Find best wan2.2 T2V

help me find the best wan2.2 t2v using wan2.2_t2v_high_noise_14B_Q6_K.gguf wan2.2_t2v_low_noise_14B_Q6_K.gguf and umt5_xxl_fp8_e4m3fn_scaled_safetensors workflow

Absolutely! To set up a *ComfyUI* workflow using the **Wan 2.2 T2V (Text‑to‑Video)** models—specifically the **wan2.2_t2v_high_noise_14B_Q6_K.gguf**, **wan2.2_t2v_low_noise_14B_Q6_K.gguf**, and **umt5_xxl_fp8_e4m3fn_scaled.safetensors**—here’s a clear, step‑by‑step guide: --- ## Setup Overview ### 1. **Install ComfyUI (and GGUF support)** Make sure you have ComfyUI installed. Within the ComfyUI Manager, install or update the **ComfyUI‑GGUF** custom node to enable GGUF support.citeturn0search1turn0search3 ### 2. **Download Required Models** - **High-noise expert model**: `wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors` (GGUF variant likely named `wan2.2_t2v_high_noise_14B_Q6_K.gguf`) - **Low-noise expert model**: `wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors` (`wan2.2_t2v_low_noise_14B_Q6_K.gguf`) - **Text encoder**: `umt5_xxl_fp8_e4m3fn_scaled.safetensors` - **VAE**: `wan_2.1_vae.safetensors` (14B models require Wan 2.1 VAE)citeturn0search1turn0search3turn0search16turn0search7 ### 3. **Organize Files in ComfyUI Folders** | Component | Destination Folder | |----------------|----------------------------------------------| | High-noise & Low-noise GGUF models | `ComfyUI/models/diffusion_models/` | | `umt5_xxl_fp8_e4m3fn_scaled.safetensors` | `ComfyUI/models/text_encoders/` | | `wan_2.1_vae.safetensors` | `ComfyUI/models/vae/` | citeturn0search3turn0search7 ### 4. **Load the T2V Workflow** In ComfyUI: - Go to **Workflows → Browse Templates** → **Video**. - Choose the **Wan2.2‑14B T2V** workflow (`Wan2.2_14B_T2V.json`)—should appear if ComfyUI is up to date.citeturn0search1turn0search0turn0search16 - Alternatively, drag-and-drop the workflow JSON if obtained externally. ### 5. **Configure the Workflow** Inside the workflow graph: - Use **UNet Loader (GGUF)** nodes for the high-noise and low-noise models. - Ensure the **CLIPLoaderGGUF** node points to your `umt5_xxl_fp8_e4m3fn_scaled.safetensors`. - Use the VAE node pointing to the `wan_2.1_vae.safetensors`. - The KSampler Advanced or equivalent node orchestrates denoising—start with the high-noise model for early steps (structure/layout), then switch to the low-noise model for refinement.citeturn0search1 ### 6. **Adjust Settings & Run** - Recommended settings: ~20 sampling steps, CFG around 5, using Uni-PC or similar sampler. - Write your prompt—with possible camera-motion keywords like *"zoom in/out," "pan," "tilt," "dolly"* to enhance dynamics.citeturn0search7turn0search11 - Hit **Run** to generate. --- ## Why This Setup Works Wan 2.2 uses a **Mixture‑of‑Experts architecture**, where: - The **high-noise expert** handles early, rough structure. - The **low-noise expert** adds finer details as denoising progresses.citeturn0search5turn0search16 This approach balances high visual fidelity with manageable inference costs.citeturn0search5turn0search10 --- ### Summary You’ll need: - Two GGUF models (high-noise + low-noise) - UM-T5 XXL text encoder - Wan 2.1 VAE - The Wan2.2 T2V workflow in ComfyUI This setup provides cinematic control, rich detail, and smooth motion in your text‑to‑video generation. If you’re targeting better performance or lower VRAM usage, we can explore options like the 5 B hybrid TI2V model or FP8 quantization too! Let me know if you'd like that.

