Feature Extraction
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README.md CHANGED
@@ -11,14 +11,17 @@ Project Page: https://foundationvision.github.io/UniTok/
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  Code: https://github.com/FoundationVision/UniTok
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- ![teaser](assets/teaser.png)
 
 
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  UniTok encodes fine-grained details for generation and captures high-level semantics for understanding. It's compatible with autoregressive generative models (e.g., LlamaGen), multimodal understanding models (e.g., LLaVA), and unified MLLMs (e.g., Chameleon and Liquid).
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-
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  Built upon UniTok, we construct an MLLM capable of both multimodal generation and understanding, which sets a new state-of-the-art among unified autoregressive MLLMs. The weights of our MLLM will be released soon.
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- ![teaser](assets/samples.png)
 
 
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  ## Performance
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@@ -105,22 +108,10 @@ Built upon UniTok, we construct an MLLM capable of both multimodal generation an
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  </table>
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- &dagger; indicates the model uses pretrained CLIP weights for initialization. Although CLIP weight initialization boosts ImageNet zero-shot accuracy,
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- we notice that random initialization leads to better downstream understanding performance.
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- We thus release the model checkpoint of UniTok that is trained from scratch.
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-
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-
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- ## Model Weights
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-
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- | Model | Res. | #Token | Code Shape | rFID | Checkpoint |
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- |:------------:|:----:|:------:|:-------------------------:|:----:|:------------:|
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- | UniTok-Large | 256 | 256 | 16 $\times$ 16 $\times$ 8 | 0.39 | [Download](https://huggingface.co/FoundationVision/UniTok/blob/main/unitok_tokenizer.pth) |
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-
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- ## Usage
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- (... rest of README content ...)
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  ## Citation
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  Code: https://github.com/FoundationVision/UniTok
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+ <p align="center">
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+ <img src="https://github.com/FoundationVision/UniTok/blob/main/assets/teaser.png" width=93%>
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+ <p>
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  UniTok encodes fine-grained details for generation and captures high-level semantics for understanding. It's compatible with autoregressive generative models (e.g., LlamaGen), multimodal understanding models (e.g., LLaVA), and unified MLLMs (e.g., Chameleon and Liquid).
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  Built upon UniTok, we construct an MLLM capable of both multimodal generation and understanding, which sets a new state-of-the-art among unified autoregressive MLLMs. The weights of our MLLM will be released soon.
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+ <p align="center">
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+ <img src="https://github.com/FoundationVision/UniTok/blob/main/assets/samples.png" width=93%>
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+ <p>
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  ## Performance
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  </table>
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+ This repo is used for hosting UniTok's checkpoints.
 
 
 
 
 
 
 
 
 
 
 
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+ For more details or tutorials see https://github.com/FoundationVision/UniTok.
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  ## Citation
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config.json ADDED
@@ -0,0 +1,115 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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