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OneDiff Enterprise

OneDiff Enterprise offers a quantization method that reduces memory usage, increases speed, and maintains quality without any loss.

Note: Before proceeding with this document, please ensure you are familiar with the OneDiff Community features by referring to the OneDiff Community README.

Get the license key

Purchase license key from SiliconFlow website or contact [email protected] if you encounter any issues.Alternatively, you can contact us to inquire about purchasing the OneDiff Enterprise license.

Once you have completed your purchase, you can execute the following
export SILICON_ONEDIFF_LICENSE_KEY= $YOUR_KEY
in the terminal

Install OneDiff Enterprise

For NA/EU users

CUDA 11.8

python3 -m pip install --pre oneflow -f https://github.com/siliconflow/oneflow_releases/releases/expanded_assets/enterprise_cu118 && \
python3 -m pip install --pre onediff-quant -f https://github.com/siliconflow/onediff_releases/releases/expanded_assets/enterprise && \
python3 -m pip install git+https://github.com/siliconflow/onediff.git@main#egg=onediff

CUDA 12.1

python3 -m pip install --pre oneflow -f https://github.com/siliconflow/oneflow_releases/releases/expanded_assets/enterprise_cu121 && \
python3 -m pip install --pre onediff-quant -f https://github.com/siliconflow/onediff_releases/releases/expanded_assets/enterprise && \
python3 -m pip install git+https://github.com/siliconflow/onediff.git@main#egg=onediff

CUDA 12.2

python3 -m pip install --pre oneflow -f https://github.com/siliconflow/oneflow_releases/releases/expanded_assets/enterprise_cu122 && \
python3 -m pip install --pre onediff-quant -f https://github.com/siliconflow/onediff_releases/releases/expanded_assets/enterprise && \
python3 -m pip install git+https://github.com/siliconflow/onediff.git@main#egg=onediff

For CN users

CUDA 11.8

python3 -m pip install --pre oneflow -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/branch/main/cu118/ && \
python3 -m pip install --pre onediff-quant -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/onediff-quant/ && \
python3 -m pip install git+https://github.com/siliconflow/onediff.git@main#egg=onediff

CUDA 12.1

python3 -m pip install --pre oneflow -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/branch/main/cu121/ && \
python3 -m pip install --pre onediff-quant -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/onediff-quant/ && \
python3 -m pip install git+https://github.com/siliconflow/onediff.git@main#egg=onediff

CUDA 12.2

python3 -m pip install --pre oneflow -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/branch/main/cu122/ && \
python3 -m pip install --pre onediff-quant -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/onediff-quant/ && \
python3 -m pip install git+https://github.com/siliconflow/onediff.git@main#egg=onediff

ComfyUI with OneDiff Enterprise

Ensure that you have installed OneDiff ComfyUI Nodes and follow the instructions below.

NOTE1: Place the *.pt files from the HuggingFace repositories into the ComfyUI/models/onediff_quant subfolder. If the onediff_quant folder does not exist, please create it.

NOTE2: It should be noted that you need to make sure you are logged in to Hugging Face before clicking the "Workflow" link below.

Online Quantization for ComfyUI

We provide ComfyUI with an online quantification tool, which has significant acceleration effects. We provide detailed documentation tutorials ComfyUI online quantization usage documentation for users who want to use.

ComfyUI SD 1.5

For more information and to access the model files and Workflow below, please visit Hugging Face - stable-diffusion-v1-5-onediff-enterprise-v1.

Download the required model files
  1. Download the v1-5-pruned.safetensors file and place it in the ComfyUI/models/checkpoints/ directory:
wget https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.safetensors -O ComfyUI/models/checkpoints/v1-5-pruned.safetensors
  1. Download the v1-5-pruned_quantize_info.pt file and place it in the ComfyUI/models/onediff_quant/ directory:
wget https://huggingface.co/siliconflow/stable-diffusion-v1-5-onediff-comfy-enterprise-v1/resolve/main/v1-5-pruned_quantize_info.pt -O ComfyUI/models/onediff_quant/v1-5-pruned_quantize_info.pt

Click the links below to view the workflow images, or load them directly into ComfyUI.

ComfyUI SD 2.1

For more information and to access the model files and Workflow below, please visit Hugging Face - stable-diffusion-v2-1-onediff-enterprise.

