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add amused model support and update reference list

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Vladimir Mandic
2023-12-27 10:15:36 -05:00
parent 0df94404c4
commit 1de3c6af0f
10 changed files with 44 additions and 23 deletions
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html/reference.json
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@@ -144,6 +144,16 @@
"desc": "DeepFloyd-IF is a pixel-based text-to-image triple-cascaded diffusion model, that can generate pictures with new state-of-the-art for photorealism and language understanding. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID-30K score of 6.66 on the COCO dataset. It is modular and composed of frozen text mode and three pixel cascaded diffusion modules, each designed to generate images of increasing resolution: 64x64, 256x256, and 1024x1024.",
"preview": "DeepFloyd--IF-I-M-v1.0.jpg"
},
"aMUSEd 256": {
"path": "amused/amused-256",
"desc": "Amused is a lightweight text to image model based off of the muse architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.",
"preview": "amused--amused-256.jpg"
},
"aMUSEd 512": {
"path": "amused/amused-512",
"desc": "Amused is a lightweight text to image model based off of the muse architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.",
"preview": "amused--amused-512.jpg"
},
"Tsinghua UniDiffuser": {
"path": "thu-ml/unidiffuser-v1",
"desc": "UniDiffuser is a unified diffusion framework to fit all distributions relevant to a set of multi-modal data in one transformer. UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead.\nSpecifically, UniDiffuser employs a variation of transformer, called U-ViT, which parameterizes the joint noise prediction network. Other components perform as encoders and decoders of different modalities, including a pretrained image autoencoder from Stable Diffusion, a pretrained image ViT-B/32 CLIP encoder, a pretrained text ViT-L CLIP encoder, and a GPT-2 text decoder finetuned by ourselves.",