ltx-2.5-22b-ic-lora-pixel-spatial-upscaler-x2-1.0.safetensors
LTX 2.5 IC-LoRA Pixel Spatial Upscaler x2
Pixel-space spatial upscaler IC-LoRA for LTX 2.5, published as its own separate gated HuggingFace repo. Attaches to the dev/distilled transformer. Place in models/loras/.
Download ltx-2.5-22b-ic-lora-pixel-spatial-upscaler-x2-1.0.safetensors
Official HuggingFace repo — sign in and click "Agree and Access" to accept the LTX 2.5 license before this downloads. 0.327 GB · Free.
No 24GB GPU? Try ltx-2.5-22b-ic-lora-pixel-spatial-upscaler-x2-1.0.safetensors online — free generation included
Skip the 0.327 GB download and ComfyUI setup. Generate a 6-second video using this exact model in your browser, ~30 seconds.
Will this run on my GPU?
Minimum: 24GB VRAM.
How to use ltx-2.5-22b-ic-lora-pixel-spatial-upscaler-x2-1.0.safetensors
- Download the file from HuggingFace.
- Place it in ComfyUI/models/loras/ inside your ComfyUI directory.
- Restart ComfyUI (or refresh the model list from the menu).
- Load a compatible workflow — see below.
See the LTX 2.5 ComfyUI setup guide for the full file list and install order.
Don't want to run this locally? Try ltx-2.5-22b-ic-lora-pixel-spatial-upscaler-x2-1.0.safetensors online with a free generation — no GPU, no install, ~30 seconds per clip.
Common issues
ComfyUI doesn't see the file after I downloaded it▼
Make sure the file is in ComfyUI/models/loras/ (not a subfolder). Restart ComfyUI fully — the menu refresh sometimes misses new files. Filename must match exactly: ltx-2.5-22b-ic-lora-pixel-spatial-upscaler-x2-1.0.safetensors.
CUDA out of memory error when loading the model▼
ltx-2.5-22b-ic-lora-pixel-spatial-upscaler-x2-1.0.safetensors needs ~24GB VRAM minimum. If you're hitting OOM: • Enable Sequential Offloading in ComfyUI settings • Lower the resolution (768×512 instead of 1280×704) — both dimensions must be divisible by 32 • Reduce frame count (65 frames instead of 161) — must be 8n+1 • Use a smaller variant — see Related models below.
How do I apply this LoRA in ComfyUI?▼
Load it in a 'LoraLoader' node and connect it after your model loader. Pair this LoRA with the dev base model (not the distilled one) for the right behavior. LoRA strength 1.0 is the trained value — start there.
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