72 lines
3.2 KiB
YAML
72 lines
3.2 KiB
YAML
# LLM backend: "local" or "lmstudio"
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llm:
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backend: local # change to "lmstudio" to use LM Studio instead
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max_cache_tokens: 4096 # max KV-cache size per session (tokens); 0 to disable caching
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system_prompt: >-
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You are a helpful voice assistant.
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Keep your responses extremely concise but natural for spoken conversation.
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Do not use markdown, bullet points, code blocks, emojis, or any formatting that doesn't work in speech.
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# Settings used only when backend = "lmstudio"
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lmstudio:
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url: http://host.docker.internal:1234 # host.docker.internal resolves to your PC from inside Docker
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model: "" # leave empty to use whatever model LM Studio has loaded
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# Avatar video generation (Wan2.2-TI2V-5B-Turbo GGUF via LightX2V + MuseTalk lip-sync)
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video:
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enabled: true # master toggle — when false, video models are not loaded
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backend: lightx2v # only option for now
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mode: reflective # "library" (pre-baked clips) | "reflective" (fresh per turn)
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resolution: 480 # 480 or 720
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fps: 16 # Wan2.2 native rate; MuseTalk resamples as needed
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library:
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base_clip_count: 4 # how many speaking base clips to pre-generate per avatar
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base_clip_seconds: 6 # duration of each pre-baked clip
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# MuseTalk audio-driven lip-sync. When disabled, Wan2.2 base frames are
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# used as-is without a lip-sync pass — useful when MuseTalk isn't installed
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# or while iterating on the base pipeline.
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musetalk:
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enabled: false # toggle lip-sync on/off
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reflective:
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clip_seconds: 5 # target length of each fresh Wan2.2 clip per turn
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clip_prompt_template: >-
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webcam view of a person speaking, {reply_hint},
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casual gestures, natural lighting, soft focus background
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prompt_reply_words: 18 # max words lifted from reply to inject as {reply_hint}
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# Model sources for the video stack. T5/VAE/tokenizer come from the
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# Wan-AI base repo. The single dense DIT comes from wan22_dit_repo as
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# GGUF (Turbo 4-step distill). Both repos download on first run into
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# HF_HOME=/cache/huggingface.
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#
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# Supported dit_quant_scheme values (dense 5B Turbo — GGUF only):
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# gguf-Q8_0 — 8-bit, ~6 GB DIT, ~6.5 GB VRAM at load (default)
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# gguf-Q4_K_M — 4-bit, ~3.5 GB DIT, lower VRAM for tight budgets
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# (any gguf-<level> published in hum-ma/Wan2.2-TI2V-5B-Turbo-GGUF)
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models:
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wan22_base_repo: Wan-AI/Wan2.2-TI2V-5B
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wan22_dit_repo: hum-ma/Wan2.2-TI2V-5B-Turbo-GGUF
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wan22_dit_quant_scheme: gguf-Q8_0
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wan22_t5_quantized: true
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wan22_model_cls: wan2.2
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wan22_config_json: /app/configs/lightx2v/wan22_i2v_gguf_5b_turbo.json
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musetalk_path: TMElyralab/MuseTalk
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# LoRAs applied to the dense 5B DIT at load time via LightX2V's
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# lora_dynamic_apply path (merged during GGUF dequant). Dense has a
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# single set of weights so `target` is always `both`.
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#
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# The old MoE-trained wan22-H-e8 / wan22-L-e8 LoRAs are NOT compatible
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# with the 5B DIT and are disabled here. Future 5B-compatible LoRAs
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# should follow the shape shown below.
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loras: []
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# loras:
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# - path: /cache/loras/your-5b-lora.safetensors
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# weight: 1.0
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# target: both
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# name: your-5b-lora
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