Text-to-Image
Diffusers
stable-diffusion
stable-diffusion-diffusers
controlnet
control-lora-v3
diffusers-training
Instructions to use urllamadrama/my_lora_weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use urllamadrama/my_lora_weights with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("urllamadrama/my_lora_weights") pipe = StableDiffusionControlNetPipeline.from_pretrained( "SG161222/Realistic_Vision_V4.0_noVAE", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
sd-control-lora-v3-urllamadrama/my_lora_weights
These are control-lora-v3 weights trained on SG161222/Realistic_Vision_V4.0_noVAE 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]
- Downloads last month
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Model tree for urllamadrama/my_lora_weights
Base model
SG161222/Realistic_Vision_V4.0_noVAE