Update app.py

app.py

@@ -3,7 +3,6 @@ import random
 import gradio as gr
 from diffusers import StableDiffusionXLPipeline
 
-# Modell laden
 pipe = StableDiffusionXLPipeline.from_pretrained(
     "John6666/wai-real-mix-v10-sdxl",
     use_safetensors=True,
@@ -11,16 +10,17 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
     low_cpu_mem_usage=True
 ).to("cpu")
 
-# Inferenzfunktion
-def generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps, seed, width, height):
+def generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps, seed, width, height, progress=gr.Progress()):
     if seed == -1:
         seed = random.randint(0, 2**32 - 1)
     generator = torch.Generator(device="cpu").manual_seed(seed)
 
-    # Fallback für negative_prompt, falls leer
     if not negative_prompt.strip():
         negative_prompt = "blurry, low quality, distorted"
 
+    def callback(step: int, timestep: int, latents):
+        progress((step + 1) / num_inference_steps)
+
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -28,39 +28,39 @@ def generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps,
         num_inference_steps=num_inference_steps,
         generator=generator,
         width=width,
-        height=height
+        height=height,
+        callback=callback,
+        callback_steps=1
     ).images[0]
 
     return image, seed
 
-# UI mit Gradio
 with gr.Blocks() as demo:
     gr.Markdown("# 🧠 WAI Real Mix v10 (SDXL on CPU)")
-    gr.Markdown("Generate images with full control over prompts, resolution, and more.")
 
     with gr.Row():
         with gr.Column():
             prompt = gr.Textbox(label="Prompt", placeholder="A futuristic city in sunset, ultra-detailed")
             negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="blurry, low quality, distorted")
-
             with gr.Row():
                 width = gr.Slider(512, 1024, value=768, step=64, label="Width")
                 height = gr.Slider(512, 1024, value=768, step=64, label="Height")
-
             guidance_scale = gr.Slider(1.0, 20.0, value=7.5, step=0.1, label="Guidance Scale (CFG)")
             num_inference_steps = gr.Slider(10, 50, value=30, step=1, label="Inference Steps")
             seed = gr.Number(value=-1, precision=0, label="Seed (-1 = random)")
-
             run_button = gr.Button("Generate")
 
         with gr.Column():
             output_image = gr.Image(label="Generated Image")
             used_seed = gr.Textbox(label="Used Seed", interactive=False)
+            progress_bar = gr.Progress(label="Progress")
 
     run_button.click(
         fn=generate_image,
         inputs=[prompt, negative_prompt, guidance_scale, num_inference_steps, seed, width, height],
-        outputs=[output_image, used_seed]
+        outputs=[output_image, used_seed],
+        progress=progress_bar
     )
 
 demo.launch()
+