workflows / WAN /malcolmrey-wan21-workflow_v2.json
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"Use lower one for movies and the one to the right for photos"
],
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},
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"For movies I would suggest to use my other WAN 2.2 workflow, but you can of course use WAN 2.1 movies as well.\n\nLoras trained on WAN 2.1 work really nicely with WAN 2.2\n\nThe difference in images between 2.1 and 2.2 to me is negligible quality-wise (unless you consider prompting, which WAN 2.2 is just better at)\n\nIf you do decide to stick with WAN 2.1 for movies, still check my WAN 2.2 workflow and grab the interpolation node that converts nicely from 16 fps to 32 fps, the difference is noticable!"
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"pix_fmt": "yuv420p",
"crf": 19,
"save_metadata": true,
"trim_to_audio": false,
"pingpong": true,
"save_output": true,
"videopreview": {
"hidden": false,
"paused": false,
"params": {
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"subfolder": "WAN\\i2v\\2025_09_21",
"type": "output",
"format": "video/h264-mp4",
"frame_rate": 24,
"workflow": "WANi2v__00002.png",
"fullpath": "C:\\Development\\ComfyUI\\output\\WAN\\i2v\\2025_09_21\\WANi2v__00002.mp4"
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"Since we use the Light lora, we need to stick to CFG 1.\n\nLight lora kills movie fluidity abit but for images it is perfectly fine."
],
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"bgcolor": "#653"
},
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"For images, set length to 1\n\nDisable the Video Combine node\n\n\n---\n\n\nFor videos, set length to your desired value (I test with short clips of 13-17 but the final ones have 81-85, the longest I was able to generate was 135 frames but your results may vary)\n\nDisable the Save Image node (unless you want all the frames saved)\n\n---\n\nBe sure to change your resolution accordingly, the longer the video is - the more memorey it needs.\n\n---\n\nIn general single frame can have high resolution (higher than Flux with the same memory limits), but as soon as you generate a movie - you gonna have to know your limits :)"
],
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},
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"mode": 0,
"inputs": [
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"name": "clip",
"type": "CLIP",
"link": 52
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"slot_index": 0,
"links": [
2,
16
]
}
],
"title": "CLIP Text Encode (Negative Prompt)",
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.36",
"Node name for S&R": "CLIPTextEncode",
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},
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"色调艳丽,过曝,静态,细节模糊不清,字幕,风格,作品,画作,画面,静止,整体发灰,最差质量,低质量,JPEG压缩残留,丑陋的,残缺的,多余的手指,画得不好的手部,画得不好的脸部,畸形的,毁容的,形态畸形的肢体,手指融合,静止不动的画面,杂乱的背景,三条腿,背景人很多,倒着走,\npoorly drawn, bad anatomy, bad hands, bad eyes, missing fingers, extra fingers, ugly, deformed, disfigured, blurry, grainy, out of focus, low resolution, amateur, poorly lit, oversaturated, undersaturated, watermark, signature, text, writing, noise, artifacts"
],
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"bgcolor": "#533"
},
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"Some people like the grain filter applied.\n\nIMHO it looks nice for images but not as much for videos :)"
],
"color": "#432",
"bgcolor": "#653"
},
{
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"type": "FastFilmGrain",
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},
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"mode": 4,
"inputs": [
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"type": "IMAGE",
"link": 5
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
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]
}
],
"properties": {
"aux_id": "vrgamegirl19/comfyui-vrgamedevgirl",
"ver": "0.1.0",
"Node name for S&R": "FastFilmGrain",
"cnr_id": "comfyui-aspect-ratio-crop-node",
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"widgets_values": [
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},
{
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"type": "Note",
"pos": [
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"I usually use here 10 steps, but you can lower it. I believe 8 was the default?\n\nIt is quite fast for me at 10 so I didn't bother to check the quality vs speed sweet spot :)"
