[WIP] [Core] Add support for ControlNet LoRA

[shauray8]

What does this PR do?

Adds support for ControlNet LoRA. continuing on @sayakpaul design #4899

Fixes #5800

Before submitting

• [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• [ ] Did you read the contributor guideline?
• [ ] Did you read our philosophy doc (important for complex PRs)?
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• [ ] Did you make sure to update the documentation with your changes? Here are the documentation guidelines, and here are tips on formatting docstrings.
• [ ] Did you write any new necessary tests?

The current design

Load things

from diffusers import (
    StableDiffusionXLControlNetPipeline, 
    ControlNetModel, 
    UNet2DConditionModel
)
import torch

pipe_id = "stabilityai/stable-diffusion-xl-base-1.0"
lora_id = "stabilityai/control-lora"
lora_filename = "control-LoRAs-rank128/control-lora-canny-rank128.safetensors"


unet = UNet2DConditionModel.from_pretrained(pipe_id, subfolder="unet", torch_dtype=torch.float16).to("cuda")
controlnet = ControlNetModel.from_unet(unet).to(device="cuda", dtype=torch.float16)
controlnet.load_lora_weights(lora_id, weight_name=lora_filename, controlnet_config=controlnet.config)

Infer

from diffusers import AutoencoderKL
from diffusers.utils import load_image, make_image_grid
from PIL import Image
import numpy as np
import cv2

prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
negative_prompt = "low quality, bad quality, sketches"

image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")

controlnet_conditioning_scale = 0.5  # recommended for good generalization

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    pipe_id,
    unet=unet,
    controlnet=controlnet,
    vae=vae,
    torch_dtype=torch.float16,
).to("cuda")

image = np.array(image)
image = cv2.Canny(image, 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
image = Image.fromarray(image)

images = pipe(
    prompt, negative_prompt=negative_prompt, image=image,
    controlnet_conditioning_scale=controlnet_conditioning_scale,
    num_images_per_prompt=4
).images

final_image = [image] + images
grid = make_image_grid(final_image, 1, 5)
grid

Who can review?

@sayakpaul can you provide me with access to the notebook you mentioned

[sayakpaul]

Here you go: https://colab.research.google.com/drive/1S-NDshYL7N4S1ugF9Y86d3SRY-Fe0Qe-?usp=sharing

[George0726]

Hi folks, are there any other updates on the PR?

[andypotato]

+1 for this

[sayakpaul]

We don't have the bandwidth for this at the moment and hence https://github.com/huggingface/diffusers/pull/4899

[github-actions[bot]]

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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[AbnerCSZ]

@sayakpaul @shauray8 @George0726 @andypotato Requesting assistance from everyone, thank you very much. controlnet = ControlNetModel.from_unet(unet).to(device="cuda", dtype=torch.float16) controlnet.load_lora_weights(lora_id, weight_name=lora_filename, controlnet_config=controlnet.config)

The following error occurred during runtime: AttributeError: 'ControlNetModel' object has no attribute 'load_lora_weights' Is this due to a version issue? May I know which version you are using? My running version is as follows: diffusers 0.27.0.dev0 transformers 4.35.2

[sayakpaul]

This feature hasn't been shipped yet.

[AbnerCSZ]

This feature hasn't been shipped yet.

@sayakpaul Is there a test version that has been implemented that can be used? thanks！