Upload README.md
Browse files
README.md
CHANGED
@@ -18,9 +18,9 @@ tags:
|
|
18 |
| ![waitan](examples/waitan.jpeg) | ![gf](examples/gf.jpeg) | ![ssh](examples/ssh.jpeg) |
|
19 |
| ![cat](examples/cat.jpeg) | ![robot](examples/robot.jpeg) | ![castle](examples/castle.jpeg) |
|
20 |
|
21 |
-
大概是Huggingface 🤗社区首个开源的Stable diffusion 2 中文模型。该模型基于stable diffusion V2.1模型,在约500
|
22 |
|
23 |
-
Probably the first open sourced Chinese Stable Diffusion 2 model in Huggingface🤗 community. This model is finetuned based on stable diffusion V2.1 with 5M chinese style filtered data. Dataset is composed of several different chinese open source dataset such as [LAION-5B](https://laion.ai/blog/laion-5b/), [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/), [Zero](https://zero.so.com/) and some web data.
|
24 |
|
25 |
|
26 |
|
@@ -34,7 +34,7 @@ Text encoder is frozen [lyua1225/clip-huge-zh-75k-steps-bs4096](https://huggingf
|
|
34 |
|
35 |
#### Unet
|
36 |
|
37 |
-
|
38 |
|
39 |
Training on 5M chinese style filtered data for 150k steps. Exponential moving average(EMA) is applied to keep the original Stable Diffusion 2 drawing capability and reach a balance between chinese style and original drawing capability.
|
40 |
|
|
|
18 |
| ![waitan](examples/waitan.jpeg) | ![gf](examples/gf.jpeg) | ![ssh](examples/ssh.jpeg) |
|
19 |
| ![cat](examples/cat.jpeg) | ![robot](examples/robot.jpeg) | ![castle](examples/castle.jpeg) |
|
20 |
|
21 |
+
大概是Huggingface 🤗社区首个开源的Stable diffusion 2 中文模型。该模型基于[stable diffusion V2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1)模型,在约500万条的中国风格筛选过的中文数据上进行微调,数据来源于多个开源数据集如[LAION-5B](https://laion.ai/blog/laion-5b/), [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/), [Zero](https://zero.so.com/)和一些网络数据。
|
22 |
|
23 |
+
Probably the first open sourced Chinese Stable Diffusion 2 model in Huggingface🤗 community. This model is finetuned based on [stable diffusion V2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) with 5M chinese style filtered data. Dataset is composed of several different chinese open source dataset such as [LAION-5B](https://laion.ai/blog/laion-5b/), [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/), [Zero](https://zero.so.com/) and some web data.
|
24 |
|
25 |
|
26 |
|
|
|
34 |
|
35 |
#### Unet
|
36 |
|
37 |
+
在筛选过的的500万中文数据集上训练了150K steps,使用指数移动平均值(EMA)做原绘画能力保留,使模型能够在中文风格和原绘画能力之间获得权衡。
|
38 |
|
39 |
Training on 5M chinese style filtered data for 150k steps. Exponential moving average(EMA) is applied to keep the original Stable Diffusion 2 drawing capability and reach a balance between chinese style and original drawing capability.
|
40 |
|