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vibevoice-cpp
VibeVoice Realtime 0.5B (C++ / GGML, Q8_0) - native C++ port of Microsoft VibeVoice
via the vibevoice-cpp backend. 24kHz mono TTS with voice cloning from a single
reference voice prompt. Default voice prompt: en-Carter_man.
Repository: localaiLicense: mit
vibevoice-cpp-asr
VibeVoice ASR 7B (C++ / GGML, Q4_K) - long-form speech-to-text with speaker
diarization. Returns per-speaker JSON segments with start/end timestamps.
English-only. ~10 GB download.
Repository: localaiLicense: mit
rfdetr-cpp-nano
RF-DETR Nano object detection model, served via the native rfdetr.cpp backend (ggml + purego, no Python).
Q8_0 quantization is the recommended default for CPU: same accuracy as F16/F32, ~20MB on disk, fastest CPU latency.
Pure C++/ggml runtime; no Python dependencies. Drop-in for the /v1/detection endpoint.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-small
RF-DETR Small object detection model (DINOv2-small backbone, 512px input, 3 decoder layers), served
via the native rfdetr.cpp backend (ggml + purego, no Python). A step up from Nano in accuracy while
staying lightweight on CPU. F16 quantization is the recommended default: identical accuracy to F32
at roughly half the size. Drop-in for the /v1/detection endpoint.
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
wan-2.1-i2v-14b-480p-ggml
Wan 2.1 I2V 14B 480P — image-to-video diffusion, GGUF Q4 quantization.
Animates a reference image into a 33-frame 480p clip. Requires more
RAM than the 1.3B T2V variant; CPU offload enabled by default.
Repository: localaiLicense: apache-2.0
wan-2.1-flf2v-14b-720p-ggml
Wan 2.1 FLF2V 14B 720P — first-last-frame-to-video diffusion, GGUF Q4_K_M.
Takes a start and end reference image and interpolates a 33-frame clip
between them. Unlike the plain I2V variant this model feeds the end
frame through clip_vision as well, so it conditions semantically (not
just in pixel-space) on both endpoints. That makes it the right choice
for seamless loops (start_image == end_image) and clean narrative cuts.
Native 720p but accepts 480p resolutions; shares the same VAE, t5xxl
text encoder, and clip_vision_h as I2V 14B.
Repository: localaiLicense: apache-2.0
wan-2.1-i2v-14b-720p-ggml
Wan 2.1 I2V 14B 720P — image-to-video diffusion, GGUF Q4_K_M.
Native 720p sibling of the 480p I2V model: animates a single
reference image into a 33-frame clip at up to 1280x720. Trained
purely as image-to-video (no first-last-frame interpolation path),
so motion is freer and better-suited to single-anchor animation
than repurposing the FLF2V 720P variant for i2v. Shares the same
VAE, umt5_xxl text encoder, and clip_vision_h as the I2V 14B 480P
and FLF2V 14B 720P entries.
Repository: localaiLicense: apache-2.0
sd-1.5-ggml
Stable Diffusion 1.5
Repository: localaiLicense: creativeml-openrail-m
sd-3.5-medium-ggml
Stable Diffusion 3.5 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
Repository: localaiLicense: stabilityai-ai-community
sd-3.5-large-ggml
Stable Diffusion 3.5 Large is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
Repository: localaiLicense: stabilityai-ai-community
flux.1-dev-ggml
FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post.
Key Features
Cutting-edge output quality, second only to our state-of-the-art model FLUX.1 [pro].
Competitive prompt following, matching the performance of closed source alternatives .
Trained using guidance distillation, making FLUX.1 [dev] more efficient.
Open weights to drive new scientific research, and empower artists to develop innovative workflows.
Generated outputs can be used for personal, scientific, and commercial purposes as described in the flux-1-dev-non-commercial-license.
This model is quantized with GGUF
Repository: localaiLicense: flux-1-dev-non-commercial-license
flux.1-dev-ggml-q8_0
FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post.
Key Features
Cutting-edge output quality, second only to our state-of-the-art model FLUX.1 [pro].
Competitive prompt following, matching the performance of closed source alternatives .
Trained using guidance distillation, making FLUX.1 [dev] more efficient.
Open weights to drive new scientific research, and empower artists to develop innovative workflows.
Generated outputs can be used for personal, scientific, and commercial purposes as described in the flux-1-dev-non-commercial-license.
Repository: localaiLicense: flux-1-dev-non-commercial-license
flux.1-dev-ggml-abliterated-v2-q8_0
FLUX.1 [dev] is an abliterated version of FLUX.1 [dev]
Repository: localaiLicense: flux-1-dev-non-commercial-license
flux.1-krea-dev-ggml
FLUX.1 Krea [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post and Krea's blog post.
Cutting-edge output quality, with a focus on aesthetic photography.
Competitive prompt following, matching the performance of closed source alternatives.
Trained using guidance distillation, making FLUX.1 Krea [dev] more efficient.
Open weights to drive new scientific research, and empower artists to develop innovative workflows.
Generated outputs can be used for personal, scientific, and commercial purposes, as described in the flux-1-dev-non-commercial-license.
Repository: localaiLicense: flux-1-dev-non-commercial-license
flux.1-krea-dev-ggml-q8_0
FLUX.1 Krea [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post and Krea's blog post.
Cutting-edge output quality, with a focus on aesthetic photography.
Competitive prompt following, matching the performance of closed source alternatives.
Trained using guidance distillation, making FLUX.1 Krea [dev] more efficient.
Open weights to drive new scientific research, and empower artists to develop innovative workflows.
Generated outputs can be used for personal, scientific, and commercial purposes, as described in the flux-1-dev-non-commercial-license.
Repository: localaiLicense: flux-1-dev-non-commercial-license
whisper-1
Port of OpenAI's Whisper model in C/C++
Repository: localaiLicense: mit
whisper-base
Port of OpenAI's Whisper model in C/C++
Repository: localaiLicense: mit
whisper-tiny
Port of OpenAI's Whisper model in C/C++
Repository: localaiLicense: mit
silero-vad-ggml
Silero VAD - pre-trained enterprise-grade Voice Activity Detector.
Repository: localai
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
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