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metadata
license: openrail++
library_name: diffusers
inference: false
tags:
  - lora
  - text-to-image
  - stable-diffusion

Hyper-SD

Official Repository of the paper: Hyper-SD.

Project Page: https://hyper-sd.github.io/

News🔥🔥🔥

  • Aug.19, 2024. 💥💥💥 SD3-related CFG LoRAs are available now! We recommend setting guidance scale to 3.0/5.0/7.0 at 4/8/16-steps. Don't forget to fuse lora with a relatively small scale (e.g. 0.125 that is adaptive with training) before inference with diffusers. Note that 8-steps and 16-steps LoRA can also inference on a little bit smaller steps like 6-steps and 12-steps, respectively. Hope to hear your feedback, FLUX-related models will be coming next week. 💥💥💥
  • May.13, 2024. The 12-Steps CFG-Preserved Hyper-SDXL-12steps-CFG-LoRA and Hyper-SD15-12steps-CFG-LoRA is also available now(support 5~8 guidance scales), this could be more practical with better trade-off between performance and speed. Enjoy!
  • Apr.30, 2024. Our 8-Steps CFG-Preserved Hyper-SDXL-8steps-CFG-LoRA and Hyper-SD15-8steps-CFG-LoRA is available now(support 5~8 guidance scales), we strongly recommend making the 8-step CFGLora a standard configuration for all SDXL and SD15 models!!!
  • Apr.28, 2024. ComfyUI workflows on 1-Step Unified LoRA 🥰 with TCDScheduler to inference on different steps are released! Remember to install ⭕️ ComfyUI-TCD in your ComfyUI/custom_nodes folder!!! You're encouraged to adjust the eta parameter to get better results 🌟!
  • Apr.26, 2024. Thanks to @Pete for contributing to our scribble demo with larger canvas right now 👏.
  • Apr.24, 2024. The ComfyUI workflow and checkpoint on 1-Step SDXL UNet ✨ is also available! Don't forget ⭕️ to install the custom scheduler in your ComfyUI/custom_nodes folder!!!
  • Apr.23, 2024. ComfyUI workflows on N-Steps LoRAs are released! Worth a try for creators 💥!
  • Apr.23, 2024. Our technical report 📚 is uploaded to arXiv! Many implementation details are provided and we welcome more discussions👏.
  • Apr.21, 2024. Hyper-SD ⚡️ is highly compatible and work well with different base models and controlnets. To clarify, we also append the usage example of controlnet here.
  • Apr.20, 2024. Our checkpoints and two demos 🤗 (i.e. SD15-Scribble and SDXL-T2I) are publicly available on HuggingFace Repo.

Try our Hugging Face demos:

Hyper-SD Scribble demo host on 🤗 scribble

Hyper-SDXL One-step Text-to-Image demo host on 🤗 T2I

Introduction

Hyper-SD is one of the new State-of-the-Art diffusion model acceleration techniques. In this repository, we release the models distilled from SD3-Medium, SDXL Base 1.0 and Stable-Diffusion v1-5。

Checkpoints

  • Hyper-SD3-Nsteps-CFG-lora.safetensors: Lora checkpoint, for SD3-related models.
  • Hyper-SDXL-Nstep-lora.safetensors: Lora checkpoint, for SDXL-related models.
  • Hyper-SD15-Nstep-lora.safetensors: Lora checkpoint, for SD1.5-related models.
  • Hyper-SDXL-1step-unet.safetensors: Unet checkpoint distilled from SDXL-Base.

Text-to-Image Usage

SD3-related models 

import torch
from diffusers import StableDiffusion3Pipeline
from huggingface_hub import hf_hub_download
base_model_id = "stabilityai/stable-diffusion-3-medium-diffusers"
repo_name = "ByteDance/Hyper-SD"
# Take 8-steps lora as an example
ckpt_name = "Hyper-SD3-8steps-CFG-lora.safetensors"
# Load model, please fill in your access tokens since SD3 repo is a gated model.
pipe = StableDiffusion3Pipeline.from_pretrained(base_model_id, token="xxx")
pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
pipe.fuse_lora(lora_scale=0.125)
pipe.to("cuda", dtype=torch.float16)
image=pipe(prompt="a photo of a cat", num_inference_steps=8, guidance_scale=5.0).images[0]
image.save("output.png")

SDXL-related models

2-Steps, 4-Steps, 8-steps LoRA

Take the 2-steps LoRA as an example, you can also use other LoRAs for the corresponding inference steps setting. 

