Upload app (16).py

app (16).py

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+import random
+
+import gradio as gr
+import numpy as np
+import torch
+import spaces
+from diffusers import FluxPipeline
+from PIL import Image
+from diffusers.utils import export_to_gif
+
+HEIGHT = 256
+WIDTH = 1024
+MAX_SEED = np.iinfo(np.int32).max
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    torch_dtype=torch.bfloat16
+).to(device)
+
+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
+
+@spaces.GPU(duration=190)
+def predict(prompt, seed=42, randomize_seed=False, guidance_scale=5.0, num_inference_steps=28, 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 of {prompt}.
+    """
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    image = pipe(
+        prompt=prompt_template,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        num_images_per_prompt=1,
+        generator=torch.Generator("cpu").manual_seed(seed),
+        height=HEIGHT,
+        width=WIDTH
+    ).images[0]
+
+    return export_to_gif(split_image(image, 4), "flux.gif", fps=4), image, seed
+
+demo = gr.Interface(fn=predict, inputs="text", outputs="image")
+
+css = """
+footer {
+    visibility: hidden;
+}
+"""
+
+
+examples = [
+    "a cat waving its paws in the air",
+    "a panda moving their hips from side to side",
+    "a flower going through the process of blooming"
+]
+
+with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+
+        with gr.Row():
+            prompt = gr.Text(label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt")
+            submit = gr.Button("Submit", scale=0)
+
+        output = gr.Image(label="GIF", show_label=False)
+        output_stills = gr.Image(label="stills", show_label=False, elem_id="stills")
+        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():
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=15,
+                    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,
+            fn=predict,
+            inputs=[prompt],
+            outputs=[output, output_stills, seed],
+            cache_examples="lazy"
+        )
+        gr.on(
+            triggers=[submit.click, prompt.submit],
+            fn=predict,
+            inputs=[prompt, seed, randomize_seed, guidance_scale, num_inference_steps],
+            outputs = [output, output_stills, seed]
+        )
+
+demo.launch()