Merge branch 'comfyanonymous:master' into update_use_env_to_load_custom_nodes

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.")
 

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 +40,21 @@ 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)
+
+        for k in outputs:
+            t = outputs[k]
+            if t is not None:
+                outputs[k] = t.cpu()
         return outputs
 
 def convert_to_transformers(sd, prefix):

comfy/controlnet.py

@@ -0,0 +1,480 @@
+import torch
+import math
+import os
+import comfy.utils
+import comfy.model_management
+import comfy.model_detection
+import comfy.model_patcher
+
+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.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):
+        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 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]:
+            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 = cond['c_crossattn']
+        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 = comfy.model_management.resolve_lowvram_weight(sd[k], diffusion_model, k)
+            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
+    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)
+    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 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:
+        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)

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)

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(True)
                     if cleared_cache == False:
                         cleared_cache = True
                         print("out of memory error, emptying cache and trying again")
@@ -402,8 +401,6 @@ 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)
 

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
@@ -74,11 +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)
@@ -176,6 +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
@@ -275,25 +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
 
@@ -603,9 +606,6 @@ 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):
@@ -614,8 +614,7 @@ 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)

comfy/lora.py

@@ -0,0 +1,199 @@
+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)
+
+
+        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)
+    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

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
@@ -18,8 +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:
@@ -48,10 +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)
@@ -93,7 +95,11 @@ 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:
@@ -105,6 +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 = []
@@ -142,12 +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]

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

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
 
@@ -57,8 +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:
@@ -69,13 +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()
@@ -87,7 +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
@@ -111,9 +118,6 @@ 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
@@ -148,17 +152,32 @@ 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
 
-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 is_intel_xpu():
+    VAE_DTYPE = torch.bfloat16
+
+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)
@@ -210,7 +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:
@@ -220,7 +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))
@@ -230,6 +248,7 @@ try:
 except:
     print("Could not pick default device.")
 
+print("VAE dtype:", VAE_DTYPE)
 
 current_loaded_models = []
 
@@ -249,7 +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
@@ -270,7 +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
@@ -450,12 +468,8 @@ 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'):
@@ -464,12 +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
@@ -496,7 +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()
@@ -508,7 +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']
@@ -570,7 +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:
@@ -593,7 +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():
@@ -628,18 +639,24 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True):
 
     return True
 
-def soft_empty_cache():
-    global xpu_available
+def soft_empty_cache(force=False):
     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
+        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()
 
+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
 

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:", key)
+                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

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

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
@@ -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)

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):

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
 }

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):

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):
@@ -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 = {}
@@ -164,8 +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):
@@ -180,7 +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):

comfy/supported_models_base.py

@@ -1,21 +1,7 @@
 import torch
 from . import model_base
 from . import utils
-
-
-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
-
+from . import latent_formats
 
 class ClipTarget:
     def __init__(self, tokenizer, clip):
@@ -33,6 +19,8 @@ class BASE:
     clip_prefix = []
     clip_vision_prefix = None
     noise_aug_config = None
+    beta_schedule = "linear"
+    latent_format = latent_formats.LatentFormat
 
     @classmethod
     def matches(s, unet_config):
@@ -54,25 +42,26 @@ 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
 
     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)
 

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",
@@ -223,6 +237,13 @@ 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:
@@ -237,6 +258,20 @@ 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'''

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
+
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+
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+
+      "Legal Entity" shall mean the union of the acting entity and all
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
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comfy_extras/chainner_models/architecture/LICENSE-SCUNet

@@ -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. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
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comfy_extras/chainner_models/architecture/LICENSE-mat

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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):

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)

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

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

comfy_extras/chainner_models/architecture/mat/utils.py

@@ -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

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:

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):

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 = {

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,

folder_paths.py

@@ -195,17 +195,22 @@ 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):
+        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)
     return result, dirs
 
@@ -233,7 +238,7 @@ 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}
 

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
 
@@ -158,6 +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):
@@ -243,14 +269,16 @@ 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": 512, "min": 320, "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 // 8, tile_y=tile_size // 8, ), )
 
 class VAEEncode:
     @classmethod
@@ -279,15 +307,17 @@ 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": 320, "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:
@@ -444,7 +474,7 @@ 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
@@ -470,7 +500,7 @@ 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:
@@ -569,7 +599,7 @@ 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 +615,7 @@ 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,)
 
