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import gradio as gr |
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import numpy as np |
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import spaces |
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import torch |
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import random |
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from PIL import Image |
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from kontext_pipeline import FluxKontextPipeline |
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from diffusers import FluxTransformer2DModel |
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from diffusers.utils import load_image |
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from huggingface_hub import hf_hub_download |
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kontext_path = hf_hub_download(repo_id="diffusers/kontext", filename="kontext.safetensors") |
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MAX_SEED = np.iinfo(np.int32).max |
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transformer = FluxTransformer2DModel.from_single_file(kontext_path, torch_dtype=torch.bfloat16) |
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pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16).to("cuda") |
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@spaces.GPU |
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def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, 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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input_image = input_image.convert("RGB") |
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original_width, original_height = input_image.size |
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if original_width >= original_height: |
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new_width = 1024 |
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new_height = int(original_height * (new_width / original_width)) |
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else: |
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new_height = 1024 |
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new_width = int(original_width * (new_height / original_height)) |
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input_image_resized = input_image.resize((new_width, new_height), Image.LANCZOS) |
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image = pipe( |
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image=input_image_resized, |
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prompt=prompt, |
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guidance_scale=guidance_scale, |
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generator=torch.Generator().manual_seed(seed), |
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).images[0] |
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return image, 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: 520px; |
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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 Kontext [dev] |
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""") |
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input_image = gr.Image(label="Upload the image for editing", type="pil") |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0) |
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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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guidance_scale = gr.Slider( |
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label="Guidance Scale", |
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minimum=1, |
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maximum=10, |
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step=0.1, |
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value=2.5, |
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) |
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gr.on( |
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triggers=[run_button.click, prompt.submit], |
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fn = infer, |
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inputs = [input_image, prompt, seed, randomize_seed, guidance_scale], |
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outputs = [result, seed] |
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) |
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demo.launch() |
