ControlNet Inpainting 104

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]

--- base_model: runwayml/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet - diffusers-training --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # controlnet-nqaKU/controlnet_inpainting_104 These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. prompt: bp 3000,3 13,6 13,2 18,ge 900,s A1,ga 190,time 200,sp 3000,5 13,1 18,7 13,4 13  prompt: 1 16,7 11,sp 3000,2 16,3 11,5 11,4 11,ga 190,s A1,time 200,bp 2400,ge 900,6 11  prompt: time 200,ga 222.5,bp 3000,6 11,5 11,2 16,4 11,1 16,s A1,3 11,sp 3000,ge 867.5,7 11  prompt: 4 13,bp 3000,3 13,time 200,5 13,s A1,1 18,7 13,ge 900,6 13,ga 190,sp 3000,2 18  prompt: 7 15,sp 3000,2 20,s A1,1 20,3 15,4 15,5 15,time 200,bp 3000,6 15,ga 156,ge 934  prompt: bp 3000,6 15,4 15,ga 222.5,time 200,sp 3000,5 15,ge 867.5,3 15,7 15,1 20,s A1,2 20  prompt: 5 13,7 13,time 200,6 13,ge 900,1 18,3 13,4 13,2 18,s A1,sp 3000,bp 3000,ga 190  prompt: 4 11,1 16,3 11,5 11,s A1,2 16,6 11,7 11,ge 900,ga 190,bp 3000,time 200,sp 3000  ## Intended uses & limitations #### How to use ```python # 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]