| 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", |
| "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", |
| "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 |
|
|
| |
| 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}") |
|
|
| |
| 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) |
|
|
| |
| 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) |
|
|
