[AudioLDM] Generalise conversion script (#3328)

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

scripts/convert_original_audioldm_to_diffusers.py

@@ -774,6 +774,8 @@ def load_pipeline_from_original_audioldm_ckpt(
     extract_ema: bool = False,
     scheduler_type: str = "ddim",
     num_in_channels: int = None,
+    model_channels: int = None,
+    num_head_channels: int = None,
     device: str = None,
     from_safetensors: bool = False,
 ) -> AudioLDMPipeline:
@@ -784,23 +786,36 @@ def load_pipeline_from_original_audioldm_ckpt(
     global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is
     recommended that you override the default values and/or supply an `original_config_file` wherever possible.
 
-    :param checkpoint_path: Path to `.ckpt` file. :param original_config_file: Path to `.yaml` config file
-    corresponding to the original architecture.
-            If `None`, will be automatically instantiated based on default values.
-    :param image_size: The image size that the model was trained on. Use 512 for original AudioLDM checkpoints. :param
-    prediction_type: The prediction type that the model was trained on. Use `'epsilon'` for original
-            AudioLDM checkpoints.
-    :param num_in_channels: The number of input channels. If `None` number of input channels will be automatically
-            inferred.
-    :param scheduler_type: Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler",
-            "euler-ancestral", "dpm", "ddim"]`.
-    :param extract_ema: Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract
-            the EMA weights or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually
-            yield higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.
-    :param device: The device to use. Pass `None` to determine automatically. :param from_safetensors: If
-    `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors
-            instead of PyTorch.
-    :return: An AudioLDMPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
+    Args:
+        checkpoint_path (`str`): Path to `.ckpt` file.
+        original_config_file (`str`):
+            Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically
+            set to the audioldm-s-full-v2 config.
+        image_size (`int`, *optional*, defaults to 512):
+            The image size that the model was trained on.
+        prediction_type (`str`, *optional*):
+            The prediction type that the model was trained on. If `None`, will be automatically
+            inferred by looking for a key in the config. For the default config, the prediction type is `'epsilon'`.
+        num_in_channels (`int`, *optional*, defaults to None):
+            The number of UNet input channels. If `None`, it will be automatically inferred from the config.
+        model_channels (`int`, *optional*, defaults to None):
+            The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override
+            to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large.
+        num_head_channels (`int`, *optional*, defaults to None):
+            The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override
+            to 32 for the small and medium checkpoints, and 64 for the large.
+        scheduler_type (`str`, *optional*, defaults to 'pndm'):
+            Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm",
+            "ddim"]`.
+        extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for
+            checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to
+            `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for
+            inference. Non-EMA weights are usually better to continue fine-tuning.
+        device (`str`, *optional*, defaults to `None`):
+            The device to use. Pass `None` to determine automatically.
+        from_safetensors (`str`, *optional*, defaults to `False`):
+            If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.
+        return: An AudioLDMPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
     """
 
     if not is_omegaconf_available():
@@ -837,6 +852,12 @@ def load_pipeline_from_original_audioldm_ckpt(
     if num_in_channels is not None:
         original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
 
+    if model_channels is not None:
+        original_config["model"]["params"]["unet_config"]["params"]["model_channels"] = model_channels
+
+    if num_head_channels is not None:
+        original_config["model"]["params"]["unet_config"]["params"]["num_head_channels"] = num_head_channels
+
     if (
         "parameterization" in original_config["model"]["params"]
         and original_config["model"]["params"]["parameterization"] == "v"
@@ -960,6 +981,20 @@ if __name__ == "__main__":
         type=int,
         help="The number of input channels. If `None` number of input channels will be automatically inferred.",
     )
+    parser.add_argument(
+        "--model_channels",
+        default=None,
+        type=int,
+        help="The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override"
+        " to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large.",
+    )
+    parser.add_argument(
+        "--num_head_channels",
+        default=None,
+        type=int,
+        help="The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override"
+        " to 32 for the small and medium checkpoints, and 64 for the large.",
+    )
     parser.add_argument(
         "--scheduler_type",
         default="ddim",
@@ -1009,6 +1044,8 @@ if __name__ == "__main__":
         extract_ema=args.extract_ema,
         scheduler_type=args.scheduler_type,
         num_in_channels=args.num_in_channels,
+        model_channels=args.model_channels,
+        num_head_channels=args.num_head_channels,
         from_safetensors=args.from_safetensors,
         device=args.device,
     )