125 lines
5.7 KiB
Python
125 lines
5.7 KiB
Python
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, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]):
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mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
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std = torch.tensor(std, 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.image_size = config.get("image_size", 224)
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self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
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self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
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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), size=self.image_size, mean=self.image_mean, std=self.image_std).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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if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152:
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json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
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elif sd["vision_model.embeddings.position_embedding.weight"].shape[0] == 577:
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json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
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else:
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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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sd.pop(k)
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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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