Fix CUDA device mismatch - resize image and add autocast

handler.py

@@ -0,0 +1,113 @@
+import torch
+import base64
+import io
+import os
+from typing import Optional
+from PIL import Image
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+
+app = FastAPI()
+
+# Global pipeline
+pipe = None
+export_to_video = None
+
+class InferenceRequest(BaseModel):
+    image: str  # base64 or URL
+    prompt: str
+    negative_prompt: str = "ugly, static, blurry, low quality"
+    num_frames: int = 93
+    num_inference_steps: int = 35
+    guidance_scale: float = 7.0
+    seed: Optional[int] = None
+
+@app.on_event("startup")
+async def load_model():
+    global pipe, export_to_video
+    from diffusers import Cosmos2VideoToWorldPipeline
+    from diffusers.utils import export_to_video as etv
+    
+    export_to_video = etv
+    model_id = "nvidia/Cosmos-Predict2-2B-Video2World"
+    
+    print("Loading model...")
+    pipe = Cosmos2VideoToWorldPipeline.from_pretrained(
+        model_id,
+        torch_dtype=torch.bfloat16,
+        token=os.environ.get("HF_TOKEN"),
+    )
+    pipe.to("cuda")
+    print("Model loaded successfully!")
+
+@app.post("/predict")
+@app.post("/")
+async def predict(request: dict):
+    global pipe, export_to_video
+    
+    # Handle both direct and nested input formats
+    inputs = request.get("inputs", request)
+    
+    image_data = inputs.get("image")
+    if not image_data:
+        raise HTTPException(status_code=400, detail="No image provided")
+    
+    prompt = inputs.get("prompt", "")
+    if not prompt:
+        raise HTTPException(status_code=400, detail="No prompt provided")
+    
+    # Load image
+    try:
+        if image_data.startswith("http"):
+            from diffusers.utils import load_image
+            image = load_image(image_data)
+        else:
+            image_bytes = base64.b64decode(image_data)
+            image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        
+        # Resize to expected dimensions for Cosmos Video2World
+        image = image.resize((1280, 704))
+        
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Failed to load image: {str(e)}")
+
+    negative_prompt = inputs.get("negative_prompt", "ugly, static, blurry, low quality")
+    num_frames = inputs.get("num_frames", 93)
+    num_inference_steps = inputs.get("num_inference_steps", 35)
+    guidance_scale = inputs.get("guidance_scale", 7.0)
+    seed = inputs.get("seed")
+
+    # Create generator on correct device
+    generator = None
+    if seed is not None:
+        generator = torch.Generator(device="cuda").manual_seed(int(seed))
+
+    try:
+        with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+            output = pipe(
+                image=image,
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                num_frames=num_frames,
+                num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale,
+                generator=generator,
+            )
+
+        video_path = "/tmp/output.mp4"
+        export_to_video(output.frames[0], video_path, fps=16)
+
+        with open(video_path, "rb") as f:
+            video_b64 = base64.b64encode(f.read()).decode("utf-8")
+
+        return {"video": video_b64, "content_type": "video/mp4"}
+
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Inference failed: {str(e)}")
+
+@app.get("/health")
+@app.get("/")
+async def health():
+    return {"status": "healthy", "message": "Cosmos-Predict2 Video2World API"}