Update app.py

app.py

@@ -6,14 +6,47 @@ model_id = "SG161222/RealVisXL_V4.0"
 pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 pipe.to("cpu")  # Use "cuda" if GPU is available
 
-def generate_image(prompt):
-    # Ensure prompt is passed correctly
-    inputs = pipe.tokenizer(prompt, return_tensors="pt")
-    with torch.no_grad():
-        # Ensure inputs are provided correctly
-        output = pipe(prompt, **inputs)
-    image = output.images[0]
-    return image
+def generate_image(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    style: str = DEFAULT_STYLE_NAME,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    randomize_seed: bool = False,
+    use_resolution_binning: bool = True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if check_text(prompt, negative_prompt):
+        raise ValueError("Prompt contains restricted words.")
+    
+    prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator().manual_seed(seed)
+
+    if not use_negative_prompt:
+        negative_prompt = ""  # type: ignore
+    negative_prompt += default_negative    
+
+    options = {
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "width": width,
+        "height": height,
+        "guidance_scale": guidance_scale,
+        "num_inference_steps": 25,
+        "generator": generator,
+        "num_images_per_prompt": NUM_IMAGES_PER_PROMPT,
+        "use_resolution_binning": use_resolution_binning,
+        "output_type": "pil",
+    }
+    
+    images = pipe(**options).images + pipe2(**options).images
+
+    image_paths = [save_image(img) for img in images]
+    return image_paths, seed
 
 def chatbot(prompt):
     # Generate the image based on the user's input