Sdxs

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SDXS is an open-source image generation tool that offers real-time one-step latent diffusion models with image conditions. It achieves significantly faster inference speeds compared to SD v1.5 and SDXL.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is sdxs?

SDXS provides real-time one-step latent diffusion models with image conditions, enabling rapid image generation. It boasts impressive inference speeds, generating 512x512 images at 100 FPS and 1024x1024 images at 30 FPS on a single GPU, making it 30x faster than SD v1.5 and 60x faster than SDXL for comparable image quality within a one-second generation limit. The tool also supports training ControlNet, expanding its applications to image-conditioned control and efficient image-to-image translation. SDXS utilizes a lightweight image decoder and a block removal distillation strategy for model acceleration, alongside a feature matching loss for efficient one-step model finetuning.

Best used for

Ideal for developers and researchers who need to generate high-quality images rapidly, experiment with image-conditioned control, and perform efficient image-to-image translation. Especially valuable for those requiring significantly faster inference speeds than traditional latent diffusion models.

Common actions

generate images

accelerate image generation

perform image-to-image translation

train ControlNet models

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Capabilities

Key features

Real-time image generation
One-step latent diffusion
Image-conditioned control
ControlNet training support
High inference speed
Lightweight image decoder

Target Audience

ai researchersmachine learning engineersgame developerscreative professionals

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the performance benefits of SDXS?

SDXS-512 can generate images at approximately 100 FPS, which is 30x faster than SD v1.5. SDXS-1024 achieves about 30 FPS, making it 60x faster than SDXL, while producing clear images within a one-second generation limit.

Can SDXS be used for image-to-image translation?

Yes, SDXS extends its training strategy to support ControlNet, demonstrating efficacy in image-to-image conversions. This includes transformations utilizing canny edges and depth maps, facilitating various image-conditioned control applications.

Is the training code for SDXS available?

While the demo code is provided, the SDXS training code is not open-sourced and is unlikely to be updated. Users can refer to the DMD2 project, which has an identical training scheme to the new version of SDXS.

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Coding & Development › Open Source & Models Research & Education › Scientific Computing

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