138 lines
5.7 KiB
Python
Executable File
138 lines
5.7 KiB
Python
Executable File
# import os
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#
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# import torch
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#
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# from modules import shared, paths, sd_disable_initialization, devices
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#
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# sd_configs_path = shared.sd_configs_path
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# # sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
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# # sd_xl_repo_configs_path = os.path.join(paths.paths['Stable Diffusion XL'], "configs", "inference")
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#
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#
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# config_default = shared.sd_default_config
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# # config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
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# config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
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# config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
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# config_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
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# config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
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# config_sdxl_inpainting = os.path.join(sd_configs_path, "sd_xl_inpaint.yaml")
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# config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
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# config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
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# config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
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# config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
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# config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
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# config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
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# config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
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# config_sd3 = os.path.join(sd_configs_path, "sd3-inference.yaml")
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#
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#
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# def is_using_v_parameterization_for_sd2(state_dict):
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# """
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# Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
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# """
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#
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# import ldm.modules.diffusionmodules.openaimodel
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#
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# device = devices.device
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#
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# with sd_disable_initialization.DisableInitialization():
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# unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
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# use_checkpoint=False,
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# use_fp16=False,
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# image_size=32,
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# in_channels=4,
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# out_channels=4,
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# model_channels=320,
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# attention_resolutions=[4, 2, 1],
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# num_res_blocks=2,
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# channel_mult=[1, 2, 4, 4],
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# num_head_channels=64,
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# use_spatial_transformer=True,
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# use_linear_in_transformer=True,
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# transformer_depth=1,
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# context_dim=1024,
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# legacy=False
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# )
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# unet.eval()
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#
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# with torch.no_grad():
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# unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
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# unet.load_state_dict(unet_sd, strict=True)
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# unet.to(device=device, dtype=devices.dtype_unet)
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#
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# test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
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# x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
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#
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# with devices.autocast():
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# out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().cpu().item()
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#
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# return out < -1
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#
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#
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# def guess_model_config_from_state_dict(sd, filename):
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# sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
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# diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
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# sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
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#
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# if "model.diffusion_model.x_embedder.proj.weight" in sd:
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# return config_sd3
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#
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# if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
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# if diffusion_model_input.shape[1] == 9:
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# return config_sdxl_inpainting
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# else:
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# return config_sdxl
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#
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# if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
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# return config_sdxl_refiner
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# elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
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# return config_depth_model
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# elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
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# return config_unclip
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# elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
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# return config_unopenclip
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#
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# if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
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# if diffusion_model_input.shape[1] == 9:
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# return config_sd2_inpainting
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# # elif is_using_v_parameterization_for_sd2(sd):
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# # return config_sd2v
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# else:
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# return config_sd2v
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#
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# if diffusion_model_input is not None:
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# if diffusion_model_input.shape[1] == 9:
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# return config_inpainting
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# if diffusion_model_input.shape[1] == 8:
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# return config_instruct_pix2pix
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#
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# if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
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# if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
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# return config_alt_diffusion_m18
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# return config_alt_diffusion
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#
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# return config_default
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#
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#
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# def find_checkpoint_config(state_dict, info):
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# if info is None:
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# return guess_model_config_from_state_dict(state_dict, "")
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#
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# config = find_checkpoint_config_near_filename(info)
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# if config is not None:
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# return config
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#
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# return guess_model_config_from_state_dict(state_dict, info.filename)
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#
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#
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# def find_checkpoint_config_near_filename(info):
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# if info is None:
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# return None
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#
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# config = f"{os.path.splitext(info.filename)[0]}.yaml"
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# if os.path.exists(config):
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# return config
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#
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# return None
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#
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