improve ddpm conversion script

scripts/convert_ddpm_original_checkpoint_to_diffusers.py

-from diffusers import UNetUnconditionalModel
+from diffusers import UNetUnconditionalModel, DDPMScheduler, DDPMPipeline
 import argparse
 import json
 import torch
@@ -56,7 +56,7 @@ def assign_to_checkpoint(paths, checkpoint, old_checkpoint, attention_paths_to_s
 
     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'.")
+            raise ValueError("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]
@@ -86,7 +86,6 @@ def assign_to_checkpoint(paths, checkpoint, old_checkpoint, attention_paths_to_s
             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:
@@ -97,7 +96,6 @@ 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']
@@ -121,7 +119,6 @@ def convert_ddpm_checkpoint(checkpoint, config):
 
     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']
@@ -138,7 +135,6 @@ def convert_ddpm_checkpoint(checkpoint, config):
                     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)}
@@ -148,7 +144,6 @@ def convert_ddpm_checkpoint(checkpoint, config):
                     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]
@@ -186,7 +181,6 @@ def convert_ddpm_checkpoint(checkpoint, config):
                     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)}
@@ -220,12 +214,21 @@ if __name__ == "__main__":
         "--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)
+    converted_checkpoint = convert_ddpm_checkpoint(checkpoint, config)
+
+    if "ddpm" in config:
+        del config["ddpm"]
+
+    model = UNetUnconditionalModel(**config)
+    model.load_state_dict(converted_checkpoint)
+
+    scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
+
+    pipe = DDPMPipeline(unet=model, scheduler=scheduler)
+    pipe.save_pretrained(args.dump_path)