ExpSoft Uncensored Captioner

ExpSoft Uncensored Captioner is a Windows desktop tool built by Nicolas Riquier that captions images using abliterated (uncensored) QwenVL vision-language models combined with the WD14 SwinV2 booru-style tagger. It ships with 5 caption presets — basic, dataset-focused, artistic, booru-style, and uncensored complete — and supports single image, batch folder, and tag-forced modes where WD14 detects tags first and injects them into the caption prompt via a {tags} template.

ExpSoft Uncensored Captioner is distributed through ExpSoft's Patreon. Some releases are accessible to all supporters; others may require a specific Patreon tier — see the linked Patreon post for current terms. The underlying QwenVL and WD14 models are open-weights; ExpSoft's value is wrapping abliterated QwenVL + WD14 SwinV2 in a Windows GUI with model downloads, 4-bit quantization and the {tags} prompt template baked in, so dataset captioning works in clicks instead of Python pain.

Standard QwenVL and most VLMs refuse to describe NSFW, violent or controversial imagery. "Abliterated" variants are open-weights modifications that remove the refusal layer, letting the model describe what it actually sees. This is essential for dataset captioning where you need accurate descriptions of artistic, anatomical or mature content — not euphemisms or refusals.

Three build variants: RTX 40xx (cu124), RTX 50xx Blackwell (cu128), and CPU-only. 4-bit bitsandbytes quantization is enabled by default, so 8-billion-parameter QwenVL models run on a single 8 GB VRAM GPU — and bigger models (32B+) fit in 24 GB VRAM.

Yes — once models are downloaded, captioning runs entirely on your local GPU. The Setup tab fetches QwenVL and WD14 weights from HuggingFace (with xet-accelerated transfer for faster downloads). After that, no internet calls.

ExpSoft's tool ships with multiple abliterated QwenVL variants you can switch between, integrates WD14 SwinV2 tag detection as a caption-prompt prefix (so the VLM sees the booru tags and weaves them into a natural description), and runs entirely on Windows with no Python proficiency required. Compared to typical captioning workflows that pin you to a single model, expect command-line proficiency, or skip the tag-injection step, this is a one-window pipeline geared for AI dataset prep.

Tag-forced mode runs WD14 SwinV2 on the image first to extract booru-style tags, then injects them into the caption prompt via the {tags} template. This anchors the VLM's caption in concrete recognised concepts — useful when the model would otherwise hallucinate or be overly generic. Ideal for dataset prep where consistency across thousands of captions matters.

ExpSoft Uncensored Captioner was built by Nicolas Riquier as part of the ExpSoft catalogue. It combines a FastAPI Python backend (model loading + inference) with a .NET 8 WPF frontend for the Windows UX.