Create app.py

app.py

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+import gradio as gr
+import torch
+import spaces
+from diffusers import FluxPipeline, FluxTransformer2DModel
+from PIL import Image
+from diffusers.utils import export_to_gif
+import uuid
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+if torch.cuda.is_available():
+    torch_dtype = torch.bfloat16
+else:
+    torch_dtype = torch.float32
+
+def split_image(input_image, num_splits=4):
+    # Create a list to store the output images
+    output_images = []
+
+    # Split the image into four 256x256 sections
+    for i in range(num_splits):
+        left = i * 256
+        right = (i + 1) * 256
+        box = (left, 0, right, 256)
+        output_images.append(input_image.crop(box))
+
+    return output_images
+
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell",
+    torch_dtype=torch_dtype
+)
+pipe.to(device)
+
+@spaces.GPU 
+def infer(prompt, seed, randomize_seed, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+    prompt_template = f"A side by side 4 frame image showing consecutive stills from a looped gif moving from left to right. The gif is {prompt}"
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+        
+    generator = torch.Generator().manual_seed(seed)
+    
+    image = pipe(
+        prompt=prompt,
+        num_inference_steps=num_inference_steps,
+        num_images_per_prompt=1,
+        generator=torch.Generator(device).manual_seed(seed),
+        height=height,
+        width=width
+    ).images[0]
+    gif_name = f"{uuid.uuid4().hex}-flux.gif"
+    export_to_gif(split_image(image, 4), gif_name, fps=4)
+    return gif_name, 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 Schnell Animations
+        Generate gifs with 
+        """)
+        
+        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)
+
+        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)
+
+            num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=12,
+                    step=1,
+                    value=4, 
+            )    
+                
+        
+        gr.Examples(
+            examples = examples,
+            inputs = [prompt]
+        )
+    gr.on(
+        trigger=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result, seed]
+    )
+
+demo.queue().launch()