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Running
on
Zero
| import gradio as gr | |
| import numpy as np | |
| import spaces | |
| import torch | |
| import spaces | |
| import random | |
| from diffusers import FluxFillPipeline | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 2048 | |
| pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16, revision="refs/pr/4").to("cuda") | |
| def infer(edit_images, prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)): | |
| print(edit_images) | |
| image = edit_images["background"] | |
| mask = edit_images["layers"][0] | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| image = pipe( | |
| prompt=prompt, | |
| image=image, | |
| mask_image=mask, | |
| height=width, | |
| width=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| generator=torch.Generator("cpu").manual_seed(seed) | |
| ).images[0] | |
| return image, seed | |
| examples = [ | |
| "a tiny astronaut hatching from an egg on the moon", | |
| "a cat holding a sign that says hello world", | |
| "an anime illustration of a wiener schnitzel", | |
| ] | |
| 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 Fill [dev] | |
| 12B param rectified flow transformer structural conditioning tuned, guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) | |
| [[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)] | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| edit_image = gr.ImageEditor( | |
| label='Upload and draw mask for inpainting', | |
| type='pil', | |
| sources=["upload", "webcam"], | |
| image_mode='RGB', | |
| layers=False, | |
| brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"), | |
| height=600 | |
| ) | |
| 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) | |
| 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(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, | |
| ) | |
| 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.on( | |
| triggers=[run_button.click, prompt.submit], | |
| fn = infer, | |
| inputs = [edit_image, prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
| outputs = [result, seed] | |
| ) | |
| demo.launch() |