88 lines
3.8 KiB
Python
88 lines
3.8 KiB
Python
import json
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import os
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import yaml
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import folder_paths
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from comfy.sd import load_checkpoint
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import os.path as osp
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import re
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import torch
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from safetensors.torch import load_file, save_file
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from . import diffusers_convert
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def load_diffusers(model_path, fp16=True, output_vae=True, output_clip=True, embedding_directory=None):
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diffusers_unet_conf = json.load(open(osp.join(model_path, "unet/config.json")))
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diffusers_scheduler_conf = json.load(open(osp.join(model_path, "scheduler/scheduler_config.json")))
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# magic
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v2 = diffusers_unet_conf["sample_size"] == 96
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if 'prediction_type' in diffusers_scheduler_conf:
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v_pred = diffusers_scheduler_conf['prediction_type'] == 'v_prediction'
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if v2:
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if v_pred:
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config_path = folder_paths.get_full_path("configs", 'v2-inference-v.yaml')
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else:
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config_path = folder_paths.get_full_path("configs", 'v2-inference.yaml')
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else:
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config_path = folder_paths.get_full_path("configs", 'v1-inference.yaml')
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with open(config_path, 'r') as stream:
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config = yaml.safe_load(stream)
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model_config_params = config['model']['params']
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clip_config = model_config_params['cond_stage_config']
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scale_factor = model_config_params['scale_factor']
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vae_config = model_config_params['first_stage_config']
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vae_config['scale_factor'] = scale_factor
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model_config_params["unet_config"]["params"]["use_fp16"] = fp16
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unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors")
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vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors")
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text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors")
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# Load models from safetensors if it exists, if it doesn't pytorch
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if osp.exists(unet_path):
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unet_state_dict = load_file(unet_path, device="cpu")
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else:
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unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
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unet_state_dict = torch.load(unet_path, map_location="cpu")
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if osp.exists(vae_path):
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vae_state_dict = load_file(vae_path, device="cpu")
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else:
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vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
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vae_state_dict = torch.load(vae_path, map_location="cpu")
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if osp.exists(text_enc_path):
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text_enc_dict = load_file(text_enc_path, device="cpu")
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else:
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text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
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text_enc_dict = torch.load(text_enc_path, map_location="cpu")
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# Convert the UNet model
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unet_state_dict = diffusers_convert.convert_unet_state_dict(unet_state_dict)
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unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
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# Convert the VAE model
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vae_state_dict = diffusers_convert.convert_vae_state_dict(vae_state_dict)
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vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
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# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
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is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
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if is_v20_model:
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# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
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text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()}
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text_enc_dict = diffusers_convert.convert_text_enc_state_dict_v20(text_enc_dict)
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text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
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else:
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text_enc_dict = diffusers_convert.convert_text_enc_state_dict(text_enc_dict)
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text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
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# Put together new checkpoint
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sd = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
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return load_checkpoint(embedding_directory=embedding_directory, state_dict=sd, config=config)
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