Diving into LTXV, my latest video diffusion experiments.

I’ve been experimenting with LTXV (ltxv-2b-0.9.8-distilled-fp8), combined with the text encoder umt5_xxl_fp8_e4m3fn_scaled.

The renderings showcase the hackercat, cherry blossoms, and a surreal city tour.

What it does:
- Generates latent video clips from text prompts
- Can produce a wide range of scenes, from surreal to photorealistic and beyond
- Perfect for short 1-2 second clips with creative prompts

Caution! 12 GB VRAM is tight:
- On my RX 6700 XT, it easily runs into OOM
- Frames, steps, and resolution need careful tuning
- FP8 helps, but some layers get upcast → memory can still fill up

Conclusion: Extremely powerful, but you need to tweak VRAM and settings to get stable results.

#AI #VideoDiffusion #LTXV #FP8 #GPU #CreativeAI #ShortVideos #Surreal #Photorealistic #StableVRAM #RX6700XT #AMD #ROCm #ComfyUI

YES SUCCEEDED!!!

Just rendered an image at 944×1152 (slightly above 1024×1024) using Flux1-Schnell-FP8 on my 6700 XT, and it works! (Image 1 is the Real-ESRGAN 2× upscaled version)

Workflow 1: Sampling (Image 2)

Prompt executed → UNet generates the latent

Step 1 (model load + latent generation) took 419 seconds

Output: Latent tensor saved to disk

Workflow 2 : VAE Decode (Image 3)

Latent loaded → VAE decodes the image

Duration: 7.5 seconds

Advantage: UNet doesn’t need to stay in VRAM → VRAM freed, even on 12 GB GPUs

The problem with the stock LoadLatent Node

Dropdown only shows files if they were produced / annotated by a previous SaveLatent Node

Node is designed to pass latents inside a graph, not load arbitrary files from disk

Purpose: prevents accidentally loading wrong files

Workaround (Image 4)

Edited /ComfyUI/nodes.py, class LoadLatent

Hardcoded latent path → Node now loads directly from disk

Result: Workflow 2 runs instantly, UNet can be unloaded

Timing

Step 1 (model load + latent generation): 419 s

Step 2 (VAE decode): 7.5 s

Result: High-res images on a 12 GB RDNA2 GPU are now possible on Flux1-Schnell-FP8 without ComfyUI crashing! (Image 5 is the original output)

This might actually become my new Flux workflow: render quick 512×512 previews first (which works perfectly on RDNA2 GPUs), sort out the good ones, extract the seed from the PNG metadata, and then re-render only the selected images with the same seed using the split workflow at higher resolutions. This way, high-resolution Flux1-Schnell-FP8 renders become possible on 12 GB RDNA2 GPUs D:

Question at the end: Has anyone ever done this before? Because I have no clue xD

#ComfyUI #flux #Flux1SchnellFP8 #FP8 #AMD #RDNA2 #VAE #AIArt #Pixelfed #HighResolution #GPUOptimization #LatentWorkflow #AIWorkflow #AIHacks #RealESRGAN #Upscale #AIExperiment #CreativeAI #DigitalArt #AICommunity #python #linux #opensource #foss