badroobot/FLUX.1-dev

1import gradio as gr2import numpy as np3import random4import spaces5import torch6from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler7from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast8 9dtype = torch.bfloat1610device = "cuda" if torch.cuda.is_available() else "cpu"11 12pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)13 14MAX_SEED = np.iinfo(np.int32).max15MAX_IMAGE_SIZE = 204816 17@spaces.GPU(duration=190)18def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):19    if randomize_seed:20        seed = random.randint(0, MAX_SEED)21    generator = torch.Generator().manual_seed(seed)22    image = pipe(23        prompt = prompt, 24        width = width,25        height = height,26        num_inference_steps = num_inference_steps, 27        generator = generator,28        guidance_scale=guidance_scale29    ).images[0] 30    return image, seed31 32examples = [33    "a tiny astronaut hatching from an egg on the moon",34    "a cat holding a sign that says hello world",35    "an anime illustration of a wiener schnitzel",36]37 38css="""39#col-container {40    margin: 0 auto;41    max-width: 520px;42}43"""44 45with gr.Blocks(css=css) as demo:46    47    with gr.Column(elem_id="col-container"):48        gr.Markdown(f"""# FLUX.1 [dev]4912B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  50[[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)]51        """)52        53        with gr.Row():54            55            prompt = gr.Text(56                label="Prompt",57                show_label=False,58                max_lines=1,59                placeholder="Enter your prompt",60                container=False,61            )62            63            run_button = gr.Button("Run", scale=0)64        65        result = gr.Image(label="Result", show_label=False)66        67        with gr.Accordion("Advanced Settings", open=False):68            69            seed = gr.Slider(70                label="Seed",71                minimum=0,72                maximum=MAX_SEED,73                step=1,74                value=0,75            )76            77            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)78            79            with gr.Row():80                81                width = gr.Slider(82                    label="Width",83                    minimum=256,84                    maximum=MAX_IMAGE_SIZE,85                    step=32,86                    value=1024,87                )88                89                height = gr.Slider(90                    label="Height",91                    minimum=256,92                    maximum=MAX_IMAGE_SIZE,93                    step=32,94                    value=1024,95                )96            97            with gr.Row():98 99                guidance_scale = gr.Slider(100                    label="Guidance Scale",101                    minimum=1,102                    maximum=15,103                    step=0.1,104                    value=3.5,105                )106  107                num_inference_steps = gr.Slider(108                    label="Number of inference steps",109                    minimum=1,110                    maximum=50,111                    step=1,112                    value=28,113                )114        115        gr.Examples(116            examples = examples,117            fn = infer,118            inputs = [prompt],119            outputs = [result, seed],120            cache_examples="lazy"121        )122 123    gr.on(124        triggers=[run_button.click, prompt.submit],125        fn = infer,126        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],127        outputs = [result, seed]128    )129 130demo.launch()