import torch
from diffusers import DiffusionPipeline, DDIMScheduler
from huggingface_hub import hf_hub_download
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "ByteDance/Hyper-SD"
# Take 2-steps lora as an example
ckpt_name = "Hyper-SDXL-2steps-lora.safetensors"
# Load model.
pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
pipe.fuse_lora()
# Ensure ddim scheduler timestep spacing set as trailing !!!
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
# lower eta results in more detail
prompt="a photo of a cat"
image=pipe(prompt=prompt, num_inference_steps=2, guidance_scale=0).images[0]

Unified LoRA (support 1 to 8 steps inference)

You can flexibly adjust the number of inference steps and eta value to achieve best performance. 

import torch
from diffusers import DiffusionPipeline, TCDScheduler
from huggingface_hub import hf_hub_download
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "ByteDance/Hyper-SD"
ckpt_name = "Hyper-SDXL-1step-lora.safetensors"
# Load model.
pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
pipe.fuse_lora()
# Use TCD scheduler to achieve better image quality
pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
# Lower eta results in more detail for multi-steps inference
eta=1.0
prompt="a photo of a cat"
image=pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, eta=eta).images[0]

1-step SDXL Unet

Only for the single step inference.

import torch
from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "ByteDance/Hyper-SD"
ckpt_name = "Hyper-SDXL-1step-Unet.safetensors"
# Load model.
unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(load_file(hf_hub_download(repo_name, ckpt_name), device="cuda"))
pipe = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
# Use LCM scheduler instead of ddim scheduler to support specific timestep number inputs
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# Set start timesteps to 800 in the one-step inference to get better results
prompt="a photo of a cat"
image=pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[800]).images[0]

SD1.5-related models

2-Steps, 4-Steps, 8-steps LoRA

Take the 2-steps LoRA as an example, you can also use other LoRAs for the corresponding inference steps setting.

import torch
from diffusers import DiffusionPipeline, DDIMScheduler
from huggingface_hub import hf_hub_download
base_model_id = "runwayml/stable-diffusion-v1-5"
repo_name = "ByteDance/Hyper-SD"
# Take 2-steps lora as an example
ckpt_name = "Hyper-SD15-2steps-lora.safetensors"
# Load model.
pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
pipe.fuse_lora()
# Ensure ddim scheduler timestep spacing set as trailing !!!
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
prompt="a photo of a cat"
image=pipe(prompt=prompt, num_inference_steps=2, guidance_scale=0).images[0]

Unified LoRA (support 1 to 8 steps inference)

You can flexibly adjust the number of inference steps and eta value to achieve best performance.

import torch
from diffusers import DiffusionPipeline, TCDScheduler
from huggingface_hub import hf_hub_download
base_model_id = "runwayml/stable-diffusion-v1-5"
repo_name = "ByteDance/Hyper-SD"
ckpt_name = "Hyper-SD15-1step-lora.safetensors"
# Load model.
pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
pipe.fuse_lora()
# Use TCD scheduler to achieve better image quality
pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
# Lower eta results in more detail for multi-steps inference
eta=1.0
prompt="a photo of a cat"
image=pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, eta=eta).images[0]

ControlNet Usage

SDXL-related models

2-Steps, 4-Steps, 8-steps LoRA

Take Canny Controlnet and 2-steps inference as an example:

import torch
from diffusers.utils import load_image
import numpy as np
import cv2
from PIL import Image
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, DDIMScheduler
from huggingface_hub import hf_hub_download

# Load original image
image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")
image = np.array(image)
# Prepare Canny Control Image
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
control_image = Image.fromarray(image)
control_image.save("control.png")
control_weight = 0.5  # recommended for good generalization

# Initialize pipeline
controlnet = ControlNetModel.from_pretrained(
    "diffusers/controlnet-canny-sdxl-1.0",
    torch_dtype=torch.float16
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16).to("cuda")

pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SDXL-2steps-lora.safetensors"))
# Ensure ddim scheduler timestep spacing set as trailing !!!
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
pipe.fuse_lora()
image = pipe("A chocolate cookie", num_inference_steps=2, image=control_image, guidance_scale=0, controlnet_conditioning_scale=control_weight).images[0]
image.save('image_out.png')

Unified LoRA (support 1 to 8 steps inference)

Take Canny Controlnet as an example:

import torch
from diffusers.utils import load_image
import numpy as np
import cv2
from PIL import Image
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, TCDScheduler
from huggingface_hub import hf_hub_download

