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| import re | |
| import os | |
| import torch | |
| from modules import shared, paths, sd_disable_initialization | |
| sd_configs_path = shared.sd_configs_path | |
| sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion") | |
| config_default = shared.sd_default_config | |
| config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml") | |
| config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml") | |
| config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml") | |
| config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml") | |
| config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml") | |
| config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml") | |
| config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml") | |
| def is_using_v_parameterization_for_sd2(state_dict): | |
| """ | |
| Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome. | |
| """ | |
| import ldm.modules.diffusionmodules.openaimodel | |
| from modules import devices | |
| device = devices.cpu | |
| with sd_disable_initialization.DisableInitialization(): | |
| unet = ldm.modules.diffusionmodules.openaimodel.UNetModel( | |
| use_checkpoint=True, | |
| use_fp16=False, | |
| image_size=32, | |
| in_channels=4, | |
| out_channels=4, | |
| model_channels=320, | |
| attention_resolutions=[4, 2, 1], | |
| num_res_blocks=2, | |
| channel_mult=[1, 2, 4, 4], | |
| num_head_channels=64, | |
| use_spatial_transformer=True, | |
| use_linear_in_transformer=True, | |
| transformer_depth=1, | |
| context_dim=1024, | |
| legacy=False | |
| ) | |
| unet.eval() | |
| with torch.no_grad(): | |
| unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k} | |
| unet.load_state_dict(unet_sd, strict=True) | |
| unet.to(device=device, dtype=torch.float) | |
| test_cond = torch.ones((1, 2, 1024), device=device) * 0.5 | |
| x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5 | |
| out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item() | |
| return out < -1 | |
| def guess_model_config_from_state_dict(sd, filename): | |
| sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None) | |
| diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None) | |
| if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None: | |
| return config_depth_model | |
| if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024: | |
| if diffusion_model_input.shape[1] == 9: | |
| return config_sd2_inpainting | |
| elif is_using_v_parameterization_for_sd2(sd): | |
| return config_sd2v | |
| else: | |
| return config_sd2 | |
| if diffusion_model_input is not None: | |
| if diffusion_model_input.shape[1] == 9: | |
| return config_inpainting | |
| if diffusion_model_input.shape[1] == 8: | |
| return config_instruct_pix2pix | |
| if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None: | |
| return config_alt_diffusion | |
| return config_default | |
| def find_checkpoint_config(state_dict, info): | |
| if info is None: | |
| return guess_model_config_from_state_dict(state_dict, "") | |
| config = find_checkpoint_config_near_filename(info) | |
| if config is not None: | |
| return config | |
| return guess_model_config_from_state_dict(state_dict, info.filename) | |
| def find_checkpoint_config_near_filename(info): | |
| if info is None: | |
| return None | |
| config = os.path.splitext(info.filename)[0] + ".yaml" | |
| if os.path.exists(config): | |
| return config | |
| return None | |