Instructions to use AiArtLab/qwen3-0.6b-4b-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AiArtLab/qwen3-0.6b-4b-adapter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AiArtLab/qwen3-0.6b-4b-adapter", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
media: split the card into three illustrations
Browse files- before_after.png: the main one (pose/light/sign text transfer)
- tags_before_after.png: danbooru tags + anime-style prompt
- limitations.png: known issue — object identity (bulldog)
- README.md +12 -6
- media/before_after.png +2 -2
- media/limitations.png +3 -0
- media/tags_before_after.png +2 -2
README.md
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@@ -33,12 +33,15 @@ Train status: trained on one Vast.ai GPU (RTX 5090, 32 GB), text only — no ima
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[](media/before_after.png)
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Same seed, same settings, same prompt — klein with its native Qwen3-4B encoder on the left,
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klein driven by Qwen3-0.6B + this adapter on the right.
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[](media/tags_before_after.png)
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-
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## What it is
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## Limitations
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scene, light, pose and clothing stay faithful. Numerically the adapter keeps improving on these
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prompts (`a bulldog` 29.2% → 26.6% per-token error, `striped bowtie` 29.4% → 25.2%), but the
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breed itself is not transferred yet.
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src/adapter_lib.py adapter schema + loading
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src/klein_condition.py encode_prompt replacement for the klein pipeline
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src/train_adapter.py training (online encoders, no cached embeddings)
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media/before_after.png native Qwen3-4B vs Qwen3-0.6B + adapter
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media/tags_before_after.png
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```
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## Donations
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[](media/before_after.png)
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Same seed, same settings, same prompt — klein with its native Qwen3-4B encoder on the left,
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klein driven by Qwen3-0.6B + this adapter on the right. Pose, light, clothing and the rendered
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sign text all carry over.
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## Anime and danbooru tags
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[](media/tags_before_after.png)
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Both prose and danbooru-style tag prompts work — a tag prompt (top) and an anime-style prompt
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(bottom), native encoder vs. this adapter.
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## What it is
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## Limitations
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[](media/limitations.png)
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* Fine identity attributes still drift: the bulldog above comes out as a terrier-like dog, although
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scene, light, pose and clothing stay faithful. Numerically the adapter keeps improving on these
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prompts (`a bulldog` 29.2% → 26.6% per-token error, `striped bowtie` 29.4% → 25.2%), but the
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breed itself is not transferred yet.
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src/adapter_lib.py adapter schema + loading
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src/klein_condition.py encode_prompt replacement for the klein pipeline
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src/train_adapter.py training (online encoders, no cached embeddings)
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media/before_after.png main illustration: native Qwen3-4B vs Qwen3-0.6B + adapter
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media/tags_before_after.png danbooru tags and anime-style prompts
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media/limitations.png known issue: object identity (bulldog)
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```
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## Donations
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media/before_after.png
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Git LFS Details
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Git LFS Details
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media/limitations.png
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Git LFS Details
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media/tags_before_after.png
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Git LFS Details
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Git LFS Details
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