Image-to-Image
Diffusers
Safetensors
StableDiffusionControlNetCCSRPipeline
image-super-resolution
controlnet
stable-diffusion
ccsr
Instructions to use kharma1/ccsr_v2_repost with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kharma1/ccsr_v2_repost with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("kharma1/ccsr_v2_repost") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base,isometricneko/stable-diffusion-v2.1-clone", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
File size: 711 Bytes
3ad5b5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"_class_name": "AutoencoderKL",
"_diffusers_version": "0.21.0",
"_name_or_path": "/home/notebook/data/group/LowLevelLLM/models/diffusion_models/stable-diffusion-2-1-base",
"act_fn": "silu",
"block_out_channels": [
128,
256,
512,
512
],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D"
],
"force_upcast": true,
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 2,
"norm_num_groups": 32,
"out_channels": 3,
"sample_size": 768,
"scaling_factor": 0.18215,
"up_block_types": [
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D"
]
}
|