URSA/scripts/app_ursa_t2i.py

# Copyright (c) 2024-present, BAAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
"""URSA T2I application."""

import argparse
import os

import gradio as gr
import numpy as np
import torch

from diffnext.pipelines import URSAPipeline
from diffnext.utils import export_to_image

# Switch to the allocator optimized for dynamic shape.
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"


def parse_args():
    """Parse arguments."""
    parser = argparse.ArgumentParser(description="Serve URSA T2I application")
    parser.add_argument("--model", default="", help="model path")
    parser.add_argument("--device", type=int, default=0, help="device index")
    parser.add_argument("--precision", default="float16", help="compute precision")
    return parser.parse_args()


def generate_image(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
):
    """Generate an image."""
    args = locals()
    seed = np.random.randint(2147483647) if randomize_seed else seed
    device = getattr(pipe, "_offload_device", pipe.device)
    generator = torch.Generator(device=device).manual_seed(seed)
    images = pipe(generator=generator, **args).frames
    return [export_to_image(image, quality=95) for image in images] + [seed]


css = """#col-container {margin: 0 auto; max-width: 1366px}"""
title = "Uniform Discrete Diffusion with Metric Path for Video Generation"
header = (
    "<div align='center'>"
    "<h2>Uniform Discrete Diffusion with Metric Path for Video Generation</h2>"
    "<h3><a href='https://arxiv.org/abs/2510.24717' target='_blank' rel='noopener'>[paper]</a>"
    "<a href='https://github.com/baaivision/URSA' target='_blank' rel='noopener'>[code]</a></h3>"
    "</div>"
)

examples = [
    "a selfie of an old man with a white beard.",
    "a woman with long hair next to a luminescent bird.",
    "a digital artwork of a cat styled in a whimsical fashion. The overall vibe is quirky and artistic.",  # noqa
    "a lone grizzly bear walks through a misty forest at dawn, sunlight catching its fur.",
    "a beautiful afghan women by red hair and green eyes.",
    "beautiful fireworks in the sky with red, white and blue.",
    "A dragon perched majestically on a craggy, smoke-wreathed mountain.",
    "A photo of llama wearing sunglasses standing on the deck of a spaceship with the Earth in the background.",  # noqa
    "Two pandas in fluffy slippers and bathrobes, lazily munching on bamboo.",
]


if __name__ == "__main__":
    args = parse_args()

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu", args.device)
    model_args = {"torch_dtype": getattr(torch, args.precision.lower()), "trust_remote_code": True}
    pipe = URSAPipeline.from_pretrained(args.model, **model_args).to(device)

    # Main Application.
    app = gr.Blocks(css=css, theme="origin").__enter__()
    container = gr.Column(elem_id="col-container").__enter__()
    _, main_row = gr.Markdown(header), gr.Row().__enter__()

    # Input.
    input_col = gr.Column().__enter__()
    prompt = gr.Text(
        label="Prompt",
        placeholder="Describe the video you want to generate",
        value="A lone grizzly bear walks through a misty forest at dawn, sunlight catching its fur.",  # noqa
        lines=5,
    )
    negative_prompt = gr.Text(
        label="Negative Prompt",
        placeholder="Describe what you don't want in the image",
        value="worst quality, low quality, inconsistent motion, static, still, blurry, jittery, distorted, ugly",  # noqa
        lines=5,
    )
    # fmt: off
    options = gr.Accordion("Options", open=False).__enter__()
    seed = gr.Slider(label="Seed", maximum=2147483647, step=1, value=0)
    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
    guidance_scale = gr.Slider(label="Guidance scale", minimum=1, maximum=10, step=0.1, value=7)
    with gr.Row():
        width = gr.Slider(label="Width", minimum=256, maximum=1024, step=32, value=1024)
        height = gr.Slider(label="Height", minimum=256, maximum=1024, step=32, value=1024)
        num_inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=25)  # noqa
    options.__exit__()
    generate_btn = gr.Button("Generate Image", variant="primary", size="lg")
    input_col.__exit__()
    # fmt: on

    # Results.
    result = gr.Image(label="Result", height=720, show_label=False)
    main_row.__exit__()

    # Examples.
    with gr.Row():
        gr.Examples(examples=examples, inputs=[prompt])

    # Events.
    container.__exit__()
    gr.on(
        triggers=[generate_btn.click, prompt.submit, negative_prompt.submit],
        fn=generate_image,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )
    app.__exit__(), app.launch(share=False)