import argparse import os import torch from diffusers import ( CMStochasticIterativeScheduler, ConsistencyModelPipeline, UNet2DModel, ) TEST_UNET_CONFIG = { "sample_size": 32, "in_channels": 3, "out_channels": 3, "layers_per_block": 2, "num_class_embeds": 1000, "block_out_channels": [32, 64], "attention_head_dim": 8, "down_block_types": [ "ResnetDownsampleBlock2D", "AttnDownBlock2D", ], "up_block_types": [ "AttnUpBlock2D", "ResnetUpsampleBlock2D", ], "resnet_time_scale_shift": "scale_shift", "attn_norm_num_groups": 32, "upsample_type": "resnet", "downsample_type": "resnet", } IMAGENET_64_UNET_CONFIG = { "sample_size": 64, "in_channels": 3, "out_channels": 3, "layers_per_block": 3, "num_class_embeds": 1000, "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4], "attention_head_dim": 64, "down_block_types": [ "ResnetDownsampleBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", ], "up_block_types": [ "AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "ResnetUpsampleBlock2D", ], "resnet_time_scale_shift": "scale_shift", "attn_norm_num_groups": 32, "upsample_type": "resnet", "downsample_type": "resnet", } LSUN_256_UNET_CONFIG = { "sample_size": 256, "in_channels": 3, "out_channels": 3, "layers_per_block": 2, "num_class_embeds": None, "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4], "attention_head_dim": 64, "down_block_types": [ "ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", ], "up_block_types": [ "AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D", ], "resnet_time_scale_shift": "default", "upsample_type": "resnet", "downsample_type": "resnet", } CD_SCHEDULER_CONFIG = { "num_train_timesteps": 40, "sigma_min": 0.002, "sigma_max": 80.0, } CT_IMAGENET_64_SCHEDULER_CONFIG = { "num_train_timesteps": 201, "sigma_min": 0.002, "sigma_max": 80.0, } CT_LSUN_256_SCHEDULER_CONFIG = { "num_train_timesteps": 151, "sigma_min": 0.002, "sigma_max": 80.0, } def str2bool(v): """ https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse """ if isinstance(v, bool): return v if v.lower() in ("yes", "true", "t", "y", "1"): return True elif v.lower() in ("no", "false", "f", "n", "0"): return False else: raise argparse.ArgumentTypeError("boolean value expected") def convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=False): new_checkpoint[f"{new_prefix}.norm1.weight"] = checkpoint[f"{old_prefix}.in_layers.0.weight"] new_checkpoint[f"{new_prefix}.norm1.bias"] = checkpoint[f"{old_prefix}.in_layers.0.bias"] new_checkpoint[f"{new_prefix}.conv1.weight"] = checkpoint[f"{old_prefix}.in_layers.2.weight"] new_checkpoint[f"{new_prefix}.conv1.bias"] = checkpoint[f"{old_prefix}.in_layers.2.bias"] new_checkpoint[f"{new_prefix}.time_emb_proj.weight"] = checkpoint[f"{old_prefix}.emb_layers.1.weight"] new_checkpoint[f"{new_prefix}.time_emb_proj.bias"] = checkpoint[f"{old_prefix}.emb_layers.1.bias"] new_checkpoint[f"{new_prefix}.norm2.weight"] = checkpoint[f"{old_prefix}.out_layers.0.weight"] new_checkpoint[f"{new_prefix}.norm2.bias"] = checkpoint[f"{old_prefix}.out_layers.0.bias"] new_checkpoint[f"{new_prefix}.conv2.weight"] = checkpoint[f"{old_prefix}.out_layers.3.weight"] new_checkpoint[f"{new_prefix}.conv2.bias"] = checkpoint[f"{old_prefix}.out_layers.3.bias"] if has_skip: new_checkpoint[f"{new_prefix}.conv_shortcut.weight"] = checkpoint[f"{old_prefix}.skip_connection.weight"] new_checkpoint[f"{new_prefix}.conv_shortcut.bias"] = checkpoint[f"{old_prefix}.skip_connection.bias"] return new_checkpoint def convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_dim=None): weight_q, weight_k, weight_v = checkpoint[f"{old_prefix}.qkv.weight"].chunk(3, dim=0) bias_q, bias_k, bias_v = checkpoint[f"{old_prefix}.qkv.bias"].chunk(3, dim=0) new_checkpoint[f"{new_prefix}.group_norm.weight"] = checkpoint[f"{old_prefix}.norm.weight"] new_checkpoint[f"{new_prefix}.group_norm.bias"] = checkpoint[f"{old_prefix}.norm.bias"] new_checkpoint[f"{new_prefix}.to_q.weight"] = weight_q.squeeze(-1).squeeze(-1) new_checkpoint[f"{new_prefix}.to_q.bias"] = bias_q.squeeze(-1).squeeze(-1) new_checkpoint[f"{new_prefix}.to_k.weight"] = weight_k.squeeze(-1).squeeze(-1) new_checkpoint[f"{new_prefix}.to_k.bias"] = bias_k.squeeze(-1).squeeze(-1) new_checkpoint[f"{new_prefix}.to_v.weight"] = weight_v.squeeze(-1).squeeze(-1) new_checkpoint[f"{new_prefix}.to_v.bias"] = bias_v.squeeze(-1).squeeze(-1) new_checkpoint[f"{new_prefix}.to_out.0.weight"] = ( checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1).squeeze(-1) ) new_checkpoint[f"{new_prefix}.to_out.0.bias"] = checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1).squeeze(-1) return new_checkpoint def con_pt_to_diffuser(checkpoint_path: str, unet_config): checkpoint = torch.load(checkpoint_path, map_location="cpu") new_checkpoint = {} new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["time_embed.0.weight"] new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["time_embed.0.bias"] new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["time_embed.2.weight"] new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["time_embed.2.bias"] if