232 lines
10 KiB
Python
232 lines
10 KiB
Python
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from diffusers import UNetUnconditionalModel
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import argparse
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import json
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import torch
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def shave_segments(path, n_shave_prefix_segments=1):
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"""
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Removes segments. Positive values shave the first segments, negative shave the last segments.
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"""
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if n_shave_prefix_segments >= 0:
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return '.'.join(path.split('.')[n_shave_prefix_segments:])
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else:
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return '.'.join(path.split('.')[:n_shave_prefix_segments])
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def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
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mapping = []
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for old_item in old_list:
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new_item = old_item
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new_item = new_item.replace('block.', 'resnets.')
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new_item = new_item.replace('conv_shorcut', 'conv1')
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new_item = new_item.replace('nin_shortcut', 'conv_shortcut')
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new_item = new_item.replace('temb_proj', 'time_emb_proj')
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({'old': old_item, 'new': new_item})
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return mapping
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def renew_attention_paths(old_list, n_shave_prefix_segments=0, in_mid=False):
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mapping = []
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for old_item in old_list:
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new_item = old_item
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# In `model.mid`, the layer is called `attn`.
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if not in_mid:
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new_item = new_item.replace('attn', 'attentions')
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new_item = new_item.replace('.k.', '.key.')
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new_item = new_item.replace('.v.', '.value.')
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new_item = new_item.replace('.q.', '.query.')
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new_item = new_item.replace('proj_out', 'proj_attn')
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new_item = new_item.replace('norm', 'group_norm')
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({'old': old_item, 'new': new_item})
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return mapping
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def assign_to_checkpoint(paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None):
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assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
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if attention_paths_to_split is not None:
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if config is None:
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raise ValueError(f"Please specify the config if setting 'attention_paths_to_split' to 'True'.")
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for path, path_map in attention_paths_to_split.items():
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old_tensor = old_checkpoint[path]
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channels = old_tensor.shape[0] // 3
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target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
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num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
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old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
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query, key, value = old_tensor.split(channels // num_heads, dim=1)
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checkpoint[path_map['query']] = query.reshape(target_shape).squeeze()
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checkpoint[path_map['key']] = key.reshape(target_shape).squeeze()
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checkpoint[path_map['value']] = value.reshape(target_shape).squeeze()
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for path in paths:
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new_path = path['new']
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if attention_paths_to_split is not None and new_path in attention_paths_to_split:
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continue
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new_path = new_path.replace('down.', 'downsample_blocks.')
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new_path = new_path.replace('up.', 'upsample_blocks.')
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if additional_replacements is not None:
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for replacement in additional_replacements:
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new_path = new_path.replace(replacement['old'], replacement['new'])
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if 'attentions' in new_path:
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checkpoint[new_path] = old_checkpoint[path['old']].squeeze()
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else:
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checkpoint[new_path] = old_checkpoint[path['old']]
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def convert_ddpm_checkpoint(checkpoint, config):
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"""
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Takes a state dict and a config, and returns a converted checkpoint.
