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82 lines
3.6 KiB
82 lines
3.6 KiB
from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor, modeling_utils |
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from .utils import load_torch_file, transformers_convert |
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import os |
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import torch |
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import comfy.ops |
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class ClipVisionModel(): |
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def __init__(self, json_config): |
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config = CLIPVisionConfig.from_json_file(json_config) |
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with comfy.ops.use_comfy_ops(): |
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with modeling_utils.no_init_weights(): |
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self.model = CLIPVisionModelWithProjection(config) |
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self.processor = CLIPImageProcessor(crop_size=224, |
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do_center_crop=True, |
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do_convert_rgb=True, |
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do_normalize=True, |
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do_resize=True, |
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image_mean=[ 0.48145466,0.4578275,0.40821073], |
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image_std=[0.26862954,0.26130258,0.27577711], |
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resample=3, #bicubic |
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size=224) |
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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 encode_image(self, image): |
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img = torch.clip((255. * image), 0, 255).round().int() |
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img = list(map(lambda a: a, img)) |
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inputs = self.processor(images=img, return_tensors="pt") |
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outputs = self.model(**inputs) |
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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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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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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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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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print("missing clip vision:", 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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