118 lines
5.0 KiB
Python
118 lines
5.0 KiB
Python
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 contextlib
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import fcbh.ops
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import fcbh.model_patcher
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import fcbh.model_management
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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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self.load_device = fcbh.model_management.text_encoder_device()
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offload_device = fcbh.model_management.text_encoder_offload_device()
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self.dtype = torch.float32
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if fcbh.model_management.should_use_fp16(self.load_device, prioritize_performance=False):
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self.dtype = torch.float16
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with fcbh.ops.use_fcbh_ops(offload_device, self.dtype):
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with modeling_utils.no_init_weights():
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self.model = CLIPVisionModelWithProjection(config)
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self.model.to(self.dtype)
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self.patcher = fcbh.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
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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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fcbh.model_management.load_model_gpu(self.patcher)
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pixel_values = inputs['pixel_values'].to(self.load_device)
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if self.dtype != torch.float32:
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precision_scope = torch.autocast
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else:
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precision_scope = lambda a, b: contextlib.nullcontext(a)
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with precision_scope(fcbh.model_management.get_autocast_device(self.load_device), torch.float32):
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outputs = self.model(pixel_values=pixel_values, output_hidden_states=True)
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for k in outputs:
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t = outputs[k]
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if t is not None:
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if k == 'hidden_states':
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outputs["penultimate_hidden_states"] = t[-2].cpu()
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outputs["hidden_states"] = None
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else:
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outputs[k] = t.cpu()
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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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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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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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