personal use

auto_pipeline_test.py

@@ -0,0 +1,16 @@
+import torch
+from diffusers import AutoPipelineForText2Video
+from diffusers.utils import export_to_video
+
+wan_list = [
+    "Wan-AI/Wan2.1-T2V-14B-Diffusers",
+    "Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers",
+    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+    "Wan-AI/Wan2.1-VACE-1.3B-diffusers",
+    "Wan-AI/Wan2.2-I2V-A14B-Diffusers",
+]
+
+pipe = AutoPipelineForText2Video.from_pretrained(
+    wan_list[2],
+    torch_dtype=torch.bfloat16,
+)

src/diffusers/__init__.py

@@ -303,6 +303,7 @@
             "AutoPipelineForImage2Image",
             "AutoPipelineForInpainting",
             "AutoPipelineForText2Image",
+            "AutoPipelineForText2Video",
             "ConsistencyModelPipeline",
             "DanceDiffusionPipeline",
             "DDIMPipeline",

src/diffusers/pipelines/__init__.py

@@ -46,6 +46,7 @@
         "AutoPipelineForImage2Image",
         "AutoPipelineForInpainting",
         "AutoPipelineForText2Image",
+        "AutoPipelineForText2Video",
     ]
     _import_structure["consistency_models"] = ["ConsistencyModelPipeline"]
     _import_structure["dance_diffusion"] = ["DanceDiffusionPipeline"]

src/diffusers/pipelines/auto_pipeline.py

@@ -117,7 +117,7 @@
     StableDiffusionXLInpaintPipeline,
     StableDiffusionXLPipeline,
 )
-from .wan import WanImageToVideoPipeline, WanPipeline, WanVideoToVideoPipeline
+from .wan import WanAnimatePipeline, WanImageToVideoPipeline, WanPipeline, WanVACEPipeline, WanVideoToVideoPipeline
 from .wuerstchen import WuerstchenCombinedPipeline, WuerstchenDecoderPipeline
 
 
@@ -218,6 +218,10 @@
 AUTO_TEXT2VIDEO_PIPELINES_MAPPING = OrderedDict(
     [
         ("wan", WanPipeline),
+        ("wan-animate", WanAnimatePipeline),
+        ("wan-image-to-video", WanImageToVideoPipeline),
+        ("wan-vace", WanVACEPipeline),
+        ("wan-video-to-video", WanVideoToVideoPipeline),
     ]
 )
 
@@ -1203,3 +1207,39 @@ def from_pipe(cls, pipeline, **kwargs):
         model.register_to_config(**unused_original_config)
 
         return model
+
+
+class AutoPipelineForText2Video(ConfigMixin):
+    config_name = "model_index.json"
+
+    def __init__(self, *args, **kwargs):
+        raise EnvironmentError(
+            f"{self.__class__.__name__} is designed to be instantiated "
+            f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
+            f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
+        )
+
+    @classmethod
+    @validate_hf_hub_args
+    def from_pretrained(cls, pretrained_model_or_path, **kwargs):
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        token = kwargs.pop("token", None)
+        local_files_only = kwargs.pop("local_files_only", False)
+        revision = kwargs.pop("revision", None)
+
+        load_config_kwargs = {
+            "cache_dir": cache_dir,
+            "force_download": force_download,
+            "proxies": proxies,
+            "token": token,
+            "local_files_only": local_files_only,
+            "revision": revision,
+        }
+
+        config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
+        orig_class_name = config["_class_name"]
+        text_to_video_cls = _get_task_class(AUTO_TEXT2VIDEO_PIPELINES_MAPPING, orig_class_name)
+        kwargs = {**load_config_kwargs, **kwargs}
+        return text_to_video_cls.from_pretrained(pretrained_model_or_path, **kwargs)