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Wan_2.2_T2V_Low/README.md
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---
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language:
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- en
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tags:
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- art
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---
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# BBC Ride Wan (2.2!)
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**Creator**: [dngstn32](https://civitai.com/user/dngstn32)
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**Type**: LORA
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**Base Model**: Wan Video 2.2 T2V-A14B
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**Version**: Wan 2.2 (T2V Low)
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**Trigger Words**: `N/A`
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**Civitai Model ID**: 1452829
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**Civitai Version ID**: 2105553
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**Stats (at time of fetch for this version)**:
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* Downloads: 2009
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* Rating: 0 (0 ratings)
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* Favorites: N/A
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---
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## 📄 Description (Parent Model)
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Wan 2.2!
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Re-trained the dataset on Wan 2.2, make sure to grab BOTH Loras (High and Low)!
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Getting great results with
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K3NK
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's workflow -
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https://civitai.com/models/1824027/wan-22-t2v-i2v4-stepskijais-wrapper-workflowk3nk
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Update 6.17
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I realized while trying to improve this LoRA that I never uploaded a T2V version, so... here it is. Enjoy!
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-- -- --
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My second LoRA... I've made some drastic changes to my captioning approach, and the results show some nice improvement.
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Please share anything you make with this, I'd love to see some better prompts!
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This was trained on WAN 14B I2V, on 45 videos normalized to 480P / 24FPS and trimmed to 3 seconds using diffusion-pipe. However, it works okay with the T2V model, and I've included some examples from that.
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My captioning approach:
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45 videos, resized to 480P, 3 seconds, 24FPS.
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Ran each one through
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ComfyUI_Qwen2-VL-Instruct
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to generate a base video description, but this unfortunately doesn't pick up on any NSFW bits. This usually took a few tries on the same image, since the LLM almost seemed "disgusted" by the suggestion. :D
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Grabbed my "favorite" frame and ran that through Joy Caption 2, then I manually combined the Qwen description and the Joy Caption Two caption to make the final .txt file.
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## Civitai Links
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* **[🔗 View This Version on Civitai →](https://civitai.com/models/1452829?modelVersionId=2105553)**
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* [View Full Model Page →](https://civitai.com/models/1452829)
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* [View Creator Profile →](https://civitai.com/user/dngstn32)
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---
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## File Information
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* **Filename**: `bbcRide_wan22_T2V_low_e20.safetensors`
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* **Size**: 292.59 MB
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* **Hash (AutoV2)**: `E2D9E03C6D`
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* **Hash (SHA256)**: `E2D9E03C6D30EC26092406EE2F5DC26AAFA8ACDEE3C8ECDD158CE2ECDD57AFDF`
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