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import gradio as gr |
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import numpy as np |
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import torch |
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import spaces |
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from diffusers import FluxPipeline, FluxTransformer2DModel |
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from diffusers.utils import export_to_gif |
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from huggingface_hub import hf_hub_download |
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from PIL import Image |
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import uuid |
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import random |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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if torch.cuda.is_available(): |
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torch_dtype = torch.bfloat16 |
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else: |
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torch_dtype = torch.float32 |
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def split_image(input_image, num_splits=4): |
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output_images = [] |
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for i in range(num_splits): |
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left = i * 320 |
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right = (i + 1) * 320 |
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box = (left, 0, right, 320) |
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output_images.append(input_image.crop(box)) |
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return output_images |
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pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch_dtype) |
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MAX_SEED = np.iinfo(np.int32).max |
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@spaces.GPU |
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def infer(prompt, seed=1, randomize_seed=False, num_inference_steps=28): |
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prompt_template = f"A side by side 4 frame image showing high quality consecutive stills from a looped gif animation moving from left to right. The scene has motion. The stills are of {prompt}" |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator().manual_seed(seed) |
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image = pipe( |
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prompt=prompt_template, |
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num_inference_steps=num_inference_steps, |
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num_images_per_prompt=1, |
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generator=generator, |
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height=320, |
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width=1280 |
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).images[0] |
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gif_name = f"{uuid.uuid4().hex}-flux.gif" |
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export_to_gif(split_image(image, 4), gif_name, fps=4) |
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return gif_name, image, seed |
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examples = [ |
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"a cute cat raising a sign that reads \"Flux does Video?\"", |
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"Chris Rock eating pizza", |
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"A flying saucer over the white house", |
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] |
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css=""" |
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#col-container { |
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margin: 0 auto; |
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max-width: 640px; |
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} |
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#strip{ |
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max-height: 160px |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(f""" |
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# FLUX Gif Animations |
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Generate gifs with FLUX [dev]. Concept idea by [fofr](https://x.com/fofrAI). Diffusers implementation by [Dhruv](_DhruvNair_) |
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""") |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0) |
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result = gr.Image(label="Result", show_label=False) |
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result_full = gr.Image(label="Gif Strip", elem_id="strip") |
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with gr.Accordion("Advanced Settings", open=False): |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=1, |
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maximum=32, |
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step=1, |
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value=28, |
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) |
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gr.Examples( |
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examples = examples, |
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inputs = [prompt], |
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outputs = [result, result_full, seed], |
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fn=infer, |
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cache_examples="lazy" |
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) |
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gr.on( |
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triggers=[run_button.click, prompt.submit], |
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fn = infer, |
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inputs = [prompt, seed, randomize_seed, num_inference_steps], |
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outputs = [result, result_full, seed] |
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) |
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demo.queue().launch() |
