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- "## Diffusion Model\n\n*Install into models/diffusion_models/*\n\n**FP16:** [wan2.2_ti2v_5B_fp16.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/blob/main/split_files/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors)\n\n**FP8:** [Wan2_2-TI2V-5B_fp8_e5m2_scaled_KJ.safetensors](https://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled/blob/main/TI2V/Wan2_2-TI2V-5B_fp8_e5m2_scaled_KJ.safetensors)\n\n## Recommended LoRA\n\n*Install into models/lora/*\n\n[Live Wallpaper Style](https://civitai.com/models/1264662)\n\n>**LoRA Tip:** If you're using Live Wallpaper Style, try changing *strength* in the **WanVideo Lora Select** between 1 & 0.75 to see how your results change. Higher LoRA strengths skew results towards the style bias it was trained for!\n\n## Text Encoder\n\n*Install into models/text_encoders/*\n\n**FP16:** [umt5_xxl_fp16.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp16.safetensors)\n\n**FP8:** [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\n## VAE\n\n*Install into models/vae/*\n\n[wan2.2_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/blob/main/split_files/vae/wan2.2_vae.safetensors)\n\n### Which versions should I get?\n\nLower precision (FP8) runs faster and uses less VRAM, but you’ll take a hit on detail/temporal stability and are more likely to see artifacts. FP16 is heavier but gives the best quality.\n\n### Install Tip\n\nTo stay organized, it helps to create subfolders inside of the target models/ folder. It will save you from getting confused later as your collection grows.\n\nSo, for example, you can put your WAN 2.2 models into subfolders \"wan2.2/\" eg \"comfyui/models/vae/wan2.2/"
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- "Gemma-3 is popular among WAN users for getting good starting points for prompts. If you want to run one locally, check out **[the list of available Gemma models](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d)**. You’ll need to research which model is most appropriate for your machine.\n\nYou can also run Gemma in the cloud. I trust [Featherless.ai](https://featherless.ai) for cloud compute; they have a strict no-logging policy, they’re the most privacy-focused provider I’m aware of, and they charge one upfront monthly cost, which keeps my costs predictable.\n\nIt helps me out if you use my **[referral link](https://featherless.ai/register?referrer=4Z0BCRPO)** — or you can **[register normally](https://featherless.ai/register)**.\n\nI wrote a program for using cloud vision models over API you might find useful: see [CloudInterrogator](https://github.com/Artificial-Sweetener/CloudInterrogator) on GitHub.\n"
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- "# WAN Vision Prompt — Quickstart Tutorial\n\nYou *can* write a prompt by hand, but many people feel intimidated by crafting detailed natural-language prompts for **WAN**—or at least want to see examples first. This guide shows a fast, low-friction way to generate a strong **starting prompt** from an image using a vision model.\n\n---\n\n## What you’ll do\n\n1. **Pick a vision model** (hosted or local).\n2. **Upload your input image** to the model.\n3. **Paste the “Suggested Prompt for Vision Model”** into the chat with that model.\n4. **Copy the model’s description of movement** and use it as your WAN seed prompt.\n\n---\n\n## Option A — Use a hosted vision model (fastest)\n\n- **[Gemini](https://gemini.google.com/)** or **[ChatGPT](https://chat.openai.com/)** both work well.\n- Upload your input image and **paste** the “Suggested Prompt for Vision Model.”\n- Copy the output description and use it in WAN.\n\n> **Note on privacy:** Hosted services process your data on their servers. If privacy is critical, review the provider’s data policy or use the local option below.\n\n---\n\n## Option B — Keep the image private (local vision model)\n\nIf your image is lewd or you simply want complete local control, run a **local vision model**.\n\n- Many people report good results with **Gemma-3**. Browse available models here: **[Gemma-3 models](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d)**\n- You can run models locally with **[Ollama](https://ollama.com/)**, or choose a privacy-focused cloud you control.\n\n> **Tip:** Choose the **smallest** model that fits your hardware and latency needs, then scale up if you need more detail or coherence.\n"
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Open \"🧩 Manager\"\n2. Click \"Install PIP Packages\"\n3. Install these two, leave the quotes out: \"SageAttention\", \"Triton-Windows\".\n3.1 Obviously Triton-Windows is only for Windows users. If you get this error on Linux, I would guess the package for Triton is just \"Triton\".\n\n---\n\n## 1. FULL CLIP INTERPOLATION\n\n`WAN 2.2 5b` generates clips at **24fps**. If you want to increase the framerate, you need to interpolate between each frames. You have two ways to do this and there is an advantage to stacking them.\n\n- **Roll Clip & Multiply:** This offsets the location of each frame in your batch so that the seam between the first and last frame is now in the middle of the batch. Then, it multiplies your framrate by the `INTERPOLATION MULTIPLIER`. So if your multiplier is 3, you'll end up with a framerate of 72 assuming the default model framerate. Of course, then we set the clip back so that frame 1 = first in batch again.