Update app.py

app.py

@@ -1,30 +1,150 @@
 import gradio as gr
+import numpy as np
+import random
+import os
+
+import spaces
+from diffusers import AutoPipelineForText2Image
+import torch
+
+from huggingface_hub import login
+login(os.environ.get("HF_TOKEN"))
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+pipe = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+pipe.load_lora_weights('enhanceaiteam/Flux-uncensored', weight_name='lora.safetensors')
+pipe = pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+
+@spaces.GPU
+def infer(
+    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)
+
+    generator = torch.Generator().manual_seed(seed)
+
+    image = pipe(
+        prompt=prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator,
+    ).images[0]
+
+    return image, seed
+
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 640px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# [FLUX.1](https://blackforestlabs.ai/)
+Generate any type of image.  
+""")
 
-def main():
-    # Laden des Flux-Modells
-    demo = gr.load("models/enhanceaiteam/Flux-Uncensored-V2")
-    
-    # Anpassung der Benutzeroberfläche
-    with gr.Blocks() as flux_interface:
-        gr.Markdown("# Flux Image Generator 🖼️")
-        gr.Markdown("Generieren Sie hochwertige Bilder mit Flux AI")
-        
         with gr.Row():
-            # Der geladene Flux-Komponente wird automatisch hinzugefügt
-            demo.render()
-        
-        gr.Markdown("""
-        ### Tipps zur Nutzung:
-        - Geben Sie detaillierte Beschreibungen ein
-        - Experimentieren Sie mit verschiedenen Stilen
-        - Mehr Details führen zu präziseren Ergebnissen
-        """)
-    
-    # Interface starten
-    flux_interface.launch(
-        debug=True,   # Detaillierte Fehlermeldungen
-        show_error=True  # Zeige Fehler im Interface
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=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):
+            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,  
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,  
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=3.5,  
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=28,  
+                )
+
+        gr.Examples(examples=examples, inputs=[prompt])
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, seed],
     )
 
 if __name__ == "__main__":
-    main()
+    demo.launch()