Update app.py

app.py

@@ -1,154 +1,56 @@
-import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+import random
+import gradio as gr
+from diffusers import StableDiffusionXLPipeline
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+# Lade das Modell
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "John6666/wai-real-mix-v10-sdxl",
+    use_safetensors=True,
+    torch_dtype=torch.float32
+).to("cpu")  # Wichtig: CPU-Modus
 
-    generator = torch.Generator().manual_seed(seed)
+# Inferenzfunktion
+def generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps, seed):
+    if seed == -1:
+        seed = random.randint(0, 2**32 - 1)
+    generator = torch.Generator(device="cpu").manual_seed(seed)
 
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
+        generator=generator
     ).images[0]
 
     return image, seed
 
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# WAI Real Mix v10 (SDXL - CPU)")
+    gr.Markdown("Generate images with advanced controls (may be slow on CPU).")
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="A castle in the clouds, cinematic lighting")
+            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="blurry, low quality, distorted")
 
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+            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)")
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+            run_button = gr.Button("Generate")
 
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image")
+            used_seed = gr.Textbox(label="Used Seed", interactive=False)
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+    run_button.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, guidance_scale, num_inference_steps, seed],
+        outputs=[output_image, used_seed]
     )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()
+