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I converted "stablediffusionapi/juggernaut-xl-v5" this model to a single safetensors file using this (https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_sdxl.py). Conversion happened properly but unable to load it using StableDiffusionXLPipeline.from_single_file()
Below is the error I am facing:
ValueError Traceback (most recent call last) /home/example_notebook.ipynb Cell 3 line 1 ----> 1 pipe = StableDiffusionXLPipeline.from_single_file(model_path,local_files_only = True)
File ~/diff_test_env/lib/python3.8/site-packages/diffusers/loaders.py:2308, in FromSingleFileMixin.from_single_file(cls, pretrained_model_link_or_path, **kwargs) 2294 file_path = file_path[len("main/") :] 2296 pretrained_model_link_or_path = hf_hub_download( 2297 repo_id, 2298 filename=file_path, (...) 2305 force_download=force_download, 2306 ) -> 2308 pipe = download_from_original_stable_diffusion_ckpt( 2309 pretrained_model_link_or_path, 2310 pipeline_class=cls, 2311 model_type=model_type, 2312 stable_unclip=stable_unclip, 2313 controlnet=controlnet, 2314 from_safetensors=from_safetensors, 2315 extract_ema=extract_ema, 2316 image_size=image_size, 2317 scheduler_type=scheduler_type, 2318 num_in_channels=num_in_channels, 2319 upcast_attention=upcast_attention, 2320 load_safety_checker=load_safety_checker, 2321 prediction_type=prediction_type, 2322 text_encoder=text_encoder, 2323 vae=vae, 2324 tokenizer=tokenizer, 2325 original_config_file=original_config_file, 2326 config_files=config_files, 2327 ) 2329 if torch_dtype is not None: 2330 pipe.to(torch_dtype=torch_dtype)
File ~/diff_test_env/lib/python3.8/site-packages/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py:1605, in download_from_original_stable_diffusion_ckpt(checkpoint_path_or_dict, original_config_file, image_size, prediction_type, model_type, extract_ema, scheduler_type, num_in_channels, upcast_attention, device, from_safetensors, stable_unclip, stable_unclip_prior, clip_stats_path, controlnet, load_safety_checker, pipeline_class, local_files_only, vae_path, vae, text_encoder, tokenizer, config_files) 1603 config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k" 1604 config_kwargs = {"projection_dim": 1280} -> 1605 text_encoder_2 = convert_open_clip_checkpoint( 1606 checkpoint, config_name, prefix="conditioner.embedders.1.model.", has_projection=True, **config_kwargs 1607 ) 1609 if is_accelerate_available(): # SBM Now move model to cpu. 1610 if model_type in ["SDXL", "SDXL-Refiner"]:
File ~/diff_test_env/lib/python3.8/site-packages/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py:971, in convert_open_clip_checkpoint(checkpoint, config_name, prefix, has_projection, local_files_only, **config_kwargs) 969 if is_accelerate_available(): 970 for param_name, param in text_model_dict.items(): --> 971 set_module_tensor_to_device(text_model, param_name, "cpu", value=param) 972 else: 973 if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
File ~/diff_test_env/lib/python3.8/site-packages/accelerate/utils/modeling.py:285, in set_module_tensor_to_device(module, tensor_name, device, value, dtype, fp16_statistics) 283 if value is not None: 284 if old_value.shape != value.shape: --> 285 raise ValueError( 286 f'Trying to set a tensor of shape {value.shape} in "{tensor_name}" (which has shape {old_value.shape}), this look incorrect.' 287 ) 289 if dtype is None: 290 # For compatibility with PyTorch load_state_dict which converts state dict dtype to existing dtype in model 291 value = value.to(old_value.dtype)
ValueError: Trying to set a tensor of shape torch.Size([1024]) in "bias" (which has shape torch.Size([1280])), this look incorrect.