Text-to-Image
Diffusers
Safetensors
stable-diffusion-xl
stable-diffusion-xl-diffusers
controlnet
diffusers-training
Instructions to use dyamagishi/human_place with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dyamagishi/human_place with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("dyamagishi/human_place") pipe = StableDiffusionControlNetPipeline.from_pretrained( "cagliostrolab/animagine-xl-3.1", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
controlnet-dyamagishi/human_place
These are controlnet weights trained on cagliostrolab/animagine-xl-3.1 with new type of conditioning. You can find some example images below.
prompt: outdoors, scenery, cloud, multiple_girls, sky, day, tree, grass, architecture, 2girls, blue_sky, building, standing, skirt, long_hair, mountain, east_asian_architecture, from_behind, castle, facing_away, black_skirt, school_uniform, pagoda, waterfall, white_shirt, white_hair, shirt, cloudy_sky, bag

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 dyamagishi/human_place
Base model
stabilityai/stable-diffusion-xl-base-1.0 Finetuned
Linaqruf/animagine-xl-2.0 Finetuned
cagliostrolab/animagine-xl-3.0 Finetuned
cagliostrolab/animagine-xl-3.1