Model reference · open weights
sd-controlnet-canny is an open-weight image model from lllyasviel, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
Controlnet - Canny Version ControlNet is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on Canny edges. It can be used in combination with Stable Diffusion. Model Details - Developed by: Lvmin Zhang, Maneesh Agrawala - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See also the article about the BLOOM Open RAIL license on which our license is based. - Resources for more information: GitHub Repository, Paper. - Cite as: @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } Introduction Controlnet was proposed in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang, Maneesh Agrawala. The abstract reads as follows: We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications. Released Checkpoints The authors released 8 different checkpoints, each trained with Stable Diffusion v1-5 on a different type of conditioning: Example It is recommended to use the checkpoint with Stable Diffusion v1-5 as the checkpoint has been t
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | lllyasviel |
|---|---|
| Type | Image models |
| Parameters (lead) | 361M |
| Variants | 1 |
| Runs with | diffusers |
| Based on | runwayml/stable-diffusion-v1-5 |
| Released | 2023-02-24 |
| Popularity | 27k downloads / month |
| Likes | 250 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| sd-controlnet-canny | 361M | BF16 | ~0.8 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys sd-controlnet-canny for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (sd-controlnet-canny 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":"sd-controlnet-canny","prompt":"a red bicycle","size":"1024x1024"}'
Licence
Open weights under openrail — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