\n- **Sample Framerate to Target:** This is a straight conversion from any arbitrary framereate to any other framerate. You could turn off Multiply and just do this if you want, but I think by over-shooting our frame target and then downsampling, we give ourselves an opportunity to smooth out issues at the clip seam.\n\n---\n\n## 2. INTERPOLATION MULTIPLIER\n\nNumber to multiply framerate by with **Roll Clip & Multiply**. If that is disabled, this does nothing.\n\n---\n\n## 3. SAMPLE FRAMERATE TO TARGET\n\nTarget framerate to interpolate down or up to. If you have this option disabled in **Full Clip Interpolation**, it does nothing.\n\n---\n\n## 4. LoRA Strength\n\nOnly controls the first LoRA. You can add another LoRA by chaining them in series.\n\n---\n\n## 5. DURATION HINT\n\nLength of your video. Anything longer than 3 seconds seems to have a high cost, though `WAN 2.2 5b` is optimized for up to 5 seconds.\n\n- Because we are doing some frame cropping and interpolation to get a loop, the final duration of your clip will not be exact, especially if you use \"Fancy\".\n\n---\n\n## 6. WORKING SIZE\n\nSize of your video on the longest edge. The shape of the video is determined by your input image.\n\n---\n\n## 7. DURATION\n\nLength of your video. Anything longer than 3 seconds seems to have exponential cost. `WAN 2.2 5b` is optimized for up to 5 seconds.\n\n> **Please note:** Because we are doing some frame cropping and interpolation to get a loop, the final duration of your clip will not be exact.\n\n---\n\n## 8. STEPS\n\nThis is the number of times the sampler denoises the clip. Each step moves along a fixed schedule that decides how much noise to remove.\n\n`WAN 2.2 5b` prefers **30 steps**. Going higher can slightly reduce artifacts, but cost (time/VRAM) climbs quickly and returns diminish past ~30.\n\n---\n\n## 9. CFG\n\nCFG, or \"Guidance Scale\" can be understood as \"the rate at which the model follows your prompt\". Higher values should adhere more to what you've written, lower values allow the model to \"improvise\".\n\nIn the context of WAN, higher CFG seems to translate into more motion, but can also cause strange things to appear. Of course, this is highly dependent on your prompt.\n\n**5** is a good place to start.\n\n---\n\n## 10. SCHEDULE SHIFT\n\nOversimplified: Think of this as a \"motion vs stability\" dial. Higher values push toward more motion and variation; lower values keep things steadier and has less of a risk of introducing artifacts.\n\nTechnical: Transforms the sampler’s noise schedule by remapping each per-step noise level by a non-linear offset that preserves start and end points. Higher values bias sampling toward earlier/noisier timesteps which will focus the sampler on developing low frequency details while lower values biases toward later/cleaner timesteps, trending instead towards high frequency details.\n\n---\n\n## 11. SEED\n\nThe number used by the noising algorithm to produce the noise we inject into our latents at the start. This is where \"randomness\" is introduced in the system, so if you lock the seed and change nothing else, you'll get the same result over and over.\n\nIf you get a result you really like but with small problems, you might be able to lock the seed and make small changes to some other value to bring it in line with your vision. For example, try locking the seed and tweaking your prompt, or CFG, or Shift.\n\n---\n\n## 12. USE TILED ENCODER\n\nThis is a technique to avoid out of memory errors when encoding the image into latent space. It's slower to have it turned on, but you may need to if you try to push the limits of the resolution you can do.\n\n---\n\n## 13. ENABLE UPSCALE\n\nTurns the post-process upscaler **on/off**. When enabled, frames are upscaled **after** generation and initial loop interpolation, but **before** full clip interpolation (multiply, framerate to target) using the model you pick below. Leave it off to save at your **Working Size** (faster export, lower VRAM).\n\n- If you’re troubleshooting quality, disable this first so you can judge the raw clip.\n- Upscaling increases render time and VRAM use in proportion to your **Upscale Target**.\n\n---\n\n## 14. CHOOSE UPSCALE MODEL\n\nSelect which **upscale model** to use. The dropdown mirrors whatever you have in `models/upscale_models/`.\n\nNeed more options? Browse and download from [OpenModelDB](https://openmodeldb.info/), drop the files into that folder, then reload for the options to show up here.\n\n---\n\n## 15. UPSCALE TARGET\n\nSets the **longest edge** (in pixels) the upscaler should aim for.\n\n- **1280** ≈ 720p long edge \n- **1920** ≈ 1080p long edge \n- **3840** ≈ 4K long edge\n\nHigher targets cost more VRAM/time. If you want the working resolution, either disable **Enable Upscale** or set the target near your **Working Size**.\n\n---\n\n## 16. UPSCALE TILE SIZE\n\nControls how large each tile is when the upscaler runs.\n\n- **Larger tiles** → faster and fewer tile boundaries, but **more VRAM**.\n- **Smaller tiles** → safer on VRAM, but slower; can increase the chance of subtle tile transitions.\n\nIf you hit out-of-memory, lower **Tile Size** before changing anything else here.\n\n> **Note:** This is separate from **Use Tiled Encoder** above. That one tiles the **VAE encode/decode**; this one tiles the **upscaler** pass.\n\n---\n\n## 17. 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