Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use yeq6x/CN_SMPL_sd15_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yeq6x/CN_SMPL_sd15_v2 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("yeq6x/CN_SMPL_sd15_v2") pipe = StableDiffusionControlNetPipeline.from_pretrained( "yeq6x/v1-5-pruned_pls_SMPL_LoRA_sd1.5_v1.fp16", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
controlnet-yeq6x/CN_SMPL_sd15_v2
These are controlnet weights trained on yeq6x/v1-5-pruned_pls_SMPL_LoRA_sd1.5_v1.fp16 with new type of conditioning. You can find some example images below.
prompt: yellow smpl, hand, black background
prompt: blue smpl, hand, black background
prompt: green smpl, hand, black background
prompt: orange smpl, full body, black background
prompt: yellow smpl, full body, black background
prompt: yellow smpl, full body, black background

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 yeq6x/CN_SMPL_sd15_v2
Base model
yeq6x/v1-5-pruned_SMPL_LoRA_sd1.5_v1.fp16