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.
Scanned 9/8/2026
Install to Claude Code
npx -y skills add anubhavg-icpl/vibe --skill token-gen-cost-analyzer --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Token Gen Cost Analyzer?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/anubhavg-icpl-token-gen-cost-analyzer)More formats (shields.io, HTML) on the badges page.
---
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.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!