💡 HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models

Shen Zhang, Zhaowei Chen, Zhenyu Zhao, Yuhao Chen, Yao Tang, Jiajun Liang

(Select HiDiffusion samples for various diffusion models, resolutions, and aspect ratios.)

👉 Why HiDiffusion

A training-free method that increases the resolution and speed of pretrained diffusion models.
Designed as a plug-and-play implementation. It can be integrated into diffusion pipelines by only adding a single line of code!
Supports various tasks, including text-to-image, image-to-image, inpainting.

(Faster, and better image details.)

(2K results of ControlNet and inpainting tasks.)

🔥 Update

2024.8.15 - 💥 Diffusers documentation has added HiDiffusion, see here. Thank Diffusers team!
2024.7.3 - 💥 Accepted by ECCV 2024!
2024.6.19 - 💥 Integrated into OpenBayes, see the demo. Thank OpenBayes team!
2024.6.16 - 💥 Support PyTorch 2.X.
2024.6.16 - 💥 Fix non-square generation issue. Now HiDiffusion supports more image sizes and aspect ratios.
2024.5.7 - 💥 Support image-to-image task, see here.
2024.4.16 - 💥 Release source code.

📢 Supported Models

✅ Stable Diffusion XL
✅ Stable Diffusion XL Turbo
✅ Stable Diffusion v2
✅ Stable Diffusion v1

Note: HiDiffusion also supports the downstream diffusion models based on these repositories, such as Ghibli-Diffusion, Playground, etc.

💣 Supported Tasks

✅ Text-to-image
✅ ControlNet, including text-to-image, image-to-image
✅ Inpainting

🔎 Main Requirements

This repository is tested on

Python==3.8
torch>=1.13.1
diffusers>=0.25.0
transformers
accelerate
xformers

🔑 Install HiDiffusion

After installing the packages in the main requirements, install HiDiffusion:

pip3 install hidiffusion

Installing from source

Alternatively, you can install from github source. Clone the repository and install:

git clone https://github.com/megvii-model/HiDiffusion.git
cd HiDiffusion
python3 setup.py install

🚀 Usage

Generating outputs with HiDiffusion is super easy based on 🤗 diffusers. You just need to add a single line of code.

Text-to-image generation

Stable Diffusion XL

from hidiffusion import apply_hidiffusion, remove_hidiffusion
from diffusers import StableDiffusionXLPipeline, DDIMScheduler
import torch
pretrain_model = "stabilityai/stable-diffusion-xl-base-1.0"
scheduler = DDIMScheduler.from_pretrained(pretrain_model, subfolder="scheduler")
pipe = StableDiffusionXLPipeline.from_pretrained(pretrain_model, scheduler = scheduler, torch_dtype=torch.float16, variant="fp16").to("cuda")

# # Optional. enable_xformers_memory_efficient_attention can save memory usage and increase inference speed. enable_model_cpu_offload and enable_vae_tiling can save memory usage.
# pipe.enable_xformers_memory_efficient_attention()
# pipe.enable_model_cpu_offload()
# pipe.enable_vae_tiling()

# Apply hidiffusion with a single line of code.
apply_hidiffusion(pipe)

prompt = "Standing tall amidst the ruins, a stone golem awakens, vines and flowers sprouting from the crevices in its body."
negative_prompt = "blurry, ugly, duplicate, poorly drawn face, deformed, mosaic, artifacts, bad limbs"
image = pipe(prompt, guidance_scale=7.5, height=2048, width=2048, eta=1.0, negative_prompt=negative_prompt).images[0]
image.save(f"golem.jpg")

Output:

Set height = 4096, width = 4096, and you can get output with 4096x4096 resolution.

Stable Diffusion XL Turbo

from hidiffusion import apply_hidiffusion, remove_hidiffusion
from diffusers import AutoPipelineForText2Image
import torch
pretrain_model = "stabilityai/sdxl-turbo"
pipe = AutoPipelineForText2Image.from_pretrained(pretrain_model, torch_dtype=torch.float16, variant="fp16").to('cuda')

# # Optional. enable_xformers_memory_efficient_attention can save memory usage and increase inference speed. enable_model_cpu_offload and enable_vae_tiling can save memory usage.
# pipe.enable_xformers_memory_efficient_attention()
# pipe.enable_model_cpu_offload()
# pipe.enable_vae_tiling()

# Apply hidiffusion with a single line of code.
apply_hidiffusion(pipe)

prompt = "In the depths of a mystical forest, a robotic owl with night vision lenses for eyes watches over the nocturnal creatures."
image = pipe(prompt, num_inference_steps=4, height=1024, width=1024, guidance_scale=0.0).images[0]
image.save(f"./owl.jpg")

Output:

Stable Diffusion v2-1

from hidiffusion import apply_hidiffusion, remove_hidiffusion
from diffusers import DiffusionPipeline, DDIMScheduler
import torch
pretrain_model = "stabilityai/stable-diffusion-2-1-base"
scheduler = DDIMScheduler.from_pretrained(pretrain_model, subfolder="scheduler")
pipe = DiffusionPipeline.from_pretrained(pretrain_model, scheduler = scheduler, torch_dtype=torch.float16).to("cuda")

