Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

### Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation
Peize Sun1 Yi Jiang2† Shoufa Chen1 Shilong Zhang1 Bingyue Peng2 Ping Luo1∗ Zehuan Yuan2∗, 1 The University of Hong Kong,  2 ByteDance
https://arxiv.org/pdf/2406.06525

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# 単語帳
数日ぶりの作業で小文字にするのを間違えて大文字にしちゃった。ごめんなさい。
処理がうまくなくて、arxivが53と44の２種類に分類している。
短い文字が、４種類個別に集計しているものがあった。



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|EMBEDDING|11|
|HO|11|
|IEEE|11|
|LEARNING|11|
|LIU|11|
|LLAMAGEN|11|
|LU|11|
|NICHOL|11|
|PEEBLES|11|
|SHOWN|11|
|TOKENIZERS|11|
|TRAINED|11|
|VISION|11|
|ARCHITECTURE|10|
|BETTER|10|
|FIGURE|10|
|HTTPS|10|
|IT|10|
|LEE|10|
|LLM|10|
|NOT|10|
|PATTERN|10|
|RADFORD|10|
|RECOGNITION|10|
|SAME|10|
|SAMPLING|10|
|VECTOR|10|
|VLLM|10|
|WHEN|10|
|AT|9|
|BE|9|
|BLUE|9|
|DOWNSAMPLE|9|
|J|9|
|LEARNING|9|
|MORE|9|
|P|9|
|SALIMANS|9|
|SET|9|
|TABLE|9|
|THESE|9|
|TWO|9|
|UP|9|
|WHICH|9|
|BASED|8|
|CAN|8|
|COM|8|
|COMMUNITY|8|
|CVF|8|
|DESIGNS|8|
|FIGURE|8|
|GENERATED|8|
|II|8|
|LLAMA|8|
|LOSS|8|
|METHODS|8|
|MODELS|8|
|NEXT|8|
|OPENAI|8|
|OR|8|
|POPULAR|8|
|RESEARCH|8|
|T|8|
|TIM|8|
|UNIFIED|8|
|USED|8|
|WU|8|
|XL|8|
|YANG|8|
|ADVERSARIAL|7|
|AESTHETIC|7|
|ALWAYS|7|
|C|7|
|CODES|7|
|COMPETITIVE|7|
|DAVID|7|
|DECODER|7|
|EPOCHS|7|
|ET|7|
|FOUNDATION|7|
|HAN|7|
|LABEL|7|
|MACHINE|7|
|MASKED|7|
|METRICS|7|
|MODELING|7|
|QUALITY|7|
|RATIO|7|
|SAMPLES|7|
|SETTING|7|
|SONG|7|
|SPEED|7|
|TEAM|7|
|TOUVRON|7|
|TRAINING|7|
|VQGAN|7|
|WE|7|
|WORK|7|
|ACHIEVE|6|
|ADOPT|6|
|BROWN|6|
|CAPTION|6|
|CHRISTOPHER|6|
|DEEP|6|
|DEN|6|
|DONG|6|
|EFFECT|6|
|ENCODER|6|
|EXAMPLES|6|
|EXPERIMENTS|6|
|FEATURE|6|
|FIDELITY|6|
|FURTHER|6|
|GE|6|
|HAS|6|
|HUANG|6|
|IMPROVE|6|
|JIANG|6|
|KWON|6|
|MULTIMODAL|6|
|NO|6|
|OORD|6|
|PODELL|6|
|PRE|6|
|PREVIOUS|6|
|Q|6|
|RAMESH|6|
|RESEARCH|6|
|SCALABLE|6|
|SCORE|6|
|SERVING|6|
|SHOWS|6|
|SOURCE|6|
|SUN|6|
|SYNTHESIS|6|
|TEMPERATURE|6|
|THAN|6|
|THEIR|6|
|UNDERSTANDING|6|
|VAN|6|
|VANILLA|6|
|WEI|6|
|WEIGHT|6|
|WHERE|6|
|WILLIAM|6|
|XU|6|
|YI|6|
|ZHAO|6|
|ZHU|6|
|ADVANCED|5|
|AFTER|5|
|ALEXANDER|5|
|ALIGNMENT|5|
|ALSO|5|
|AR|5|
|ARE|5|
|BAI|5|
|BROCK|5|
|DAI|5|
|DENG|5|
|DESIGN|5|
|DETAILED|5|
|DEVELOPED|5|
|DIMENSION|5|
|DIT|5|
|E|5|
|EFFECT|5|
|ERMON|5|
|EVALUATING|5|
|FAN|5|
|FID竊|5|
|FLOWERS|5|
|FOR|5|
|FUTURE|5|
|GENERATION|5|
|GRADIENT|5|
|HAVE|5|
|INCEPTION|5|
|INCREASING|5|
|INSTRUCTION|5|
|INTERNAL|5|
|INTO|5|
|IS竊|5|
|JUN|5|
|L|5|
|LUO|5|
|ORIGINAL|5|
|OTHER|5|
|OVER|5|
|PENG|5|
|PHOTO|5|
|PIXELS|5|
|PROCESS|5|
|PSNR竊|5|
|RECALL|5|
|REPORT|5|
|RESIZED|5|
|RESULTS|5|
|ROOM|5|
|SCALABILITY|5|
|SEBASTIAN|5|
|SHAZEER|5|
|SITTING|5|
|SPACE|5|
|SSIM竊|5|
|THEY|5|
|TRANSFORMERS|5|
|USAGE|5|
|VALIDATION|5|
|VQGAN|5|
|WORKS|5|
|Z|5|

# docker

```shell-session:bash
$ docker commit c6430640806f kaizenjapan/wc
sha256:10a0f931326a53dc72485350888d90403714c059c878c7280660f5e5a9ee3f36
$ docker push kaizenjapan/wc
Using default tag: latest
The push refers to repository [docker.io/kaizenjapan/wc]
93bb6265160c: Pushed 
afa85b6e2808: Mounted from kaizenjapan/llm 
8da92be08e15: Mounted from kaizenjapan/llm 
latest: digest: sha256:f02a9142be5b988c7aafe65bdb08eeebf5b487c34f342bab15cc60be3cef990c size: 954
```