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| # import os | |
| # | |
| # import torch | |
| # | |
| # from modules import shared, paths, sd_disable_initialization, devices | |
| # | |
| # sd_configs_path = shared.sd_configs_path | |
| # # sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion") | |
| # # sd_xl_repo_configs_path = os.path.join(paths.paths['Stable Diffusion XL'], "configs", "inference") | |
| # | |
| # | |
| # 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_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml") | |
| # config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml") | |
| # config_sdxl_inpainting = os.path.join(sd_configs_path, "sd_xl_inpaint.yaml") | |
| # config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml") | |
| # config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml") | |
| # config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-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") | |
| # config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml") | |
| # config_sd3 = os.path.join(sd_configs_path, "sd3-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 | |
| # | |
| # device = devices.device | |
| # | |
| # with sd_disable_initialization.DisableInitialization(): | |
| # unet = ldm.modules.diffusionmodules.openaimodel.UNetModel( | |
| # use_checkpoint=False, | |
| # 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=devices.dtype_unet) | |
| # | |
| # test_cond = torch.ones((1, 2, 1024), device=device) * 0.5 | |
| # x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5 | |
| # | |
| # with devices.autocast(): | |
| # out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().cpu().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) | |
| # sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None) | |
| # | |
| # if "model.diffusion_model.x_embedder.proj.weight" in sd: | |
| # return config_sd3 | |
| # | |
| # if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None: | |
| # if diffusion_model_input.shape[1] == 9: | |
| # return config_sdxl_inpainting | |
| # else: | |
| # return config_sdxl | |
| # | |
| # if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None: | |
| # return config_sdxl_refiner | |
| # elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None: | |
| # return config_depth_model | |
| # elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768: | |
| # return config_unclip | |
| # elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024: | |
| # return config_unopenclip | |
| # | |
| # 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_sd2v | |
| # | |
| # 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: | |
| # if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024: | |
| # return config_alt_diffusion_m18 | |
| # 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 = f"{os.path.splitext(info.filename)[0]}.yaml" | |
| # if os.path.exists(config): | |
| # return config | |
| # | |
| # return None | |
| # | |