mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-01-26 19:09:45 +00:00
119 lines
4.2 KiB
Python
119 lines
4.2 KiB
Python
import os
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import torch
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import logging
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import importlib
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import huggingface_guess
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from diffusers import DiffusionPipeline
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from transformers import modeling_utils
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from backend.state_dict import try_filter_state_dict, load_state_dict
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from backend.operations import using_forge_operations
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from backend.nn.vae import IntegratedAutoencoderKL
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from backend.nn.clip import IntegratedCLIP, CLIPTextConfig
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from backend.nn.unet import IntegratedUNet2DConditionModel
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from backend.diffusion_engine.sd15 import StableDiffusion
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from backend.diffusion_engine.sd20 import StableDiffusion2
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from backend.diffusion_engine.sdxl import StableDiffusionXL
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possible_models = [StableDiffusion, StableDiffusion2, StableDiffusionXL]
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logging.getLogger("diffusers").setLevel(logging.ERROR)
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dir_path = os.path.dirname(__file__)
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def load_huggingface_component(guess, component_name, lib_name, cls_name, repo_path, state_dict):
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config_path = os.path.join(repo_path, component_name)
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if component_name in ['feature_extractor', 'safety_checker']:
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return None
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if lib_name in ['transformers', 'diffusers']:
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if component_name in ['scheduler'] or component_name.startswith('tokenizer'):
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cls = getattr(importlib.import_module(lib_name), cls_name)
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return cls.from_pretrained(os.path.join(repo_path, component_name))
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if cls_name in ['AutoencoderKL']:
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config = IntegratedAutoencoderKL.load_config(config_path)
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with using_forge_operations():
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model = IntegratedAutoencoderKL.from_config(config)
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load_state_dict(model, state_dict)
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return model
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if component_name.startswith('text_encoder') and cls_name in ['CLIPTextModel', 'CLIPTextModelWithProjection']:
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config = CLIPTextConfig.from_pretrained(config_path)
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with modeling_utils.no_init_weights():
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with using_forge_operations():
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model = IntegratedCLIP(config)
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load_state_dict(model, state_dict, ignore_errors=[
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'transformer.text_projection.weight',
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'transformer.text_model.embeddings.position_ids',
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'logit_scale'
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], log_name=cls_name)
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return model
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if cls_name == 'UNet2DConditionModel':
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with using_forge_operations():
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model = IntegratedUNet2DConditionModel.from_config(guess.unet_config)
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model._internal_dict = guess.unet_config
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load_state_dict(model, state_dict)
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return model
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print(f'Skipped: {component_name} = {lib_name}.{cls_name}')
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return None
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def split_state_dict(sd):
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guess = huggingface_guess.guess(sd)
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state_dict = {
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'unet': try_filter_state_dict(sd, ['model.diffusion_model.']),
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'vae': try_filter_state_dict(sd, guess.vae_key_prefix)
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}
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sd = guess.process_clip_state_dict(sd)
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guess.clip_target = guess.clip_target(sd)
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for k, v in guess.clip_target.items():
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state_dict[v] = try_filter_state_dict(sd, [k + '.'])
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state_dict['ignore'] = sd
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print_dict = {k: len(v) for k, v in state_dict.items()}
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print(f'StateDict Keys: {print_dict}')
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del state_dict['ignore']
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return state_dict, guess
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@torch.no_grad()
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def forge_loader(sd):
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state_dicts, estimated_config = split_state_dict(sd)
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repo_name = estimated_config.huggingface_repo
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local_path = os.path.join(dir_path, 'huggingface', repo_name)
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config: dict = DiffusionPipeline.load_config(local_path)
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huggingface_components = {}
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for component_name, v in config.items():
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if isinstance(v, list) and len(v) == 2:
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lib_name, cls_name = v
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component_sd = state_dicts.get(component_name, None)
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component = load_huggingface_component(estimated_config, component_name, lib_name, cls_name, local_path, component_sd)
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if component_sd is not None:
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del state_dicts[component_name]
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if component is not None:
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huggingface_components[component_name] = component
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for M in possible_models:
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if any(isinstance(estimated_config, x) for x in M.matched_guesses):
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return M(estimated_config=estimated_config, huggingface_components=huggingface_components)
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print('Failed to recognize model type!')
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return None
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