sd

About

What sd is

SD-Turbo Model Card SD-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. We release SD-Turbo as a research artifact, and to study small, distilled text-to-image models. For increased quality and prompt understanding, we recommend SDXL-Turbo. Please note: For commercial use, please refer to https://stability.ai/license. Model Details Model Description SD-Turbo is a distilled version of Stable Diffusion 2.1, trained for real-time synthesis. SD-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the technical report), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high image quality. This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. - Developed by: Stability AI - Funded by: Stability AI - Model type: Generative text-to-image model - Finetuned from model: Stable Diffusion 2.1 Model Sources For research purposes, we recommend our generative-models Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference). - Repository: https://github.com/Stability-AI/generative-models - Paper: https://stability.ai/research/adversarial-diffusion-distillation - Demo [for the bigger SDXL-Turbo]: http://clipdrop.co/stable-diffusion-turbo Evaluation The charts above evaluate user preference for SD-Turbo over other single- and multi-step models. SD-Turbo evaluated at a single step is preferred by human voters in terms of image quality and prompt following over LCM-Lora XL and LCM-Lora 1.5. Note: For increased quality, we recommend the bigger version SDXL-Turbo. For details on the user study, we refer to the research paper. Uses Direct Use The model is intended for both non-commercial and commercial usage. Possible research areas and tasks include - Research on generative models. - Research on real-time applications of generat

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How image models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys sd for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (sd below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/images/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"sd","prompt":"a red bicycle","size":"1024x1024"}'

Details

Languages, data & research

Licence

Commercial licence needed

Sources

Weights & code

Explore

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