[A] Add 24.06 MAR paper#19
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Pull Request Overview
Adds a full summary of the new paper “Autoregressive Image Generation without Vector Quantization”, covering background, methodology, implementation details, experiments, and references
- Introduces paper metadata, author link, and Chinese translation
- Details vector quantization background and the proposed diffusion-based autoregressive method
- Provides experiments on loss functions, tokenizers, MLP ablations, and system comparisons
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papers/image-generation/2406-mar/index.md:35
- [nitpick] List indentation is inconsistent here and in subsequent bullet points. Use uniform indent levels for nested lists to improve readability.
+- 以[VQ-VAE, 2017]為例
| - Diffusion Loss：consine形狀的noise schedule，訓練時DDPM有1000 step而推論則僅有100 step | ||
| - Denosing MLP(small MLP)：3層1024個channel的block，每一個block包含LayerNorm, linear layer， SiLU 激活函數並使用residual connection連接，實作上是使用AdaLN將transformer的輸出z加入到LayerNorm層當中 | ||
| - Tokenizer：使用LDM提供的公開tokenizer，包括VQ-16和KL-16。其中VQ-16是基於VQ-GAN的量化模型，使用GAN loss和感知loss，KL-16則透過KL散度做regularization且不依賴VQ | ||
| - Transformer：使用 ViT 來接收 tokenizer 處理後的 token sequene，加上位置編碼和類別token [CLS]，然後通過32層1024個channel的transformer block |
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Typo in 'sequene'; it should be 'sequence'.
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| - Transformer：使用 ViT 來接收 tokenizer 處理後的 token sequene，加上位置編碼和類別token [CLS]，然後通過32層1024個channel的transformer block | |
| - Transformer：使用 ViT 來接收 tokenizer 處理後的 token sequence，加上位置編碼和類別token [CLS]，然後通過32層1024個channel的transformer block |
| - Tokenizer：使用LDM提供的公開tokenizer，包括VQ-16和KL-16。其中VQ-16是基於VQ-GAN的量化模型，使用GAN loss和感知loss，KL-16則透過KL散度做regularization且不依賴VQ | ||
| - Transformer：使用 ViT 來接收 tokenizer 處理後的 token sequene，加上位置編碼和類別token [CLS]，然後通過32層1024個channel的transformer block | ||
| - Masked autoregressive models：在訓練階段使用 [0.7, 1.0] 的masking ratio，0.7代表隨機遮蔽掉70%的token，另外為了避免抽樣出來的序列太短，他們始終pad 64個[cls] token到其中。在推理階段會逐步將1.0的masking ratio降低到0，並使用cosine schedule來調整步數，預設是64步 | ||
| - Baseline Autoregressive Model: casual attention的GPT模型，輸入有append一個[cls]，並且有使用kv cache以及溫度參數 |
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Typo in 'casual attention'; it should be 'causal attention'.
Suggested change
| - Baseline Autoregressive Model: casual attention的GPT模型，輸入有append一個[cls]，並且有使用kv cache以及溫度參數 | |
| - Baseline Autoregressive Model: causal attention的GPT模型，輸入有append一個[cls]，並且有使用kv cache以及溫度參數 |
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Pull Request Overview
This PR introduces a new markdown document detailing a paper on autoregressive image generation without using vector quantization.
- Added a new markdown file with paper details, experimental setups, and comparison figures.
- Provides background, methodology, and implementation details for the proposed approach.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
zephyr-sh
approved these changes
May 17, 2025
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