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williamberman · 2023-03-06T08:49:14Z

These are community controlnet pipelines inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

They are not exhaustively tested but I ran through a few examples manually and they seem to work ok. Please note that they will not be officially supported. Feel free to open issues and I can get to them when I have time but they won't be prioritized.

I do not know how sound the img2img pipeline is. I haven't thought too hard about if noising the encoded latents as such + using the additional controlnet conditioning works as intended.

note that community pipelines can also be loaded as such https://huggingface.co/docs/diffusers/v0.14.0/en/using-diffusers/custom_pipeline_overview#loading-official-community-pipelines

inpainting

import numpy as np
import torch
from PIL import Image
from stable_diffusion_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline

from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
from diffusers import ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image

def ade_palette():
    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
            [102, 255, 0], [92, 0, 255]]


image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")

controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)

pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
)

pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()

def image_to_seg(image):
    pixel_values = image_processor(image, return_tensors="pt").pixel_values
    with torch.no_grad():
        outputs = image_segmentor(pixel_values)
    seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)  # height, width, 3
    palette = np.array(ade_palette())
    for label, color in enumerate(palette):
        color_seg[seg == label, :] = color
    color_seg = color_seg.astype(np.uint8)
    seg_image = Image.fromarray(color_seg)
    return seg_image

image = load_image(
    "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
)

mask_image = load_image(
    "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
)

controlnet_conditioning_image = image_to_seg(image)

image = pipe(
    "Face of a yellow cat, high resolution, sitting on a park bench",
    image,
    mask_image,
    controlnet_conditioning_image,
    num_inference_steps=20,
).images[0]

image.save("out.png")

inpainting + image variation

import numpy as np
import torch
from PIL import Image
from stable_diffusion_controlnet_inpaint_img2img import StableDiffusionControlNetInpaintImg2ImgPipeline

from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
from diffusers import ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image

def ade_palette():
    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
            [102, 255, 0], [92, 0, 255]]


image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")

controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)

pipe = StableDiffusionControlNetInpaintImg2ImgPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
)

pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()

def image_to_seg(image):
    pixel_values = image_processor(image, return_tensors="pt").pixel_values
    with torch.no_grad():
        outputs = image_segmentor(pixel_values)
    seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)  # height, width, 3
    palette = np.array(ade_palette())
    for label, color in enumerate(palette):
        color_seg[seg == label, :] = color
    color_seg = color_seg.astype(np.uint8)
    seg_image = Image.fromarray(color_seg)
    return seg_image

image = load_image(
    "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
)

mask_image = load_image(
    "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
)

controlnet_conditioning_image = image_to_seg(image)

image = pipe(
    "Face of a yellow cat, high resolution, sitting on a park bench",
    image,
    mask_image,
    controlnet_conditioning_image,
    num_inference_steps=20,
).images[0]

image.save("out.png")

cc @Suhail @haofanwang

HuggingFaceDocBuilderDev · 2023-03-06T08:57:10Z

patrickvonplaten

Thanks!

pcuenca

Thanks! I made a comment about citing the original authors.

yiyixuxu

awesome!

@pcuenca

* community controlnet inpainting pipelines * add community member attribution re: @pcuenca

@pcuenca

* community controlnet inpainting pipelines * add community member attribution re: @pcuenca

CrazyBoyM · 2023-04-26T17:16:46Z

Hi, happy to see your great work! but can you tell me what's the different with inpainting + image variation

CrazyBoyM · 2023-04-26T17:35:27Z

And will the inpaint pipe support the 'MultiControlNetModel' in pipeline_stable_diffusion_controlnetand and stable_diffusion_controlnet_img2img ?

patrickvonplaten · 2023-04-27T12:07:27Z

@williamberman @yiyixuxu I wonder whether controlnet is popular enough to actually move those two pipelines into src/diffusers

RustyKettle · 2023-05-27T23:01:06Z

@williamberman @yiyixuxu I wonder whether controlnet is popular enough to actually move those two pipelines into src/diffusers

I am using the controlnet+inpainting+img2img extensively, and would love to see it moved into being officially supported. I would like to see all the recent Lora updates from the other pipelines worked into controlnet+img2img+inpaint.

patrickvonplaten · 2023-05-30T10:14:44Z

Hey @RustyKettle,

We've merged exactly this PR a couple of days ago: #3533

RustyKettle · 2023-05-30T21:53:33Z

@pcuenca

* community controlnet inpainting pipelines * add community member attribution re: @pcuenca

community controlnet inpainting pipelines

cc72daa

williamberman force-pushed the controlnet_stable_diffusion_inpainting branch from 68d0a2d to cc72daa Compare March 6, 2023 09:03

williamberman mentioned this pull request Mar 6, 2023

Add a ControlNet model & pipeline #2407

Merged

5 tasks

williamberman requested review from patrickvonplaten, pcuenca, sayakpaul and yiyixuxu March 6, 2023 09:07

patrickvonplaten approved these changes Mar 6, 2023

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pcuenca approved these changes Mar 6, 2023

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Comment thread examples/community/stable_diffusion_controlnet_inpaint.py

takuma104 mentioned this pull request Mar 6, 2023

Adding support for ControlNet pipeline for img2img flows #2562

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yiyixuxu approved these changes Mar 6, 2023

View reviewed changes

williamberman added 2 commits March 6, 2023 11:02

add community member attribution re: @pcuenca

0faebd0

Merge branch 'main' into controlnet_stable_diffusion_inpainting

d02d5f3

williamberman merged commit ca7ca11 into huggingface:main Mar 6, 2023

yiyixuxu mentioned this pull request Mar 6, 2023

diffusers community pipeline based on your repo haofanwang/ControlNet-for-Diffusers#27

Closed

mikegarts mentioned this pull request Mar 7, 2023

community stablediffusion controlnet img2img pipeline #2584

Merged

yiyixuxu mentioned this pull request Mar 23, 2023

Create a img2img (+ inpainting) controlnet pipeline #2783

Closed

mengfei25 pushed a commit to mengfei25/diffusers that referenced this pull request Mar 27, 2023

community controlnet inpainting pipelines (huggingface#2561)

b18553e

* community controlnet inpainting pipelines * add community member attribution re: @pcuenca

w4ffl35 pushed a commit to w4ffl35/diffusers that referenced this pull request Apr 14, 2023

community controlnet inpainting pipelines (huggingface#2561)

5a683ae

* community controlnet inpainting pipelines * add community member attribution re: @pcuenca

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community controlnet inpainting pipelines#2561

community controlnet inpainting pipelines#2561
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inpainting

inpainting + image variation

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