abc.Iterable): - return x - return tuple(repeat(x, n)) - - return parse - - -to_2tuple = _ntuple(2) - - -def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): - """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.""" - # Validate arguments. - assert isinstance(x, torch.Tensor) and x.ndim == 4 - if f is None: - f = torch.ones([1, 1], dtype=torch.float32, device=x.device) - assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] - assert f.dtype == torch.float32 and not f.requires_grad - batch_size, num_channels, in_height, in_width = x.shape - # upx, upy = _parse_scaling(up) - # downx, downy = _parse_scaling(down) - - upx, upy = up, up - downx, downy = down, down - - # padx0, padx1, pady0, pady1 = _parse_padding(padding) - padx0, padx1, pady0, pady1 = padding[0], padding[1], padding[2], padding[3] - - # Upsample by inserting zeros. - x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1]) - x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1]) - x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) - - # Pad or crop. - x = torch.nn.functional.pad( - x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)] - ) - x = x[ - :, - :, - max(-pady0, 0) : x.shape[2] - max(-pady1, 0), - max(-padx0, 0) : x.shape[3] - max(-padx1, 0), - ] - - # Setup filter. - f = f * (gain ** (f.ndim / 2)) - f = f.to(x.dtype) - if not flip_filter: - f = f.flip(list(range(f.ndim))) - - # Convolve with the filter. - f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim) - if f.ndim == 4: - x = conv2d(input=x, weight=f, groups=num_channels) - else: - x = conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels) - x = conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels) - - # Downsample by throwing away pixels. - x = x[:, :, ::downy, ::downx] - return x - - -def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl="cuda"): - r"""Pad, upsample, filter, and downsample a batch of 2D images. - Performs the following sequence of operations for each channel: - 1. Upsample the image by inserting N-1 zeros after each pixel (`up`). - 2. Pad the image with the specified number of zeros on each side (`padding`). - Negative padding corresponds to cropping the image. - 3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it - so that the footprint of all output pixels lies within the input image. - 4. Downsample the image by keeping every Nth pixel (`down`). - This sequence of operations bears close resemblance to scipy.signal.upfirdn(). - The fused op is considerably more efficient than performing the same calculation - using standard PyTorch ops. It supports gradients of arbitrary order. - Args: - x: Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f: Float32 FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - up: Integer upsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - down: Integer downsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - padding: Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - flip_filter: False = convolution, True = correlation (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - # assert isinstance(x, torch.Tensor) - # assert impl in ['ref', 'cuda'] - return _upfirdn2d_ref( - x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain - ) - - -def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl="cuda"): - r"""Upsample a batch of 2D images using the given 2D FIR filter. - By default, the result is padded so that its shape is a multiple of the input. - User-specified padding is applied on top of that, with negative values - indicating cropping. Pixels outside the image are assumed to be zero. - Args: - x: Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f: Float32 FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - up: Integer upsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - padding: Padding with respect to the output. Can be a single number or a - list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - flip_filter: False = convolution, True = correlation (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - upx, upy = _parse_scaling(up) - # upx, upy = up, up - padx0, padx1, pady0, pady1 = _parse_padding(padding) - # padx0, padx1, pady0, pady1 = padding, padding, padding, padding - fw, fh = _get_filter_size(f) - p = [ - padx0 + (fw + upx - 1) // 2, - padx1 + (fw - upx) // 2, - pady0 + (fh + upy - 1) // 2, - pady1 + (fh - upy) // 2, - ] - return upfirdn2d( - x, - f, - up=up, - padding=p, - flip_filter=flip_filter, - gain=gain * upx * upy, - impl=impl, - ) - - -class FullyConnectedLayer(torch.nn.Module): - def __init__( - self, - in_features, # Number of input features. - out_features, # Number of output features. - bias=True, # Apply additive bias before the activation function? - activation="linear", # Activation function: 'relu', 'lrelu', etc. - lr_multiplier=1, # Learning rate multiplier. - bias_init=0, # Initial value for the additive bias. - ): - super().