Transform Low-Res Videos into HD Masterpieces — The Intelligent Way

Introduction: Beyond Traditional Upscaling

Traditional AI upscalers like RealESRGAN are great for images, but they often struggle with videos. They can introduce artifacts, fail to add meaningful detail, and leave footage looking blurry and unconvincing.

This workflow, "Wan 2.2 5B - Latent Video Upscaler," offers a paradigm shift. Instead of just guessing pixels, it uses the immense power of the Wan 2.2 5B Text-to-Video model to intelligently reinterpret and reconstruct your video in high definition. It doesn't just scale up; it dreams up the missing details, resulting in a cleaner, more detailed, and more coherent HD video than any conventional upscaler can achieve.

TL;DR: Stop using image upscalers on video. Use a diffusion model to truly enhance and upscale your footage with intelligent detail.

Key Features & Highlights

• 🤖 Intelligent Enhancement: Leverages the Wan 2.2 5B model to add semantically correct details, textures, and coherence, far surpassing the capabilities of traditional upscalers.

• ⚡ Fast & Efficient: Built on the lightweight 5B parameter model, this workflow performs latent upscaling and denoising significantly faster than generating from scratch.

• 🎨 Quality Preservation: Applies a light touch (denoise=0.2) to enhance and upscale without altering the original motion or content of the video drastically.

• 📈 2x Resolution Boost: Doubles the resolution of your input video directly in the latent space before decoding.

• 🎬 Smooth Final Output: Includes an optional RIFE frame interpolation pass to double the frame rate (from 16fps to 32fps) for buttery-smooth motion in the final render.

• 🔊 Audio Passthrough: Automatically carries over the original audio track from your source video to the final enhanced output.

Workflow Overview & Strategy

This workflow is a sophisticated video processing chain:

1. Input: Load your low-resolution source video using VHS_LoadVideo.

2. Initial Upscale: The video is immediately 2x upscaled using a Lanczos filter to get to the target size. This provides a better starting point for the model.

3. Latent Processing: The upscaled frames are encoded into the latent space.

4. Intelligent Enhancement: The core of the workflow. The Wan 2.2 5B model, guided by quality-positive and detail-negative prompts, gently denoises (denoise=0.2) the latents over just 8 steps with UniPC. This step is where the "magic" happens—the model fills in plausible, high-quality details.

5. Decoding: The enhanced latents are decoded back into a high-resolution image sequence.

6. Final Output:

   • Option A: Save the immediately upscaled video at 16fps.

   • Option B (Recommended): Pass the sequence through RIFE VFI to interpolate frames to 32fps, creating a final video that is both high-resolution and super smooth.

Technical Details & Requirements

🧰 Models Required:

• Base Model: (GGUF Format)

  • Wan2.2-TI2V-5B-Q8_0.gguf

  • Source: Likely from HuggingFace or other model repositories.

• LoRA:

  • Wan2_2_5B_FastWanFullAttn_lora_rank_128_bf16.safetensors (Applied at strength 0.5)

• VAE:

  • Wan2.2_VAE.safetensors

• CLIP Vision: (For GGUF Loader)

  • umt5-xxl-encoder-q4_k_m.gguf

• Interpolation Model:

  • rife47.pth (For RIFE VFI node)

⚙️ Recommended Hardware:

• A GPU with a good amount of VRAM (e.g., 12GB+) is recommended for comfortable operation, especially when processing longer videos.

🔌 Custom Nodes:
This workflow uses:

• comfyui-videohelpersuite (VHS) - For video loading/combining

• comfyui-frame-interpolation - For RIFE VFI

• comfyui-gguf / gguf - For model loading

• comfyui-easy-use - For memory management

• comfyui-kjnodes - For performance patches (Sage Attention)

Usage Instructions

1. Load the JSON: Import the provided .json file into your ComfyUI.

2. Load the Models: Ensure all required models are in their correct folders. Check the paths in the LoaderGGUF, VAELoader, and LoraLoaderModelOnly nodes.

3. Select Your Video: In the VHS_LoadVideo node, click the video icon to select your low-resolution input video.

4. Queue Prompt: Run the workflow!

5. Retrieve Output: Find your two enhanced videos in the output directory:

   • .../Wan 2.2 5B Upscales/Denoise 0.2_xxxxx.mp4 (16fps)

   • .../Wan 2.2 5B Upscales/Denoise 0.2_32fps_xxxxx.mp4 (32fps - Smoother)

Tips & Tricks

• Denoise Strength: The denoise parameter in the KSampler (default 0.2) is key.

  • ~0.1-0.3: Best for upscaling/enhancement. Preserves the original content while improving quality.

  • >0.5: Will start to significantly alter the content and style, moving towards a new generation based on your video.

• Source Quality: This workflow excels at breathing new life into low-quality, pixelated, or noisy source videos from older generators.

• Prompt Engineering: The positive prompt (high detail, high quality...) is generic to encourage enhancement. For stylistic changes, you can modify this prompt (e.g., "cinematic, film grain, photorealism").

Conclusion: The Future of Video Upscaling

This workflow demonstrates a powerful new application for diffusion models: not just as generators, but as intelligent enhancement tools. By leveraging the knowledge within the Wan 2.2 model, we can upscale videos with a level of coherence and detail that traditional methods simply cannot match. It’s faster than full generation and smarter than simple scaling.

Upload your low-res clips and witness the intelligent upscaling revolution.

Credit: Crafted by the ComfyUI community. Special thanks to the creators of the Wan 2.2 models and the FastWanFullAttn LoRA.