Create generate.py

generate.py

@@ -0,0 +1,37 @@
+import torch
+from diffusers import AnimateDiffPipeline, DDIMScheduler
+
+# Load model only once (on import)
+def load_model():
+    # Example AnimateDiff model from HF Hub
+    model_id = "guoyww/animatediff-motion-adapter-v1-5-2"  
+
+    pipe = AnimateDiffPipeline.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16
+    )
+
+    scheduler = DDIMScheduler.from_pretrained(
+        "runwayml/stable-diffusion-v1-5",
+        subfolder="scheduler"
+    )
+
+    pipe.scheduler = scheduler
+    pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+
+    return pipe
+
+
+# Global model instance (loaded once)
+pipe = load_model()
+
+
+# Generation function
+def generate(prompt: str, num_inference_steps: int = 50, guidance_scale: float = 7.5):
+    with torch.autocast("cuda" if torch.cuda.is_available() else "cpu"):
+        result = pipe(
+            prompt,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale
+        )
+    return result.frames[0]  # returning first frame (you can adapt this to video/gif)