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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() |