74 lines
3.2 KiB
Python
74 lines
3.2 KiB
Python
from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor, modeling_utils
|
|
from .utils import load_torch_file, transformers_convert
|
|
import os
|
|
import torch
|
|
import comfy.ops
|
|
|
|
class ClipVisionModel():
|
|
def __init__(self, json_config):
|
|
config = CLIPVisionConfig.from_json_file(json_config)
|
|
with comfy.ops.use_comfy_ops():
|
|
with modeling_utils.no_init_weights():
|
|
self.model = CLIPVisionModelWithProjection(config)
|
|
self.processor = CLIPImageProcessor(crop_size=224,
|
|
do_center_crop=True,
|
|
do_convert_rgb=True,
|
|
do_normalize=True,
|
|
do_resize=True,
|
|
image_mean=[ 0.48145466,0.4578275,0.40821073],
|
|
image_std=[0.26862954,0.26130258,0.27577711],
|
|
resample=3, #bicubic
|
|
size=224)
|
|
|
|
def load_sd(self, sd):
|
|
return self.model.load_state_dict(sd, strict=False)
|
|
|
|
def encode_image(self, image):
|
|
img = torch.clip((255. * image[0]), 0, 255).round().int()
|
|
inputs = self.processor(images=[img], return_tensors="pt")
|
|
outputs = self.model(**inputs)
|
|
return outputs
|
|
|
|
def convert_to_transformers(sd):
|
|
sd_k = sd.keys()
|
|
if "embedder.model.visual.transformer.resblocks.0.attn.in_proj_weight" in sd_k:
|
|
keys_to_replace = {
|
|
"embedder.model.visual.class_embedding": "vision_model.embeddings.class_embedding",
|
|
"embedder.model.visual.conv1.weight": "vision_model.embeddings.patch_embedding.weight",
|
|
"embedder.model.visual.positional_embedding": "vision_model.embeddings.position_embedding.weight",
|
|
"embedder.model.visual.ln_post.bias": "vision_model.post_layernorm.bias",
|
|
"embedder.model.visual.ln_post.weight": "vision_model.post_layernorm.weight",
|
|
"embedder.model.visual.ln_pre.bias": "vision_model.pre_layrnorm.bias",
|
|
"embedder.model.visual.ln_pre.weight": "vision_model.pre_layrnorm.weight",
|
|
}
|
|
|
|
for x in keys_to_replace:
|
|
if x in sd_k:
|
|
sd[keys_to_replace[x]] = sd.pop(x)
|
|
|
|
if "embedder.model.visual.proj" in sd_k:
|
|
sd['visual_projection.weight'] = sd.pop("embedder.model.visual.proj").transpose(0, 1)
|
|
|
|
sd = transformers_convert(sd, "embedder.model.visual", "vision_model", 32)
|
|
return sd
|
|
|
|
def load_clipvision_from_sd(sd):
|
|
sd = convert_to_transformers(sd)
|
|
if "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
|
|
json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
|
|
else:
|
|
json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
|
|
clip = ClipVisionModel(json_config)
|
|
m, u = clip.load_sd(sd)
|
|
u = set(u)
|
|
keys = list(sd.keys())
|
|
for k in keys:
|
|
if k not in u:
|
|
t = sd.pop(k)
|
|
del t
|
|
return clip
|
|
|
|
def load(ckpt_path):
|
|
sd = load_torch_file(ckpt_path)
|
|
return load_clipvision_from_sd(sd)
|