Update app.py

app.py

@@ -1,3 +1,136 @@
+
+
 import gradio as gr
+import numpy as np
+import random
+import spaces
+import torch
+from diffusers import DiffusionPipeline
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype).to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+@spaces.GPU()
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, guidance_scale=7.5, progress=gr.Progress(track_tqdm=True)):
+    if width > MAX_IMAGE_SIZE or height > MAX_IMAGE_SIZE:
+        raise ValueError("Image size exceeds the maximum allowed dimensions.")
+    
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+    
+    try:
+        image = pipe(
+                prompt=prompt, 
+                width=width,
+                height=height,
+                num_inference_steps=num_inference_steps, 
+                generator=generator,
+                guidance_scale=guidance_scale
+        ).images[0] 
+    except Exception as e:
+        return None, seed, f"Error: {str(e)}"
+    
+    return image, seed, None
+ 
+examples = [
+    "a tiny astronaut hatching from an egg on the moon",
+    "a cat holding a sign that says hello world",
+    "an anime illustration of a wiener schnitzel",
+    # Add more diverse examples
+]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# Custom Image Creator
+12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
+[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1)]
+        """)
+        
+        with gr.Row():
+            prompt = gr.Textbox(
+                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)
+        
+        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():
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=4,
+                )
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=0.0,
+                    maximum=20.0,
+                    step=0.5,
+                    value=7.5,
+                )
+        
+        gr.Examples(
+            examples=examples,
+            fn=infer,
+            inputs=[prompt],
+            outputs=[result, seed],
+            cache_examples="lazy"
+        )
+
+    run_button.click(
+        fn=infer,
+        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps, guidance_scale],
+        outputs=[result, seed],
+    )
 
-gr.load("models/black-forest-labs/FLUX.1-dev").launch()
+    gr.Markdown("""
+    ## Save Your Image
+    Right-click on the image and select 'Save As' to download the generated image.
+    """)
+    
+demo.launch()