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Zero
Running
on
Zero
| import torch | |
| import spaces | |
| import gradio as gr | |
| from diffusers import FluxInpaintPipeline | |
| import random | |
| import numpy as np | |
| MARKDOWN = """ | |
| # FLUX.1 Inpainting 🎨 | |
| Shoutout to [Black Forest Labs](https://huggingface.co./black-forest-labs) team for | |
| creating this amazing model, and a big thanks to [Gothos](https://github.com/Gothos) | |
| for taking it to the next level by enabling inpainting with the FLUX. | |
| """ | |
| MAX_SEED = np.iinfo(np.int32).max | |
| DEVICE = "cuda" #if torch.cuda.is_available() else "cpu" | |
| pipe = FluxInpaintPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE) | |
| def process(input_image_editor, input_text, strength, seed, randomize_seed, num_inference_steps, guidance_scale=3.5, progress=gr.Progress(track_tqdm=True)): | |
| if not input_text: | |
| raise gr.Error("Please enter a text prompt.") | |
| image = input_image_editor['background'] | |
| if not image: | |
| raise gr.Error("Please upload an image.") | |
| width, height = image.size | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator(device=DEVICE).manual_seed(seed) | |
| result = pipe(prompt=input_text, image=image, mask_image=mask_image, width=width, height=height, | |
| strength=strength, num_inference_steps=num_inference_steps, generator=generator, | |
| guidance_scale=guidance_scale).images[0] | |
| return result, mask_image, seed | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown(MARKDOWN) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| input_image_component = gr.ImageEditor( | |
| label='Image', | |
| type='pil', | |
| sources=["upload", "webcam"], | |
| image_mode='RGB', | |
| layers=False, | |
| brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed")) | |
| input_text_component = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| strength_slider = gr.Slider( | |
| minimum=0.0, | |
| maximum=1.0, | |
| value=0.7, | |
| step=0.01, | |
| label="Strength" | |
| ) | |
| num_inference_steps = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| value=30, | |
| step=1, | |
| label="Number of inference steps" | |
| ) | |
| guidance_scale = gr.Slider( | |
| label="Guidance Scale", | |
| minimum=1, | |
| maximum=15, | |
| step=0.1, | |
| value=3.5, | |
| ) | |
| seed_number = gr.Number( | |
| label="Seed", | |
| value=42, | |
| precision=0 | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Accordion("Upload a mask", open=False): | |
| uploaded_mask_component = gr.Image(label="Already made mask (black pixels will be preserved, white pixels will be redrawn)", sources=["upload"], type="pil") | |
| submit_button_component = gr.Button( | |
| value='Inpaint', variant='primary') | |
| with gr.Column(scale=1): | |
| output_image_component = gr.Image( | |
| type='pil', image_mode='RGB', label='Generated Image') | |
| output_mask_component = gr.Image( | |
| type='pil', image_mode='RGB', label='Generated Mask') | |
| with gr.Accordion("Debug Info", open=False): | |
| output_seed = gr.Number(label="Used Seed") | |
| submit_button_component.click( | |
| fn=process, | |
| inputs=[input_image_component, input_text_component, strength_slider, seed_number, randomize_seed, num_inference_steps, guidance_scale], | |
| outputs=[output_image_component, output_mask_component, output_seed] | |
| ) | |
| demo.launch(debug=False, show_error=True) |