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117 lines
4.9 KiB
117 lines
4.9 KiB
from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace |
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import os |
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import torch |
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import json |
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import logging |
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import comfy.ops |
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import comfy.model_patcher |
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import comfy.model_management |
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import comfy.utils |
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import comfy.clip_model |
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class Output: |
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def __getitem__(self, key): |
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return getattr(self, key) |
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def __setitem__(self, key, item): |
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setattr(self, key, item) |
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def clip_preprocess(image, size=224): |
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mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype) |
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std = torch.tensor([0.26862954,0.26130258,0.27577711], device=image.device, dtype=image.dtype) |
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image = image.movedim(-1, 1) |
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if not (image.shape[2] == size and image.shape[3] == size): |
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scale = (size / min(image.shape[2], image.shape[3])) |
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image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode="bicubic", antialias=True) |
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h = (image.shape[2] - size)//2 |
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w = (image.shape[3] - size)//2 |
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image = image[:,:,h:h+size,w:w+size] |
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image = torch.clip((255. * image), 0, 255).round() / 255.0 |
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return (image - mean.view([3,1,1])) / std.view([3,1,1]) |
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class ClipVisionModel(): |
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def __init__(self, json_config): |
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with open(json_config) as f: |
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config = json.load(f) |
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self.load_device = comfy.model_management.text_encoder_device() |
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offload_device = comfy.model_management.text_encoder_offload_device() |
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self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) |
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self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops.manual_cast) |
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self.model.eval() |
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self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) |
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def load_sd(self, sd): |
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return self.model.load_state_dict(sd, strict=False) |
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def get_sd(self): |
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return self.model.state_dict() |
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def encode_image(self, image): |
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comfy.model_management.load_model_gpu(self.patcher) |
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pixel_values = clip_preprocess(image.to(self.load_device)).float() |
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out = self.model(pixel_values=pixel_values, intermediate_output=-2) |
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outputs = Output() |
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outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device()) |
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outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device()) |
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outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device()) |
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return outputs |
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def convert_to_transformers(sd, prefix): |
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sd_k = sd.keys() |
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if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k: |
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keys_to_replace = { |
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"{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding", |
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"{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight", |
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"{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight", |
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"{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias", |
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"{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight", |
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"{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias", |
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"{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight", |
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} |
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for x in keys_to_replace: |
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if x in sd_k: |
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sd[keys_to_replace[x]] = sd.pop(x) |
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if "{}proj".format(prefix) in sd_k: |
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sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1) |
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sd = transformers_convert(sd, prefix, "vision_model.", 48) |
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else: |
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replace_prefix = {prefix: ""} |
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sd = state_dict_prefix_replace(sd, replace_prefix) |
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return sd |
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def load_clipvision_from_sd(sd, prefix="", convert_keys=False): |
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if convert_keys: |
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sd = convert_to_transformers(sd, prefix) |
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if "vision_model.encoder.layers.47.layer_norm1.weight" in sd: |
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json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json") |
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elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd: |
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json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") |
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elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd: |
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json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") |
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else: |
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return None |
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clip = ClipVisionModel(json_config) |
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m, u = clip.load_sd(sd) |
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if len(m) > 0: |
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logging.warning("missing clip vision: {}".format(m)) |
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u = set(u) |
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keys = list(sd.keys()) |
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for k in keys: |
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if k not in u: |
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t = sd.pop(k) |
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del t |
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return clip |
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def load(ckpt_path): |
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sd = load_torch_file(ckpt_path) |
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if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd: |
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return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True) |
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else: |
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return load_clipvision_from_sd(sd)
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