| import gradio as gr | |
| gr.load("models/black-forest-labs/FLUX.1-schnell").launch(share=True) | |
| # import gradio as gr | |
| # import numpy as np | |
| # import random | |
| # import spaces | |
| # import torch | |
| # from diffusers import DiffusionPipeline | |
| # from transformers import pipeline | |
| # pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell") | |
| # def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, 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=0.0 | |
| # ).images[0] | |
| # return image, seed | |
| # with gr.Blocks(css=css) as demo: | |
| # with gr.Column(elem_id="col-container"): | |
| # gr.Markdown(f"""# FLUX.1 [schnell] | |
| # 12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation | |
| # [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)] | |
| # """) | |
| # with gr.Row(): | |
| # prompt = gr.Text( | |
| # label="Prompt", | |
| # show_label=False, | |
| # max_lines=1, | |
| # placeholder="Enter your prompt", | |
| # container=False, | |
| # ) | |
| # run_button = gr.Button("Run", scale=0) | |
| # 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=0, | |
| # ) | |
| # randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| # with gr.Row(): | |
| # width = gr.Slider( | |
| # label="Width", | |
| # minimum=256, | |
| # maximum=MAX_IMAGE_SIZE, | |
| # step=32, | |
| # value=1024, | |
| # ) | |
| # height = gr.Slider( | |
| # label="Height", | |
| # minimum=256, | |
| # maximum=MAX_IMAGE_SIZE, | |
| # step=32, | |
| # value=1024, | |
| # ) | |
| # with gr.Row(): | |
| # num_inference_steps = gr.Slider( | |
| # label="Number of inference steps", | |
| # minimum=1, | |
| # maximum=50, | |
| # step=1, | |
| # value=4, | |
| # ) | |
| # 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, num_inference_steps], | |
| # outputs = [result, seed] | |
| # ) | |
| # demo.launch() |