Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use dyamagishi/model_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dyamagishi/model_out with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("dyamagishi/model_out") pipe = StableDiffusionControlNetPipeline.from_pretrained( "Azher/Anything-v4.5-vae-fp16-diffuser", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
controlnet-dyamagishi/model_out
These are controlnet weights trained on Azher/Anything-v4.5-vae-fp16-diffuser with new type of conditioning.
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 dyamagishi/model_out
Base model
Azher/Anything-v4.5-vae-fp16-diffuser