Update app.py

app.py

@@ -1,142 +1,68 @@
 import gradio as gr
 import numpy as np
 import random
-#import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
 
 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
+import gradio as gr
+import replicate
 
-#@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)
-        
-    generator = torch.Generator().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
-    ).images[0] 
-    
-    return image, seed
+def generate_image(model, lora_scale, guidance_scale, prompt_strength, num_steps, prompt):
+    output = replicate.run(
+        "dd-ds-ai/lora_test_01:70c669221124d8aaf0fc494f9553468bd069483a19e74b2753262008b1e8fbb2",
+        input={
+            "model": model,
+            "lora_scale": lora_scale,
+            "num_outputs": 1,
+            "aspect_ratio": "1:1",
+            "output_format": "webp",
+            "guidance_scale": guidance_scale,
+            "output_quality": 90,
+            "prompt_strength": prompt_strength,
+            "extra_lora_scale": 1,
+            "num_inference_steps": num_steps,
+            "prompt": prompt
+        }
+    )
+    image_url = output[0] if output else None
+    return image_url
 
-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;
-}
-"""
+# Gradio-Interface erstellen
+def create_gradio_interface():
+    lora_scale = gr.Slider(0, 2, value=1, step=0.1, label="Lora Scale")
+    guidance_scale = gr.Slider(1, 10, value=3.5, step=0.1, label="Guidance Scale")
+    prompt_strength = gr.Slider(0, 1, value=0.8, step=0.1, label="Prompt Strength")
+    num_steps = gr.Slider(1, 50, value=28, step=1, label="Number of Inference Steps")
+    prompt = gr.Textbox(label="Prompt", value="a person reading the hamburger abendblatt newspaper")
 
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
+    # Erstelle ein Button-Interface für die Bildgenerierung
+    generate_btn = gr.Button("Bild generieren")
 
-        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]
+    # Gradio Interface
+    interface = gr.Interface(
+        fn=generate_image,  # Die Funktion, die aufgerufen wird
+        inputs=[model, lora_scale, guidance_scale, prompt_strength, num_steps, prompt],  # Eingaben
+        outputs=gr.Image(label="Generated Image"),  # Ausgabe als Bild
     )
 
-demo.queue().launch()
+    # Binde den Button an die Bildgenerierung
+    interface.launch(share=True)
+
+
+# Starte die Gradio-App
+if __name__ == "__main__":
+    create_gradio_interface()
+
+
+# demo.queue().launch()