How to use from the
Use from the
Diffusers library
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
)

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 images_0) 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 images_1) 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 images_2) 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 images_3) 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 images_4) 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 images_5) 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 images_6) 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 images_7)

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
1
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for chaeyeonl33/controlnet_inpainting_shuffle_processedpromp_changemask_condition_random_mask

Adapter
(2743)
this model