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