# # Optional. enable_xformers_memory_efficient_attention can save memory usage and increase inference speed. enable_model_cpu_offload and enable_vae_tiling can save memory usage.
# pipe.enable_xformers_memory_efficient_attention()
# pipe.enable_model_cpu_offload()
# pipe.enable_vae_tiling()

# Apply hidiffusion with a single line of code.
apply_hidiffusion(pipe)

prompt = "An adorable happy brown border collie sitting on a bed, high detail."
negative_prompt = "ugly, tiling, out of frame, poorly drawn face, extra limbs, disfigured, deformed, body out of frame, blurry, bad anatomy, blurred, artifacts, bad proportions."
image = pipe(prompt, guidance_scale=7.5, height=1024, width=1024, eta=1.0, negative_prompt=negative_prompt).images[0]
image.save(f"collie.jpg")

Output:

Set height = 2048, width = 2048, and you can get output with 2048x2048 resolution.

Stable Diffusion v1-5

from hidiffusion import apply_hidiffusion, remove_hidiffusion
from diffusers import DiffusionPipeline, DDIMScheduler
import torch
pretrain_model = "runwayml/stable-diffusion-v1-5"
scheduler = DDIMScheduler.from_pretrained(pretrain_model, subfolder="scheduler")
pipe = DiffusionPipeline.from_pretrained(pretrain_model, scheduler = scheduler, torch_dtype=torch.float16).to("cuda")

# # Optional. enable_xformers_memory_efficient_attention can save memory usage and increase inference speed. enable_model_cpu_offload and enable_vae_tiling can save memory usage.
# pipe.enable_xformers_memory_efficient_attention()
# pipe.enable_model_cpu_offload()
# pipe.enable_vae_tiling()

# Apply hidiffusion with a single line of code.
apply_hidiffusion(pipe)

prompt = "thick strokes, bright colors, an exotic fox, cute, chibi kawaii. detailed fur, hyperdetailed , big reflective eyes, fairytale, artstation,centered composition, perfect composition, centered, vibrant colors, muted colors, high detailed, 8k."
negative_prompt = "ugly, tiling, poorly drawn face, out of frame, disfigured, deformed, blurry, bad anatomy, blurred."
image = pipe(prompt, guidance_scale=7.5, height=1024, width=1024, eta=1.0, negative_prompt=negative_prompt).images[0]
image.save(f"fox.jpg")

Output:

Set height = 2048, width = 2048, and you can get output with 2048x2048 resolution.

Remove HiDiffusion

If you want to remove HiDiiffusion, simply use remove_hidiffusion(pipe).

ControlNet

Text-to-image generation

from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, DDIMScheduler
import numpy as np
import torch
import cv2
from PIL import Image
from hidiffusion import apply_hidiffusion, remove_hidiffusion

# load Yoshua_Bengio.jpg in the assets file.
path = './assets/Yoshua_Bengio.jpg'
image = Image.open(path)
# get canny image
image = np.array(image)
image = cv2.Canny(image, 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)

# initialize the models and pipeline
controlnet_conditioning_scale = 0.5  # recommended for good generalization
controlnet = ControlNetModel.from_pretrained(
    "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
)
scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16,
    scheduler = scheduler
)

# Apply hidiffusion with a single line of code.
apply_hidiffusion(pipe)

pipe.enable_model_cpu_offload()
pipe.enable_xformers_memory_efficient_attention()

prompt = "The Joker, high face detail, high detail, muted color, 8k"
negative_prompt = "blurry, ugly, duplicate, poorly drawn, deformed, mosaic."

image = pipe(
    prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image,
    height=2048, width=2048, guidance_scale=7.5, negative_prompt = negative_prompt, eta=1.0
).images[0]

image.save('joker.jpg')

Output:

Image-to-image generation

import torch
import numpy as np
from PIL import Image
from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, DDIMScheduler
from hidiffusion import apply_hidiffusion, remove_hidiffusion
import cv2 

controlnet = ControlNetModel.from_pretrained(
    "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
).to("cuda")
scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")

pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=controlnet,
    scheduler = scheduler,
    torch_dtype=torch.float16,
).to("cuda")

# Apply hidiffusion with a single line of code.
apply_hidiffusion(pipe)

pipe.enable_model_cpu_offload()
pipe.enable_xformers_memory_efficient_attention()

path = './assets/lara.jpeg'
ori_image = Image.open(path)
# get canny image
image = np.array(ori_image)
image = cv2.Canny(image, 50, 120)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)

controlnet_conditioning_scale = 0.5  # recommended for good generalization
prompt = "Lara Croft with brown hair, and is wearing a tank top, a brown backpack. The room is dark and has an old-fashioned decor with a patterned floor and a wall featuring a design with arches and a dark area on the right side, muted color, high detail, 8k high definition award winning"
negative_prompt = "underexposed, poorly drawn hands, duplicate hands, overexposed, bad art, beginner, amateur, abstract, disfigured, deformed, close up, weird colors, watermark"

image = pipe(prompt,
    image=ori_image,
    control_image=canny_image,
    height=1536,
    width=2048,
    strength=0.99,
    num_inference_steps=50,
    controlnet_conditioning_scale=controlnet_conditioning_scale,
    guidance_scale=12.5,
    negative_prompt = negative_prompt,
    eta=1.0
).images[0]

image.save("lara.jpg")

Output:

Inpainting

import torch
from diffusers import AutoPipelineForInpainting, DDIMScheduler
from diffusers.utils import load_image
from hidiffusion import apply_hidiffusion, remove_hidiffusion
from PIL import Image

scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
pipeline = AutoPipelineForInpainting.from_pretrained(
    "diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16", 
    scheduler=scheduler
)

# Apply hidiffusion with a single line of code.
apply_hidiffusion(pipeline)

pipeline.enable_model_cpu_offload()
# remove following line if xFormers is not installed
pipeline.enable_xformers_memory_efficient_attention()

# load base and mask image
img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
init_image = load_image(img_url)
# load mask_image.jpg in the assets file.
mask_image = Image.open("./assets/mask_image.png")

prompt =  "A steampunk explorer in a leather aviator cap and goggles, with a brass telescope in hand, stands amidst towering ancient trees, their roots entwined with intricate gears and pipes."

negative_prompt = "blurry, ugly, duplicate, poorly drawn, deformed, mosaic"
image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, height=2048, width=2048, strength=0.85, guidance_scale=12.5, negative_prompt = negative_prompt, eta=1.0).images[0]
image.save('steampunk_explorer.jpg')

Output:

Integration into downstream models

HiDiffusion supports models based on supported models, such as Ghibli-Diffusion, Playground, etc.

Ghibli-Diffusion

from diffusers import StableDiffusionPipeline
import torch
from hidiffusion import apply_hidiffusion, remove_hidiffusion

model_id = "nitrosocke/Ghibli-Diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

# Apply hidiffusion with a single line of code.
apply_hidiffusion(pipe)

prompt = "ghibli style magical princess with golden hair"
negative_prompt="blurry, ugly, duplicate, poorly drawn face, deformed, mosaic, artifacts, bad limbs"
image = pipe(prompt, height=1024, width=1024, eta=1.0, negative_prompt=negative_prompt).images[0]

image.save("./magical_princess.jpg")

Output:

Playground

from diffusers import DiffusionPipeline
import torch
from hidiffusion import apply_hidiffusion, remove_hidiffusion

pipe = DiffusionPipeline.from_pretrained(
    "playgroundai/playground-v2-1024px-aesthetic",
    torch_dtype=torch.float16,
    use_safetensors=True,
    add_watermarker=False,
    variant="fp16"
)
pipe.to("cuda")
pipe.enable_xformers_memory_efficient_attention()

# Apply hidiffusion with a single line of code.
apply_hidiffusion(pipe)

prompt = "The little girl riding a bike, in a beautiful anime scene by Hayao Miyazaki: a snowy Tokyo city with massive Miyazaki clouds floating in the blue sky, enchanting snowscapes of the city with bright sunlight, Miyazaki's landscape imagery, Japanese art"
negative_prompt="blurry, ugly, duplicate, poorly drawn, deformed, mosaic"
image  = pipe(prompt=prompt, guidance_scale=3.0, height=2048, width=2048, negative_prompt=negative_prompt).images[0]
image.save('girl.jpg')

Note: You may change guidance scale from 3.0 to 5.0 and design appropriate negative prompt to generate satisfactory results.

Output:

🙏 Acknowledgements

This codebase is based on tomesd and diffusers. Thanks!

🎓 Citation

@inproceedings{zhang2025hidiffusion,
  title={HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models},
  author={Zhang, Shen and Chen, Zhaowei and Zhao, Zhenyu and Chen, Yuhao and Tang, Yao and Liang, Jiajun},
  booktitle={European Conference on Computer Vision},
  pages={145--161},
  year={2025},
  organization={Springer}
}

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README.md

README.md

💡 HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models

👉 Why HiDiffusion

🔥 Update

📢 Supported Models

💣 Supported Tasks

🔎 Main Requirements

🔑 Install HiDiffusion

Installing from source

🚀 Usage

Text-to-image generation

Stable Diffusion XL

Stable Diffusion XL Turbo

Stable Diffusion v2-1

Stable Diffusion v1-5

Remove HiDiffusion

ControlNet

Text-to-image generation

Image-to-image generation

Inpainting

Integration into downstream models

Ghibli-Diffusion

Playground

🙏 Acknowledgements

🎓 Citation

Files

README.md

Latest commit

History

README.md

File metadata and controls

💡 HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models

👉 Why HiDiffusion

🔥 Update

📢 Supported Models

💣 Supported Tasks

🔎 Main Requirements

🔑 Install HiDiffusion

Installing from source

🚀 Usage

Text-to-image generation

Stable Diffusion XL

Stable Diffusion XL Turbo

Stable Diffusion v2-1

Stable Diffusion v1-5

Remove HiDiffusion

ControlNet

Text-to-image generation

Image-to-image generation

Inpainting

Integration into downstream models

Ghibli-Diffusion

Playground

🙏 Acknowledgements

🎓 Citation