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vllm-omni-wan2.2-t2v
Wan2.2-T2V-A14B via vLLM-Omni - Text-to-video generation model from Wan-AI. Generates high-quality videos from text prompts using a 14B parameter diffusion model.

Repository: localaiLicense: apache-2.0

wan-2.1-t2v-1.3b-ggml
Wan 2.1 T2V 1.3B — text-to-video diffusion model, GGUF-quantized for the stable-diffusion.cpp backend. Generates short (33-frame) 832x480 clips from a text prompt. Cheapest Wan variant, suitable for CPU-offloaded inference with ~10 GB of usable RAM.

Repository: localaiLicense: apache-2.0

ltx-2.3-22b-dev-ggml
LTX-2.3 22B dev - DiT-based audio-video foundation model from Lightricks, GGUF-quantized for the stable-diffusion.cpp backend. Generates synchronized video and audio from a text prompt (T2V), a reference image (I2V), or first/last frame pairs (FLF2V). Uses gemma-3-12b-it as the text encoder and ships dedicated video and audio VAEs plus an embeddings_connectors safetensors that bridges the LLM hidden states to the diffusion model. This entry uses the dynamic (UD) Q4_K_M quantization of the 22B model (~16 GB) paired with the UD-Q4_K_XL QAT Gemma encoder (~7.4 GB). Recommended generation: width=1280, height=720, video_frames=33, fps=24, sampler=euler, cfg_scale=6.0.

Repository: localaiLicense: ltx-2-community-license-agreement

ltx-2.3-22b-dev-ggml-q4_k_m
LTX-2.3 22B dev - non-dynamic Q4_K_M quantization (~14.3 GB). Same pipeline as ltx-2.3-22b-dev-ggml but with the plain Q4_K_M weights instead of the dynamic UD-Q4_K_M variant. Slightly smaller and slightly lower quality.

Repository: localaiLicense: ltx-2-community-license-agreement

ltx-2.3-22b-dev-ggml-q8_0
LTX-2.3 22B dev - Q8_0 quantization (~22.8 GB). Highest-quality quantized dev variant on the cpp backend; needs roughly twice the VRAM/RAM of the Q4 entries but produces noticeably cleaner audio and motion. Paired with the QAT Gemma-3 12B encoder.

Repository: localaiLicense: ltx-2-community-license-agreement

ltx-2.3-22b-distilled-ggml
LTX-2.3 22B distilled - faster student of the dev model, GGUF-quantized for the stable-diffusion.cpp backend. Trades a small amount of quality for substantially fewer sampling steps, making it the right pick for iterative previews and CPU-offloaded inference. Same input modalities as the dev entry (T2V / I2V / FLF2V) and the same gemma-3-12b-it text encoder. This entry uses the dynamic (UD) Q4_K_M quantization of the 22B distilled model (~16.3 GB). Recommended generation: width=1280, height=720, video_frames=33, fps=24, sampler=euler, cfg_scale=6.0.

Repository: localaiLicense: ltx-2-community-license-agreement

ltx-2.3-22b-distilled-ggml-q4_k_m
LTX-2.3 22B distilled - non-dynamic Q4_K_M quantization (~14.3 GB). Same pipeline as ltx-2.3-22b-distilled-ggml but with the plain Q4_K_M weights instead of the dynamic UD-Q4_K_M variant.

Repository: localaiLicense: ltx-2-community-license-agreement

ltx-2.3-22b-distilled-ggml-q8_0
LTX-2.3 22B distilled - Q8_0 quantization (~22.8 GB). Highest-quality distilled variant on the cpp backend; useful when you want the distilled sampling cost but the cleanest possible output.

Repository: localaiLicense: ltx-2-community-license-agreement