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| import gradio as gr | |
| import numpy as np | |
| import random | |
| import spaces | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Load the model in FP16 | |
| pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.float16) | |
| # Move the pipeline to GPU | |
| pipe = pipe.to(device) | |
| # Convert text encoders to full precision | |
| pipe.text_encoder = pipe.text_encoder.to(torch.float32) | |
| if hasattr(pipe, 'text_encoder_2'): | |
| pipe.text_encoder_2 = pipe.text_encoder_2.to(torch.float32) | |
| # Enable memory efficient attention if available | |
| if hasattr(pipe, 'enable_xformers_memory_efficient_attention'): | |
| pipe.enable_xformers_memory_efficient_attention() | |
| # Compile the UNet for potential speedups | |
| pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 2048 | |
| 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(device=device).manual_seed(seed) | |
| # Use full precision for text encoding | |
| with torch.no_grad(): | |
| text_inputs = pipe.tokenizer(prompt, return_tensors="pt").to(device) | |
| text_embeddings = pipe.text_encoder(text_inputs.input_ids)[0] | |
| # Use mixed precision for the rest of the pipeline | |
| with torch.inference_mode(), torch.autocast(device_type='cuda', dtype=torch.float16): | |
| image = pipe( | |
| prompt_embeds=text_embeddings, | |
| 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/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() |