Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use swaghjal/pixelated__model_out_2_ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use swaghjal/pixelated__model_out_2_ep with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("swaghjal/pixelated__model_out_2_ep") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
controlnet = ControlNetModel.from_pretrained("swaghjal/pixelated__model_out_2_ep")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-base", controlnet=controlnet
)controlnet-swaghjal/pixelated__model_out_2_ep
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. You can find some example images below.
prompt: High-quality close-up dslr photo of man wearing a hat with trees in the background
prompt: Girl smiling, professional dslr photograph, dark background, studio lights, high quality
prompt: Portrait of a clown face, oil on canvas, bittersweet expression
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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 swaghjal/pixelated__model_out_2_ep
Base model
stabilityai/stable-diffusion-2-1-base