CivArchive
    LTX 2.3 Video to Video Fast with GGUF - v1.0

    I improved as best I could and made it use video to video and have had amazing results. With my RTX 5090 with 64gb ram I can make these in under a minute.

    🧠 Model Configuration Overview

    🔹 Base UNet

    • Model: ltx-2-3-22b-dev-Q4_K_M.gguf

    • Type: UNet (quantized GGUF)

    ⸻

    🔹 Distilled LoRA

    • LoRA: ltx-2.3-22b-distilled-lora-dynamic_fro09_avg_rank_105_bf16.safetensors

    • Strength: 0.60

    • Type: Distilled LoRA (bf16)

    ⸻

    🔹 Text Encoders (Dual CLIP)

    • CLIP 1: gemma_3_12B_it_fp4_mixed.safetensors

    • Type: Text Encoder (Gemma, FP4 mixed)

    • CLIP 2: ltx-2.3_text_projection_bf16.safetensors

    • Type: Text Projection (bf16)

    • Mode: ltxv

    ⸻

    🔹 Audio VAE

    • Model: LTX23_audio_vae_bf16.safetensors

    • Device: main_device

    • Precision: bf16

    • Type: Audio VAE

    ⸻

    🔹 Video VAE

    • Model: LTX23_video_vae_bf16.safetensors

    • Device: main_device

    • Precision: bf16

    • Type: Video VAE

    ⸻

    🔹 Upscaler

    • Model: ltx-2.3-spatial-upscaler-x2-1.1.safetensors

    • Type: Spatial Upscaler (x2)

    Description

    Workflows
    SD 1.5

    Details

    Downloads
    9
    Platform
    CivitAI
    Platform Status
    Available
    Created
    4/1/2026
    Updated
    4/2/2026
    Deleted
    -

    Files

    ltx23VideoToVideoFast_v10.zip

    Mirrors