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vllm-omni-wan2.2-i2v
Wan2.2-I2V-A14B via vLLM-Omni - Image-to-video generation model from Wan-AI. Generates high-quality videos from images using a 14B parameter diffusion model.
Repository: localaiLicense: apache-2.0
ltx-2
**LTX-2** is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model. It brings together the core building blocks of modern video generation, with open weights and a focus on practical, local execution.
**Key Features:**
- **Joint Audio-Video Generation**: Generates synchronized video and audio in a single model
- **Image-to-Video**: Converts static images into dynamic videos with matching audio
- **High Quality**: Produces realistic video with natural motion and synchronized audio
- **Open Weights**: Available under the LTX-2 Community License Agreement
**Model Details:**
- **Model Type**: Diffusion-based audio-video foundation model
- **Architecture**: DiT (Diffusion Transformer) based
- **Developed by**: Lightricks
- **Paper**: [LTX-2: Efficient Joint Audio-Visual Foundation Model](https://arxiv.org/abs/2601.03233)
**Usage Tips:**
- Width & height settings must be divisible by 32
- Frame count must be divisible by 8 + 1 (e.g., 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97, 105, 113, 121)
- Recommended settings: width=768, height=512, num_frames=121, frame_rate=24.0
- For best results, use detailed prompts describing motion and scene dynamics
**Limitations:**
- This model is not intended or able to provide factual information
- Prompt following is heavily influenced by the prompting-style
- When generating audio without speech, the audio may be of lower quality
**Citation:**
```bibtex
@article{hacohen2025ltx2,
title={LTX-2: Efficient Joint Audio-Visual Foundation Model},
author={HaCohen, Yoav and Brazowski, Benny and Chiprut, Nisan and others},
journal={arXiv preprint arXiv:2601.03233},
year={2025}
}
```
Repository: localaiLicense: ltx-2-community-license-agreement
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
ltx-2.3
**LTX-2.3** is an improved DiT-based audio-video foundation model from Lightricks, building upon the LTX-2 architecture with enhanced capabilities for generating synchronized video and audio within a single model.
**Key Features:**
- **Joint Audio-Video Generation**: Generates synchronized video and audio in a single model
- **Image-to-Video**: Converts static images into dynamic videos with matching audio
- **Enhanced Quality**: Improved video quality and motion generation over LTX-2
- **Open Weights**: Available under the LTX-2 Community License Agreement
**Model Details:**
- **Model Type**: Diffusion-based audio-video foundation model
- **Architecture**: DiT (Diffusion Transformer) based
- **Developed by**: Lightricks
- **Parent Model**: LTX-2
**Usage Tips:**
- Width & height settings must be divisible by 32
- Frame count must be divisible by 8 + 1 (e.g., 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97, 105, 113, 121)
- Recommended settings: width=768, height=512, num_frames=121, frame_rate=24.0
- For best results, use detailed prompts describing motion and scene dynamics
**Limitations:**
- This model is not intended or able to provide factual information
- Prompt following is heavily influenced by the prompting-style
- When generating audio without speech, the audio may be of lower quality
Repository: localaiLicense: ltx-2-community-license-agreement
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