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 "<code>example.png</code> turns into a mighty Phoenix"), so I am posting the corrected version below. Apologies for the inconvenience.<hr />- 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 <code>.latent</code> 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.<hr />Original workflows:t2v:<a target="_blank" rel="ugc" href="https://civarchive.com/models/1835262/wan22-t2v-gguf-lightx2v-lora">https://civarchive.com/models/1835262/wan22-t2v-gguf-lightx2v-lora</a>I2v:<a target="_blank" rel="ugc" href="https://civarchive.com/models/1822764/wan-22-i2v-gguf-compact-speed-wf-or-lightning-lora-44-steps">https://civarchive.com/models/1822764/wan-22-i2v-gguf-compact-speed-wf-or-lightning-lora-44-steps</a>(natively very good)FLF2V:<a target="_blank" rel="ugc" href="https://civarchive.com/models/1884402/flf2v-wan-22-video-generation-first-frame-to-last-frame-with-frame-interpolation">https://civarchive.com/models/1884402/flf2v-wan-22-video-generation-first-frame-to-last-frame-with-frame-interpolation</a>(this ís my own model, it is now very far from the original that is linked)

Wan 2.2 GGUF - T2V I2V and FLF2V workflow for VRAM or RAM limited people

Now the Clip loader of the Img2vid is also a GGUF file. The FLF2V is now optimized to pass several cache files at the same time, which could reduce the number of times, the models get loaded in the memory.

Files with SHA256: e435862021abbcb9ef85aedbd0a637b5e5b37aaa9e7fbed0c0955471b3c6055f