DDPM Conversion (open-mmlab#94)

* DDPM

* Fixes

* Edit tests

scripts/convert_ddpm_original_checkpoint_to_diffusers.py

@@ -0,0 +1,231 @@
+from diffusers import UNetUnconditionalModel
+import argparse
+import json
+import torch
+
+
+def shave_segments(path, n_shave_prefix_segments=1):
+    """
+    Removes segments. Positive values shave the first segments, negative shave the last segments.
+    """
+    if n_shave_prefix_segments >= 0:
+        return '.'.join(path.split('.')[n_shave_prefix_segments:])
+    else:
+        return '.'.join(path.split('.')[:n_shave_prefix_segments])
+
+
+def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+        new_item = new_item.replace('block.', 'resnets.')
+        new_item = new_item.replace('conv_shorcut', 'conv1')
+        new_item = new_item.replace('nin_shortcut', 'conv_shortcut')
+        new_item = new_item.replace('temb_proj', 'time_emb_proj')
+
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({'old': old_item, 'new': new_item})
+
+    return mapping
+
+
+def renew_attention_paths(old_list, n_shave_prefix_segments=0, in_mid=False):
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        # In `model.mid`, the layer is called `attn`.
+        if not in_mid:
+            new_item = new_item.replace('attn', 'attentions')
+        new_item = new_item.replace('.k.', '.key.')
+        new_item = new_item.replace('.v.', '.value.')
+        new_item = new_item.replace('.q.', '.query.')
+
+        new_item = new_item.replace('proj_out', 'proj_attn')
+        new_item = new_item.replace('norm', 'group_norm')
+
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+        mapping.append({'old': old_item, 'new': new_item})
+
+    return mapping
+
+
+def assign_to_checkpoint(paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None):
+    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
+
+    if attention_paths_to_split is not None:
+        if config is None:
+            raise ValueError(f"Please specify the config if setting 'attention_paths_to_split' to 'True'.")
+
+        for path, path_map in attention_paths_to_split.items():
+            old_tensor = old_checkpoint[path]
+            channels = old_tensor.shape[0] // 3
+
+            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
+
+            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
+
+            old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
+            query, key, value = old_tensor.split(channels // num_heads, dim=1)
+
+            checkpoint[path_map['query']] = query.reshape(target_shape).squeeze()
+            checkpoint[path_map['key']] = key.reshape(target_shape).squeeze()
+            checkpoint[path_map['value']] = value.reshape(target_shape).squeeze()
+
+    for path in paths:
+        new_path = path['new']
+
+        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
+            continue
+
+        new_path = new_path.replace('down.', 'downsample_blocks.')
+        new_path = new_path.replace('up.', 'upsample_blocks.')
+
+        if additional_replacements is not None:
+            for replacement in additional_replacements:
+                new_path = new_path.replace(replacement['old'], replacement['new'])
+
+
+        if 'attentions' in new_path:
+            checkpoint[new_path] = old_checkpoint[path['old']].squeeze()
+        else:
+            checkpoint[new_path] = old_checkpoint[path['old']]
+
+
+def convert_ddpm_checkpoint(checkpoint, config):
+    """
+    Takes a state dict and a config, and returns a converted checkpoint.
+    """
+
+    new_checkpoint = {}
+
+    new_checkpoint['time_embedding.linear_1.weight'] = checkpoint['temb.dense.0.weight']
+    new_checkpoint['time_embedding.linear_1.bias'] = checkpoint['temb.dense.0.bias']
+    new_checkpoint['time_embedding.linear_2.weight'] = checkpoint['temb.dense.1.weight']
+    new_checkpoint['time_embedding.linear_2.bias'] = checkpoint['temb.dense.1.bias']
+
+    new_checkpoint['conv_norm_out.weight'] = checkpoint['norm_out.weight']
+    new_checkpoint['conv_norm_out.bias'] = checkpoint['norm_out.bias']
+
+    new_checkpoint['conv_in.weight'] = checkpoint['conv_in.weight']
+    new_checkpoint['conv_in.bias'] = checkpoint['conv_in.bias']
+    new_checkpoint['conv_out.weight'] = checkpoint['conv_out.weight']
+    new_checkpoint['conv_out.bias'] = checkpoint['conv_out.bias']
+
+    num_downsample_blocks = len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'down' in layer})
+    downsample_blocks = {layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(num_downsample_blocks)}
+
+    num_upsample_blocks = len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'up' in layer})
+    upsample_blocks = {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(num_upsample_blocks)}
+
+    for i in range(num_downsample_blocks):
+        block_id = (i - 1) // (config['num_res_blocks'] + 1)
+        layer_in_block_id = (i - 1) % (config['num_res_blocks'] + 1)
+
+        if any('downsample' in layer for layer in downsample_blocks[i]):
+            new_checkpoint[f'downsample_blocks.{i}.downsamplers.0.conv.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
+            new_checkpoint[f'downsample_blocks.{i}.downsamplers.0.conv.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
+            new_checkpoint[f'downsample_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
+            new_checkpoint[f'downsample_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
+
+        if any('block' in layer for layer in downsample_blocks[i]):
+            num_blocks = len({'.'.join(shave_segments(layer, 2).split('.')[:2]) for layer in downsample_blocks[i] if 'block' in layer})
+            blocks = {layer_id: [key for key in downsample_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(num_blocks)}
+
+            if num_blocks > 0:
+                for j in range(config['num_res_blocks']):
+                    paths = renew_resnet_paths(blocks[j])
+                    assign_to_checkpoint(paths, new_checkpoint, checkpoint)
+
+
+        if any('attn' in layer for layer in downsample_blocks[i]):
+            num_attn = len({'.'.join(shave_segments(layer, 2).split('.')[:2]) for layer in downsample_blocks[i] if 'attn' in layer})
+            attns = {layer_id: [key for key in downsample_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(num_blocks)}
+
+            if num_attn > 0:
+                for j in range(config['num_res_blocks']):
+                    paths = renew_attention_paths(attns[j])
+                    assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config)
+
+
+    mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key]
+    mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key]
+    mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key]
+
+    # Mid new 2
+    paths = renew_resnet_paths(mid_block_1_layers)
+    assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[
+        {'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}
+    ])
+
+    paths = renew_resnet_paths(mid_block_2_layers)
+    assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[
+        {'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}
+    ])
+
+    paths = renew_attention_paths(mid_attn_1_layers, in_mid=True)
+    assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[
+        {'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}
+    ])
+
+    for i in range(num_upsample_blocks):
+        block_id = num_upsample_blocks - 1 - i
+
+        if any('upsample' in layer for layer in upsample_blocks[i]):
+            new_checkpoint[f'upsample_blocks.{block_id}.upsamplers.0.conv.weight'] = checkpoint[f'up.{i}.upsample.conv.weight']
+            new_checkpoint[f'upsample_blocks.{block_id}.upsamplers.0.conv.bias'] = checkpoint[f'up.{i}.upsample.conv.bias']
+
+        if any('block' in layer for layer in upsample_blocks[i]):
+            num_blocks = len({'.'.join(shave_segments(layer, 2).split('.')[:2]) for layer in upsample_blocks[i] if 'block' in layer})
+            blocks = {layer_id: [key for key in upsample_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(num_blocks)}
+
+            if num_blocks > 0:
+                for j in range(config['num_res_blocks'] + 1):
+                    replace_indices = {'old': f'upsample_blocks.{i}', 'new': f'upsample_blocks.{block_id}'}
+                    paths = renew_resnet_paths(blocks[j])
+                    assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])
+
+
+        if any('attn' in layer for layer in upsample_blocks[i]):
+            num_attn = len({'.'.join(shave_segments(layer, 2).split('.')[:2]) for layer in upsample_blocks[i] if 'attn' in layer})
+            attns = {layer_id: [key for key in upsample_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(num_blocks)}
+
+            if num_attn > 0:
+                for j in range(config['num_res_blocks'] + 1):
+                    replace_indices = {'old': f'upsample_blocks.{i}', 'new': f'upsample_blocks.{block_id}'}
+                    paths = renew_attention_paths(attns[j])
+                    assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])
+
+    new_checkpoint = {k.replace('mid_new_2', 'mid'): v for k, v in new_checkpoint.items()}
+    return new_checkpoint
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
+    )
+
+    parser.add_argument(
+        "--config_file",
+        default=None,
+        type=str,
+        required=True,
+        help="The config json file corresponding to the architecture.",
+    )
+
+    parser.add_argument(
+        "--dump_path", default=None, type=str, required=True, help="Path to the output model."
+    )
+
+
+    args = parser.parse_args()
+    checkpoint = torch.load(args.checkpoint_path)
+
+    with open(args.config_file) as f:
+        config = json.loads(f.read())
+
+    converted_checkpoint = convert_ddpm_checkpoint(args.checkpoint_path, args.config_file)
+    torch.save(converted_checkpoint, args.dump_path)