Download the required model files
  1. Download the v2-1_768-ema-pruned.zip file and unzip ,then place the .safetensors in the ComfyUI/models/checkpoints/ directory:
wget https://huggingface.co/siliconflow/stable-diffusion-v2-1-onediff-comfy-enterprise/blob/main/v2-1_768-ema-pruned.zip -O ComfyUI/models/checkpoints/v2-1_768-ema-pruned.zip
  1. Download the v2-1-onediff-comfy-enterprise.pt file and place it in the ComfyUI/models/onediff_quant/ directory:
wget https://huggingface.co/siliconflow/stable-diffusion-v2-1-onediff-comfy-enterprise/blob/main/v2-1_768-ema-pruned.pt -O ComfyUI/models/onediff_quant/v1-5-pruned_quantize_info.pt

Click the links below to view the workflow images, or load them directly into ComfyUI.

ComfyUI SDXL

For model details and to access the model files and Workflow below, please visit Hugging Face - sdxl-base-1.0-onediff-comfy-enterprise-v1.

Download the required model files
  1. Download the sd_xl_base_1.0.safetensors file and place it in the ComfyUI/models/checkpoints/ directory:
wget https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -O ComfyUI/models/checkpoints/sd_xl_base_1.0.safetensors
  1. Download the sd_xl_base_1.0_quantize_info.pt file and place it in the ComfyUI/models/onediff_quant/ directory:
wget https://huggingface.co/siliconflow/sdxl-base-1.0-onediff-comfy-enterprise-v1/resolve/main/sd_xl_base_1.0_quantize_info.pt -O ComfyUI/models/onediff_quant/sd_xl_base_1.0_quantize_info.pt

ComfyUI SVD

For model details and to access the model files and Workflow below, visit Hugging Face - stable-video-diffusion-xt-comfyui-deepcache-int8.

Download the required model files
  1. Download the svd_xt.safetensors or svd_xt_1_1.safetensors file and place it in the ComfyUI/models/checkpoints/ directory:
wget https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors -O ComfyUI/models/checkpoints/svd_xt.safetensors
  1. Download the unet_SVD_img2vid_quantize_info.pt file and place it in the ComfyUI/models/onediff_quant/ directory:
wget https://huggingface.co/siliconflow/stable-video-diffusion-xt-comfyui-deepcache-int8/resolve/main/unet_SVD_img2vid_quantize_info.pt -O ComfyUI/models/onediff_quant/unet_SVD_img2vid_quantize_info.pt
  1. Download the unet_SVD_img2vid_quantize_deepcache_info.pt file and place it in the ComfyUI/models/onediff_quant/ directory:
wget https://huggingface.co/siliconflow/stable-video-diffusion-xt-comfyui-deepcache-int8/resolve/main/unet_SVD_img2vid_quantize_deepcache_info.pt -O ComfyUI/models/onediff_quant/unet_SVD_img2vid_quantize_deepcache_info.pt

Stable Diffusion WebUI with OneDiff Enterprise

We provide two types of quantization methods, online quantization and offline quantization.

Online quantization will quantize all Conv2d and Linear modules. Its advantages are as follows:

  • Easy to use: The only thing you need to do is just ticking the Model Quantization(int8) Speed Up option.
  • Fast: It can finish the quantification in a few seconds

Offline quantization requires you to manually run the quantization script we provide to obtain a quantized model, which can then be used in the WebUI. Its advantages are as follows:

  • Better image quality: It can identify and quantize modules with high computational costs yet minimal impact on image quality, to achieve the optimal balance between computational costs and image quality.

Online Quantization

Select onediff_diffusion_model in script, and a checkbox labeled Model Quantization(int8) Speed Up will appear below (as shown in the figure). By ticking this checkbox, you can use the online quantization function.

Note: If you can't see the checkbox and instead see a message with the content Hints: Enterprise function is not supported on your system, it means that you do not have installed the onediff enterprise correctly. Please follow the instructions in the Install OneDiff Enterprise to install it.

Offline Quantization

  1. Enter the directory where OneDiff is installed and run the script as follows to quantize the model

    python3 onediff_diffusers_extensions/tools/quantization/quantize-sd-fast.py \
  --model /path/to/your/model \
  --quantized_model /path/to/save/quantized/model \
  --height 512 \
  --width 512 \
  --conv_ssim_threshold 0.991 \
  --linear_ssim_threshold 0.991 \
  --conv_compute_density_threshold 0 \
  --linear_compute_density_threshold 0 \
  --save_as_float true \
  --cache_dir /path/to/save/quantized/cache

    The meaning of each parameter is as follows:

    --model Specifies the path of the model to be quantified, can be a diffusers format model (which is a folder containing unet, vae, text_encoder and etc.) or a single safetensors file

    --quantized_model Specifies the path to save the quantized model

    --height --width Specify the height and width of the output image which are the most commonly used height and width in your WebUI usage

    --conv_ssim_threshold A similarity threshold that quantize convolution. The higher the threshold, the lower the accuracy loss caused by quantization, should be greater than 0 and smaller than 1

    --linear_ssim_threshold A similarity threshold that quantize linear. The higher the threshold, the lower the accuracy loss caused by quantization, should be greater than 0 and smaller than 1

    --conv_compute_density_threshold The conv modules whose computational density is higher than the threshold will be quantized. Default to 900

    --linear_compute_density_threshold The linear modules whose computational density is higher than the threshold will be quantized. Default to 300

    --save_as_float If save model with floating point weights. If set to true, the weight of quantized modules will be saved as floating point dtype, otherwise int dtype.

