app.py

import gradio as gr
import numpy as np
import torch
import spaces
from diffusers import FluxPipeline, FluxTransformer2DModel
from diffusers.utils import export_to_gif
from huggingface_hub import hf_hub_download
from PIL import Image
import uuid
import random

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 * 320
        right = (i + 1) * 320
        box = (left, 0, right, 320)
        output_images.append(input_image.crop(box))

    return output_images

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch_dtype).to(device)

MAX_SEED = np.iinfo(np.int32).max

@spaces.GPU 
def infer(prompt, seed=1, randomize_seed=False, num_inference_steps=28):
    print('entered the function')
    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}"
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)
    
    image = pipe(
        prompt=prompt_template,
        num_inference_steps=num_inference_steps,
        num_images_per_prompt=1,
        generator=generator,
        height=320,
        width=1280
    ).images[0]
    
    gif_name = f"{uuid.uuid4().hex}-flux.gif"
    
    export_to_gif(split_image(image, 4), gif_name, fps=4)
    
    return gif_name, image, seed

examples = [
    "a cute cat raising a sign that reads \"Flux does Video?\"",
    "Chris Rock eating pizza",
    "A flying saucer over the white house",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
#strip{
max-height: 160px
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # FLUX Gif Animations
        Generate gifs with FLUX [dev]. Concept idea by [fofr](https://x.com/fofrAI). Diffusers implementation by [Dhruv](_DhruvNair_)
        """)
        
        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)
        result_full = gr.Image(label="Gif Strip", elem_id="strip")
        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=32,
                    step=1,
                    value=28, 
            )    
                
        
        gr.Examples(
            examples = examples,
            inputs = [prompt],
            outputs = [result, result_full, seed],
            fn=infer,
            cache_examples="lazy"
        )
        
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn = infer,
        inputs = [prompt, seed, randomize_seed, num_inference_steps],
        outputs = [result, result_full, seed]
    )

demo.queue().launch()