import random import gradio as gr import numpy as np import spaces import torch from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast from gradio_imagefeed import ImageFeed dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 2048 LICENSE=f"""# Better UI for FLUX.1 [dev] [[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]""" CSS = "#col-container { margin: 0 auto; max-width: 900px; }" EXAMPLES = ["a tiny elephant hatching from a turtle egg in the palm of a human hand, highly detailed textures, close-up"] @spaces.GPU(duration=45) def infer(prompt, seed=99999, randomize_seed=True, width=896, height=1152, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)): if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator().manual_seed(seed) image = pipe( prompt = prompt, width = width, height = height, num_inference_steps = num_inference_steps, generator = generator, guidance_scale=guidance_scale).images[0] yield image, seed with gr.Blocks(css=CSS) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(LICENSE) with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=5, placeholder="Prompt", container=False) run_button = gr.Button("Run", scale=0) result = ImageFeed(label="Result", show_label=False) # result = gr.Image(label="Result", show_label=False) with gr.Accordion("Advanced Settings", open=False): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=random.randint(0, MAX_SEED)) randomize_seed = gr.Checkbox(label="Randomize", value=True) with gr.Row(): width = gr.Slider( label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=896) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1152) with gr.Row(): guidance_scale = gr.Slider( label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3) num_inference_steps = gr.Slider( label="Inference Steps", minimum=1, maximum=50, step=1, value=28) gr.Examples( examples=EXAMPLES, fn=infer, inputs=[prompt], outputs=[result, seed], cache_examples="lazy" ) gr.on( triggers=[run_button.click, prompt.submit], fn = infer, inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], outputs = [result, seed] ) demo.launch()