Filter by type:
Filter by tags:
minimax-h3-fl2va-unpruned-q4-ggml
MiniMax-H3 served by stable-diffusion.cpp. It generates video AND stereo
audio jointly from a text prompt, so a clip comes back as an MP4 with a
soundtrack. Muxing the finished frames needs ffmpeg on the host.
Runtime: stable-diffusion.cpp (backend stablediffusion-ggml). Partition:
fl2va. DiT layout: unpruned. Format: Q4_K_M.
This entry is the FL2VA partition: text-to-video, first-frame (I2VA) and first/last-frame (FL2VA) conditioning. It refuses reference conditioning (reference image or audio): that needs minimax-h3-ref2va-pruned-q4-ggml, which is a separate checkpoint.
Render status: verified render (the stable-diffusion.cpp page shows a text-to-video and
an image-to-video render with this file).
Weights are the Q4_K_M quantisation of the DiT (~18.8 GB), the
Qwen3-VL-32B MiniMax-H3 text encoder (~18 GB), the fp16 video VAE (~5 GB)
and the fp32 audio VAE (~0.6 GB). MiniMax-H3 runs at 24 fps and the width
and height are rounded up to a multiple of 32. The frame count is
rounded up to the 17k + 5 grid.
Repository: localaiLicense: other
minimax-h3-fl2va-pruned-q4-ggml
MiniMax-H3 served by stable-diffusion.cpp. It generates video AND stereo
audio jointly from a text prompt, so a clip comes back as an MP4 with a
soundtrack. Muxing the finished frames needs ffmpeg on the host.
Runtime: stable-diffusion.cpp (backend stablediffusion-ggml). Partition:
fl2va. DiT layout: pruned. Format: Q4_K_M.
This entry is the FL2VA partition: text-to-video, first-frame (I2VA) and first/last-frame (FL2VA) conditioning. It refuses reference conditioning (reference image or audio): that needs minimax-h3-ref2va-pruned-q4-ggml, which is a separate checkpoint. It is the smaller, pruned counterpart of minimax-h3-fl2va-unpruned-q4-ggml.
Render status: rendered with stable-diffusion.cpp on CUDA (text-to-video, 512x288, 22 frames, with audio). Not yet rendered through LocalAI.
Weights are the Q4_K_M quantisation of the DiT (~11.4 GB), the
Qwen3-VL-32B MiniMax-H3 text encoder (~18 GB), the fp16 video VAE (~5 GB)
and the fp32 audio VAE (~0.6 GB). MiniMax-H3 runs at 24 fps and the width
and height are rounded up to a multiple of 32. The frame count is
rounded up to the 17k + 5 grid.
Repository: localaiLicense: other
minimax-h3-ref2va-pruned-q4-ggml
MiniMax-H3 served by stable-diffusion.cpp. It generates video AND stereo
audio jointly from a text prompt, so a clip comes back as an MP4 with a
soundtrack. Muxing the finished frames needs ffmpeg on the host.
Runtime: stable-diffusion.cpp (backend stablediffusion-ggml). Partition:
ref2va. DiT layout: pruned. Format: Q4_K_M.
This entry is the REF2VA partition, the one that takes REFERENCE conditioning: reference images, reference videos (a directory of frames, with an optional paired soundtrack) and reference audio steer the subject and the voice of the clip. It does not serve first/last-frame keyframes, and keyframes cannot be mixed with references. For plain text-to-video or keyframes use minimax-h3-fl2va-unpruned-q4-ggml.
Render status: verified render (the stable-diffusion.cpp page shows a reference-to-video
render with this file).
Weights are the Q4_K_M quantisation of the DiT (~11.4 GB), the
Qwen3-VL-32B MiniMax-H3 text encoder (~18 GB), the fp16 video VAE (~5 GB)
and the fp32 audio VAE (~0.6 GB). MiniMax-H3 runs at 24 fps and the width
and height are rounded up to a multiple of 32. The frame count is
rounded up to the 17k + 5 grid.
Repository: localaiLicense: other
realesrgan-x4plus-anime-6b
RealESRGAN x4plus anime 6B is a six-block ESRGAN model for anime images,
with a native 4x scale. Uses the official PyTorch .pth checkpoint through
stable-diffusion.cpp, without conversion. Upscale-only: not a text-to-image model.
Repository: localaiLicense: BSD-3-Clause
stable-diffusion-x4-upscaler
Stable Diffusion x4 Upscaler is Stability AI's diffusion-based super-resolution model. It enlarges low-resolution images by four times while reconstructing image detail.
Repository: localaiLicense: openrail++
dreamshaper
A text-to-image model that uses Stable Diffusion 1.5 to generate images from text prompts. This model is DreamShaper model by Lykon.
Repository: localaiLicense: other
stable-diffusion-3-medium
Stable Diffusion 3 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
Repository: localaiLicense: stabilityai-ai-community
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
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
ideogram-4-iq4nl-ggml
Ideogram 4 is a text-to-image diffusion model known for state-of-the-art prompt adherence and exceptional, accurate text rendering inside images. It is driven by a Qwen3-VL-8B text encoder and performs real classifier-free guidance from a separate unconditional diffusion model.
This is the iQ4_NL (4-bit) quantization, a good balance of quality and footprint (~5.8GB diffusion + ~5.8GB unconditional). The bundle also pulls the Qwen3-VL-8B-Instruct text encoder and the FLUX.2 VAE. Quantized GGUF weights by stduhpf for use with stable-diffusion.cpp.
Repository: localaiLicense: ideogram-non-commercial-model-agreement
ideogram-4-q8_0-ggml
Ideogram 4 is a text-to-image diffusion model known for state-of-the-art prompt adherence and exceptional, accurate text rendering inside images. It is driven by a Qwen3-VL-8B text encoder and performs real classifier-free guidance from a separate unconditional diffusion model.
This is the Q8_0 (8-bit) quantization for highest quality (~10.1GB diffusion + ~10.1GB unconditional). The bundle also pulls the Qwen3-VL-8B-Instruct text encoder and the FLUX.2 VAE. Quantized GGUF weights by stduhpf for use with stable-diffusion.cpp.
Repository: localaiLicense: ideogram-non-commercial-model-agreement
qwen-image-2.1-q4_k-ggml
Qwen-Image 2.1 is the Qwen image generation foundation model, with strong prompt adherence and accurate text rendering (English and Chinese). It uses Qwen3-VL-8B as the text encoder and its own VAE, and supports both text-to-image and image editing: pass reference images to edit them.
This is the Q4_K (4-bit) quantization (~4.2GB diffusion model) by leejet for stable-diffusion.cpp. The bundle also pulls the Qwen3-VL-8B-Instruct text encoder with its vision projector (used for image editing) and the Qwen-Image 2.1 VAE. Use image dimensions divisible by 32.
Repository: localaiLicense: qwen-research
qwen-image-2.1-q8_0-ggml
Qwen-Image 2.1 is the Qwen image generation foundation model, with strong prompt adherence and accurate text rendering (English and Chinese). It uses Qwen3-VL-8B as the text encoder and its own VAE, and supports both text-to-image and image editing: pass reference images to edit them.
This is the Q8_0 (8-bit) quantization (~7.7GB diffusion model) by leejet for stable-diffusion.cpp. The bundle also pulls the Qwen3-VL-8B-Instruct text encoder with its vision projector (used for image editing) and the Qwen-Image 2.1 VAE. Use image dimensions divisible by 32.
Repository: localaiLicense: qwen-research
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-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