87 lines
2.9 KiB
Markdown
87 lines
2.9 KiB
Markdown
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# Video Generation Research — Qdrant Quick-Start
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**Canonical data store**: Qdrant `comfyui_decisions` @ `http://10.0.0.22:6333` (64+ points)
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**Embedding**: `snowflake-arctic-embed2:latest` (1024-dim, Cosine)
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**Human companion**: `/home/n8n/workspace/comfy/research.md` (search topics + URLs only, no specs)
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---
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## What Lives Where
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| Store | Content | How to Query |
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|-------|---------|-------------|
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| **Qdrant** `comfyui_decisions` | All specs, VRAM numbers, node names, workarounds, paper abstracts, trend analysis | Vector search via Ollama embedding + POST to 10.0.0.22:6333 |
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| **Qdrant** `comfyui_kb` | General knowledge (3884 points, pre-existing) | Same method |
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| **research.md** | Human-readable search topics, URL quick-reference, comparison tables | grep / raw read |
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| **This file** | Quick-start pointers to the above | Read once, then query Qdrant |
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---
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## Qdrant Query Pattern
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```python
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import requests, json
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# 1. Get embedding
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emb_resp = requests.post("http://localhost:11434/api/embed", json={
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"model": "snowflake-arctic-embed2:latest",
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"input": "YOUR SEARCH TOPIC"
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})
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vector = emb_resp.json()["embeddings"][0]
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# 2. Search Qdrant
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search_resp = requests.post(
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"http://10.0.0.22:6333/collections/comfyui_decisions/points/search",
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json={"vector": vector, "limit": 5, "with_payload": True}
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)
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results = search_resp.json()["result"]["points"]
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```
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---
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## Example Search Topics
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Use these as `input` strings above (from `research.md`):
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- "LTX 2.3 specs, frames, VRAM, audio latent"
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- "HunyuanVideo frame limits block swapping FP8"
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- "Wan 2.2 low VRAM sound-to-video FreeLong"
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- "Google Veo 3 native audio cloud"
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- "Kling 3.0 Omni visual identity vocal tone"
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- "ID-LoRA face identity lock LTX"
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- "FreeLong spectral blending Wan motion reversal"
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- "Segment chaining last frame to first frame"
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- "LivePortrait portrait animation"
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- "MuseTalk LatentSync lip-sync"
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- "RIFE frame interpolation"
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- "MultiTalk NeurIPS multi-person conversation"
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- "StreamingT2V CVPR long video streaming"
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---
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## Quick Model Comparison (from Qdrant)
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| Tool | Native Audio | Max Frames | VRAM | Best For |
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|------|-------------|------------|------|----------|
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| LTX 2.3 | YES | 361 | 12-24 GB | Local, audio-synced |
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| HunyuanVideo 1.5 | NO | 129 | 20-24 GB | Best open-source quality |
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| Wan 2.2 | YES (S2V) | 81 / 1025 | 5-16 GB | Low VRAM, sound-driven |
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| Veo 3 | YES | API | Cloud | Highest fidelity |
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| Kling 3.0 Omni | YES | API | Cloud | Character + voice |
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---
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## Key URLs (Quick Reference)
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- LTX: https://huggingface.co/Lightricks
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- Hunyuan: https://github.com/Tencent-Hunyuan/HunyuanVideo
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- Wan wrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper
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- Veo: https://deepmind.google/models/veo/
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- Kling: https://kling.ai/
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- LivePortrait: https://github.com/kijai/ComfyUI-LivePortraitKJ
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- VHS: https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
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---
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*All detailed data is in Qdrant. This file is a navigation aid only.*
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