__init__() - self.weight = torch.nn.Parameter( - torch.randn([out_features, in_features]) / lr_multiplier - ) - self.bias = ( - torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) - if bias - else None - ) - self.activation = activation - - self.weight_gain = lr_multiplier / np.sqrt(in_features) - self.bias_gain = lr_multiplier - - def forward(self, x): - w = self.weight * self.weight_gain - b = self.bias - if b is not None and self.bias_gain != 1: - b = b * self.bias_gain - - if self.activation == "linear" and b is not None: - # out = torch.addmm(b.unsqueeze(0), x, w.t()) - x = x.matmul(w.t().to(x.device)) - out = x + b.reshape( - [-1 if i == x.ndim - 1 else 1 for i in range(x.ndim)] - ).to(x.device) - else: - x = x.matmul(w.t().to(x.device)) - out = bias_act(x, b, act=self.activation, dim=x.ndim - 1).to(x.device) - return out - - -def _conv2d_wrapper( - x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True -): - """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.""" - out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) - - # Flip weight if requested. - if ( - not flip_weight - ): # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False). - w = w.flip([2, 3]) - - # Workaround performance pitfall in cuDNN 8.0.5, triggered when using - # 1x1 kernel + memory_format=channels_last + less than 64 channels. - if ( - kw == 1 - and kh == 1 - and stride == 1 - and padding in [0, [0, 0], (0, 0)] - and not transpose - ): - if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64: - if out_channels <= 4 and groups == 1: - in_shape = x.shape - x = w.squeeze(3).squeeze(2) @ x.reshape( - [in_shape[0], in_channels_per_group, -1] - ) - x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]]) - else: - x = x.to(memory_format=torch.contiguous_format) - w = w.to(memory_format=torch.contiguous_format) - x = conv2d(x, w, groups=groups) - return x.to(memory_format=torch.channels_last) - - # Otherwise => execute using conv2d_gradfix. - op = conv_transpose2d if transpose else conv2d - return op(x, w, stride=stride, padding=padding, groups=groups) - - -def conv2d_resample( - x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False -): - r"""2D convolution with optional up/downsampling. - Padding is performed only once at the beginning, not between the operations. - Args: - x: Input tensor of shape - `[batch_size, in_channels, in_height, in_width]`. - w: Weight tensor of shape - `[out_channels, in_channels//groups, kernel_height, kernel_width]`. - f: Low-pass filter for up/downsampling. Must be prepared beforehand by - calling setup_filter(). None = identity (default). - up: Integer upsampling factor (default: 1). - down: Integer downsampling factor (default: 1). - padding: Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - groups: Split input channels into N groups (default: 1). - flip_weight: False = convolution, True = correlation (default: True). - flip_filter: False = convolution, True = correlation (default: False). - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - # Validate arguments. - assert isinstance(x, torch.Tensor) and (x.ndim == 4) - assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) - assert f is None or ( - isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32 - ) - assert isinstance(up, int) and (up >= 1) - assert isinstance(down, int) and (down >= 1) - # assert isinstance(groups, int) and (groups >= 1), f"!!!!!! groups: {groups} isinstance(groups, int) {isinstance(groups, int)} {type(groups)}" - out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) - fw, fh = _get_filter_size(f) - # px0, px1, py0, py1 = _parse_padding(padding) - px0, px1, py0, py1 = padding, padding, padding, padding - - # Adjust padding to account for up/downsampling. - if up > 1: - px0 += (fw + up - 1) // 2 - px1 += (fw - up) // 2 - py0 += (fh + up - 1) // 2 - py1 += (fh - up) // 2 - if down > 1: - px0 += (fw - down + 1) // 2 - px1 += (fw - down) // 2 - py0 += (fh - down + 1) // 2 - py1 += (fh - down) // 2 - - # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. - if kw == 1 and kh == 1 and (down > 1 and up == 1): - x = upfirdn2d( - x=x, f=f, down=down, padding=[px0, px1, py0, py1], flip_filter=flip_filter - ) - x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - return x - - # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. - if kw == 1 and kh == 1 and (up > 1 and down == 1): - x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - x = upfirdn2d( - x=x, - f=f, - up=up, - padding=[px0, px1, py0, py1], - gain=up**2, - flip_filter=flip_filter, - ) - return x - - # Fast path: downsampling only => use strided convolution. - if down > 1 and up == 1: - x = upfirdn2d(x=x, f=f, padding=[px0, px1, py0, py1], flip_filter=flip_filter) - x = _conv2d_wrapper( - x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight - ) - return x - - # Fast path: upsampling with optional downsampling => use transpose strided convolution. - if up > 1: - if groups == 1: - w = w.transpose(0, 1) - else: - w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) - w = w.transpose(1, 2) - w = w.reshape( - groups * in_channels_per_group, out_channels // groups, kh, kw - ) - px0 -= kw - 1 - px1 -= kw - up - py0 -= kh - 1 - py1 -= kh - up - pxt = max(min(-px0, -px1), 0) - pyt = max(min(-py0, -py1), 0) - x = _conv2d_wrapper( - x=x, - w=w, - stride=up, - padding=[pyt, pxt], - groups=groups, - transpose=True, - flip_weight=(not flip_weight), - ) - x = upfirdn2d( - x=x, - f=f, - padding=[px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt], - gain=up**2, - flip_filter=flip_filter, - ) - if down > 1: - x = upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) - return x - - # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. - if up == 1 and down == 1: - if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: - return _conv2d_wrapper( - x=x, w=w, padding=[py0, px0], groups=groups, flip_weight=flip_weight - ) - - # Fallback: Generic reference implementation. - x = upfirdn2d( - x=x, - f=(f if up > 1 else None), - up=up, - padding=[px0, px1, py0, py1], - gain=up**2, - flip_filter=flip_filter, - ) - x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - if down > 1: - x = upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) - return x - - -class Conv2dLayer(torch.nn.Module): - def __init__( - self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - kernel_size, # Width and height of the convolution kernel. - bias=True, # Apply additive bias before the activation function? - activation="linear", # Activation function: 'relu', 'lrelu', etc. - up=1, # Integer upsampling factor. - down=1, # Integer downsampling factor. - resample_filter=[ - 1, - 3, - 3, - 1, - ], # Low-pass filter to apply when resampling activations. - conv_clamp=None, # Clamp the output to +-X, None = disable clamping. - channels_last=False, # Expect the input to have memory_format=channels_last? - trainable=True, # Update the weights of this layer during training? - ): - super().__init__() - self.activation = activation - self.up = up - self.down = down - self.register_buffer("resample_filter", setup_filter(resample_filter)) - self.conv_clamp = conv_clamp - self.padding = kernel_size // 2 - self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2)) - self.act_gain = activation_funcs[activation].def_gain - - memory_format = ( - torch.channels_last if channels_last else torch.contiguous_format - ) - weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to( - memory_format=memory_format - ) - bias = torch.zeros([out_channels]) if bias else None - if trainable: - self.weight = torch.nn.Parameter(weight) - self.bias = torch.nn.Parameter(bias) if bias is not None else None - else: - self.register_buffer("weight", weight) - if bias is not None: - self.register_buffer("bias", bias) - else: - self.bias = None - - def forward(self, x, gain=1): - w = self.weight * self.weight_gain - x = conv2d_resample( - x=x, - w=w, - f=self.resample_filter, - up=self.up, - down=self.down, - padding=self.padding, - ) - - act_gain = self.act_gain * gain - act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - out = bias_act( - x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp - ) - return out diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py index 2e66e624..e000871c 100644 --- a/comfy_extras/chainner_models/model_loading.py +++ b/comfy_extras/chainner_models/model_loading.py @@ -1,13 +1,14 @@ import logging as logger +from .architecture.DAT import DAT from .architecture.face.codeformer import CodeFormer from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean from .architecture.face.restoreformer_arch import RestoreFormer from .architecture.HAT import HAT from .architecture.LaMa import LaMa -from .architecture.MAT import MAT from .architecture.OmniSR.OmniSR import OmniSR from .architecture.RRDB import RRDBNet as ESRGAN +from .architecture.SCUNet import SCUNet from .architecture.SPSR import SPSRNet as SPSR from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 from .architecture.SwiftSRGAN import Generator as SwiftSRGAN @@ -33,7 +34,6 @@ def load_state_dict(state_dict) -> PyTorchModel: state_dict = state_dict["params"] state_dict_keys = list(state_dict.keys()) - # SRVGGNet Real-ESRGAN (v2) if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys: model = RealESRGANv2(state_dict) @@ -46,12 +46,14 @@ def load_state_dict(state_dict) -> PyTorchModel: and "initial.cnn.depthwise.weight" in state_dict["model"].keys() ): model = SwiftSRGAN(state_dict) - # HAT -- be sure it is above swinir - elif "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" in state_dict_keys: - model = HAT(state_dict) - # SwinIR + # SwinIR, Swin2SR, HAT elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys: - if "patch_embed.proj.weight" in state_dict_keys: + if ( + "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" + in state_dict_keys + ): + model = HAT(state_dict) + elif "patch_embed.proj.weight" in state_dict_keys: model = Swin2SR(state_dict) else: model = SwinIR(state_dict) @@ -78,12 +80,15 @@ def load_state_dict(state_dict) -> PyTorchModel: or "generator.model.1.bn_l.running_mean" in state_dict_keys ): model = LaMa(state_dict) - # MAT - elif "synthesis.first_stage.conv_first.conv.resample_filter" in state_dict_keys: - model = MAT(state_dict) # Omni-SR elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys: model = OmniSR(state_dict) + # SCUNet + elif "m_head.0.weight" in state_dict_keys and "m_tail.0.weight" in state_dict_keys: + model = SCUNet(state_dict) + # DAT + elif "layers.0.blocks.2.attn.attn_mask_0" in state_dict_keys: + model = DAT(state_dict) # Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1 else: try: diff --git a/comfy_extras/chainner_models/types.py b/comfy_extras/chainner_models/types.py index 1906c0c7..193333b9 100644 --- a/comfy_extras/chainner_models/types.py +++ b/comfy_extras/chainner_models/types.py @@ -1,20 +1,32 @@ from typing import Union +from .architecture.DAT import DAT from .architecture.face.codeformer import CodeFormer from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean from .architecture.face.restoreformer_arch import RestoreFormer from .architecture.HAT import HAT from .architecture.LaMa import LaMa -from .architecture.MAT import MAT from .architecture.OmniSR.OmniSR import OmniSR from .architecture.RRDB import RRDBNet as ESRGAN +from .architecture.SCUNet import SCUNet from .architecture.SPSR import SPSRNet as SPSR from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 from .architecture.SwiftSRGAN import Generator as SwiftSRGAN from .architecture.Swin2SR import Swin2SR from .architecture.SwinIR import SwinIR -PyTorchSRModels = (RealESRGANv2, SPSR, SwiftSRGAN, ESRGAN, SwinIR, Swin2SR, HAT, OmniSR) +PyTorchSRModels = ( + RealESRGANv2, + SPSR, + SwiftSRGAN, + ESRGAN, + SwinIR, + Swin2SR, + HAT, + OmniSR, + SCUNet, + DAT, +) PyTorchSRModel = Union[ RealESRGANv2, SPSR, @@ -24,6 +36,8 @@ PyTorchSRModel = Union[ Swin2SR, HAT, OmniSR, + SCUNet, + DAT, ] @@ -39,8 +53,8 @@ def is_pytorch_face_model(model: object): return isinstance(model, PyTorchFaceModels) -PyTorchInpaintModels = (LaMa, MAT) -PyTorchInpaintModel = Union[LaMa, MAT] +PyTorchInpaintModels = (LaMa,) +PyTorchInpaintModel = Union[LaMa] def is_pytorch_inpaint_model(model: object): From a74c5dbf3764fa598b58da8c88da823aaf8364fa Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Sep 2023 22:33:37 -0400 Subject: [PATCH 53/63] Move some functions to utils.py --- comfy/supported_models.py | 12 ++++++------ comfy/supported_models_base.py | 21 +++------------------ comfy/utils.py | 14 ++++++++++++++ 3 files changed, 23 insertions(+), 24 deletions(-) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 0b3e4bcb..bb8ae214 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -68,7 +68,7 @@ class SD20(supported_models_base.BASE): def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {} replace_prefix[""] = "cond_stage_model.model." - state_dict = supported_models_base.state_dict_prefix_replace(state_dict, replace_prefix) + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) return state_dict @@ -120,7 +120,7 @@ class SDXLRefiner(supported_models_base.BASE): keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection" keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" - state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace) + state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) return state_dict def process_clip_state_dict_for_saving(self, state_dict): @@ -129,7 +129,7 @@ class SDXLRefiner(supported_models_base.BASE): if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") replace_prefix["clip_g"] = "conditioner.embedders.0.model" - state_dict_g = supported_models_base.state_dict_prefix_replace(state_dict_g, replace_prefix) + state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) return state_dict_g def clip_target(self): @@ -167,8 +167,8 @@ class SDXL(supported_models_base.BASE): keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection" keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" - state_dict = supported_models_base.state_dict_prefix_replace(state_dict, replace_prefix) - state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace) + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) return state_dict def process_clip_state_dict_for_saving(self, state_dict): @@ -183,7 +183,7 @@ class SDXL(supported_models_base.BASE): replace_prefix["clip_g"] = "conditioner.embedders.1.model" replace_prefix["clip_l"] = "conditioner.embedders.0" - state_dict_g = supported_models_base.state_dict_prefix_replace(state_dict_g, replace_prefix) + state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) return state_dict_g def clip_target(self): diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py index 395a90ab..88a1d7fd 100644 --- a/comfy/supported_models_base.py +++ b/comfy/supported_models_base.py @@ -3,21 +3,6 @@ from . import model_base from . import utils from . import latent_formats - -def state_dict_key_replace(state_dict, keys_to_replace): - for x in keys_to_replace: - if x in state_dict: - state_dict[keys_to_replace[x]] = state_dict.pop(x) - return state_dict - -def state_dict_prefix_replace(state_dict, replace_prefix): - for rp in replace_prefix: - replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) - for x in replace: - state_dict[x[1]] = state_dict.pop(x[0]) - return state_dict - - class ClipTarget: def __init__(self, tokenizer, clip): self.clip = clip @@ -70,13 +55,13 @@ class BASE: def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {"": "cond_stage_model."