字节开源 OneReward: 通过多任务人类偏好学习实现统一掩模引导的图像生成

简介

我们提出OneReward ------一种基于Qwen2.5-VL生成式奖励模型的全新视觉领域RLHF方法，通过增强多任务强化学习显著提升策略模型在多项子任务中的生成能力。基于OneReward，我们开发出Seedream 3.0 Fill 统一图像编辑模型，能高效处理图像填充、延展、物体消除和文字渲染等多样化任务，其表现超越Ideogram、Adobe Photoshop和FLUX Fill [Pro]等多家顶尖商业与开源系统。最后，基于FLUX Fill [dev]版本，我们激动地发布FLUX.1-Fill-dev-OneReward，该模型在修补与扩展绘画任务上性能优于闭源的FLUX Fill [Pro]，为未来统一图像编辑研究树立了强大的新基准。

Image Fill

Image Extend with Prompt

Image Extend without Prompt

Object Removal

快速开始

确保您的transformers版本≥4.51.3（支持Qwen2.5-VL）
安装最新版本的diffusers

bash 复制代码

pip install -U diffusers

以下内容包含一段代码示例，展示如何基于文本提示和输入遮罩使用模型生成图像，支持图像修复（inpaint）、图像扩展（outpaint）和对象擦除（object-removal）。由于模型已完全训练，需要使用带cfg的FluxFillCFGPipeline，您可以在我们的GitHub上找到相关代码。

python 复制代码

import torch
from diffusers.utils import load_image
from diffusers import FluxTransformer2DModel

from src.pipeline_flux_fill_with_cfg import FluxFillCFGPipeline

transformer_onereward = FluxTransformer2DModel.from_pretrained(
    "bytedance-research/OneReward",
    subfolder="flux.1-fill-dev-OneReward-transformer",
    torch_dtype=torch.bfloat16
)

pipe = FluxFillCFGPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Fill-dev", 
    transformer=transformer_onereward,
    torch_dtype=torch.bfloat16).to("cuda")

# Image Fill
image = load_image('assets/image.png')
mask = load_image('assets/mask_fill.png')
image = pipe(
    prompt='the words "ByteDance", and in the next line "OneReward"',
    negative_prompt="nsfw",
    image=image,
    mask_image=mask,
    height=image.height,
    width=image.width,
    guidance_scale=1.0,
    true_cfg=4.0,
    num_inference_steps=50,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save(f"image_fill.jpg")

模型

FLUX.1-Fill-dev[OneReward]，采用论文中算法1训练

python 复制代码

transformer_onereward = FluxTransformer2DModel.from_pretrained(
    "bytedance-research/OneReward",
    subfolder="flux.1-fill-dev-OneReward-transformer",
    torch_dtype=torch.bfloat16
)

pipe = FluxFillCFGPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Fill-dev", 
    transformer=transformer_onereward,
    torch_dtype=torch.bfloat16).to("cuda")

FLUX.1-Fill-dev[OneRewardDynamic]，采用论文中的算法2进行训练

python 复制代码

transformer_onereward_dynamic = FluxTransformer2DModel.from_pretrained(
    "bytedance-research/OneReward",
    subfolder="flux.1-fill-dev-OneRewardDynamic-transformer",
    torch_dtype=torch.bfloat16
)

pipe = FluxFillCFGPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Fill-dev", 
    transformer=transformer_onereward_dynamic,
    torch_dtype=torch.bfloat16).to("cuda")

Object Removal

python 复制代码

image = load_image('assets/image.png')
mask = load_image('assets/mask_remove.png')
image = pipe(
    prompt='remove',  # using fix prompt in object removal
    negative_prompt="nsfw",
    image=image,
    mask_image=mask,
    height=image.height,
    width=image.width,
    guidance_scale=1.0,
    true_cfg=4.0,
    num_inference_steps=50,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save(f"object_removal.jpg")

Image Extend with prompt

python 复制代码

image = load_image('assets/image2.png')
mask = load_image('assets/mask_extend.png')
image = pipe(
    prompt='Deep in the forest, surronded by colorful flowers',
    negative_prompt="nsfw",
    image=image,
    mask_image=mask,
    height=image.height,
    width=image.width,
    guidance_scale=1.0,
    true_cfg=4.0,
    num_inference_steps=50,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save(f"image_extend_w_prompt.jpg")

许可协议

代码采用 Apache 2.0 许可。模型采用 CC BY NC 4.0 许可。