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Wan 2.2 GGUF - T2V I2V and FLF2V workflow for VRAM or RAM limited people — v2 more GGUFed

A collection of 3 workflows forked from 3 other sources and reworked to allow them to do more with less VRAM or RAM. Prevents using the RAM while staying on the VRAM, prevents ComfyUI crashes (you will less likely...

TâcheMultipleCapacitésImage en vidéo · Texte en vidéoFamilleWanEstimé VRAMNot yet characterizedBClasse

Profil d'exécution

Accès
Flux de travail externe
Statut
Présenté
Source
Civitai
Mis à jour
2 oct. 2026

Description de la source

A collection of 3 workflows forked from 3 other sources and reworked to allow them to do more with less VRAM or RAM. Prevents using the RAM while staying on the VRAM, prevents ComfyUI crashes (you will less likely saturate the RAM), allows you to use bigger models on the same graphic card, with better settings.

The cost? The WAN 2.2 models will have to be loaded each time into memory, which is time consuming, but it is better than nothing. Loading the model from an SSD drive is recommended. Please check your extra_model_paths.yaml file in the main directory of ComfyUI.

I made several typos in the workflow's instructions. It's tedious to update the entire workflow file here (I'd have to submit the Nth " example.png turns into a mighty Phoenix"), so I am posting the corrected version below. Apologies for the inconvenience.

  • Preparation:

Make sure you have all the needed files by enabling everything. If you are not using an SSD to store your files, you're likely wasting a lot of time.

If you run Part 1 and Part 2 separately, in this order, several times, the models will be loaded into memory over and over. It reduces the overall amount of RAM or VRAM needed, but the process is time-consuming. This method is useful when the alternative is a complete workflow failure, such as a crash near the end. I spend an additional 1 minute and 20 seconds per generation with this method, but I also save time by keeping everything in VRAM. For Wan 2.2 FLF2V, this is competitive; for Wan 2.2 T2V, it is much less so.

  • First Part:

Select "Enable Part 1" and disable "Enable Part 2." Apply the usual settings. Run the workflow. It will only process the High CFG part. Once the first part of the workflow is finished, the .latent file will be saved to a cache directory. You will probably need to adapt the save path for this file.

  • Second Part:

Select "Enable Part 2" and disable "Enable Part 1." Make sure the path to the latent file is correct. Run the workflow.

Original workflows:

t2v:

https://civitai.com/models/1835262/wan22-t2v-gguf-lightx2v-lora

I2v:

https://civitai.com/models/1822764/wan-22-i2v-gguf-compact-speed-wf-or-lightning-lora-44-steps

(natively very good)

FLF2V:

https://civitai.com/models/1884402/flf2v-wan-22-video-generation-first-frame-to-last-frame-with-frame-interpolation

(this ís my own model, it is now very far from the original that is linked)

What you need

Hardware and download-size estimates are not available yet.

Exigences

Modèles

4
  • umt5-xxl-encoder-Q6_K.gguf

    Non résolu

    Text encoder · Unknown

  • wan_2.1_vae.safetensors

    Fichier non vérifié

    VAE · Unknown

  • Wan2.2-T2V-A14B-HighNoise-Q6_K.gguf

    Non résolu

    Checkpoint · Unknown

  • Wan2.2-T2V-A14B-LowNoise-Q6_K.gguf

    Non résolu

    Checkpoint · Unknown

Packs de nœuds

5

Commentaire généré par l’IA

Explication générée par l’IA à partir des détails de la source et de la configuration. Les suggestions sont clairement signalées.

Objectif

  • This collection of three Wan 2.2 GGUF workflows is for generating video from text, an image, or first and last frames on systems with limited VRAM or RAM; it produces video and can save a .latent file during a split run.

Flux

  • The collection covers text-to-video, image-to-video, and first-frame-to-last-frame generation.

  • The workflows are reworked to use less VRAM or RAM and allow bigger models on the same graphics card.

  • In a split run, Part 1 processes only the High CFG part and saves a .latent file to a cache directory for Part 2.

Entrées et sorties

  • The generation inputs can be text, an image, or first and last frames; the generated output is video.

  • A split run outputs a .latent file in a cache directory for the next part.

Installation

  • Before running, enable all needed files, check extra_model_paths.yaml in ComfyUI's main directory, and consider storing the model files on an SSD.

    Suggestion · non vérifié

  • No model-use license is listed.

Dépendances

  • The listed ComfyUI add-ons are ComfyUI-VideoHelperSuite 1.6.1, ComfyUI-KJNodes 1.1.4, and rgthree-comfy 1.0.2507112302.

  • Before setup, verify the package names for comfyui-gguf and was-node-suite-comfyui.

    Suggestion · non vérifié

  • Have these model files ready: umt5-xxl-encoder-Q6_K.gguf, wan_2.1_vae.safetensors, Wan2.2-T2V-A14B-HighNoise-Q6_K.gguf, and Wan2.2-T2V-A14B-LowNoise-Q6_K.gguf.

    Suggestion · non vérifié

Configuration

  • To reduce overall RAM or VRAM use, run Part 1 and Part 2 separately, in that order.

    Suggestion · non vérifié

  • For a split run, enable Part 1 and disable Part 2; then verify or adapt the .latent path, enable Part 2, disable Part 1, and run again.

    Suggestion · non vérifié

Précautions

  • The WAN 2.2 models load into memory each time, making split runs time-consuming; the workflow instructions also contain typos, so use the corrected instructions when configuring a run.

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Sources

1 source
CivitaiPrincipal
civitai.com/models/1893033

Vérifié 19 juil.

Extraits de sources

1 extrait

Source context: 829 downloads · Type Workflows · Base model Wan Video 2.2 I2V-A14B