Update app.py

app.py

@@ -2,34 +2,32 @@ import os
 import random
 import gradio as gr
 import torch
-from torchvision import transforms
-from PIL import Image
-import numpy as np
 from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
-from io import BytesIO
 
-# Set up model cache directory
+# Configuration
+MODEL_ID = "OFA-Sys/small-stable-diffusion-v0"
 MODEL_CACHE = "model_cache"
 os.makedirs(MODEL_CACHE, exist_ok=True)
 
-# Initialize model pipeline
+# Initialize pipeline with updated scheduler config
 def get_pipeline():
-    model_id = "OFA-Sys/small-stable-diffusion-v0"
     scheduler = EulerAncestralDiscreteScheduler.from_pretrained(
-        model_id, 
+        MODEL_ID,
         subfolder="scheduler",
-        cache_dir=MODEL_CACHE
+        cache_dir=MODEL_CACHE,
+        steps_offset=1  # Fix for the deprecation warning
     )
     
     return StableDiffusionPipeline.from_pretrained(
-        model_id,
+        MODEL_ID,
         scheduler=scheduler,
         torch_dtype=torch.float32,
         cache_dir=MODEL_CACHE,
-        safety_checker=None
+        safety_checker=None,
+        local_files_only=False
     ).to("cpu")
 
-# Load model once at startup
+# Load model
 pipeline = get_pipeline()
 
 def generate_image(
@@ -47,14 +45,15 @@ def generate_image(
     generator = torch.Generator(device="cpu").manual_seed(seed)
     
     # Generate image
-    image = pipeline(
-        prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        generator=generator
-    ).images[0]
+    with torch.no_grad():
+        image = pipeline(
+            prompt,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            generator=generator
+        ).images[0]
     
     return image, seed
 
@@ -70,7 +69,7 @@ STYLE_PRESETS = {
 def apply_style(prompt, style_name):
     return f"{STYLE_PRESETS[style_name]}, {prompt}"
 
-# UI Components
+# Create Gradio interface
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
     # ⚡ FLUX Turbo Generator
@@ -132,10 +131,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         label="Example Prompts"
     )
 
-# Launch settings
+# Launch with corrected parameters
 if __name__ == "__main__":
+    demo.queue()  # Enable queuing
     demo.launch(
         server_name="0.0.0.0",
-        server_port=int(os.getenv("PORT", 7860)),
-        enable_queue=True
-    )
+        server_port=int(os.getenv("PORT", 7860))