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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() |