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"""
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new_checkpoint = {}
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new_checkpoint['time_embedding.linear_1.weight'] = checkpoint['temb.dense.0.weight']
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new_checkpoint['time_embedding.linear_1.bias'] = checkpoint['temb.dense.0.bias']
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new_checkpoint['time_embedding.linear_2.weight'] = checkpoint['temb.dense.1.weight']
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new_checkpoint['time_embedding.linear_2.bias'] = checkpoint['temb.dense.1.bias']
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new_checkpoint['conv_norm_out.weight'] = checkpoint['norm_out.weight']
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new_checkpoint['conv_norm_out.bias'] = checkpoint['norm_out.bias']
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new_checkpoint['conv_in.weight'] = checkpoint['conv_in.weight']
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new_checkpoint['conv_in.bias'] = checkpoint['conv_in.bias']
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new_checkpoint['conv_out.weight'] = checkpoint['conv_out.weight']
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new_checkpoint['conv_out.bias'] = checkpoint['conv_out.bias']
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num_downsample_blocks = len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'down' in layer})
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downsample_blocks = {layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(num_downsample_blocks)}
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num_upsample_blocks = len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'up' in layer})
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upsample_blocks = {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(num_upsample_blocks)}
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for i in range(num_downsample_blocks):
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block_id = (i - 1) // (config['num_res_blocks'] + 1)
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layer_in_block_id = (i - 1) % (config['num_res_blocks'] + 1)
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if any('downsample' in layer for layer in downsample_blocks[i]):
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new_checkpoint[f'downsample_blocks.{i}.downsamplers.0.conv.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
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new_checkpoint[f'downsample_blocks.{i}.downsamplers.0.conv.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
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new_checkpoint[f'downsample_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
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new_checkpoint[f'downsample_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
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if any('block' in layer for layer in downsample_blocks[i]):
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num_blocks = len({'.'.join(shave_segments(layer, 2).split('.')[:2]) for layer in downsample_blocks[i] if 'block' in layer})
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blocks = {layer_id: [key for key in downsample_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(num_blocks)}
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if num_blocks > 0:
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for j in range(config['num_res_blocks']):
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paths = renew_resnet_paths(blocks[j])
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assign_to_checkpoint(paths, new_checkpoint, checkpoint)
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if any('attn' in layer for layer in downsample_blocks[i]):
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num_attn = len({'.'.join(shave_segments(layer, 2).split('.')[:2]) for layer in downsample_blocks[i] if 'attn' in layer})
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attns = {layer_id: [key for key in downsample_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(num_blocks)}
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if num_attn > 0:
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for j in range(config['num_res_blocks']):
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paths = renew_attention_paths(attns[j])
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assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config)
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mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key]
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mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key]
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mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key]
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# Mid new 2
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paths = renew_resnet_paths(mid_block_1_layers)
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assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[
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{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}
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])
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paths = renew_resnet_paths(mid_block_2_layers)
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assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[
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{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}
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])
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paths = renew_attention_paths(mid_attn_1_layers, in_mid=True)
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assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[
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{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}
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])
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for i in range(num_upsample_blocks):
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block_id = num_upsample_blocks - 1 - i
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if any('upsample' in layer for layer in upsample_blocks[i]):
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new_checkpoint[f'upsample_blocks.{block_id}.upsamplers.0.conv.weight'] = checkpoint[f'up.{i}.upsample.conv.weight']
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new_checkpoint[f'upsample_blocks.{block_id}.upsamplers.0.conv.bias'] = checkpoint[f'up.{i}.upsample.conv.bias']
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if any('block' in layer for layer in upsample_blocks[i]):
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num_blocks = len({'.'.join(shave_segments(layer, 2).split('.')[:2]) for layer in upsample_blocks[i] if 'block' in layer})
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blocks = {layer_id: [key for key in upsample_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(num_blocks)}
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if num_blocks > 0:
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for j in range(config['num_res_blocks'] + 1):
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replace_indices = {'old': f'upsample_blocks.{i}', 'new': f'upsample_blocks.{block_id}'}
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paths = renew_resnet_paths(blocks[j])
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assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])
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if any('attn' in layer for layer in upsample_blocks[i]):
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num_attn = len({'.'.join(shave_segments(layer, 2).split('.')[:2]) for layer in upsample_blocks[i] if 'attn' in layer})
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attns = {layer_id: [key for key in upsample_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(num_blocks)}
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if num_attn > 0:
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for j in range(config['num_res_blocks'] + 1):
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replace_indices = {'old': f'upsample_blocks.{i}', 'new': f'upsample_blocks.{block_id}'}
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paths = renew_attention_paths(attns[j])
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assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])
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new_checkpoint = {k.replace('mid_new_2', 'mid'): v for k, v in new_checkpoint.items()}
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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(
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"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
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)
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parser.add_argument(
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"--config_file",
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default=None,
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type=str,
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required=True,
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help="The config json file corresponding to the architecture.",
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)
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parser.add_argument(
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"--dump_path", default=None, type=str, required=True, help="Path to the output model."
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)
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args = parser.parse_args()
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checkpoint = torch.load(args.checkpoint_path)
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with open(args.config_file) as f:
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config = json.loads(f.read())
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converted_checkpoint = convert_ddpm_checkpoint(args.checkpoint_path, args.config_file)
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torch.save(converted_checkpoint, args.dump_path)
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