FisherKing-LTX2.3-[GGUF-Distilled]-I2V-ReferenceWorkflow-v1.0 [Low VRAM Compatible]

✅ Educational Reference Workflow

✅ Clean Baseline Implementation

✅ Dynamic VRAM Friendly

✅ Tiled VAE Decode

Features

✅ Wireless Modular Architecture

✅ Clean Reference Implementation

✅ Prompt Engineering Ready

✅ Two-Phase Sampling Pipeline

✅ Low VRAM Compatible

✅ Modular and Easily Extensible

Workflow Pipeline

Engine Configuration ↓ Video Clip Configuration ↓ Image Preprocessing ↓ Prompt Engineering ↓ Image Conditioning ↓ Phase 1 Sampling (Base Latent Generation) ↓ 2× Spatial Upscale ↓ Phase 2 Sampling (Detail Refinement) ↓ VAE Decode ↓ Final Video Output

Verified Settings

The following settings were used to validate this workflow and are recommended as the baseline configuration.

Sampling

CFG: 1.0

Steps: 11 (8 + 3)

Sampler: Euler Ancestral

Video

Frames: 145 (≈6 seconds @ 24 FPS)

Output FPS: 24

Tested Resolution: 1024 × 576 (16:9)

Required LoRA Configuration

No LoRAs were used during validation.

This workflow has been validated using the LTX 2.3 Distilled GGUF model. The official Distilled LoRA is not required when using the distilled model and has therefore been omitted from this reference workflow.

Hardware & Resolution Notes

Validated On

Hardware

GPU: NVIDIA RTX 2080 (8 GB VRAM)

System RAM: 64 GB

Software

ComfyUI: 0.27.x

Python: 3.13.7

PyTorch: 2.12.0 + CUDA 13.0

Validation Settings

Resolution: 1024 × 576

Length: 6 Seconds

Generation Time: ~12–14 Minutes

Although compatible with 8 GB VRAM , LTX 2.3 Text-to-Video remains computationally intensive and memory hungry.

System RAM plays an equally important role in handling intermediate tensors and memory paging during video generation.

Expected Behavior

8 GB VRAM + 16 GB RAM

Possible out-of-memory errors

Slower generation

8 GB VRAM + 32 GB RAM

Better stability

Performance depends on available system memory

8 GB VRAM + 64 GB RAM (or more)

Recommended configuration

If your system has a more capable GPU, consider increasing the output resolution to 720p or higher .

Design Philosophy

This workflow intentionally avoids unnecessary complexity.

The objective is to provide a stable, reproducible reference implementation that users can understand, learn from, and extend for their own creative projects.

Advanced optimizations such as SageAttention, Flash Attention, or other hardware-specific accelerations have been intentionally omitted. These optimizations vary across GPU architectures, CUDA versions, and operating systems. Keeping the reference workflow free from hardware-specific optimizations improves portability, simplifies debugging, and provides a consistent baseline for future experimentation.

Models Used

Model

ltx23DISTILLEDGGUF_q80.gguf

Latent Upscale Model

Not used in this reference workflow.

I intentionally omitted latent upscaling to keep the workflow simple and to explore Video-to-Video (V2V) upscaling as a dedicated post-processing workflow. A separate V2V reference workflow is planned.

Text Encoders

gemma_3_12B_it_fp4_mixed.safetensors

ltx_2.3_text_projection_bf16.safetensors

VAE

LTX23_audio_vae_bf16

LTX23_video_vae_bf16

LoRA

None

Notes

Update the resolution, frame rate, clip duration, and prompts to match your project requirements.

This workflow has been tuned and validated using the LTX 2.3 Distilled GGUF model.

Replacing the model with a non-distilled ( Dev ) version may require retuning parameters such as:

Sigma schedule

Scheduler

Sampling steps

CFG / Guidance

If your hardware allows, you can incorporate a latent upscaling stage into the generation pipeline or perform a dedicated Video-to-Video (V2V) upscaling pass after generation.

Why Tiled VAE Decode?

Native Video VAE Decode can exceed the VRAM budget on 8 GB GPUs , resulting in an out-of-memory error during decoding.

This workflow uses Tiled VAE Decode directly to provide more consistent and reliable execution on lower-VRAM hardware.

Reference Pipeline Design

This workflow intentionally generates video directly at the target resolution instead of using the common 0.5× latent generation → 2× latent upscale pipeline.

While the 0.5× approach can reduce generation time and VRAM requirements, it also introduces an additional optimization stage and associated tuning decisions.

The goal of this workflow is to demonstrate the core LTX 2.3 generation pipeline with minimal architectural complexity while providing a stable, reproducible baseline.

To keep the workflow focused and easy to understand, additional refinement passes, latent upscaling, and advanced enhancement stages have been intentionally omitted.

If you would like to incorporate latent upscaling into this workflow, refer to FisherKing-LTX2.3-[GGUF-Distilled]-T2V-ReferenceWorkflow-v1.0 , which demonstrates how to integrate a latent upscale model into the generation pipeline.

Alternatively, you can perform a dedicated Video-to-Video (V2V) upscaling pass as a post-processing workflow.

Credits

Thanks to the developers and researchers behind LTX , ComfyUI , GGUF support , and the many open-source projects that made this workflow possible.

VAE · 348 MB · SAFETENSORS · bf16 · full · Unknown