Add ControlNetUnion

[hlky]

What does this PR do?

Model

Original code
Weights

Changes include:

• Fixup imports.
  • ControlNetConditioningEmbedding, ControlNetOutput, zero_module imported from controlnet.
• ResidualAttentionMlp instead of nn.Sequential/OrderedDict.
• Remove subclassed nn.LayerNorm.
• Add num_trans_channel etc to __init__/config.
• transformer_layers as nn.ModuleList instead of nn.Sequential.
• Fix copies and other comparisons against ControlNetModel (type hints, etc)

Pipelines

StableDiffusionXLControlNetUnionPipeline

• Fixup compared to StableDiffusionXLControlNetPipeline

StableDiffusionXLControlNetUnionImg2ImgPipeline

• Fixup compared to StableDiffusionXLControlNetImg2ImgPipeline

StableDiffusionXLControlNetUnionInpaintPipeline

• Fixup compared to StableDiffusionXLControlNetInpaintPipeline

ControlNetUnionInput/ControlNetUnionInputProMax

The original requires pipeline input where image_list is a list like [0, 0, 0, controlnet_img, 0, 0] with the ControlNet conditioning image in the correct index, and union_control_type as a tensor like torch.Tensor([0, 0, 0, 1, 0, 0]) with the same indexing.

This has been replaced by ControlNetUnionInput/ControlNetUnionInputProMax, these allow the user to provide a single input like:

ControlNetUnionInput(
openpose=...,
canny=...,
)

Appropriate control_type tensor is produced in the pipeline.

Example

Note: a better example prompt/image would be great

pip install controlnet_aux

importtorchimportrandomfromdiffusersimportAutoencoderKLfromcontrolnet_auximportLineartAnimeDetectorfromdiffusersimport (
EulerAncestralDiscreteScheduler,
ControlNetUnionModel,
StableDiffusionXLControlNetUnionPipeline,
)
fromdiffusers.models.controlnetsimportControlNetUnionInputfromdiffusers.utilsimportload_imagedevice=torch.device("cuda:0")
scheduler=EulerAncestralDiscreteScheduler.from_pretrained(
"gsdf/CounterfeitXL", subfolder="scheduler"
)
vae=AutoencoderKL.from_pretrained(
"gsdf/CounterfeitXL", subfolder="vae", torch_dtype=torch.float16
)
controlnet_model=ControlNetUnionModel.from_pretrained(
"xinsir/controlnet-union-sdxl-1.0", torch_dtype=torch.float16, use_safetensors=True
)
pipe: StableDiffusionXLControlNetUnionPipeline= (
StableDiffusionXLControlNetUnionPipeline.from_pretrained(
"gsdf/CounterfeitXL",
controlnet=controlnet_model,
vae=vae,
torch_dtype=torch.float16,
variant="fp16",
scheduler=scheduler,
)
)
pipe=pipe.to(device)
processor=LineartAnimeDetector.from_pretrained("lllyasviel/Annotators").to(device)
prompt="A cat"controlnet_img=load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png"
).resize((1024, 1024))
controlnet_img=processor(controlnet_img, output_type="pil")
seed=random.randint(0, 2147483647)
generator=torch.Generator("cuda").manual_seed(seed)
union_input=ControlNetUnionInput(
canny=controlnet_img,
)
image=pipe(
prompt=prompt,
image=union_input,
generator=generator,
height=1024,
width=1024,
num_inference_steps=30,
).images[0]
image.save("anime_lineart.png")

Example output

StableDiffusionXLControlNetUnionImg2ImgPipeline example (`tile`)

