app.py

# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates. 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
# app.py
# 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.



import dataclasses
import json
from pathlib import Path
import gradio as gr
import torch
import openai
import os

from uno.flux.pipeline import UNOPipeline
from uno.utils.prompt_enhancer import enhance_prompt_with_chatgpt

os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"

openai.api_key = os.getenv("OPENAI_API_KEY")

from huggingface_hub import login


login(token=os.getenv("HUGGINGFACE_TOKEN"))

def get_examples(examples_dir: str = "assets/examples") -> list:
    examples = Path(examples_dir)
    ans = []
    for example in examples.iterdir():
        if not example.is_dir():
            continue
        with open(example / "config.json") as f:
            example_dict = json.load(f)

        example_list = [example_dict["useage"], example_dict["prompt"]]

        for key in ["image_ref1", "image_ref2", "image_ref3", "image_ref4"]:
            example_list.append(str(example / example_dict[key]) if key in example_dict else None)

        example_list.append(example_dict["seed"])
        ans.append(example_list)
    return ans

def create_demo(model_type: str, device: str = "cuda" if torch.cuda.is_available() else "cpu", offload: bool = False):
    pipeline = UNOPipeline(model_type, device, offload, only_lora=True, lora_rank=512)

    with gr.Blocks() as demo:
        gr.Markdown("# UNO by UNO team")
        gr.Markdown(
            """
            <div style="text-align: center; display: flex; justify-content: left; gap: 5px;">
                <a href="https://github.com/bytedance/UNO"><img alt="Build" src="https://img.shields.io/github/stars/bytedance/UNO"></a> 
                <a href="https://bytedance.github.io/UNO/"><img alt="Build" src="https://img.shields.io/badge/Project%20Page-UNO-yellow"></a> 
                <a href="https://arxiv.org/abs/2504.02160"><img alt="Build" src="https://img.shields.io/badge/arXiv%20paper-UNO-b31b1b.svg"></a>
                <a href="https://huggingface.co/bytedance-research/UNO"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=Model&color=orange"></a>
                <a href="https://huggingface.co/spaces/bytedance-research/UNO-FLUX"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=demo&color=orange"></a>
            </div>
            """
        )

        with gr.Row():
            with gr.Column():
                prompt = gr.Textbox(label="Prompt", value="handsome woman in the city")
                with gr.Row():
                    image_prompt1 = gr.Image(label="Ref Img1", type="pil")
                    image_prompt2 = gr.Image(label="Ref Img2", type="pil")
                    image_prompt3 = gr.Image(label="Ref Img3", type="pil")
                    image_prompt4 = gr.Image(label="Ref Img4", type="pil")

                with gr.Row():
                    with gr.Column():
                        width = gr.Slider(512, 2048, 512, step=16, label="Generation Width")
                        height = gr.Slider(512, 2048, 512, step=16, label="Generation Height")
                    with gr.Column():
                        gr.Markdown("📌 Trained on 512x512. Larger size = better quality, but less stable.")

                with gr.Accordion("Advanced Options", open=False):
                    with gr.Row():
                        num_steps = gr.Slider(1, 50, 25, step=1, label="Number of steps")
                        guidance = gr.Slider(1.0, 5.0, 4.0, step=0.1, label="Guidance")
                        seed = gr.Number(-1, label="Seed (-1 for random)")
                        num_outputs = gr.Slider(1, 5, 5, step=1, label="Number of Enhanced Prompts / Images")

                generate_btn = gr.Button("Generate Enhanced Images")

            with gr.Column():
                outputs = []
                for i in range(5):
                    outputs.append(gr.Image(label=f"Image {i+1}"))
                    outputs.append(gr.Textbox(label=f"Enhanced Prompt {i+1}"))

        def run_generation(prompt, width, height, guidance, num_steps, seed,
                           img1, img2, img3, img4, num_outputs):
            uploaded_images = [img for img in [img1, img2, img3, img4] if img is not None]

            print(f"\n📥 [DEBUG] User prompt: {prompt}")
            prompts = enhance_prompt_with_chatgpt(
                user_prompt=prompt,
                num_prompts=num_outputs,
                reference_images=uploaded_images
            )

            print(f"\n🧠 [DEBUG] Final Prompt List (len={len(prompts)}):")
            for idx, p in enumerate(prompts):
                print(f"  [{idx+1}] {p}")

            while len(prompts) < num_outputs:
                prompts.append(prompt)

            results = []
            for i in range(num_outputs):
                try:
                    seed_val = int(seed) if seed != -1 else torch.randint(0, 10**8, (1,)).item()
                    print(f"🧪 [DEBUG] Using seed: {seed_val} for image {i+1}")
                    gen_image, _ = pipeline.gradio_generate(
                        prompt=prompts[i],
                        width=width,
                        height=height,
                        guidance=guidance,
                        num_steps=num_steps,
                        seed=seed_val,
                        image_prompt1=img1,
                        image_prompt2=img2,
                        image_prompt3=img3,
                        image_prompt4=img4,
                    )
                    print(f"✅ [DEBUG] Image {i+1} generated using prompt: {prompts[i]}")
                    results.append(gen_image)
                    results.append(prompts[i])
                except Exception as e:
                    print(f"❌ [ERROR] Failed to generate image {i+1}: {e}")
                    results.append(None)
                    results.append(f"⚠️ Failed to generate: {e}")

            # Pad to 10 outputs: 5 image + prompt pairs
            while len(results) < 10:
                results.append(None if len(results) % 2 == 0 else "")

            return results

        generate_btn.click(
            fn=run_generation,
            inputs=[
                prompt, width, height, guidance, num_steps,
                seed, image_prompt1, image_prompt2, image_prompt3, image_prompt4, num_outputs
            ],
            outputs=outputs
        )

        example_text = gr.Text("", visible=False, label="Case For:")
        examples = get_examples("./assets/examples")

        gr.Examples(
            examples=examples,
            inputs=[
                example_text, prompt,
                image_prompt1, image_prompt2, image_prompt3, image_prompt4,
                seed, outputs[0]
            ],
        )

    return demo

if __name__ == "__main__":
    from typing import Literal
    from transformers import HfArgumentParser

    @dataclasses.dataclass
    class AppArgs:
        name: Literal["flux-dev", "flux-dev-fp8", "flux-schnell"] = "flux-dev"
        device: Literal["cuda", "cpu"] = "cuda" if torch.cuda.is_available() else "cpu"
        offload: bool = dataclasses.field(
            default=False,
            metadata={"help": "If True, sequentially offload unused models to CPU"}
        )
        port: int = 7860

    parser = HfArgumentParser([AppArgs])
    args = parser.parse_args_into_dataclasses()[0]

    demo = create_demo(args.name, args.device, args.offload)
    demo.launch(server_port=args.port)