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.
Modellquelle
- Art
- ControlNet
- Dateigröße
- 1,35 GB
- Basismodell
- runwayml/stable-diffusion-v1-5
- Version
- vdiffusion_pytorch_model
- Aktualisiert
- 7. Okt. 2026
- Downloads
- 2
- Quelle
- Hugging Face
Quellenbeschreibung
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]
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 7. Okt.
Modellartefakte
1 Artefakt
Modellartefakte
1 Artefaktcheckpoint-1000/controlnet/diffusion_pytorch_model.safetensors
safetensors · 1,35 GB · SHA-256 8dc60232830a…d456 · Hugging Face
Download fileQuellenauszüge
3 Auszüge
Quellenauszüge
3 Auszüge--- 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-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 ```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]
Source context: 2 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo chaeyeonl33/controlnet_inpainting_shuffle_processedpromp_changemask_condition_random_mask
Source context: 1 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo chaeyeonl33/controlnet_inpainting_shuffle_processedpromp_changemask_condition_random_mask
