mirror of
https://www.modelscope.cn/alimama-creative/FLUX.1-Turbo-Alpha.git
synced 2026-04-02 13:32:53 +08:00
81 lines
2.8 KiB
Markdown
81 lines
2.8 KiB
Markdown
---
|
||
license: other
|
||
license_name: flux-1-dev-non-commercial-license
|
||
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
|
||
language:
|
||
- en
|
||
base_model: black-forest-labs/FLUX.1-dev
|
||
library_name: diffusers
|
||
tags:
|
||
- Text-to-Image
|
||
- FLUX
|
||
- Stable Diffusion
|
||
pipeline_tag: text-to-image
|
||
---
|
||
|
||
<div style="display: flex; justify-content: center; align-items: center;">
|
||
<img src="./images/images_alibaba.png" alt="alibaba" style="width: 20%; height: auto; margin-right: 5%;">
|
||
<img src="./images/images_alimama.png" alt="alimama" style="width: 20%; height: auto;">
|
||
</div>
|
||
|
||
本仓库包含了由阿里妈妈创意团队开发的基于[FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)模型的8步蒸馏版。
|
||
|
||
# 介绍
|
||
|
||
该模型是基于FLUX.1-dev模型的8步蒸馏版lora。我们使用特殊设计的判别器来提高蒸馏质量。该模型可以用于T2I、Inpainting controlnet和其他FLUX相关模型。建议guidance_scale=3.5和lora_scale=1。我们的更低步数的版本将在后续发布。
|
||
|
||
- Text-to-Image.
|
||
|
||
|
||
|
||
- 配合[alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)。我们模型可以很好地适配Inpainting controlnet，并与原始输出保持相似的结果。
|
||
|
||
|
||
|
||
# 使用指南
|
||
## diffusers
|
||
该模型可以直接与diffusers一起使用
|
||
|
||
```python
|
||
import torch
|
||
from diffusers.pipelines import FluxPipeline
|
||
|
||
model_id = "black-forest-labs/FLUX.1-dev"
|
||
adapter_id = "alimama-creative/FLUX.1-Turbo-Alpha"
|
||
|
||
pipe = FluxPipeline.from_pretrained(
|
||
model_id,
|
||
torch_dtype=torch.bfloat16
|
||
)
|
||
pipe.to("cuda")
|
||
|
||
pipe.load_lora_weights(adapter_id)
|
||
pipe.fuse_lora()
|
||
|
||
prompt = "A DSLR photo of a shiny VW van that has a cityscape painted on it. A smiling sloth stands on grass in front of the van and is wearing a leather jacket, a cowboy hat, a kilt and a bowtie. The sloth is holding a quarterstaff and a big book."
|
||
image = pipe(
|
||
prompt=prompt,
|
||
guidance_scale=3.5,
|
||
height=1024,
|
||
width=1024,
|
||
num_inference_steps=8,
|
||
max_sequence_length=512).images[0]
|
||
```
|
||
|
||
## comfyui
|
||
|
||
- 文生图加速链路: [点击这里](./workflows/t2I_flux_turbo.json)
|
||
- Inpainting controlnet 加速链路: [点击这里](./workflows/alimama_flux_inpainting_turbo_8step.json)
|
||
|
||
|
||
# 训练细节
|
||
|
||
该模型在1M公开数据集和内部源图片上进行训练，这些数据美学评分6.3+而且分辨率大于800。我们使用对抗训练来提高质量，我们的方法将原始FLUX.1-dev transformer固定为判别器的特征提取器，并在每个transformer层中添加判别头网络。在训练期间，我们将guidance scale固定为3.5，并使用时间偏移量3。
|
||
|
||
混合精度: bf16
|
||
|
||
学习率: 2e-5
|
||
|
||
批大小: 64
|
||
|
||
训练分辨率: 1024x1024 |