Model reference · open weights
Kolors-ControlNet-Depth is an open-weight image model from Kwai-Kolors. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Maker | Kwai-Kolors |
|---|---|
| Type | Image models |
| Task | Text→image |
| Parameters (lead) | 1.3B |
| Runs with | diffusers |
| Released | 2024-07-24 |
| Popularity | 183 downloads / month |
| Licence | Open weights |
About
We provide two ControlNet weights and inference code based on Kolors-Basemodel: Canny and Depth. You can find some example images below.
1、ControlNet Demos
2、ControlNet and IP-Adapter-Plus Demos
We also support joint inference code between Kolors-IPadapter and Kolors-ControlNet.
To evaluate the performance of models, we compiled a test set of more than 200 images and text prompts. We invite several image experts to provide fair ratings for the generated results of different models. The experts rate the generated images based on four criteria: visual appeal, text faithfulness, conditional controllability, and overall satisfaction. Conditional controllability measures controlnet's ability to preserve spatial structure, while the other criteria follow the evaluation standards of BaseModel. The specific results are summarized in the table below, where Kolors-ControlNet achieved better performance in various criterias.
1、Canny
| Model | Average Overall Satisfaction | Average Visual Appeal | Average Text Faithfulness | Average Conditional Controllability |
|---|---|---|---|---|
| SDXL-ControlNet-Canny | 3.14 | 3.63 | 4.37 | 2.84 |
| Kolors-ControlNet-Canny | 4.06 | 4.64 | 4.45 | 3.52 |
2、Depth
| Model | Average Overall Satisfaction | Average Visual Appeal | Average Text Faithfulness | Average Conditional Controllability |
|---|---|---|---|---|
| SDXL-ControlNet-Depth | 3.35 | 3.77 | 4.26 | 4.5 |
| Kolors-ControlNet-Depth | 4.12 | 4.12 | 4.62 | 4.6 |
The dependencies and installation are basically the same as the Kolors-BaseModel.
# Canny - ControlNet
huggingface-cli download --resume-download Kwai-Kolors/Kolors-ControlNet-Canny --local-dir weights/Kolors-ControlNet-Canny
# Depth - ControlNet
huggingface-cli download --resume-download Kwai-Kolors/Kolors-ControlNet-Depth --local-dir weights/Kolors-ControlNet-Depth
If you intend to utilize the depth estimation network, please make sure to download its corresponding model weights.
huggingface-cli download lllyasviel/Annotators ./dpt_hybrid-midas-501f0c75.pt --local-dir ./controlnet/annotator/ckpts
a. Using canny ControlNet:
python ./controlnet/sample_controlNet.py ./controlnet/assets/woman_1.png 一个漂亮的女孩,高品质,超清晰,色彩鲜艳,超高分辨率,最佳品质,8k,高清,4K Canny
python ./controlnet/sample_controlNet.py ./controlnet/assets/dog.png 全景,一只可爱的白色小狗坐在杯子里,看向镜头,动漫风格,3d渲染,辛烷值渲染 Canny
# The image will be saved to "controlnet/outputs/"
b. Using depth ControlNet:
python ./controlnet/sample_controlNet.py ./controlnet/assets/woman_2.png 新海诚风格,丰富的色彩,穿着绿色衬衫的女人站在田野里,唯美风景,清新明亮,斑驳的光影,最好的质量,超细节,8K画质 Depth
python ./controlnet/sample_controlNet.py ./controlnet/assets/bird.png 一只颜色鲜艳的小鸟,高品质,超清晰,色彩鲜艳,超高分辨率,最佳品质,8k,高清,4K Depth
# The image will be saved to "controlnet/outputs/"
c. Using depth ControlNet + IP-Adapter-Plus:
If you intend to utilize the kolors-ip-adapter-plus, please make sure to download its corresponding model weights.
python ./controlnet/sample_controlNet_ipadapter.py ./controlnet/assets/woman_2.png ./ipadapter/asset/2.png 一个红色头发的女孩,唯美风景,清新明亮,斑驳的光影,最好的质量,超细节,8K画质 Depth
# The image will be saved to "controlnet/outputs/"
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys kolors-controlnet-depth for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (kolors-controlnet-depth below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/images/generations \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"kolors-controlnet-depth","prompt":"a red bicycle","size":"1024x1024"}'
Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.