| 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") |
|
|
| 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.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) |
| sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None) |
|
|
| 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_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: |
| 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 |
|
|
|
|