scripts/convert_ldm_original_checkpoint_to_diffusers.py

@@ -124,7 +124,7 @@ def assign_to_checkpoint(paths, checkpoint, old_checkpoint, attention_paths_to_s
 
 def convert_ldm_checkpoint(checkpoint, config):
     """
-    Takes a state dict and the path to
+    Takes a state dict and a config, and returns a converted checkpoint.
     """
     new_checkpoint = {}
 
@@ -142,15 +142,15 @@ def convert_ldm_checkpoint(checkpoint, config):
     new_checkpoint['conv_out.bias'] = checkpoint['out.2.bias']
 
     # Retrieves the keys for the input blocks only
-    num_input_blocks = len({shave_segments(layer, -2) for layer in checkpoint if 'input_blocks' in layer})
+    num_input_blocks = len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'input_blocks' in layer})
     input_blocks = {layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key] for layer_id in range(num_input_blocks)}
 
     # Retrieves the keys for the middle blocks only
-    num_middle_blocks = len({shave_segments(layer, -2) for layer in checkpoint if 'middle_block' in layer})
+    num_middle_blocks = len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'middle_block' in layer})
     middle_blocks = {layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key] for layer_id in range(num_middle_blocks)}
 
     # Retrieves the keys for the output blocks only
-    num_output_blocks = len({shave_segments(layer, -2) for layer in checkpoint if 'output_blocks' in layer})
+    num_output_blocks = len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'output_blocks' in layer})
     output_blocks = {layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key] for layer_id in range(num_output_blocks)}
 
     for i in range(1, num_input_blocks):

tests/test_modeling_utils.py

@@ -929,7 +929,7 @@ def test_from_pretrained_save_pretrained(self):
 
     @slow
     def test_from_pretrained_hub(self):
-        model_path = "fusing/ddpm-cifar10"
+        model_path = "google/ddpm-cifar10"
 
         ddpm = DDPMPipeline.from_pretrained(model_path)
         ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
@@ -947,9 +947,9 @@ def test_from_pretrained_hub(self):
 
     @slow
     def test_ddpm_cifar10(self):
-        model_id = "fusing/ddpm-cifar10"
+        model_id = "google/ddpm-cifar10"
 
-        unet = UNetUnconditionalModel.from_pretrained(model_id, ddpm=True)
+        unet = UNetUnconditionalModel.from_pretrained(model_id)
         scheduler = DDPMScheduler.from_config(model_id)
         scheduler = scheduler.set_format("pt")
 
@@ -968,9 +968,9 @@ def test_ddpm_cifar10(self):
 
     @slow
     def test_ddim_lsun(self):
-        model_id = "fusing/ddpm-lsun-bedroom-ema"
+        model_id = "google/ddpm-lsun-bedroom-ema"
 
-        unet = UNetUnconditionalModel.from_pretrained(model_id, ddpm=True)
+        unet = UNetUnconditionalModel.from_pretrained(model_id)
         scheduler = DDIMScheduler.from_config(model_id)
 
         ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
@@ -988,9 +988,9 @@ def test_ddim_lsun(self):
 
     @slow
     def test_ddim_cifar10(self):
-        model_id = "fusing/ddpm-cifar10"
+        model_id = "google/ddpm-cifar10"
 
-        unet = UNetUnconditionalModel.from_pretrained(model_id, ddpm=True)
+        unet = UNetUnconditionalModel.from_pretrained(model_id)
         scheduler = DDIMScheduler(tensor_format="pt")
 
         ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
@@ -1008,7 +1008,7 @@ def test_ddim_cifar10(self):
 
     @slow
     def test_pndm_cifar10(self):
-        model_id = "fusing/ddpm-cifar10"
+        model_id = "google/ddpm-cifar10"
 
         unet = UNetUnconditionalModel.from_pretrained(model_id, ddpm=True)
         scheduler = PNDMScheduler(tensor_format="pt")