ControlNet Test Base
These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. You can find some example images below.
Modellquelle
- Art
- ControlNet
- Dateigröße
- 4,66 GB
- Basismodell
- stabilityai/stable-diffusion-xl-base-1.0
- Version
- vdiffusion_pytorch_model
- Aktualisiert
- 7. Okt. 2026
- Downloads
- 7
- Quelle
- Hugging Face
Quellenbeschreibung
controlnet-taoshotaro/controlnet_test_base
These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. You can find some example images below.
prompt: red circle with blue background images_0) prompt: cyan circle with brown floral background images_1)
Intended uses & limitations
How to use
# 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]
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 7. Okt.
Modellartefakte
1 Artefakt
Modellartefakte
1 Artefaktcheckpoint-1000/controlnet/diffusion_pytorch_model.safetensors
safetensors · 4,66 GB · SHA-256 43fa3eb4368d…a448 · Hugging Face
Download fileQuellenauszüge
3 Auszüge
Quellenauszüge
3 Auszüge--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet - diffusers-training inference: true --- <!-- 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-taoshotaro/controlnet_test_base These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. You can find some example images below. prompt: red circle with blue background  prompt: cyan circle with brown floral background  ## 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]
Source context: 7 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo taoshotaro/controlnet_test_base
Source context: 1 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo taoshotaro/controlnet_test_base
