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"title": "Pass1 High Noise", "properties": { "cnr_id": "tiled_ksampler", "ver": "d954379b188efd8d66f4280878a7b582038729e5", "Node name for S&R": "Asymmetric Tiled KSampler" }, "widgets_values": [ 8918, "fixed", 1, 1, 4, 1, "lcm", "simple", 1 ], "color": "#332922", "bgcolor": "#593930" }, { "id": 419, "type": "Asymmetric Tiled KSampler", "pos": [ 1451.697021484375, 540.4310302734375 ], "size": [ 272.0835876464844, 310 ], "flags": {}, "order": 19, "mode": 0, "inputs": [ { "name": "model", "type": "MODEL", "link": 921 }, { "name": "positive", "type": "CONDITIONING", "link": 890 }, { "name": "negative", "type": "CONDITIONING", "link": 891 }, { "name": "latent_image", "type": "LATENT", "link": 888 } ], "outputs": [ { "name": "LATENT", "type": "LATENT", "links": [ 889 ] } ], "title": "Pass2 Low Noise", "properties": { "cnr_id": "tiled_ksampler", "ver": "d954379b188efd8d66f4280878a7b582038729e5", "Node name for S&R": "Asymmetric Tiled KSampler" }, "widgets_values": [ 8918, "fixed", 1, 1, 4, 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looking at viewer,\nthrone, ruins, castle, dark,\n(black background:1.2)" ], "color": "#232", "bgcolor": "#353" }, { "id": 418, "type": "EmptyHunyuanLatentVideo", "pos": [ 776.484130859375, 608.3308715820312 ], "size": [ 270.0943298339844, 130 ], "flags": {}, "order": 5, "mode": 0, "inputs": [], "outputs": [ { "name": "LATENT", "type": "LATENT", "links": [ 885, 887, 924 ] } ], "properties": { "cnr_id": "comfy-core", "ver": "0.3.44", "Node name for S&R": "EmptyHunyuanLatentVideo" }, "widgets_values": [ 480, 848, 57, 1 ], "color": "#2a363b", "bgcolor": "#3f5159" }, { "id": 437, "type": "MarkdownNote", "pos": [ -995.6474609375, -30 ], "size": [ 520, 1180 ], "flags": {}, "order": 26, "mode": 0, "inputs": [], "outputs": [], "title": "How to use", "properties": {}, "widgets_values": [ "## How to use (Wan 2.2 T2V 14B, GGUF low VRAM)\n\n**What it does:** a prompt -> a short video. Wan 2.2 14B text-to-video as two Q2_K GGUF experts (high noise first, then low noise) with the lightx2v speed LoRA, then RealESRGAN x2, a resize, RIFE and an MP4.\n\n**Inputs**\n1. **Positive prompt**: the scene, the motion and the camera.\n2. **Negative prompt**: what to avoid (also used by NAG).\n\n**Models and Loras** group: two GGUF loaders (high noise top, low noise below), one lightx2v T2V LoRA per expert (strength 1), T5 text encoder, VAE `wan_2.1_vae`.\n\n**Important settings**\n- **EmptyHunyuanLatentVideo**: 480 x 848 (width x height), 57 frames. This sets the video size and length (more = more VRAM and time).\n- **Pass1 High Noise / Pass2 Low Noise** (Asymmetric Tiled KSampler): seed 8918 fixed, 4 steps, cfg 1, lcm / simple. Pass 2 denoise 0.3 refines pass 1; the creator suggests 0.3 to 0.5.\n- **enhancments 2 passes** group (one row per expert): Enhance-A-Video 1.0, CFG-Zero* init, shift 6 (ModelSamplingSD3), NAG scale 11 / tau 0.25 / alpha 2.373 (NAG uses the negative prompt).\n- **Without the LoRA**: bypass both LoRA loaders, then cfg 6 and 30 to 60 steps on both samplers (creator advice).\n- **Purge VRAM** hangs off the pass 1 output to free VRAM once pass 1 is done (its output is not wired, so the run order is up to ComfyUI).