import gradio as gr import numpy as np import random import spaces import torch from diffusers import FluxPipeline, FluxTransformer2DModel,FlowMatchEulerDiscreteScheduler, AutoencoderKL from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast dtype = torch.bfloat16 device = "cuda" sd3_repo = "stabilityai/stable-diffusion-3-medium-diffusers" scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained (sd3_repo, subfolder="scheduler") text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype) tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype) text_encoder_2 = T5EncoderModel.from_pretrained(sd3_repo, subfolder="text_encoder_3", torch_dtype=dtype) tokenizer_2 = T5TokenizerFast.from_pretrained(sd3_repo, subfolder="tokenizer_3", torch_dtype=dtype) vae = AutoencoderKL.from_pretrained("diffusers-internal-dev/FLUX.1-schnell", subfolder="vae", torch_dtype=dtype) transformer = FluxTransformer2DModel.from_pretrained("diffusers-internal-dev/FLUX.1-schnell", subfolder="transformer", torch_dtype=dtype) device = "cuda" if torch.cuda.is_available() else "cpu" pipe = FluxPipeline( scheduler=scheduler, text_encoder=text_encoder, tokenizer=tokenizer, text_encoder_2=text_encoder_2, tokenizer_2=tokenizer_2, vae=vae, transformer=transformer, ).to("cuda") MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 2048 @spaces.GPU() 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 examples = [ "a tiny astronaut hatching from an egg on the moon", "a cat holding a sign that says hello world", "an anime illustration of a wiener schnitzel", ] css=""" #col-container { margin: 0 auto; max-width: 520px; } """ 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/2024/07/31/announcing-black-forest-labs/)] [[model](https://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()