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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 DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL |
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from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast |
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from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images |
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dtype = torch.bfloat16 |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device) |
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good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device) |
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device) |
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torch.cuda.empty_cache() |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 2048 |
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pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) |
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@spaces.GPU(duration=75) |
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def 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)): |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator().manual_seed(seed) |
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for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( |
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prompt=prompt, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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width=width, |
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height=height, |
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generator=generator, |
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output_type="pil", |
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good_vae=good_vae, |
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): |
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yield img, seed |
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examples = [ |
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"a tiny astronaut hatching from an egg on the moon", |
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"a cat holding a sign that says hello world", |
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"an anime illustration of a wiener schnitzel", |
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] |
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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 [dev] |
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12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) |
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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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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", |
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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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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.Examples( |
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examples = examples, |
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fn = infer, |
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inputs = [prompt], |
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outputs = [result, seed], |
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cache_examples="lazy" |
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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 = [prompt, 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() |
