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Running
on
Zero
Running
on
Zero
| import os | |
| import random | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| import spaces | |
| import uuid | |
| DESCRIPTION = """# SPRIGHT T2I | |
| [SPRIGHT T2I](https://spright-t2i.github.io/) is a framework to improve the spatial consistency of text-to-image models WITHOUT compromising their fidelity aspects. | |
| """ | |
| MAX_SEED = np.iinfo(np.int32).max | |
| CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES", "1") == "1" | |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768")) | |
| TOKEN = os.getenv("HF_TOKEN") | |
| pipe_id = "SPRIGHT-T2I/spright-t2i-sd2" | |
| pipe = DiffusionPipeline.from_pretrained( | |
| pipe_id, | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| token=TOKEN, | |
| ).to("cuda") | |
| def save_image(img): | |
| unique_name = str(uuid.uuid4()) + ".png" | |
| img.save(unique_name) | |
| return unique_name | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| def generate( | |
| prompt: str, | |
| seed: int = 0, | |
| width: int = 768, | |
| height: int = 768, | |
| guidance_scale: float = 7.5, | |
| num_inference_steps: int = 50, | |
| randomize_seed: bool = False, | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| seed = randomize_seed_fn(seed, randomize_seed) | |
| generator = torch.Generator().manual_seed(seed) | |
| image = pipe( | |
| prompt=prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| generator=generator, | |
| ).images[0] | |
| image_path = save_image(image) | |
| print(image_path) | |
| return [image_path], seed | |
| examples = [ | |
| "A cat next to a suitcase", | |
| "A candle on the left of a mouse", | |
| "A bag on the right of a dog", | |
| "A mouse on the top of a bowl", | |
| ] | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| gr.DuplicateButton( | |
| value="Duplicate Space for private use", | |
| elem_id="duplicate-button", | |
| visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
| ) | |
| with gr.Group(): | |
| 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.Gallery(label="Result", columns=1, show_label=False) | |
| with gr.Accordion("Advanced options", 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(visible=False): | |
| 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(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=1, | |
| maximum=20, | |
| step=0.1, | |
| value=7.5, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=10, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=prompt, | |
| outputs=[result, seed], | |
| fn=generate, | |
| cache_examples=CACHE_EXAMPLES, | |
| ) | |
| gr.on( | |
| triggers=[ | |
| prompt.submit, | |
| run_button.click, | |
| ], | |
| fn=generate, | |
| inputs=[prompt, seed, width, height, guidance_scale, num_inference_steps, randomize_seed], | |
| outputs=[result, seed], | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() | |