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
| { | |
| "_class_name": "StableDiffusionControlNetCCSRPipeline", | |
| "_class_module": "ccsr.pipelines.pipeline_ccsr", | |
| "_diffusers_version": "0.39.0", | |
| "controlnet": [ | |
| "ccsr.models.controlnet", | |
| "ControlNetCCSRModel" | |
| ], | |
| "feature_extractor": [ | |
| "transformers", | |
| "CLIPImageProcessor" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "DDPMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } |