hidiffusion 0.1.10

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

     

(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

• 2024.6.16 - 💥 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

@article{zhang2023hidiffusion,
  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},
  journal={arXiv preprint arXiv:2311.17528},
  year={2023}
}