Compute token counts, inference latency, and quality ceiling for Emu3-style next-token generation and pick between Emu3-family and diffusion. Use when you need help with token gen cost analyzer.
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---
name: token-gen-cost-analyzer
description: Compute token counts, inference latency, and quality ceiling for Emu3-style next-token generation and pick between Emu3-family and diffusion. Use when you need help with token gen cost analyzer.
license: CC-BY-NC-SA-4.0
phase: 12
lesson: 12
metadata:
version: 1.0.0
tags: [emu3, next-token-prediction, video-gen, diffusion, cfg]
---
Given a generation product spec (image or video, target resolution, quality tier, throughput requirement), compute token counts for Emu3-style next-token generation, estimate inference cost, and pick between Emu3-family and diffusion.
Produce:
1. Token count. Per-image tokens at chosen tokenizer reduction (typically 8x per dim for image). Per-video tokens with 3D VQ (typically 4x4x4 spatiotemporal).
2. Inference latency. Tokens / throughput (tokens-per-second) for Emu3-family; denoise-steps * step-time for diffusion. Cite concrete A100 / H100 ranges.
3. Quality ceiling. Tokenizer reconstruction PSNR (30-32 dB for IBQ-class), FID expectations on MJHQ-30K, FVD for video.
4. CFG configuration. Recommended guidance weight (gamma) per task; typical 3.0 for standard gen, 5-7 for strong prompt adherence.
5. Pick. Emu3-family if product needs unified understanding + generation or any-modality flexibility; diffusion (SDXL / SD3 / Flux) if product is image-gen-only with strict latency.
Hard rejects:
- Claiming Emu3 is faster than diffusion at inference. It is not; the autoregressive decode over thousands of image tokens is the standing cost.
- Recommending Emu3-family without specifying CFG weight. Quality collapses without it.
- Proposing Emu3 for strict 4K image generation. Token count at 2048+ resolution blows KV cache and takes minutes.
Refusal rules:
- If latency budget is <5s per image, refuse Emu3 and recommend SDXL or SD3.
- If product must emit images AND describe them AND reason about third-party images, recommend Emu3-family (the unified loss is the point); diffusion cannot do this without a separate VLM.
- If user wants open weights with permissive license for commercial use, refuse Emu3 — check its license first; some versions are research-only.
Output: one-page analysis with token counts, latency estimates, quality ceiling, CFG config, and a pick with justification. End with arXiv 2409.18869 (Emu3) and 2408.11039 (Transfusion) for the alternative.
