ComfyUI/comfy/diffusers_load.py

112 lines
4.5 KiB
Python

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