Instructions to use hoa-quickloop/tryon_controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hoa-quickloop/tryon_controlnet with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("hoa-quickloop/tryon_controlnet") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| library_name: diffusers | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - controlnet | |
| inference: true | |
| base_model: stabilityai/stable-diffusion-2-1-base | |
| <!-- This model card has been generated automatically according to the information the training script had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # controlnet-hoa-quickloop/tryon_controlnet | |
| These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. | |
| ## Intended uses & limitations | |
| #### How to use | |
| ```python | |
| # TODO: add an example code snippet for running this diffusion pipeline | |
| ``` | |
| #### Limitations and bias | |
| [TODO: provide examples of latent issues and potential remediations] | |
| ## Training details | |
| [TODO: describe the data used to train the model] |