Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use eduardoprea44/multiview-controlnet-yawangle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use eduardoprea44/multiview-controlnet-yawangle with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("eduardoprea44/multiview-controlnet-yawangle") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
controlnet-eduardoprea44/multiview-controlnet-yawangle
These are controlnet weights trained on stable-diffusion-v1-5/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below.
prompt: long sleeve upper rotated by 0 degrees
prompt: dress rotated by 60 degrees
prompt: short sleeve upper rotated by 180 degrees
prompt: long sleeve dress rotated by 270 degrees

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]
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Model tree for eduardoprea44/multiview-controlnet-yawangle
Base model
stable-diffusion-v1-5/stable-diffusion-v1-5