fromdiffusersimport (
StableDiffusionXLControlNetUnionImg2ImgPipeline,
ControlNetUnionModel,
AutoencoderKL,
)
fromdiffusers.models.controlnetsimportControlNetUnionInputProMaxfromdiffusers.utilsimportload_imageimporttorchfromPILimportImageimportnumpyasnpprompt="A cat"# download an imageimage=load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png"
)
# initialize the models and pipelinecontrolnet=ControlNetUnionModel.from_pretrained(
"brad-twinkl/controlnet-union-sdxl-1.0-promax", torch_dtype=torch.float16
)
vae=AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
)
pipe=StableDiffusionXLControlNetUnionImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
torch_dtype=torch.float16,
).to("cuda")
height=image.heightwidth=image.widthratio=np.sqrt(1024.0*1024.0/ (width*height))
# 3 * 3 upscale correspond to 16 * 3 multiply, 2 * 2 correspond to 16 * 2 multiply and so on.scale_image_factor=3base_factor=16factor=scale_image_factor*base_factorW, H=int(width*ratio) //factor*factor, int(height*ratio) //factor*factorimage=image.resize((W, H))
target_width=W//scale_image_factortarget_height=H//scale_image_factorimages= []
crops_coords_list= [
(0, 0),
(0, width//2),
(height//2, 0),
(width//2, height//2),
0,
0,
0,
0,
0,
]
foriinrange(scale_image_factor):
forjinrange(scale_image_factor):
left=j*target_widthtop=i*target_heightright=left+target_widthbottom=top+target_heightcropped_image=image.crop((left, top, right, bottom))
cropped_image=cropped_image.resize((W, H))
images.append(cropped_image)
# set ControlNetUnion inputresult_images= []
forsub_img, crops_coordsinzip(images, crops_coords_list):
union_input=ControlNetUnionInputProMax(
tile=sub_img,
)
new_width, new_height=W, Hout=pipe(
prompt=[prompt] *1,
image=sub_img,
control_image_list=union_input,
width=new_width,
height=new_height,
num_inference_steps=30,
crops_coords_top_left=(W, H),
target_size=(W, H),
original_size=(W*2, H*2),
)
result_images.append(out.images[0])
new_im=Image.new(
"RGB", (new_width*scale_image_factor, new_height*scale_image_factor)
)
new_im.paste(result_images[0], (0, 0))
new_im.paste(result_images[1], (new_width, 0))
new_im.paste(result_images[2], (new_width*2, 0))
new_im.paste(result_images[3], (0, new_height))
new_im.paste(result_images[4], (new_width, new_height))
new_im.paste(result_images[5], (new_width*2, new_height))
new_im.paste(result_images[6], (0, new_height*2))
new_im.paste(result_images[7], (new_width, new_height*2))
new_im.paste(result_images[8], (new_width*2, new_height*2))
new_im.save("upscaled.png")

Tile example output

StableDiffusionXLControlNetUnionInpaintPipeline example (`inpaint`, `outpaint`)

Note: a better example prompt/image would be great, non-optimal results in this example due to resized image+mask

fromdiffusersimportStableDiffusionXLControlNetUnionInpaintPipeline, ControlNetUnionModel, AutoencoderKLfromdiffusers.models.controlnetsimportControlNetUnionInputProMaxfromdiffusers.utilsimportload_imageimporttorchimportnumpyasnpfromPILimportImageprompt="A cat"# download an imageimage=load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint/overture-creations-5sI6fQgYIuo.png"
).resize((1024, 1024))
mask=load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
).resize((1024, 1024))
# initialize the models and pipelinecontrolnet=ControlNetUnionModel.from_pretrained(
"brad-twinkl/controlnet-union-sdxl-1.0-promax", torch_dtype=torch.float16
)
vae=AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
)
pipe=StableDiffusionXLControlNetUnionInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
torch_dtype=torch.float16,
variant="fp16",
)
pipe.enable_model_cpu_offload()
controlnet_img=image.copy()
controlnet_img_np=np.array(controlnet_img)
mask_np=np.array(mask)
controlnet_img_np[mask_np>0] =0controlnet_img=Image.fromarray(controlnet_img_np)
union_input=ControlNetUnionInputProMax(
repaint=controlnet_img,
)
# generate imageimage=pipe(prompt, image=image, mask_image=mask, control_image_list=union_input).images[0]
image.save("inpaint.png")

Inpaint example output

Fixes#9772

Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

[hlky]

Not sure why the whitespace changed here 🤷‍♂️

[hlky]

Ready for review.

[HuggingFaceDocBuilderDev]

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[yiyixuxu]

thanks!!

[yiyixuxu]

cc @asomoza
can you help test these controlnet union pipelines a bit?

[hlky]

@asomoza There are more test examples in the original repo. Note that the inpaint pipeline is only used with promax version for inpaint/outpaint, and img2img pipeline is only used with promax version for tile superresolution.

[asomoza]

I'll test as soon as I can, this is the controlnet that I use the most on a daily basis so I'll love the idea to have it in the core.

Just a bit more information on the official repository, first I tried using it without the need of a different repository, which lead me to use the code in this space and I asked the model author to do a different model repository for the promax version without any response, so at the end I also had to make my ownto avoid the custom code to just load it.

There's quite a bit of discussion about changing the model naming but the author never answers any of them.

Also the promax version came after the original one, but once we have it, there's not much incentive on using the older when the newer one does the same and more.