 
@@ -1578,6 +1608,7 @@ NODE_CLASS_MAPPINGS = {
     "ConditioningCombine": ConditioningCombine,
     "ConditioningConcat": ConditioningConcat,
     "ConditioningSetArea": ConditioningSetArea,
+    "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage,
     "ConditioningSetMask": ConditioningSetMask,
     "KSamplerAdvanced": KSamplerAdvanced,
     "SetLatentNoiseMask": SetLatentNoiseMask,
@@ -1639,6 +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)",

server.py

@@ -1,6 +1,8 @@
 import os
 import sys
 import asyncio
+import traceback
+
 import nodes
 import folder_paths
 import execution
@@ -10,6 +12,7 @@ import json
 import glob
 import struct
 from PIL import Image, ImageOps
+from PIL.PngImagePlugin import PngInfo
 from io import BytesIO
 
 try:
@@ -79,7 +82,7 @@ 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")
@@ -88,6 +91,8 @@ 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()
@@ -122,7 +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):
@@ -229,13 +234,17 @@ class PromptServer():
 
                 if os.path.isfile(file):
                     with Image.open(file) as original_pil:
+                        metadata = PngInfo()
+                        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')
 
                         # 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)
 
@@ -438,6 +447,7 @@ 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 +616,15 @@ 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

web/extensions/core/groupOptions.js

@@ -0,0 +1,167 @@
+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
+            // 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;
+                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);
+                                }
+                            }
+                        });
+                        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);
+                                }
+                            }
+                        });
+                        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: "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: "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);
+                        }
+                    }
+                });
+            }
+
+            return options
+        }
+    }
+});

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,

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 = "") {
@@ -16,8 +16,12 @@ function hideWidget(node, widget, suffix = "") {
 	widget.type = CONVERTED_TYPE + suffix;
 	widget.serializeValue = () => {
 		// Prevent serializing the widget if we have no input linked
-		const { link } = node.inputs.find((i) => i.widget?.name === widget.name);
-		if (link == null) {
+		if (!node.inputs) {
+			return undefined;
+		}
+		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;
@@ -103,6 +107,9 @@ 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 +137,20 @@ 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 +158,7 @@ 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 +395,7 @@ 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;
 						}

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) {
 			
@@ -11523,7 +11529,7 @@ LGraphNode.prototype.executeAction = function(action)
                     if (timeout) {
                         clearInterval(timeout);
                     }
-                    timeout = setTimeout(refreshHelper, 250);
+                    timeout = setTimeout(refreshHelper, 10);
                     return;
                 }
                 e.preventDefault();

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 +716,33 @@ export class ComfyApp {
 			if (workflow && workflow.version && workflow.nodes && workflow.extra) {
 				this.loadGraphData(workflow);
 			}
+			else {
+				if (e.target.type === "text" || e.target.type === "textarea") {
+					return;
+				}
+
+				// 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 +798,6 @@ 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 +808,10 @@ 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 +824,8 @@ 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 +837,28 @@ 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 +869,8 @@ export class ComfyApp {
 				return false;
 			}
 
-			return res;
+			// Fall through to Litegraph defaults
+			return origProcessKey.apply(this, arguments);
 		};
 	}
 
@@ -994,6 +1066,10 @@ 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 }) => {
@@ -1110,6 +1186,7 @@ export class ComfyApp {
 		this.#addDrawGroupsHandler();
 		this.#addApiUpdateHandlers();
 		this.#addDropHandler();
+		this.#addCopyHandler();
 		this.#addPasteHandler();
 		this.#addKeyboardHandler();
 
@@ -1151,22 +1228,25 @@ export class ComfyApp {
 						const inputData = inputs[inputName];
 						const type = inputData[0];
 
-						if(inputData[1]?.forceInput) {
-							this.addInput(inputName, type);
+						let widgetCreated = true;
+						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);
+							widgetCreated = false;
+						}
+
+						if(widgetCreated && inputData[1]?.forceInput && config?.widget) {
+							if (!config.widget.options) config.widget.options = {};
+							config.widget.options.forceInput = inputData[1].forceInput;
 						}
 					}
 

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();
@@ -617,7 +619,9 @@ 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 +643,23 @@ 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", {

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) {
@@ -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];
@@ -381,11 +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,
@@ -393,15 +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);
@@ -454,6 +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 };
 	},
 };

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;