Instructions to use chaeyeonl33/controlnet_inpainting_shuffle_processedpromp_changemask_condition_random_mask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chaeyeonl33/controlnet_inpainting_shuffle_processedpromp_changemask_condition_random_mask with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("chaeyeonl33/controlnet_inpainting_shuffle_processedpromp_changemask_condition_random_mask") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
controlnet = ControlNetModel.from_pretrained("chaeyeonl33/controlnet_inpainting_shuffle_processedpromp_changemask_condition_random_mask")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet
)controlnet-chaeyeonl33/controlnet_inpainting_shuffle_processedpromp_changemask_condition_random_mask
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below.
prompt: s A1,6 15,bp 3000,4 15,1 20,sp 3000,ge 934,5 15,3 15,ga 156,7 15,2 20,time 200
prompt: 6 11,s A1,3 11,4 11,ge 900,1 16,7 11,5 11,sp 3000,time 200,2 16,bp 2800,ga 190
prompt: 5 15,4 15,time 200,bp 3000,s A1,sp 3000,ga 156,7 15,ge 934,1 20,6 15,2 20,3 15
prompt: bp 3000,ga 190,time 200,3 15,2 20,5 15,7 15,1 20,ge 900,4 15,6 15,sp 3000,s A1
prompt: 2 20,s A1,3 15,7 15,sp 3000,4 15,1 20,ge 900,5 15,bp 3000,time 200,6 15,ga 190
prompt: 5 15,4 15,sp 3000,s A1,3 15,7 15,ga 190,2 20,ge 900,6 15,1 20,time 200,bp 3000
prompt: sp 3000,time 200,5 11,4 11,ge 867.5,7 11,3 11,bp 3000,2 16,s A1,ga 222.5,6 11,1 16
prompt: sp 3000,time 200,bp 3000,1 20,2 20,7 15,5 15,s A1,ga 156,3 15,ge 934,4 15,6 15

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 chaeyeonl33/controlnet_inpainting_shuffle_processedpromp_changemask_condition_random_mask
Base model
runwayml/stable-diffusion-v1-5