} - return state_dict_prefix_replace(state_dict, replace_prefix) + return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_unet_state_dict_for_saving(self, state_dict): replace_prefix = {"": "model.diffusion_model."} - return state_dict_prefix_replace(state_dict, replace_prefix) + return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_vae_state_dict_for_saving(self, state_dict): replace_prefix = {"": "first_stage_model."} - return state_dict_prefix_replace(state_dict, replace_prefix) + return utils.state_dict_prefix_replace(state_dict, replace_prefix) diff --git a/comfy/utils.py b/comfy/utils.py index 47f4b970..3ed32e37 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -39,6 +39,20 @@ def calculate_parameters(sd, prefix=""): params += sd[k].nelement() return params +def state_dict_key_replace(state_dict, keys_to_replace): + for x in keys_to_replace: + if x in state_dict: + state_dict[keys_to_replace[x]] = state_dict.pop(x) + return state_dict + +def state_dict_prefix_replace(state_dict, replace_prefix): + for rp in replace_prefix: + replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) + for x in replace: + state_dict[x[1]] = state_dict.pop(x[0]) + return state_dict + + def transformers_convert(sd, prefix_from, prefix_to, number): keys_to_replace = { "{}positional_embedding": "{}embeddings.position_embedding.weight", From 2da73b7073dc520ee480dee8ff911b9aa83ff70a Mon Sep 17 00:00:00 2001 From: Simon Lui <502929+simonlui@users.noreply.github.com> Date: Sat, 2 Sep 2023 20:07:52 -0700 Subject: [PATCH 54/63] Revert changes in comfy/ldm/modules/diffusionmodules/util.py, which is unused. --- comfy/ldm/modules/diffusionmodules/util.py | 24 +++++++--------------- 1 file changed, 7 insertions(+), 17 deletions(-) diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py index 9d07d935..d890c804 100644 --- a/comfy/ldm/modules/diffusionmodules/util.py +++ b/comfy/ldm/modules/diffusionmodules/util.py @@ -15,7 +15,6 @@ import torch.nn as nn import numpy as np from einops import repeat -from comfy import model_management from comfy.ldm.util import instantiate_from_config import comfy.ops @@ -140,22 +139,13 @@ class CheckpointFunction(torch.autograd.Function): @staticmethod def backward(ctx, *output_grads): ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] - if model_management.is_nvidia(): - with torch.enable_grad(), \ - torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): - # Fixes a bug where the first op in run_function modifies the - # Tensor storage in place, which is not allowed for detach()'d - # Tensors. - shallow_copies = [x.view_as(x) for x in ctx.input_tensors] - output_tensors = ctx.run_function(*shallow_copies) - elif model_management.is_intel_xpu(): - with torch.enable_grad(), \ - torch.xpu.amp.autocast(**ctx.gpu_autocast_kwargs): - # Fixes a bug where the first op in run_function modifies the - # Tensor storage in place, which is not allowed for detach()'d - # Tensors. - shallow_copies = [x.view_as(x) for x in ctx.input_tensors] - output_tensors = ctx.run_function(*shallow_copies) + with torch.enable_grad(), \ + torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) input_grads = torch.autograd.grad( output_tensors, ctx.input_tensors + ctx.input_params, From 6f70227b8cbc61d5e228857aed991a8ae1ef1a33 Mon Sep 17 00:00:00 2001 From: Michael Abrahams Date: Sun, 3 Sep 2023 11:51:50 -0400 Subject: [PATCH 55/63] Add support for pasting images into the graph It can be useful to paste images from the clipboard directly into the node graph. This commit modifies copy and paste handling to support this. When an image file is found in the clipboard, we check whether an image node is selected. If so, paste the image into that node. Otherwise, a new node is created. If no image data are found in the clipboard, we call the original Litegraph paste. To ensure that onCopy and onPaste events are fired, we override Litegraph's ctrl+c and ctrl+v handling. Try to detect whether the pasted image is a real file on disk, or just pixel data copied from e.g. Photoshop. Pasted pixel data will be called 'image.png' and have a creation time of now. If it is simply pasted data, we store it in the subfolder /input/clipboard/. This also adds support for the subfolder property in the IMAGEUPLOAD widget. --- web/scripts/app.js | 93 ++++++++++++++++++++++++++++++++++++------ web/scripts/widgets.js | 29 +++++++++---- 2 files changed, 102 insertions(+), 20 deletions(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index 3b7483cd..b5114604 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -667,11 +667,40 @@ export class ComfyApp { } /** - * Adds a handler on paste that extracts and loads workflows from pasted JSON data + * Adds a handler on paste that extracts and loads images or workflows from pasted JSON data */ #addPasteHandler() { document.addEventListener("paste", (e) => { - let data = (e.clipboardData || window.clipboardData).getData("text/plain"); + let data = (e.clipboardData || window.clipboardData); + const items = data.items; + + // Look for image paste data + for (const item of items) { + if (item.type.startsWith('image/')) { + var imageNode = null; + + // If an image node