Update pipeline_wan_i2v.py

pipeline_wan_i2v.py

@@ -1,8 +1,8 @@
 import torch
-from diffusers import DiffusionPipeline
-
-class WanImageToVideoPipeline(DiffusionPipeline):
+from diffusers import AutoencoderKL, DDIMScheduler, Transformer2DModel
+from transformers import PreTrainedTokenizerFast
 
+class WanImageToVideoPipeline:
     def __init__(
         self,
         vae,
@@ -13,7 +13,6 @@ class WanImageToVideoPipeline(DiffusionPipeline):
         text_encoder,
         tokenizer
     ):
-        super().__init__()
         self.vae = vae
         self.transformer = transformer
         self.scheduler = scheduler
@@ -22,22 +21,7 @@ class WanImageToVideoPipeline(DiffusionPipeline):
         self.text_encoder = text_encoder
         self.tokenizer = tokenizer
 
-    def __call__(self, image, num_frames=16):
-
-        # Encode image
-        img_latents = self.image_encoder.encode(image).latent_dist.sample()
-
-        frames = []
-        for _ in range(num_frames):
-
-            latents = img_latents.clone()
-
-            for t in self.scheduler.timesteps:
-                noise_pred = self.transformer(latents, t)
-                step = self.scheduler.step(noise_pred, t, latents)
-                latents = step.prev_sample
-
-            decoded = self.vae.decode(latents).sample
-            frames.append(self.image_processor.postprocess(decoded))
-
-        return type("Result", (), {"frames": frames})
+    def __call__(self, image):
+        # Dummy output so HF endpoint doesn't crash
+        # Replace later with actual generation logic
+        return {"frames": [image]}