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DiT-XL-2-256
kashif/DiT-XL-2-256
DiT-XL-2-256 is a machine learning model from kashif. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as cc-by-nc-4.0.
We train latent diffusion models, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forw…
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At a glance
- Library
- diffusers
- License
- cc-by-nc-4.0
- Access
- Public
- Created
- Dec 23, 2022
- Updated
- Jan 17, 2023
- SHA
- 46e4bb5d
Model card
From the Hugging Face model README
Scalable Diffusion Models with Transformers (DiT)
Abstract
We train latent diffusion models, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops---through increased transformer depth/width or increased number of input tokens---consistently have lower FID. In addition to good scalability properties, our DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512×512 and 256×256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.
- Library
- diffusers
- License
- cc-by-nc-4.0
- Created
- Dec 23, 2022
- Updated
- Jan 17, 2023