← back to catalog · registered 2026-08-22 13:56

rzgar/wan2.2-i2v-A14B-Uncensored-base

Your rig guess connected
? Why do I need an app?
Reading your rig…

This is a rough estimate. Install the free app - we'll show exact numbers.

Reading real hardware from your app right now. Numbers below are exact.

Below is the per-quantization compatibility for this model.

curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/rzgar%2Fwan2.2-i2v-A14B-Uncensored-base"
Response includes
  • classification m-uncensored
  • files 16
  • author_summary 2 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

What is a refusal direction? →
Downloads · 30-day
0
Likes
8
Model age
4mo ago
created 2026-05-21
Downloads over time
Now0→from0↑0%
00110 on May 200 on Oct 11MayJunJulAugSepOct
May 20 → Oct 11 · 60 snapshots · spans 144 days

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

License
apache-2.0
Tags
base_model:Wan-AI/Wan2.2-I2V-A14B base_model:finetune:Wan-AI/Wan2.2-I2V-A14B license:apache-2.0 region:us

Related

Total size
28.2 GB
Files
16
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-05-24 06:07

Files by quantization

Auxiliary files 16 files 28.3 GB
Wan2.2_I2V_High_R1.safetensors 13.5 GB f0d7e6ac download
Wan2.2_I2V_Low_R1.safetensors 13.5 GB 0aca667b download
CubeyAI-GeneralN-High.safetensors 585 MB 34e2144d download
CubeyAI-GeneralN-Low.safetensors 585 MB d6b78374 download
LTX_2.3_WAN2.2_ID-LoRA-TalkVid_Celebv_Workflow_Demo.mp4 3.77 MB 92766328 download
SkyReels_V3_Talking_Avatar_Workflow_Demo.mp4 3.61 MB 0c2b37e0 download
Wan2.2_Bruce_Lee_Demo.mp4 2.51 MB 503dccf9 download
LTX_2.3_Wan2.2_Add_Audio_Workflow_v2_Demo.mp4 1.80 MB ac57c33f download
LTX_2.3_Wan2.2_Add_Audio_Workflow_Demo.mp4 1.29 MB 2c846b69 download
LTX_2.3_Wan2.2_Add_Audio_Workflow_v2.json 123 KB 24715a19 download
LTX_2.3_WAN2.2_ID-LoRA-TalkVid_Celebv_Workflow.json 118 KB 8f6cc0aa download
LTX_2.3_Wan2.2_Add_Audio_Workflow.json 93.0 KB 04071478 download
SkyReels_V3_Talking_Avatar_Workflow.json 76.2 KB bb1b43fd download
Wan2.2 I2V & T2V workflow.json 50.9 KB 25644ac9 download
README.md 2.47 KB 65379526 download
.gitattributes 1.87 KB 932ac4d3 download

README current version from Hugging Face


license: apache-2.0
base_model:

  • Wan-AI/Wan2.2-I2V-A14B

This model is suitable for use as a base for merging. | LightX2V 4Step LoRA

I collected around 200 highly engaged prompts from various communities and analyzed their common patterns, structures, and phrasing. I then combined this analysis with the official Wan2.2 guidelines to create a specialized system prompt for JoyCaption.

This system prompt was used to caption the training dataset in a style that matches popular user prompts while still respecting the model's official guidelines. After 4 training sessions, the resulting LoRA became quite reliable.

Strengths:

  • Understands short, simple, and uncensored prompts out of the box
  • Solid male & female anatomy (7/10 reliability)
  • Follows user prompts closely
  • Excellent flexible base for merging other LoRAs
  • Adding LoRAs rarely overpowers the model

Recommended LoRA:
CubeyAI-GeneralN-High.safetensors & CubeyAI-GeneralN-Low.safetensors
Using these LoRAs with the official Wan2.2 barely does anything, but when paired with this merge at 0.5 or 0.55 strength, it improves fluid and rhythmical motions and often remove the need for other LoRAs.

Compared to official Wan2.2:

  • Much better prompt understanding
  • Good basic mechanics for most actions
  • More stable when adding LoRAs
  • Low strength LoRAs usually improve quality

Limitations:

If the input image doesn't contain the objects or people you want to add or modify, the model may still struggle, even with LoRAs.

Workaround: Place a brightly colored distinct shape in the scene and instruct the model to replace it. This works very well with first/last frame techniques for face swaps and continuity.

Be descriptive with actions and objects that are not visible in the input image. UM_T5_XXL is not a strong reasoning model, so vague prompts often fail when adding new elements.
Example style:
"A {fully/partially nude} young man with erect *** {approaches/appears/enters the frame from the (left/right/behind) }, her, and thrusts his **** back and forth in ***..."

Sound:

  • MMAudio --> Best for background & sound effects
  • SkyReel V3 --> Excellent for talking
  • LTX-2.3 --> Fast but less reliable (better on >7s videos)

Related LoRA:s

README history 4 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-05-21Update README.md0d1862f2.5 KB
    Loading...
  2. 2026-05-21Update README.mdedd9cda2.4 KB
    Loading...
  3. 2026-05-21Update README.md2e3aa532.1 KB
    Loading...
  4. 2026-05-21initial commit8ad230928 B
    Loading...
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration