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Running
on
Zero
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import gradio as gr
import numpy as np
import spaces
import torch
import random
from PIL import Image
from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
MAX_SEED = np.iinfo(np.int32).max
pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda")
@spaces.GPU
def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)):
"""
Perform image editing using the FLUX.1 Kontext pipeline.
This function takes an input image and a text prompt to generate a modified version
of the image based on the provided instructions. It uses the FLUX.1 Kontext model
for contextual image editing tasks.
Args:
input_image (PIL.Image.Image): The input image to be edited. Will be converted
to RGB format if not already in that format.
prompt (str): Text description of the desired edit to apply to the image.
Examples: "Remove glasses", "Add a hat", "Change background to beach".
seed (int, optional): Random seed for reproducible generation. Defaults to 42.
Must be between 0 and MAX_SEED (2^31 - 1).
randomize_seed (bool, optional): If True, generates a random seed instead of
using the provided seed value. Defaults to False.
guidance_scale (float, optional): Controls how closely the model follows the
prompt. Higher values mean stronger adherence to the prompt but may reduce
image quality. Range: 1.0-10.0. Defaults to 2.5.
steps (int, optional): Controls how many steps to run the diffusion model for.
Range: 1-30. Defaults to 28.
progress (gr.Progress, optional): Gradio progress tracker for monitoring
generation progress. Defaults to gr.Progress(track_tqdm=True).
Returns:
tuple: A 3-tuple containing:
- PIL.Image.Image: The generated/edited image
- int: The seed value used for generation (useful when randomize_seed=True)
- gr.update: Gradio update object to make the reuse button visible
Example:
>>> edited_image, used_seed, button_update = infer(
... input_image=my_image,
... prompt="Add sunglasses",
... seed=123,
... randomize_seed=False,
... guidance_scale=2.5
... )
"""
if randomize_seed:
seed = random.randint(0, MAX_SEED)
if input_image:
input_image = input_image.convert("RGB")
image = pipe(
image=input_image,
prompt=prompt,
guidance_scale=guidance_scale,
width = input_image.size[0],
height = input_image.size[1],
num_inference_steps=steps,
generator=torch.Generator().manual_seed(seed),
).images[0]
else:
image = pipe(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=steps,
generator=torch.Generator().manual_seed(seed),
).images[0]
return image, seed, gr.update(visible=True)
css="""
#col-container {
margin: 0 auto;
max-width: 960px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# FLUX.1 Kontext [dev]
Image editing and manipulation model guidance-distilled from FLUX.1 Kontext [pro], [[blog]](https://bfl.ai/announcements/flux-1-kontext-dev) [[model]](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev)
""")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Upload the image for editing", type="pil")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')",
container=False,
)
run_button = gr.Button("Run", scale=0)
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)
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=10,
step=0.1,
value=2.5,
)
steps = gr.Slider(
label="Steps",
minimum=1,
maximum=30,
value=28,
step=1
)
with gr.Column():
result = gr.Image(label="Result", show_label=False, interactive=False)
reuse_button = gr.Button("Reuse this image", visible=False)
gr.on(
triggers=[run_button.click, prompt.submit],
fn = infer,
inputs = [input_image, prompt, seed, randomize_seed, guidance_scale, steps],
outputs = [result, seed, reuse_button]
)
reuse_button.click(
fn = lambda image: image,
inputs = [result],
outputs = [input_image]
)
demo.launch(mcp_server=True) |