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