Instructions to use 777Radik/Qwen-Image-Cyrillic-ControlNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 777Radik/Qwen-Image-Cyrillic-ControlNet with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("777Radik/Qwen-Image-Cyrillic-ControlNet") pipe = StableDiffusionControlNetPipeline.from_pretrained( "Qwen/Qwen-Image-2512", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 1,768 Bytes
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license: apache-2.0
base_model: Qwen/Qwen-Image-2512
tags:
- diffusers
- diffsynth
- controlnet
- text-to-image
- cyrillic
---
# Qwen-Image Cyrillic Blockwise ControlNet
Blockwise Canny ControlNet checkpoint fine-tuned for copying real Cyrillic glyph controls
into Qwen-Image output. It starts from
`DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny` and keeps the DiffSynth checkpoint
key layout.
## Artifact
- `model.safetensors`
- 2,266,838,080 bytes
- SHA-256: `8f7ed8860b94283d48b72859cb7ec715a6da7ae522a4a312004e635abfc8f9cb`
## Training
- base: `Qwen/Qwen-Image-2512`
- trainable module: full `blockwise_controlnet`
- 128 prepared edge-control samples at 512×512
- 128 steps
- learning rate: `1e-5`
- trainable precision: BF16
- frozen transformer, text encoder, and VAE storage/onload: FP8
- gradient checkpointing enabled
Exact hashes and arguments are in the separate configuration repository.
## Validation
Fixed held-out benchmark: 100 unseen Russian words, 512×512, 20 steps, seed 3000 plus sample
index, fitted edge control, scale 0.85.
- manual exact Cyrillic score: 99/100
- OCR diagnostic exact: 77/100
- mean OCR CER: 0.1142
- mean glyph similarity: 0.9564
The single manual failure was `ЕЩЁ → ЁЩЁ`. Edge scale retries 0.9, 1.0, and 1.1 and a
filled-control retry at 0.7 with the same seed did not correct it. OCR is diagnostic only;
the reported 99/100 score comes from manual inspection of all five contact sheets.
## Limitations
Training data uses a narrow synthetic typography distribution: simple backgrounds, limited
fonts, and isolated words. This checkpoint is intended for glyph-guided generation, not
unguided spelling, paragraphs, arbitrary layouts, handwriting, or guaranteed OCR-perfect
output.
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