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import gradio as gr |
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import numpy as np |
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import random |
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import spaces |
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import torch |
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from diffusers import FluxPriorReduxPipeline, FluxPipeline |
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from diffusers.utils import load_image |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 2048 |
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pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained( |
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"black-forest-labs/FLUX.1-Redux-dev", |
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torch_dtype=torch.bfloat16 |
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).to("cuda") |
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pipe = FluxPipeline.from_pretrained( |
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"black-forest-labs/FLUX.1-dev" , |
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text_encoder=None, |
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text_encoder_2=None, |
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torch_dtype=torch.bfloat16 |
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).to("cuda") |
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@spaces.GPU |
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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)): |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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pipe_prior_output = pipe_prior_redux(control_image) |
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images = pipe( |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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generator=torch.Generator("cpu").manual_seed(seed), |
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**pipe_prior_output, |
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).images[0] |
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return images, seed |
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css=""" |
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#col-container { |
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margin: 0 auto; |
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max-width: 960px; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(f"""# FLUX.1 Redux [dev] |
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An adapter for FLUX [dev] to create image variations |
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[[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)] |
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""") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Image to create variations", type="pil") |
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run_button = gr.Button("Run") |
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result = gr.Image(label="Result", show_label=False) |
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with gr.Accordion("Advanced Settings", open=False): |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(): |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance Scale", |
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minimum=1, |
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maximum=15, |
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step=0.1, |
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value=3.5, |
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) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=1, |
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maximum=50, |
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step=1, |
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value=28, |
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) |
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gr.on( |
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triggers=[run_button.click], |
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fn = infer, |
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inputs = [input_image, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], |
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outputs = [result, seed] |
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) |
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demo.launch() |
