Daxamur's Wan 2.2 Workflows
-NEWS-
v1.2 out now, utilizing a triple sampler method for far better quality, prompt adherence and motion. Using the default settings included with the flow, this flow takes about 2ish minutes longer (for me), but the results are pretty amazing in my opinion.
Updates to GGUF flows will be posted separately going forward.
Thanks to @lug_L for pointing me to this method!
Doing some organizing here soon as I feel the number of flows is growing too large for a single post. This listing will be updated with links to the individual posts (GGUF, Experimental, etc...) as they're created and moved from this one.
Notes
I've done my best to place most nodes that you'd want to configure at the lower portion of the flow (roughly) sequentially, while most of the operational / backend stuff sits at the top. Nodes have been labeled according to their function as clearly as possible.
Beyond that;
NAG Attention is in use, so it is recommended to leave the CFG set to 1.
The sampler and scheduler are set to uni_pc // simple by default as I find this is the best balance of speed and quality. If you don't mind waiting (a lot, in my experience) longer for some slightly better results, then I'd recommend res_3s // bong_tangent from the RES4LYF custom node.
I have set the default number of steps to 8 (4 steps per sampler) as opposed to 4, as here is where I see the most significant quality / time tradeoff - but this is really up to your preference.
This flow will save finished videos to ComfyUI/output/WAN/<T2V|T2I|I2V>/ by default.
I2V
For I2V, I find that generally Wan 2.2 does better if the input image's resolution is above the resolution you are sampling at (as opposed to resizing to fit the sampling resolution prior to executing) - but I haven't tested this super extensively.
The custom node flow2-wan-video will cause a conflict with the Wan image to video node and must be removed to work. I have found that this node does not get completely removed from the custom_nodes folder when removing via the ComfyUI manager, so this must be deleted manually.
GGUF
All models used with the GGUF versions of the flows are the same with the exception of the base high and low noise model. You will need to determine which GGUF quant best fits your system, and then set the correct model in each respective Load WAN 2.2 GGUF node accordingly. As a rule of thumb, ideally your GGUF model should fit within your VRAM with a few GB to spare.
The examples for the GGUF flows were created using the Q6_K quant of WAN 2.2 I2V and T2V.
The WAN 2.2 GGUF quants tested with this flow come from the following locations on huggingface;
MMAUDIO
To set up MMAUDIO, you must download the MMAUDIO models below, create an "mmaudio" folder in your models directory (ComfyUI/models/mmaudio), and place every mmaudio model downloaded into this folder (even apple_DFN5B-CLIP-ViT-H-14-384_fp16.safetensors).
Block Swap Flows
I have set the "WAN Blocks to Swap (0-40)" node to 40 as a default, which is as aggressive as possible. If you find that you have extra VRAM with 40 blocks, decrease this until you find a good balance.
I am troubleshooting sampler output issues when using block swap with WAN 2.2 I2V, so this is still being worked on.
Models Used
T2V (Text to Video)
I2V (Image to Video)
lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank64_bf16.safetensors
model.safetensors (renamed to clip-vit-large-patch14.safetensors)
MMAUDIO
Flows
T2V: UP
T2V + MMAUDIO: UP
T2V GGUF: UP
T2V Block Swap: UP
I2V: UP
I2V + MMAUDIO: UP
I2V GGUF: UP
I2V Block Swap: In progress...
DM to inquire about custom work.
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