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| import spaces | |
| import gradio as gr | |
| import numpy as np | |
| import random | |
| import torch | |
| from diffusers import AuraFlowPipeline | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Initialize the AuraFlow v0.3 pipeline | |
| pipe = AuraFlowPipeline.from_pretrained( | |
| "fal/AuraFlow-v0.3", | |
| torch_dtype=torch.float16 | |
| ).to(device) | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 1024 | |
| def infer(prompt, | |
| negative_prompt="", | |
| seed=42, | |
| randomize_seed=False, | |
| width=1024, | |
| height=1024, | |
| guidance_scale=5.0, | |
| num_inference_steps=28, | |
| progress=gr.Progress(track_tqdm=True)): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| image = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| generator=generator | |
| ).images[0] | |
| return image, seed | |
| with gr.Blocks(theme="bethecloud/storj_theme") as demo: | |
| gr.HTML( | |
| """ | |
| <h1 style='text-align: center'> | |
| AuraFlow v0.3 | |
| </h1> | |
| """ | |
| ) | |
| gr.HTML( | |
| """ | |
| <h3 style='text-align: center'> | |
| Follow me for more! | |
| <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a> | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a> | |
| </h3> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| prompt = gr.Text(label="Prompt", placeholder="Enter your prompt") | |
| negative_prompt = gr.Text(label="Negative prompt", placeholder="Enter a negative prompt") | |
| seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| 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) | |
| guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=5.0) | |
| num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=28) | |
| run_button = gr.Button("Generate") | |
| with gr.Column(scale=1): | |
| result = gr.Image(label="Generated Image") | |
| seed_output = gr.Number(label="Seed used") | |
| run_button.click( | |
| fn=infer, | |
| inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
| outputs=[result, seed_output] | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| "A photo of a lavender cat", | |
| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
| "An astronaut riding a green horse", | |
| "A delicious ceviche cheesecake slice", | |
| ], | |
| inputs=prompt, | |
| ) | |
| demo.queue().launch(server_name="0.0.0.0", share=False) | |