|
--- |
|
license: openrail |
|
base_model: runwayml/stable-diffusion-v1-5 |
|
tags: |
|
- art |
|
- controlnet |
|
- stable-diffusion |
|
--- |
|
|
|
# Controlnet |
|
|
|
Controlnet is an auxiliary model which augments pre-trained diffusion models with an additional conditioning. |
|
|
|
Controlnet comes with multiple auxiliary models, each which allows a different type of conditioning |
|
|
|
Controlnet's auxiliary models are trained with stable diffusion 1.5. Experimentally, the auxiliary models can be used with other diffusion models such as dreamboothed stable diffusion. |
|
|
|
The auxiliary conditioning is passed directly to the diffusers pipeline. If you want to process an image to create the auxiliary conditioning, external dependencies are required. |
|
|
|
Some of the additional conditionings can be extracted from images via additional models. We extracted these |
|
additional models from the original controlnet repo into a separate package that can be found on [github](https://github.com/patrickvonplaten/controlnet_aux.git). |
|
|
|
## HED Boundary |
|
|
|
### Diffusers |
|
|
|
Install the additional controlnet models package. |
|
|
|
```sh |
|
$ pip install git+https://github.com/patrickvonplaten/controlnet_aux.git |
|
``` |
|
|
|
```py |
|
from PIL import Image |
|
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler |
|
import torch |
|
from controlnet_aux import HEDdetector |
|
|
|
hed = HEDdetector.from_pretrained('lllyasviel/ControlNet') |
|
|
|
image = Image.open('images/man.png') |
|
|
|
image = hed(image) |
|
|
|
controlnet = ControlNetModel.from_pretrained( |
|
"fusing/stable-diffusion-v1-5-controlnet-hed", torch_dtype=torch.float16 |
|
) |
|
|
|
pipe = StableDiffusionControlNetPipeline.from_pretrained( |
|
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 |
|
) |
|
|
|
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) |
|
|
|
# Remove if you do not have xformers installed |
|
# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers |
|
# for installation instructions |
|
pipe.enable_xformers_memory_efficient_attention() |
|
|
|
pipe.enable_model_cpu_offload() |
|
|
|
image = pipe("oil painting of handsome old man, masterpiece", image, num_inference_steps=20).images[0] |
|
|
|
image.save('images/man_hed_out.png') |
|
``` |
|
|
|
 |
|
|
|
 |
|
|
|
 |
|
|
|
### Training |
|
|
|
The HED Edge model was trained on 3M edge-image, caption pairs. The model was trained for 600 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model. |
|
|
