Guide
ComfyUI Video Model Selection for Mid-Range GPUs
Choose the best model and workflow for high-quality, photorealistic video generation on RTX 4070/4080-class GPUs with strong prompt adherence.
What You'll Achieve
By the end of this guide, you'll know:
- Which video generation models work best on mid-range GPUs (12-16GB VRAM)
- When to use image-to-video (I2V) vs. text-to-video (T2V)
- Optimal settings for photorealistic output and prompt compliance
- Workflow strategies to maximize quality within your VRAM budget
🎮 GPU Assumptions
This guide assumes a mid-range NVIDIA GPU:
| GPU | VRAM | Recommendation |
|---|
| RTX 4070 | 12GB | LTX-Video, Wan 2.1 (quantized) |
| RTX 4070 Ti Super | 16GB | Wan 2.1, Wan 2.2, LTX-Video |
| RTX 4080 | 16GB | Any model except Mochi |
⚠️ VRAM is the bottleneck. 12GB limits you to quantized models or shorter videos. 16GB opens up full-precision Wan 2.1/2.2.
📊 Model Comparison (2025-2026)
| Model | Best For | VRAM Needs | Prompt Adherence | Speed | Mid-Range GPU? |
|---|
| Wan 2.2 | Photorealistic I2V | 14-16GB (fp16) | ⭐⭐⭐⭐⭐ | Medium | ✅ 4080 only |
| Wan 2.1 | Versatile I2V/T2V | 12-16GB | ⭐⭐⭐⭐ | Medium | ✅ Yes |
| LTX-Video | Speed, iteration | 8-12GB (FP8) | ⭐⭐⭐ | Fast | ✅ Great |
| CogVideoX | I2V quality | 14-16GB | ⭐⭐⭐⭐ | Slow | ⚠️ 16GB recommended |
| HunyuanVideo | Professional T2V | 16GB+ | ⭐⭐⭐⭐ | Slow | ⚠️ Stretch |
| Mochi 1 | Natural movement | 24GB+ | ⭐⭐⭐⭐ | Slow | ❌ No |
🏆 Recommended: Wan 2.1 (or 2.2) + LTX-Video combo
Use Wan 2.1 for your primary high-quality generations. Use LTX-Video for quick iterations and testing prompts before committing to a full Wan render.
🔧 Recommended Workflow for Photorealism
Step 1: Choose Your Model Variant
- Wan 2.1 I2V — Best for turning a static image into video with excellent prompt following
- Wan 2.2 — Newer, slightly better quality but requires more VRAM
- LTX-Video — Use for rapid prototyping, then upscale the result
Step 2: Optimal Settings for Quality
| Setting | Recommended Value | Why |
|---|
| Steps | 25-30 | Balances quality and generation time |
| Sampler | euler_a or ddim | Best results for video |
| CFG Scale | 3.5-5 | Too high = artifacts, too low = weak prompt adherence |
| Frame Rate | 16-24 fps | Higher = smoother but more VRAM |
| Resolution | 704x480 or 832x480 | Safe for 12GB; 1280x720 needs 16GB+ |
| Noise Strategy | High noise for prompt, low for details | Wan 2.1's dual-sampler approach |
Step 3: Prompt Engineering for Photorealism
- Start with:
photorealistic, 8k, high detail, cinematic lighting
- Add camera movement:
slow dolly forward, cinematic, shallow depth of field
- Specify subject:
human subject, realistic skin texture, natural colors
- Avoid: artistic styles, abstract descriptions
⚠️ LoRA tradeoff: Adding a LoRA often reduces prompt adherence. For maximum compliance, run without LoRA at high steps.
🛠️ Troubleshooting Common Issues
| Problem | Solution |
|---|
| Out of memory errors | Use FP8 quantized models, reduce resolution, or enable model offloading |
| Poor prompt adherence | Increase steps to 25+, use high-noise sampler, write more specific prompts |
| Artifacts/flickering | Lower CFG (3.5-4), use ddim sampler, enable temporal consistency |
| Too slow | Switch to LTX-Video for drafts, use FP8 variants, reduce frame count |
| Black frames | Check VAE settings, ensure proper image input dimensions (multiple of 16) |