# Load original image
image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")
image = np.array(image)
# Prepare Canny Control Image
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
control_image = Image.fromarray(image)
control_image.save("control.png")
control_weight = 0.5  # recommended for good generalization

# Initialize pipeline
controlnet = ControlNetModel.from_pretrained(
    "diffusers/controlnet-canny-sdxl-1.0",
    torch_dtype=torch.float16
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=controlnet, vae=vae, torch_dtype=torch.float16).to("cuda")

# Load Hyper-SD15-1step lora
pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SDXL-1step-lora.safetensors"))
pipe.fuse_lora()
# Use TCD scheduler to achieve better image quality
pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
# Lower eta results in more detail for multi-steps inference
eta=1.0
image = pipe("A chocolate cookie", num_inference_steps=4, image=control_image, guidance_scale=0, controlnet_conditioning_scale=control_weight, eta=eta).images[0]
image.save('image_out.png')

SD1.5-related models

2-Steps, 4-Steps, 8-steps LoRA

Take Canny Controlnet and 2-steps inference as an example:

import torch
from diffusers.utils import load_image
import numpy as np
import cv2
from PIL import Image
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, DDIMScheduler

from huggingface_hub import hf_hub_download

controlnet_checkpoint = "lllyasviel/control_v11p_sd15_canny"

# Load original image
image = load_image("https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/input.png")
image = np.array(image)
# Prepare Canny Control Image
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
control_image = Image.fromarray(image)
control_image.save("control.png")

# Initialize pipeline
controlnet = ControlNetModel.from_pretrained(controlnet_checkpoint, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16).to("cuda")
pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SD15-2steps-lora.safetensors"))
pipe.fuse_lora()
# Ensure ddim scheduler timestep spacing set as trailing !!!
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
image = pipe("a blue paradise bird in the jungle", num_inference_steps=2, image=control_image, guidance_scale=0).images[0]
image.save('image_out.png')

Unified LoRA (support 1 to 8 steps inference)

Take Canny Controlnet as an example:

import torch
from diffusers.utils import load_image
import numpy as np
import cv2
from PIL import Image
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, TCDScheduler
from huggingface_hub import hf_hub_download

controlnet_checkpoint = "lllyasviel/control_v11p_sd15_canny"

# Load original image
image = load_image("https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/input.png")
image = np.array(image)
# Prepare Canny Control Image
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
control_image = Image.fromarray(image)
control_image.save("control.png")

# Initialize pipeline
controlnet = ControlNetModel.from_pretrained(controlnet_checkpoint, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16).to("cuda")
# Load Hyper-SD15-1step lora
pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SD15-1step-lora.safetensors"))
pipe.fuse_lora()
# Use TCD scheduler to achieve better image quality
pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
# Lower eta results in more detail for multi-steps inference
eta=1.0
image = pipe("a blue paradise bird in the jungle", num_inference_steps=1, image=control_image, guidance_scale=0, eta=eta).images[0]
image.save('image_out.png')

Comfyui Usage

  • Hyper-SDXL-Nsteps-lora.safetensors: text-to-image workflow
  • Hyper-SD15-Nsteps-lora.safetensors: text-to-image workflow
  • Hyper-SDXL-1step-Unet-Comfyui.fp16.safetensors: text-to-image workflow
    • REQUIREMENT / INSTALL for 1-Step SDXL UNet: Please install our scheduler folder into your ComfyUI/custom_nodes to enable sampling from 800 timestep instead of 999.
    • i.e. making sure the ComfyUI/custom_nodes/ComfyUI-HyperSDXL1StepUnetScheduler folder exist.
    • For more details, please refer to our technical report.
  • Hyper-SD15-1step-lora.safetensors: text-to-image workflow
  • Hyper-SDXL-1step-lora.safetensors: text-to-image workflow
    • REQUIREMENT / INSTALL for 1-Step Unified LoRAs: Please install the ComfyUI-TCD into your ComfyUI/custom_nodes to enable TCDScheduler with support of different inference steps (1~8) using single checkpoint.
    • i.e. making sure the ComfyUI/custom_nodes/ComfyUI-TCD folder exist.
    • You're encouraged to adjust the eta parameter in TCDScheduler to get better results.

Citation 

@misc{ren2024hypersd,
      title={Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis}, 
      author={Yuxi Ren and Xin Xia and Yanzuo Lu and Jiacheng Zhang and Jie Wu and Pan Xie and Xing Wang and Xuefeng Xiao},
      year={2024},
      eprint={2404.13686},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}