is selected, paste into it + if (this.canvas.current_node && + this.canvas.current_node.is_selected && + ComfyApp.isImageNode(this.canvas.current_node)) { + imageNode = this.canvas.current_node; + } + + // No image node selected: add a new one + if (!imageNode) { + const newNode = LiteGraph.createNode("LoadImage"); + newNode.pos = [...this.canvas.graph_mouse]; + imageNode = this.graph.add(newNode); + this.graph.change(); + } + const blob = item.getAsFile(); + imageNode.pasteFile(blob); + return; + } + } + + // No image found. Look for node data + data = data.getData("text/plain"); let workflow; try { data = data.slice(data.indexOf("{")); @@ -687,9 +716,29 @@ export class ComfyApp { if (workflow && workflow.version && workflow.nodes && workflow.extra) { this.loadGraphData(workflow); } + else { + // Litegraph default paste + this.canvas.pasteFromClipboard(); + } + + + }); + } + + + /** + * Adds a handler on copy that serializes selected nodes to JSON + */ + #addCopyHandler() { + document.addEventListener("copy", (e) => { + // copy + if (this.canvas.selected_nodes) { + this.canvas.copyToClipboard(); + } }); } + /** * Handle mouse * @@ -745,12 +794,6 @@ export class ComfyApp { const self = this; const origProcessKey = LGraphCanvas.prototype.processKey; LGraphCanvas.prototype.processKey = function(e) { - const res = origProcessKey.apply(this, arguments); - - if (res === false) { - return res; - } - if (!this.graph) { return; } @@ -761,9 +804,10 @@ export class ComfyApp { return; } - if (e.type == "keydown") { + if (e.type == "keydown" && !e.repeat) { + // Ctrl + M mute/unmute - if (e.keyCode == 77 && e.ctrlKey) { + if (e.key === 'm' && e.ctrlKey) { if (this.selected_nodes) { for (var i in this.selected_nodes) { if (this.selected_nodes[i].mode === 2) { // never @@ -776,7 +820,8 @@ export class ComfyApp { block_default = true; } - if (e.keyCode == 66 && e.ctrlKey) { + // Ctrl + B bypass + if (e.key === 'b' && e.ctrlKey) { if (this.selected_nodes) { for (var i in this.selected_nodes) { if (this.selected_nodes[i].mode === 4) { // never @@ -788,6 +833,28 @@ export class ComfyApp { } block_default = true; } + + // Ctrl+C Copy + if ((e.key === 'c') && (e.metaKey || e.ctrlKey)) { + if (e.shiftKey) { + this.copyToClipboard(true); + block_default = true; + } + // Trigger default onCopy + return true; + } + + // Ctrl+V Paste + if ((e.key === 'v') && (e.metaKey || e.ctrlKey)) { + if (e.shiftKey) { + this.pasteFromClipboard(true); + block_default = true; + } + else { + // Trigger default onPaste + return true; + } + } } this.graph.change(); @@ -798,7 +865,8 @@ export class ComfyApp { return false; } - return res; + // Fall through to Litegraph defaults + return origProcessKey.apply(this, arguments); }; } @@ -1110,6 +1178,7 @@ export class ComfyApp { this.#addDrawGroupsHandler(); this.#addApiUpdateHandlers(); this.#addDropHandler(); + this.#addCopyHandler(); this.#addPasteHandler(); this.#addKeyboardHandler(); diff --git a/web/scripts/widgets.js b/web/scripts/widgets.js index 5a4644b1..45ac9b89 100644 --- a/web/scripts/widgets.js +++ b/web/scripts/widgets.js @@ -76,7 +76,7 @@ export function addValueControlWidget(node, targetWidget, defaultValue = "random targetWidget.value = max; } } - return valueControl; + return valueControl; }; function seedWidget(node, inputName, inputData, app) { @@ -387,11 +387,12 @@ export const ComfyWidgets = { } }); - async function uploadFile(file, updateNode) { + async function uploadFile(file, updateNode, pasted = false) { try { // Wrap file in formdata so it includes filename const body = new FormData(); body.append("image", file); + if (pasted) body.append("subfolder", "pasted"); const resp = await api.fetchApi("/upload/image", { method: "POST", body, @@ -399,15 +400,17 @@ export const ComfyWidgets = { if (resp.status === 200) { const data = await resp.json(); - // Add the file as an option and update the widget value - if (!imageWidget.options.values.includes(data.name)) { - imageWidget.options.values.push(data.name); + // Add the file to the dropdown list and update the widget value + let path = data.name; + if (data.subfolder) path = data.subfolder + "/" + path; + + if (!imageWidget.options.values.includes(path)) { + imageWidget.options.values.push(path); } if (updateNode) { - showImage(data.name); - - imageWidget.value = data.name; + showImage(path); + imageWidget.value = path; } } else { alert(resp.status + " - " + resp.statusText); @@ -460,6 +463,16 @@ export const ComfyWidgets = { return handled; }; + node.pasteFile = function(file) { + if (file.type.startsWith("image/")) { + const is_pasted = (file.name === "image.png") && + (file.lastModified - Date.now() < 2000); + uploadFile(file, true, is_pasted); + return true; + } + return false; + } + return { widget: uploadWidget }; }, }; From 1938f5c5fe479996802c46d5c2233887e3598a40 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 4 Sep 2023 00:58:18 -0400 Subject: [PATCH 56/63] Add a force argument to soft_empty_cache to force a cache empty. --- comfy/ldm/modules/attention.py | 2 +- comfy/ldm/modules/diffusionmodules/model.py | 1 + comfy/model_management.py | 4 ++-- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 8f953d33..34484b28 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -323,7 +323,7 @@ class CrossAttentionDoggettx(nn.Module): break except model_management.OOM_EXCEPTION as e: if first_op_done == False: - model_management.soft_empty_cache() + model_management.soft_empty_cache(True) if cleared_cache == False: cleared_cache = True print("out of memory error, emptying cache and trying again") diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py index 43154848..5f38640c 100644 --- a/comfy/ldm/modules/diffusionmodules/model.py +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -186,6 +186,7 @@ def slice_attention(q, k, v): del s2 break except model_management.OOM_EXCEPTION as e: + model_management.soft_empty_cache(True) steps *= 2 if steps > 128: raise e diff --git a/comfy/model_management.py b/comfy/model_management.py index bdbbbd84..b663e8f5 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -639,14 +639,14 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True): return True -def soft_empty_cache(): +def soft_empty_cache(force=False): global cpu_state if cpu_state == CPUState.MPS: torch.mps.empty_cache() elif is_intel_xpu(): torch.xpu.empty_cache() elif torch.cuda.is_available(): - if is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda + if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda torch.cuda.empty_cache() torch.cuda.ipc_collect() From d19684707922af9c2399307c8d9bccc1b267cc3b Mon Sep 17 00:00:00 2001 From: Michael Poutre Date: Wed, 23 Aug 2023 16:37:31 -0700 Subject: [PATCH 57/63] feat: Add support for excluded_dirs to folder_paths.recursive_search Refactored variable names to better match what they represent --- folder_paths.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/folder_paths.py b/folder_paths.py index e321690d..16de1bb6 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -121,18 +121,25 @@ def add_model_folder_path(folder_name, full_folder_path): def get_folder_paths(folder_name): return folder_names_and_paths[folder_name][0][:] -def recursive_search(directory): +def recursive_search(directory, excluded_dir_names=None): if not os.path.isdir(directory): return [], {} + + if excluded_dir_names is None: + excluded_dir_names = [] + result = [] dirs = {directory: os.path.getmtime(directory)} - for root, subdir, file in os.walk(directory, followlinks=True): - for filepath in file: - #we os.path,join directory with a blank string to generate a path separator at the end. - result.append(os.path.join(root, filepath).replace(os.path.join(directory,''),'')) - for d in subdir: - path = os.path.join(root, d) + for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): + print("Checking directory: " + dirpath) + subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] + for file_name in filenames: + relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) + result.append(relative_path) + for d in subdirs: + path = os.path.join(dirpath, d) dirs[path] = os.path.getmtime(path) + print("Returning from recursive_search" + repr(result)) return result, dirs def filter_files_extensions(files, extensions): From 3e00fa433216335a874bd408de421f9d65432daf Mon Sep 17 00:00:00 2001 From: Michael Poutre Date: Wed, 23 Aug 2023 16:50:41 -0700 Subject: [PATCH 58/63] feat: Exclude .git when retrieving filename lists In the future could support user provided excluded dirs via config file --- folder_paths.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/folder_paths.py b/folder_paths.py index 16de1bb6..a1805285 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -166,7 +166,7 @@ def get_filename_list_(folder_name): folders = folder_names_and_paths[folder_name] output_folders = {} for x in folders[0]: - files, folders_all = recursive_search(x) + files, folders_all = recursive_search(x, excluded_dir_names=[".git"]) output_list.update(filter_files_extensions(files, folders[1])) output_folders = {**output_folders, **folders_all} From f368e5ac7d31649b22c0c1e44bc9fa8002fcb117 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 5 Sep 2023 01:22:03 -0400 Subject: [PATCH 59/63] Don't paste nodes when target is a textarea or a text box. --- web/scripts/app.js | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index 7f507357..9c380d3f 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -717,8 +717,12 @@ export class ComfyApp { this.loadGraphData(workflow); } else { + if (e.target.type === "text" || e.target.type === "textarea") { + return; + } + // Litegraph default paste - this.canvas.pasteFromClipboard(); + this.canvas.pasteFromClipboard(); } From bc1f6e21856f7be25db5c5c2956b89c27db93b3d Mon Sep 17 00:00:00 2001 From: Michael Poutre Date: Tue, 5 Sep 2023 15:06:46 -0700 Subject: [PATCH 60/63] fix(ui/widgets): Only set widget forceInput option if a widget is added --- web/scripts/app.js | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index 9c380d3f..a3661da6 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -1228,6 +1228,7 @@ export class ComfyApp { const inputData = inputs[inputName]; const type = inputData[0]; + let widgetCreated = true; if (Array.isArray(type)) { // Enums Object.assign(config, widgets.COMBO(this, inputName, inputData, app) || {}); @@ -1240,8 +1241,10 @@ export class ComfyApp { } else { // Node connection inputs this.addInput(inputName, type); + widgetCreated = false; } - if(inputData[1]?.forceInput && config?.widget) { + + if(widgetCreated && inputData[1]?.forceInput && config?.widget) { if (!config.widget.options) config.widget.options = {}; config.widget.options.forceInput = inputData[1].forceInput; } From 21a563d385ff520e1f7fdaada722212b35fb8d95 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 5 Sep 2023 23:46:37 -0400 Subject: [PATCH 61/63] Remove prints. --- folder_paths.