Instructions to use rzgar/Wan2.2_I2V_R1_base_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Wan2.2
How to use rzgar/Wan2.2_I2V_R1_base_v2 with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
This is an improved version of wan2.2-i2v-A14B-Uncensored-base.
The base model has been changed to KJ FP8, and an improved lightx2v (tuned specifically for this version) has been merged into it.
This version not only works great with I2V but also performs very well in text-to-video style. Use a blank image or the canvas.png provided in the demo folder.
As a rule of thumb, and to preserve the flexibility of the model, the LoRAs are merged at a strength of the “sweet spot” - 20%. The model responds very well when used with style LoRAs or any other wan2.2 I2V and T2V LoRAs, without overpowering the base model. If you use your own LoRA stacks with this model, try strengths in the range of 0.5–0.65. The model already understands the majority of explicit prompts and the mechanics behind them. Using NSFW-Wan-UMT5-XXL-V2 will improve it further.
- (Optional VAE): Cinematic The Matrix theme | B & W
Image To Video Clips
Text To Video style. Input image: canvas.png
Files
| File | download |
|---|---|
Wan2.2_I2V_High_R1_n54w_v2.safetensors |
High noise |
Wan2.2_I2V_Low_R1_n54w_v2.safetensors |
Low noise |
Appearance-Only Enhancers
Rarely needed in I2V scenes, but in T2V scenes they can enhance appearances if the visual results otherwise look underfit.
| File | Download | Strength |
|---|---|---|
male_genitalia_enhancer_high.safetensors |
High noise | 0.55 |
male_genitalia_enhancer_low.safetensors |
Low noise | 0.55 |
female_genitalia_enhancer_high.safetensors |
High noise | 0.5 |
female_genitalia_enhancer_low.safetensors |
Low noise | 0.5 |
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Model tree for rzgar/Wan2.2_I2V_R1_base_v2
Base model
Wan-AI/Wan2.2-I2V-A14B