],
"color": "#432",
"bgcolor": "#653"
},
{
"id": 62,
"type": "Power Lora Loader (rgthree)",
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],
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}
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"name": "MODEL",
"shape": 3,
"type": "MODEL",
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},
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"name": "CLIP",
"shape": 3,
"type": "CLIP",
"links": [
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]
}
],
"properties": {
"cnr_id": "rgthree-comfy",
"ver": "dbc5fa5e89b6a8b6a1a1dda787505b690f18026c",
"Show Strengths": "Single Strength"
},
"widgets_values": [
{},
{
"type": "PowerLoraLoaderHeaderWidget"
},
{
"on": false,
"lora": "WAN\\celebs\\wan_aliashawkat_v1.safetensors",
"strength": 0.65,
"strengthTwo": null
},
{
"on": false,
"lora": "WAN\\celebs\\wan_aliashawkat_v2.safetensors",
"strength": 0.65,
"strengthTwo": null
},
{
"on": false,
"lora": "WAN\\celebs\\wan_jamieleecurtis_v1.safetensors",
"strength": 0.65,
"strengthTwo": null
},
{
"on": false,
"lora": "WAN\\celebs\\wan_jamieleecurtis_v2.safetensors",
"strength": 0.65,
"strengthTwo": null
},
{
"on": false,
"lora": "WAN\\celebs\\wan_jenniferconnelly_v1.safetensors",
"strength": 0.65,
"strengthTwo": null
},
{
"on": false,
"lora": "WAN\\celebs\\wan_jenniferconnelly_v2.safetensors",
"strength": 0.65,
"strengthTwo": null
},
{},
""
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"Existence of v2 (v3, etc) models means:\n\n1) Either I was dissatisfied with previous version and made another one\n\n2) Or I wanted an additional model (different dataset) to be able to use both (or more) at the same time at lower weights.\n\nI made an article about it in the SD1.5 times but the same concept worked fine in Flux and now in WAN -> https://civitai.com/articles/1721/improving-results-by-using-multiple-models-of-the-same-concept-turning-it-to-11\n\nTL;DR is that training the same subject on different datasets and then combining the models will give you better likeness in general (there is a chance you will hit the jackpot in a single lora).\n\nIn SD1.5 and Flux it also helped with consistency, you were getting good results more often. In WAN I feel like if the model is good (or combination of models) then you will always get good results, if the model is bad - you will always get bad results.\n\n---\n\nI didn't list all the pairs that I've already made, just three to show you an example."
],
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"Strenght of LORAs.\n\nFor WAN2.1 and single lora, I usually go with 1.0 (sometimes some are better at 1.1 but I try to make sure the model works at 1.0)\n\nFor multiple loras. If you use two, then I would start with my default of 0.65 each (Yes, it surpasses the 1.0 that you would have on single lora)\n\nFor three loras, 0.45-0.5 each. For four loras, 0.3-0.35 each.\n\nFor WAN2.2 in my other workflow, use the same strength for LOW. Multiply the value with 1.5 for HIGH \n\nSo, 1 Lora at 1.0 in WAN 2.1 would be HIGH 1.5 and LOW 1.0 in WAN 2.2\n\nTwo loras at 0.65 in WAN 2.1 would be HIGH 0.95 and LOW 1.0"
],
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{
"id": 8,
"type": "PathchSageAttentionKJ",
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],
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"Original workflow by https://www.reddit.com/user/yanokusnir/\n\n## Civitai Links:\nWan 2.1 T2V 14b-Q4-K-M GGUF\n🔗 [Download Link](https://civitai.com/models/1757056)\n\nUmt5-xxl-encoder Q5_K_M GGUF\n🔗 [Download Link](https://civitai.com/models/1757056)\n\nWan21 T2V 14B lightx2v LoRA\n🔗 [Download Link](https://civitai.com/models/1757056)\n\nWAN 2.1 VAE\n🔗 [Download Link](https://civitai.com/models/1757056)\n\n---\n\n## Original Links:\n### WAN 2.1 T2V GGUF MODEL\n🔗 [Download Link](https://huggingface.co/city96/Wan2.1-T2V-14B-gguf/tree/main)\n\n---\n\n### WAN 2.1 GGUF TEXT ENCODER\n🔗 [Download Link](https://huggingface.co/city96/umt5-xxl-encoder-gguf/tree/main)\n\n---\n\n### WAN 2.1 VAE \n🔗 [Download Link](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors)\n\n---\n\n### LORA\n🔗 [Download Link](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank32.safetensors)\n\n\n\n\n\n\n\n\n\n\n"
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