black-forest-labs/FLUX.1-dev

1import gradio as gr2import numpy as np3import random4import spaces5import torch6from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL7from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast8from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images9 10dtype = torch.bfloat1611device = "cuda" if torch.cuda.is_available() else "cpu"12 13taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)14good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)15pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)16torch.cuda.empty_cache()17 18MAX_SEED = np.iinfo(np.int32).max19MAX_IMAGE_SIZE = 204820 21pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)22 23@spaces.GPU(duration=75)24def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):25    if randomize_seed:26        seed = random.randint(0, MAX_SEED)27    generator = torch.Generator().manual_seed(seed)28    29    for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(30            prompt=prompt,31            guidance_scale=guidance_scale,32            num_inference_steps=num_inference_steps,33            width=width,34            height=height,35            generator=generator,36            output_type="pil",37            good_vae=good_vae,38        ):39            yield img, seed40    41examples = [42    "a tiny astronaut hatching from an egg on the moon",43    "a cat holding a sign that says hello world",44    "an anime illustration of a wiener schnitzel",45]46 47css="""48#col-container {49    margin: 0 auto;50    max-width: 520px;51}52"""53 54with gr.Blocks(css=css) as demo:55    56    with gr.Column(elem_id="col-container"):57        gr.Markdown(f"""> FLUX.2 [dev] is here! ✨ [Try it out here](https://huggingface.co/spaces/black-forest-labs/FLUX.2-dev)58 59# FLUX.1 [dev]6012B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  61[[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)]62        """)63        64        with gr.Row():65            66            prompt = gr.Text(67                label="Prompt",68                show_label=False,69                max_lines=1,70                placeholder="Enter your prompt",71                container=False,72            )73            74            run_button = gr.Button("Run", scale=0)75        76        result = gr.Image(label="Result", show_label=False)77        78        with gr.Accordion("Advanced Settings", open=False):79            80            seed = gr.Slider(81                label="Seed",82                minimum=0,83                maximum=MAX_SEED,84                step=1,85                value=0,86            )87            88            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)89            90            with gr.Row():91                92                width = gr.Slider(93                    label="Width",94                    minimum=256,95                    maximum=MAX_IMAGE_SIZE,96                    step=32,97                    value=1024,98                )99                100                height = gr.Slider(101                    label="Height",102                    minimum=256,103                    maximum=MAX_IMAGE_SIZE,104                    step=32,105                    value=1024,106                )107            108            with gr.Row():109 110                guidance_scale = gr.Slider(111                    label="Guidance Scale",112                    minimum=1,113                    maximum=15,114                    step=0.1,115                    value=3.5,116                )117  118                num_inference_steps = gr.Slider(119                    label="Number of inference steps",120                    minimum=1,121                    maximum=50,122                    step=1,123                    value=28,124                )125        126        gr.Examples(127            examples = examples,128            fn = infer,129            inputs = [prompt],130            outputs = [result, seed],131            cache_examples="lazy"132        )133 134    gr.on(135        triggers=[run_button.click, prompt.submit],136        fn = infer,137        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],138        outputs = [result, seed]139    )140 141demo.launch()