[asomoza]

I did the tests and it works as expected, really nice!

an example of the diffusers image fill with this:

source	mask	result

Code

importrandomimporttorchfromPILimportImage, ImageChopsfromdiffusersimportAutoencoderKL, ControlNetUnionModel, StableDiffusionXLControlNetUnionPipelinefromdiffusers.models.controlnetsimportControlNetUnionInputProMaxfromdiffusers.utilsimportload_imagedevice=torch.device("cuda:0")
source_image=load_image(
"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/diffusers_fill/jefferson-sees-OCQjiB4tG5c-unsplash.jpg"
)
width, height=source_image.sizemin_dimension=min(width, height)
left= (width-min_dimension) /2top= (height-min_dimension) /2right= (width+min_dimension) /2bottom= (height+min_dimension) /2final_source=source_image.crop((left, top, right, bottom))
final_source=final_source.resize((1024, 1024), Image.LANCZOS).convert("RGBA")
final_source.save("source.png")
mask=load_image(
"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/diffusers_fill/car_mask_good.png"
).convert("L")
binary_mask=mask.point(lambdap: 255ifp>0else0)
inverted_mask=ImageChops.invert(binary_mask)
alpha_image=Image.new("RGBA", final_source.size, (0, 0, 0, 0))
cnet_image=Image.composite(final_source, alpha_image, inverted_mask)
vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
controlnet_model=ControlNetUnionModel.from_pretrained(
"OzzyGT/controlnet-union-promax-sdxl-1.0",
torch_dtype=torch.float16,
variant="fp16",
)
pipe: StableDiffusionXLControlNetUnionPipeline=StableDiffusionXLControlNetUnionPipeline.from_pretrained(
"SG161222/RealVisXL_V5.0",
controlnet=controlnet_model,
vae=vae,
torch_dtype=torch.float16,
variant="fp16",
)
pipe=pipe.to(device)
prompt="high quality"seed=random.randint(0, 2147483647)
generator=torch.Generator("cuda").manual_seed(seed)
union_input=ControlNetUnionInputProMax(
repaint=cnet_image,
)
image=pipe(
prompt=prompt,
image=union_input,
generator=generator,
height=1024,
width=1024,
num_inference_steps=30,
).images[0]
image.save("fill.png")

[yiyixuxu]

ok merged! thank you both
still need test, but can be added in a follow-up PR

[vladmandic]

@hlky@yiyixuxu@asomoza

the design here makes it a nightmare to use and very different than any other controlnet model.
i'm talking specifically about concept:

image: Union[ControlNetUnionInput, ControlNetUnionInputProMax]

so image is some new class and need to set image via its properties instead? how does that make sense?
its not even a new argument, its still called image, so no way to differentiate.
and everywhere else in diffusers, image is exactly that - an image!

i understand that you need to signal the union controlnet whats the input type, but this is not a unique scenario.
the FluxControlNetPipeline introduced concept of control_mode, why not use that here?
if not, at the very least, do not call it image (its not!) so it can be differentiated what to set and how.

[hlky]

This ControlNet works differently than Flux ControlNet Union. The design is explained in the opening comment, also see the original code, we avoid sync on every step.

https://github.com/xinsir6/ControlNetPlus/blob/b48420576eac63c04388cb65fb74513cbd17405a/models/controlnet_union.py#L842-L872

https://github.com/xinsir6/ControlNetPlus/blob/b48420576eac63c04388cb65fb74513cbd17405a/models/controlnet_union.py#L854

[vladmandic]

I understand it works differently, but you can't override image param to become something completely else. at least create a new property then.

[asomoza]

just to take note on this, we also have some inconsistencies with the naming of the normal controlnet conditioning images across some pipelines, IMO it should never be image but this is a breaking change so at some point we will need to fix this for the 1.0 version release.

[yiyixuxu]

the controlnet union pipelines introduced a new parameter called control_image_list (not control_image/image like our regular ones) so I don't think we override the image param, no?

we can change the argument for check_input here to something else if that's what you want

diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl.py

Line 796 in f2d348d

image: Union[ControlNetUnionInput, ControlNetUnionInputProMax],

[yiyixuxu]

agree with @asomoza. Here, we have some inconsistency with the parameter names; control_image and image are used interchangeably sometimes. and we should fix it

But it is irrelevant to this PR, this PR does not have this issue at all

[yiyixuxu]

ok I just saw the refactor
that works too:)

[john09282922]

Hi, Can I use promax version for now?
And I would like to check the promax code, But I can't find it.
Also, in the promax, is there controlnet option, like canny, pose, depth?

Thanks,

[vladmandic]

Hi, Can I use promax version for now? And I would like to check the promax code, But I can't find it. Also, in the promax, is there controlnet option, like canny, pose, depth?

there is no separate promax code, its the same architecture so same limitations.
only difference is control_mode identifiers since union and promax don't have same conditioning models.

[john09282922]

Hi, Can I use promax version for now? And I would like to check the promax code, But I can't find it. Also, in the promax, is there controlnet option, like canny, pose, depth?

there is no separate promax code, its the same architecture so same limitations. only difference is control_mode identifiers since union and promax don't have same conditioning models.

Thanks for replying my question. I can't find the code on promax input and union input. where those are?

[hlky]

@john09282922 There is no separate promax code.