add AIBOM
#10
by
RiccardoDav
- opened
- stabilityai_sd-turbo.json +45 -0
stabilityai_sd-turbo.json
ADDED
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{
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"bomFormat": "CycloneDX",
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"specVersion": "1.6",
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"serialNumber": "urn:uuid:520aa74b-77fd-45ad-86f4-39110dbbcbda",
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"version": 1,
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"metadata": {
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"timestamp": "2025-06-05T09:36:18.912990+00:00",
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"component": {
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"type": "machine-learning-model",
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"bom-ref": "stabilityai/sd-turbo-ee31968f-8aa4-5c6d-827a-b66124849b83",
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"name": "stabilityai/sd-turbo",
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"externalReferences": [
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{
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"url": "https://huggingface.co/stabilityai/sd-turbo",
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"type": "documentation"
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}
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],
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"modelCard": {
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"modelParameters": {
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"task": "text-to-image"
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},
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"properties": [
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{
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"name": "library_name",
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"value": "diffusers"
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}
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]
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},
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"authors": [
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{
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"name": "stabilityai"
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}
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],
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"description": "SD-Turbo is a distilled version of [Stable Diffusion 2.1](https://huggingface.co/stabilityai/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](https://stability.ai/research/adversarial-diffusion-distillation)), which allows sampling large-scale foundationalimage 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 anadversarial 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](https://huggingface.co/stabilityai/stable-diffusion-2-1)",
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"tags": [
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"diffusers",
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"safetensors",
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"text-to-image",
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"autotrain_compatible",
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"diffusers:StableDiffusionPipeline",
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"region:us"
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]
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}
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}
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}
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