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/folder_paths.py b/folder_paths.py index a1805285..82aedd43 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -131,7 +131,6 @@ def recursive_search(directory, excluded_dir_names=None): result = [] dirs = {directory: os.path.getmtime(directory)} for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): - print("Checking directory: " + dirpath) subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] for file_name in filenames: relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) @@ -139,7 +138,6 @@ def recursive_search(directory, excluded_dir_names=None): for d in subdirs: path = os.path.join(dirpath, d) dirs[path] = os.path.getmtime(path) - print("Returning from recursive_search" + repr(result)) return result, dirs def filter_files_extensions(files, extensions): From f88f7f413afbe04b42c4422e9deedbaa3269ce76 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 6 Sep 2023 03:26:55 -0400 Subject: [PATCH 62/63] Add a ConditioningSetAreaPercentage node. --- comfy/samplers.py | 15 ++++++++++++--- nodes.py | 27 +++++++++++++++++++++++++++ 2 files changed, 39 insertions(+), 3 deletions(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index 103ac33f..3250b2ed 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -390,11 +390,20 @@ def get_mask_aabb(masks): return bounding_boxes, is_empty -def resolve_cond_masks(conditions, h, w, device): +def resolve_areas_and_cond_masks(conditions, h, w, device): # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. # While we're doing this, we can also resolve the mask device and scaling for performance reasons for i in range(len(conditions)): c = conditions[i] + if 'area' in c[1]: + area = c[1]['area'] + if area[0] == "percentage": + modified = c[1].copy() + area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w)) + modified['area'] = area + c = [c[0], modified] + conditions[i] = c + if 'mask' in c[1]: mask = c[1]['mask'] mask = mask.to(device=device) @@ -622,8 +631,8 @@ class KSampler: positive = positive[:] negative = negative[:] - resolve_cond_masks(positive, noise.shape[2], noise.shape[3], self.device) - resolve_cond_masks(negative, noise.shape[2], noise.shape[3], self.device) + resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], self.device) + resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], self.device) calculate_start_end_timesteps(self.model_wrap, negative) calculate_start_end_timesteps(self.model_wrap, positive) diff --git a/nodes.py b/nodes.py index fa26e593..77d18052 100644 --- a/nodes.py +++ b/nodes.py @@ -159,6 +159,31 @@ class ConditioningSetArea: c.append(n) return (c, ) +class ConditioningSetAreaPercentage: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, x, y, strength): + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + n[1]['area'] = ("percentage", height, width, y, x) + n[1]['strength'] = strength + n[1]['set_area_to_bounds'] = False + c.append(n) + return (c, ) + class ConditioningSetMask: @classmethod def INPUT_TYPES(s): @@ -1583,6 +1608,7 @@ NODE_CLASS_MAPPINGS = { "ConditioningCombine": ConditioningCombine, "ConditioningConcat": ConditioningConcat, "ConditioningSetArea": ConditioningSetArea, + "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage, "ConditioningSetMask": ConditioningSetMask, "KSamplerAdvanced": KSamplerAdvanced, "SetLatentNoiseMask": SetLatentNoiseMask, @@ -1644,6 +1670,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { "ConditioningAverage ": "Conditioning (Average)", "ConditioningConcat": "Conditioning (Concat)", "ConditioningSetArea": "Conditioning (Set Area)", + "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", "ConditioningSetMask": "Conditioning (Set Mask)", "ControlNetApply": "Apply ControlNet", "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)", From cb080e771e1e792e18611ef63d2d6a49aa50a524 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 6 Sep 2023 16:18:02 -0400 Subject: [PATCH 63/63] Lower refresh timeout for search in litegraph. --- web/lib/litegraph.core.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/web/lib/litegraph.core.js b/web/lib/litegraph.core.js index 4bb2f0d9..4a21a1b3 100644 --- a/web/lib/litegraph.core.js +++ b/web/lib/litegraph.core.js @@ -11529,7 +11529,7 @@ LGraphNode.prototype.executeAction = function(action) if (timeout) { clearInterval(timeout); } - timeout = setTimeout(refreshHelper, 250); + timeout = setTimeout(refreshHelper, 10); return; } e.preventDefault();