    --cache_dir Specifies the path to save the cache when quantizing. You can use the cache to re-quantize one model without re-computing

  2. convert quantized model to origial Stable Diffusion single safetensors file by running the scirpt (if you want to convert SDXL model, use onediff_sd_webui_extensions/tools/convert_diffusers_to_sdxl.py instead)

    python3 onediff_sd_webui_extensions/tools/convert_diffusers_to_sd.py \
  --model_path /path/to/saved/quantized/model \
  --checkpoint_path /path/to/save/quantized/safetensors \
  --use_safetensors

Then you can get the offline quantized model in the path specified by --checkpoint_pathand use it in WebUI (remember to tick the Model Quantization(int8) Speed Up option).

Note:

  • Make sure that the safetensors file and the {model_name}_sd_calibrate_info.txt file are in the same folder, so that the OneDiff script can read the calibration file for this offline quantization model.

  • When you set conv_ssim_threshold and linear_ssim_threshold to a too high value, the number of quantized modules will be very few, and you will obtain too low acceleration benefits.

  • When you set conv_ssim_threshold and linear_ssim_threshold to a too low value, the number of quantized modules will be very large, and you will obtain a higher acceleration benefits, but the quality of generated image may decrease significantly

  • When you are using offline quantized model, do remember to tick the Model Quantization(int8) Speed Up option, otherwise abnormal image may be generated.

Diffusers with OneDiff Enterprise

Accessing Diffusers Models

To download the necessary models, please visit the siliconflow/stable-diffusion-v1-5-onediff-enterprise on HuggingFace. Please visit Hugging Face - stable-diffusion-v2-1-onediff-enterprise. Additionally, to use the quantized model customized for DeepCache, please visit the siliconflow/stable-diffusion-v1-5-onediff-deepcache-int8.

Diffusers-SD-1.5

Scripts

Run text_to_image_sd_enterprise.py by command:

python text_to_image_sd_enterprise.py --model $model_path --saved_image output_sd.png

Type python3 text_to_image_sd_enterprise.py -h for more options.

Diffusers-SD-2.1

python text_to_image_sd_enterprise.py --model $model_path --saved_image output_sd.png

Diffusers-SD-1.5 + DeepCache

Ensure that you have installed OneDiffX and then run text_to_image_deep_cache_sd_sdxl_enterprise.py by command:

python text_to_image_deep_cache_sd_sdxl_enterprise.py --model $model_path --model_type sd --saved_image output_deepcache_sd.png

Diffusers SDXL

Accessing Diffusers Models

To download the necessary models, please visit the siliconflow/sdxl-base-1.0-onediff-enterprise-v2 on HuggingFace.

Scripts

Run text_to_image_sdxl_enterprise.py by command:

python text_to_image_sdxl_enterprise.py --model $model_path --saved_image output_sdxl.png

Type python3 text_to_image_sdxl_enterprise.py -h for more options.

SDXL + DeepCache

Ensure that you have installed OneDiffX and then run text_to_image_deep_cache_sd_sdxl_enterprise.py by command:

python text_to_image_deep_cache_sd_sdxl_enterprise.py --model $model_path --model_type sdxl --saved_image output_deepcache.png

Diffusers SVD

Accessing Diffusers Models

To download the necessary models, please visit the siliconflow/stable-video-diffusion-img2vid-xt-deepcache-int8 on HuggingFace.

Scripts

Run image_to_video.py:

python3 benchmarks/image_to_video.py \
  --model $model_path \
  --input-image path/to/input_image.jpg \
  --output-video path/to/output_image.mp4

SVD + DeepCache

python3 benchmarks/image_to_video.py \
  --model $model_path \
  --deepcache \
  --input-image path/to/input_image.jpg \
  --output-video path/to/output_image.mp4

Quantitative model

Due to space limitations, specific quantification-related documents can be found in the quantization document.

Contact

For users of OneDiff Community, please visit GitHub Issues for bug reports and feature requests.

For users of OneDiff Enterprise, you can contact [email protected] for commercial support.

Feel free to join our Discord community for discussions and to receive the latest updates.