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Victor Kuznetsov
76e3d4154c
A third diffusion pipeline alongside sdxl/controlnet: Qwen-Image (20B MMDiT, Apache-2.0 code AND weights) img2img. The scrub still comes from the img2img strength; Qwen preserves text (incl. CJK) and structure markedly better than SDXL at the scrub floor, so it over-regenerates real photos far less (directly targets the controlnet over-regeneration that degrades real uploads). - watermark_profiles: QWEN_MODEL_ID, normalize_profile accepts "qwen". - WatermarkRemover: _load_qwen_pipeline (bf16, loads Qwen base unless --model overridden, clear ImportError if diffusers lacks the class), _run_qwen (no MPS fallback -- 20B is CUDA/cloud-class), dispatch in _generate_one/preload, pure _build_qwen_kwargs (true_cfg_scale, not guidance_scale). - Shared _base_load_kwargs() across all three loaders (dtype + token). - CLI --pipeline gains "qwen"; invisible_engine threads it through. - scripts/qwen_scrub_prototype.py: standalone PEP 723 GPU experiment. Prototype oracle floors (Modal A100-80GB, single seed, controls SynthID-positive, PENDING seed-repeat cert): OpenAI clears at strength ~0.10, Gemini at ~0.30 (0.20 still detected), with CJK text + faces faithful where controlnet plasticizes. The Gemini floor is higher than the shared default ladder, so pass an explicit --strength for Gemini on this pipeline until a Qwen-specific ladder is certified. The model-running path is CUDA-only (untestable locally); unit tests cover the pure call-shape (_build_qwen_kwargs) and profile normalization without torch. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>