Spaces:
Runtime error
Runtime error
| import spaces | |
| import gradio as gr | |
| import numpy as np | |
| import random | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| dtype = torch.bfloat16 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device) | |
| 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().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 | |
| # Example prompt | |
| example_prompt = "A vibrant red origami crane on a white background, intricate paper folds, studio lighting" | |
| # Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# FLUX.1 [schnell] Image Generator") | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| gr.Markdown(""" | |
| ## About FLUX.1 [schnell] | |
| - Fast text-to-image model optimized for local development and personal use | |
| - Part of the FLUX.1 model family by Black Forest Labs | |
| - Open-source: Available under Apache 2.0 license | |
| - Supports resolutions between 0.1 and 2.0 megapixels | |
| - Outperforms many larger models in quality and prompt adherence | |
| - Uses advanced transformer architecture with flow matching techniques | |
| - Capable of generating high-quality images in just a few inference steps | |
| """) | |
| with gr.Column(scale=3): | |
| prompt = gr.Textbox(label="Prompt", placeholder="Enter your image description here...", value=example_prompt) | |
| run_button = gr.Button("Generate") | |
| result = gr.Image(label="Generated Image") | |
| gr.Markdown(""" | |
| ## Example Prompt | |
| Try this example prompt or modify it to see how FLUX.1 [schnell] performs: | |
| ``` | |
| A vibrant red origami crane on a white background, intricate paper folds, studio lighting | |
| ``` | |
| """) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| seed = gr.Slider(minimum=0, maximum=MAX_SEED, step=1, label="Seed", randomize=True) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| width = gr.Slider(minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, label="Width") | |
| height = gr.Slider(minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, label="Height") | |
| num_inference_steps = gr.Slider(minimum=1, maximum=50, step=1, value=4, label="Number of inference steps") | |
| gr.Markdown(""" | |
| **Note:** FLUX.1 [schnell] is optimized for speed and can produce high-quality results with just a few inference steps. | |
| Adjust the number of steps based on your speed/quality preference. More steps may improve quality but will increase generation time. | |
| """) | |
| gr.Markdown(""" | |
| ## Additional Information | |
| - FLUX.1 [schnell] is based on a hybrid architecture of multimodal and parallel diffusion transformer blocks | |
| - It supports various aspect ratios within the 0.1 to 2.0 megapixel range | |
| - The model uses bfloat16 precision for efficient computation | |
| - For optimal performance, running on a CUDA-enabled GPU is recommended | |
| - For more details and other FLUX.1 variants, visit [Black Forest Labs](https://blackforestlabs.ai) | |
| """) | |
| run_button.click( | |
| infer, | |
| inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps], | |
| outputs=[result, seed] | |
| ) | |
| demo.launch() |