Fix FluxFillPipeline and accelerate

app.py

@@ -1,6 +1,6 @@
 import os
 import torch
-from diffusers import StableDiffusionInpaintPipeline
+from diffusers import FluxFillPipeline
 import gradio as gr
 from PIL import Image
 import cv2
@@ -26,29 +26,13 @@ else:
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-try:
-    # Load with only available components, ignoring missing ones
-    pipe = StableDiffusionInpaintPipeline.from_pretrained(
-        "black-forest-labs/FLUX.1-Fill-dev",
-        torch_dtype=torch.float16,
-        token=hf_token,
-        safety_checker=None,  # Disable safety checker if missing
-        feature_extractor=None,  # Disable if missing
-        image_encoder=None,  # Disable if missing
-    ).to(device)
-    pipe.enable_model_cpu_offload()
-except ValueError as e:
-    print(
-        f"Pipeline loading failed due to missing components: {e}. Falling back to compatible model."
-    )
-    # Fallback to a fully supported inpainting model
-    pipe = StableDiffusionInpaintPipeline.from_pretrained(
-        "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
-    ).to(device)
-    pipe.enable_model_cpu_offload()
-except Exception as e:
-    print(f"Unexpected error loading pipeline: {e}")
-    raise
+# Load the correct pipeline
+pipe = FluxFillPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Fill-dev",
+    torch_dtype=torch.bfloat16,
+    token=hf_token,
+).to(device)
+pipe.enable_model_cpu_offload()
 
 
 def inpaint(
@@ -70,6 +54,7 @@ def inpaint(
             mask_image=mask,
             num_inference_steps=50,
             guidance_scale=7.5,
+            max_sequence_length=512,
         ).images[0]
         return output
     except Exception as e:

requirements.txt

@@ -5,4 +5,5 @@ transformers
 gradio
 opencv-python-headless
 numpy
-huggingface_hub
+huggingface_hub
+accelerate