---
title: Bernini-R-n4w
canonical_url: "https://www.modelscope.cn/models/rzgar/Bernini-R-n4w"
md_url: "https://www.modelscope.cn/models/rzgar/Bernini-R-n4w.md"
repository: rzgar/Bernini-R-n4w
last_updated: 2026-07-09
license: apache-2.0
model_type:
  - bernini-r
base_model:
  - ByteDance/Bernini-R
base_model_relation: finetune
parameters: 57.4B
tensor_type:
  - F16
  - F32
  - U8
  - F8_E4M3
  - BF16
  - I8
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 43
stars: 0
---

# Bernini-R-n4w

> Bernini-R-n4w - rzgar 在 ModelScope 开源的模型。Bernini-R-n4w Your browser does not support the video tag. Anatomy & Generation Quality

rzgar/Bernini-R-n4w 是 ModelScope 魔搭社区上的 57.4B 参数机器学习模型，采用 apache-2.0 许可，基于 ByteDance/Bernini-R 构建。

- **Repository**: rzgar/Bernini-R-n4w
- **License**: apache-2.0
- **Parameters**: 57.4B
- **Base model**: ByteDance/Bernini-R
- **Downloads**: 43
- **Stars**: 0
- **Last updated**: 2026-07-09

Source: https://www.modelscope.cn/models/rzgar/Bernini-R-n4w

---

## Bernini-R-n4w
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  <source src="https://huggingface.co/rzgar/Bernini-R-n4w/resolve/main/video__00179.mp4" type="video/mp4">
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### 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](https://civitai.red/models/1995784/smooth-mix-wan-22-14b-i2vt2v?modelVersionId=2259006)) as a refiner often further improves anatomy and fine details if the initial video isn’t too messy.

<video controls width="100%">
  <source src="https://huggingface.co/rzgar/Bernini-R-n4w/resolve/main/video__00180.mp4" type="video/mp4">
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</video>

### LightX2V LoRA
- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors)
- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors)
### Text Encoders
- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)

### VAE
- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)

### Clip Vision
- [clip_vision_h.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/clip_vision/clip_vision_h.safetensors)