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| import gradio as gr | |
| import numpy as np | |
| import random | |
| import spaces | |
| import torch | |
| from diffusers import FluxPriorReduxPipeline, FluxPipeline | |
| from diffusers.utils import load_image | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 2048 | |
| pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained( | |
| "black-forest-labs/FLUX.1-Redux-dev", | |
| torch_dtype=torch.bfloat16 | |
| ).to("cuda") | |
| pipe = FluxPipeline.from_pretrained( | |
| "black-forest-labs/FLUX.1-dev" , | |
| text_encoder=None, | |
| text_encoder_2=None, | |
| torch_dtype=torch.bfloat16 | |
| ).to("cuda") | |
| def infer(control_image, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| pipe_prior_output = pipe_prior_redux(control_image) | |
| images = pipe( | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| generator=torch.Generator("cpu").manual_seed(seed), | |
| **pipe_prior_output, | |
| ).images[0] | |
| return images, seed | |
| 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 Redux [dev] | |
| An adapter for FLUX [dev] to create image variations | |
| [[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(): | |
| input_image = gr.Image(label="Image to create variations", type="pil") | |
| run_button = gr.Button("Run") | |
| 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], | |
| fn = infer, | |
| inputs = [input_image, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
| outputs = [result, seed] | |
| ) | |
| demo.launch() |