unet_config["num_class_embeds"] is not None: new_checkpoint["class_embedding.weight"] = checkpoint["label_emb.weight"] new_checkpoint["conv_in.weight"] = checkpoint["input_blocks.0.0.weight"] new_checkpoint["conv_in.bias"] = checkpoint["input_blocks.0.0.bias"] down_block_types = unet_config["down_block_types"] layers_per_block = unet_config["layers_per_block"] attention_head_dim = unet_config["attention_head_dim"] channels_list = unet_config["block_out_channels"] current_layer = 1 prev_channels = channels_list[0] for i, layer_type in enumerate(down_block_types): current_channels = channels_list[i] downsample_block_has_skip = current_channels != prev_channels if layer_type == "ResnetDownsampleBlock2D": for j in range(layers_per_block): new_prefix = f"down_blocks.{i}.resnets.{j}" old_prefix = f"input_blocks.{current_layer}.0" has_skip = True if j == 0 and downsample_block_has_skip else False new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip) current_layer += 1 elif layer_type == "AttnDownBlock2D": for j in range(layers_per_block): new_prefix = f"down_blocks.{i}.resnets.{j}" old_prefix = f"input_blocks.{current_layer}.0" has_skip = True if j == 0 and downsample_block_has_skip else False new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip) new_prefix = f"down_blocks.{i}.attentions.{j}" old_prefix = f"input_blocks.{current_layer}.1" new_checkpoint = convert_attention( checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim ) current_layer += 1 if i != len(down_block_types) - 1: new_prefix = f"down_blocks.{i}.downsamplers.0" old_prefix = f"input_blocks.{current_layer}.0" new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) current_layer += 1 prev_channels = current_channels # hardcoded the mid-block for now new_prefix = "mid_block.resnets.0" old_prefix = "middle_block.0" new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) new_prefix = "mid_block.attentions.0" old_prefix = "middle_block.1" new_checkpoint = convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim) new_prefix = "mid_block.resnets.1" old_prefix = "middle_block.2" new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) current_layer = 0 up_block_types = unet_config["up_block_types"] for i, layer_type in enumerate(up_block_types): if layer_type == "ResnetUpsampleBlock2D": for j in range(layers_per_block + 1): new_prefix = f"up_blocks.{i}.resnets.{j}" old_prefix = f"output_blocks.{current_layer}.0" new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True) current_layer += 1 if i != len(up_block_types) - 1: new_prefix = f"up_blocks.{i}.upsamplers.0" old_prefix = f"output_blocks.{current_layer-1}.1" new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) elif layer_type == "AttnUpBlock2D": for j in range(layers_per_block + 1): new_prefix = f"up_blocks.{i}.resnets.{j}" old_prefix = f"output_blocks.{current_layer}.0" new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True) new_prefix = f"up_blocks.{i}.attentions.{j}" old_prefix = f"output_blocks.{current_layer}.1" new_checkpoint = convert_attention( checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim ) current_layer += 1 if i != len(up_block_types) - 1: new_prefix = f"up_blocks.{i}.upsamplers.0" old_prefix = f"output_blocks.{current_layer-1}.2" new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) new_checkpoint["conv_norm_out.weight"] = checkpoint["out.0.weight"] new_checkpoint["conv_norm_out.bias"] = checkpoint["out.0.bias"] new_checkpoint["conv_out.weight"] = checkpoint["out.2.weight"] new_checkpoint["conv_out.bias"] = checkpoint["out.2.bias"] return new_checkpoint if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.") parser.add_argument( "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model." ) parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.") args = parser.parse_args() args.class_cond = str2bool(args.class_cond) ckpt_name = os.path.basename(args.unet_path) print(f"Checkpoint: {ckpt_name}") # Get U-Net config if "imagenet64" in ckpt_name: unet_config = IMAGENET_64_UNET_CONFIG elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)): unet_config = LSUN_256_UNET_CONFIG elif "test" in ckpt_name: unet_config = TEST_UNET_CONFIG else: raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.") if not args.class_cond: unet_config["num_class_embeds"] = None converted_unet_ckpt = con_pt_to_diffuser(args.unet_path, unet_config) image_unet = UNet2DModel(**unet_config) image_unet.load_state_dict(converted_unet_ckpt) # Get scheduler config if "cd" in ckpt_name or "test" in ckpt_name: scheduler_config = CD_SCHEDULER_CONFIG elif "ct" in ckpt_name and "imagenet64" in ckpt_name: scheduler_config = CT_IMAGENET_64_SCHEDULER_CONFIG elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)): scheduler_config = CT_LSUN_256_SCHEDULER_CONFIG else: raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.") cm_scheduler = CMStochasticIterativeScheduler(**scheduler_config) consistency_model = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler) consistency_model.save_pretrained(args.dump_path)