rzgar/Bernini-R-n4w

Bernini-R-n4w

### Anatomy & Generation Quality

This merge handles male and female anatomy well. Results become significantly stronger and more consistent when guided by reference images or reference videos.

Success Rates (based on internal testing):

• I2V / Reference Video-to-Video: Over 75% good results in a single generation
• Text-to-Video (T2V): Above 65% when using detailed prompts (quality varies with prompt strength)

Prompting Advice

Bernini-R is noticeably more complex than standard Wan2.2 models, so the short/simple prompts many people use with Wan2.2 often don’t perform well here.

• Text-to-Video (T2V): Prompts need to be detailed and descriptive. In some cases, running a second generation was required to get a strong result.
• Image-to-Video with reference video: One generation was enough in roughly 7 out of 10 tests.

Tip – Refiner for Difficult Scenes

For tricky or complex scenes, using the low-noise expert from another Wan2.2 merge (I used SmoothMix I2V V1 low-noise) as a refiner often further improves anatomy and fine details if the initial video isn’t too messy.

LightX2V LoRA

• wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors
• wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors

Text Encoders

• umt5_xxl_fp8_e4m3fn_scaled.safetensors

VAE

• wan_2.1_vae.safetensors

Clip Vision

• clip_vision_h.safetensors