iturn0image5turn0image8turn0image10turn0image11To achieve optimal performance with the Wan2.2 T2V (Text-to-Video) model using the specified components—`wan2.2_t2v_high_noise_14B_Q6_K.gguf`, `wan2.2_t2v_low_noise_14B_Q6_K.gguf`, and `umt5_xxl_fp8_e4m3fn_scaled_safetensors`—follow this comprehensive workflow setup: --- ### 🔧 Required Components 1. **High-Noise Model**: `wan2.2_t2v_high_noise_14B_Q6_K.gguf` 2. **Low-Noise Model**: `wan2.2_t2v_low_noise_14B_Q6_K.gguf` 3. **CLIP Model**: `umt5_xxl_fp8_e4m3fn_scaled.safetensors` 4. **VAE Model**: `wan2.2_vae.safetensors` --- ### 🛠️ Workflow Configuration in ComfyUI 1. **Load Diffusion Models**: - First node: Load `wan2.2_t2v_high_noise_14B_Q6_K.gguf`. - Second node: Load `wan2.2_t2v_low_noise_14B_Q6_K.gguf`. 2. **Load CLIP Model**: - Use the `umt5_xxl_fp8_e4m3fn_scaled.safetensors` model. 3. **Load VAE Model**: - Integrate the `wan2.2_vae.safetensors` model. 4. **Input Source**: - Utilize the `Load Image` node to upload your starting image. 5. **Video Generation**: - Configure the `Wan22ImageToVideoLatent` node to set the desired resolution and frame count. --- ### ⚙️ Additional Configuration - **Model Quantization**: Ensure that the models are quantized to FP8 for optimal performance. - **VAE Compatibility**: The `wan2.2_vae.safetensors` model is compatible with the T2V models, ensuring accurate color reproduction and detail preservation. - **Expert Model Usage**: The high-noise model focuses on the overall layout, while the low-noise model refines video details, leveraging the Mixture-of-Experts (MoE) architecture for efficient computation citeturn0search2. --- ### 📥 Download Links - **High-Noise Model**: [wan2.2_t2v_high_noise_14B_Q6_K.gguf](https://huggingface.co/bullerwins/Wan2.2-T2V-A14B-GGUF/blob/main/wan2.2_t2v_high_noise_14B_Q6_K.gguf) - **Low-Noise Model**: [wan2.2_t2v_low_noise_14B_Q6_K.gguf](https://huggingface.co/bullerwins/Wan2.2-T2V-A14B-GGUF/blob/main/wan2.2_t2v_low_noise_14B_Q6_K.gguf) - **CLIP Model**: [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/wan-ai/umt5_xxl_fp8_e4m3fn_scaled) - **VAE Model**: [wan2.2_vae.safetensors](https://huggingface.co/wan-ai/wan2.2-vae) --- ### 🖼️ Visual Workflow Example For a visual representation of the workflow, refer to the ComfyUI Wan 2.2 + Lightx2v V2 Examples:  --- By following this setup, you can effectively utilize the Wan2.2 T2V model with the specified components to generate high-quality video content.

KSamplerAdvanced No module named 'triton'

The error: ``` KSamplerAdvanced No module named 'triton' ``` means that a required dependency (`triton`) is missing. This usually happens when using **ComfyUI** with **KSamplerAdvanced** or **Diffusers**-based models that rely on **xformers** or **triton** for efficient sampling. --- ### ✅ Solution #### 🔧 Step 1: Install `triton` If you're using Python 3.10 or similar with PyTorch, run: ```bash pip install triton ``` **If you get errors**, instead use: ```bash pip install -U git+https://github.com/openai/triton.git@main ``` This ensures compatibility with current CUDA/PyTorch setups. --- ### 📦 Optional: Install `xformers` too Often used together with `triton` in advanced samplers (like `KSamplerAdvanced`). ```bash pip install xformers ``` --- ### 🧪 Verify After installing, restart ComfyUI and try running your workflow again. You should see the sampler working without errors. --- ### ❗ If Using Conda or Python Virtual Environments Make sure you're installing into the **same environment** that ComfyUI is running in: ```bash which python # or python -m site ``` To confirm you're installing `triton` into the correct Python path. --- Let me know if you're using a specific platform (like Windows, Mac M1/M2, or Linux) or you want a Docker or Conda-based setup – I can give a tailored install command.