\n- **Upscaler and 2nd frame interpolaration** group: RealESRGAN x2, then Lanczos to 1080 x 1920 (portrait), then RIFE (multiplier 1 = no new frames; 2 doubles the frame count).\n- **Export**: MP4 h264, 24 fps, crf 19, ping-pong on (plays forward then back). Lands in `ComfyUI/output/` with the prefix `AnimateDiff`.\n\nThen **Run**.\n\n**Tips**\n- 14B needs BOTH GGUFs (high + low noise) and the **wan_2.1_vae**. The Wan 2.2 VAE is for the 5B model only; the wrong VAE gives black or garbled frames.\n- The T5 text encoder runs on the CPU (CLIPLoader device `cpu`) to save VRAM.\n- Out of memory: fewer frames, a smaller size, or a smaller tile on VAE Decode (Tiled).\n- Landscape video: swap width and height, and set Image Scale to 1920 x 1080.\n- Wan wants 4n+1 frames (for example 33 or 81).\n- The 5B TI2V graphs are in `source/` (`Wan2.2- 5B T2V.json`, `Wan2.2- 5B I2V.json`); they use the Wan 2.2 VAE.\n\nFaster attention: start ComfyUI with --use-sage-attention (less VRAM). --use-ck-attention changes the image for the same seed.\n\n_Tested on: RTX 3050 Laptop 4 GB (creator). Checked on ComfyUI 0.45.3: the graph converts; not run there yet._" ], "color": "#432", "bgcolor": "#653" }, { "id": 438, "type": "MarkdownNote", "pos": [ -995.6474609375, 1190 ], "size": [ 520, 640 ], "flags": {}, "order": 27, "mode": 0, "inputs": [], "outputs": [], "title": "Downloads", "properties": {}, "widgets_values": [ "## Downloads - put each file in the ComfyUI folder shown\n\nClick a file name to download it. Put it in the folder shown (inside `ComfyUI/models/`).\n\n| Folder | File (click = download) | Size |\n|---|---|---|\n| `diffusion_models/` | [wan2.2_t2v_high_noise_14B_Q2_K.gguf](https://huggingface.co/bullerwins/Wan2.2-T2V-A14B-GGUF/resolve/main/wan2.2_t2v_high_noise_14B_Q2_K.gguf) | 5.3 GB |\n| `diffusion_models/` | [wan2.2_t2v_low_noise_14B_Q2_K.gguf](https://huggingface.co/bullerwins/Wan2.2-T2V-A14B-GGUF/resolve/main/wan2.2_t2v_low_noise_14B_Q2_K.gguf) | 5.3 GB |\n| `loras/` | [lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank128_bf16.safetensors](https://huggingface.co/Kijai/WanVideo_comfy/resolve/main/Lightx2v/lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank128_bf16.safetensors) | |\n| `text_encoders/` | [t5xxl_um_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/calcuis/wan-gguf/resolve/main/t5xxl_um_fp8_e4m3fn_scaled.safetensors) | 6.7 GB |\n| `vae/` | [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors) | |\n| `upscale_models/` | [RealESRGAN_x2.pth](https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth) | 0.07 GB |\n\n`rife49.pth` is downloaded by ComfyUI-Frame-Interpolation on first run.\n\n**Custom nodes:** [gguf](https://github.com/calcuis/gguf), [tiled_ksampler](https://github.com/FlyingFireCo/tiled_ksampler), [ComfyUI-KJNodes](https://github.com/kijai/ComfyUI-KJNodes), [ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite), [ComfyUI-Frame-Interpolation](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation), [ComfyUI-tbox](https://github.com/ai-shizuka/ComfyUI-tbox).\n\nOne command from the repo root: `python scripts/frizzy.py doctor wan2.2/gguf-lowvram --comfy \"C:/path/to/ComfyUI\"`. 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