Model Gallery

3 models from 1 repositories

Filter by type:

Filter by tags:

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

minimax-h3-fl2va-q4
MiniMax-H3 served by vllm.cpp, LocalAI's own C++ port of vLLM. It generates video AND audio jointly from a text prompt, so a clip comes back as an MP4 with a real soundtrack rather than a silent render: ask for speech in the prompt and the model lip-syncs it. This is the Q4_K_M quantisation of the FL2VA partition, which serves text-to-video (t2va) and first/last-frame conditioning (fl2va). Reference conditioning (ref2va) is a different checkpoint and is refused by this one. Roughly 40 GB of weights across five files, plus the two VAE configs that carry the latent statistics. The default canvas is 1344x768 at 124 frames and 24 fps, about 5.2 seconds. Generation is slow — measured at roughly 176 s per denoise step at that canvas on a 20-SM device, so the 50-step default is a multi-hour job. Muxing the finished frames needs ffmpeg on the host.

Repository: localaiLicense: other

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