inpaint-mask

runway.py

@@ -110,23 +110,39 @@ def fit_image_to_mask(original_image: Image, reference_image: Image, mask_x1: in
 @app.post("/inpaint/")
 async def inpaint_image(
     image: UploadFile = File(...),
-    prompt: str = "Fill the masked area with appropriate content.",
-    mask_x1: int = 100,
-    mask_y1: int = 100,
-    mask_x2: int = 200,
-    mask_y2: int = 200
+    mask: UploadFile = File(...),
+    prompt: str = "Fill the masked area with appropriate content."
 ):
     """
-    Endpoint for image inpainting using a text prompt and autogenerated mask.
+    Endpoint for image inpainting using a text prompt and an uploaded mask.
+    - `image`: Original image file (PNG/JPG).
+    - `mask`: Mask file indicating areas to inpaint (white for masked areas, black for unmasked).
+    - `prompt`: Text prompt describing the desired output.
+    
+    Returns:
+    - The inpainted image as a PNG file.
     """
     try:
+        # Load the uploaded image and mask
         image_bytes = await image.read()
+        mask_bytes = await mask.read()
+
         original_image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-        mask_image = generate_rectangular_mask(original_image.size, mask_x1, mask_y1, mask_x2, mask_y2)
+        mask_image = Image.open(io.BytesIO(mask_bytes)).convert("L")
+
+        # Ensure dimensions match between image and mask
+        if original_image.size != mask_image.size:
+            raise HTTPException(status_code=400, detail="Image and mask dimensions must match.")
+
+        # Perform inpainting using the pipeline
         result = pipe(prompt=prompt, image=original_image, mask_image=mask_image).images[0]
+
+        # Convert result to bytes for response
         result_bytes = io.BytesIO()
         result.save(result_bytes, format="PNG")
         result_bytes.seek(0)
+
+        # Return the image as a streaming response
         return StreamingResponse(
             result_bytes,
             media_type="image/png",
@@ -146,7 +162,7 @@ async def inpaint_with_reference(
     mask_y2: int = 200
 ):
     """
-    Endpoint for replacing masked areas with reference image content, refined to look natural.
+    Endpoint for replacing masked areas with reference image content, refined to look natural, using an autogenerated mask.
     """
     try:
         image_bytes = await image.read()
@@ -190,7 +206,7 @@ async def fit_image_to_mask(
     mask_y2: int = 200
 ):
     """
-    Endpoint for fitting a reference image into a masked region of the original image, refined to look natural.
+    Endpoint for fitting a reference image into a masked region of the original image, refined to look natural, using an autogenerated mask.
     """
     try:
         # Load the uploaded images
@@ -202,7 +218,7 @@ async def fit_image_to_mask(
         # Fit the reference image into the masked region
         result = fit_image_to_mask(original_image, reference_image, mask_x1, mask_y1, mask_x2, mask_y2)
         if not isinstance(result, tuple) or len(result) != 2:
-            raise ValueError(f"Expected tuple of (guided_image, mask_image), got {type(result)}")
+            raise ValueError(f"Expected tuple of (guided_image, mask_image), got {type(result)}: {result}")
         guided_image, mask_image = result
 
         # Soften the mask for smoother transitions