Wan 2.7 Blog

Wan 2.2 Remix v3 workflow guide with practical tips. Learn how Remix differs from I2V and T2V, which NSFW checkpoint to download (5B vs 14B), what safetensors naming conventions mean, and the prompt adjustments that actually improve Remix output — based on 300+ test generations.

How to train Wan 2.2 LoRAs for character consistency in I2V and T2V workflows. Covers T2V vs I2V LoRA differences, AI Toolkit training settings for 12GB VRAM, face drift causes and fixes, and when to train a LoRA instead of relying on prompts or reference images.

A complete breakdown of Wan 2.7 credit costs per generation type — text-to-video, image-to-video, 9-grid, reference-to-video, and editing. Learn how credits work, what each mode costs, and how to minimize your spend.

Discover 15 practical Wan 2.7 tips and lesser-known features — from prompt expansion tricks and seed locking to ComfyUI optimization, credit-saving workflows, and multi-reference techniques that experienced users rely on.

I tested over 2,000 prompts on Wan 2.2 across image-to-video, text-to-video, and Remix workflows. Here is exactly how to structure your prompts for camera control, character consistency, and motion quality.

I tested Wan 2.2 and LTX 2.3 side by side for 3 weeks on real projects. Here is which model wins for image-to-video, prompt adherence, speed, and NSFW flexibility — and when each one makes sense.