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CalamitousFelicitousness
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Comprehensive review of modules/caption/ addressing memory management, consistency, and code quality: Inference correctness: - Add devices.inference_context() to _qwen(), _smol(), _sa2() handlers - Remove redundant @torch.no_grad() decorator from joycaption predict() - Remove dead dtype=torch.bfloat16 kwarg from Florence loader Memory management: - Bound moondream3 image cache with LRU eviction (max 8 entries) - Replace fragile id(image) cache keys with content-based md5 hash - Add devices.torch_gc() after model loading in deepseek - Move deepbooru model to CPU before dropping reference on unload - Add external handler delegation to VQA.unload() (moondream3, joycaption, joytag, deepseek) - Protect batch offload mutation with try/finally Code deduplication: - Extract strip_think_xml_tags() shared helper for Qwen/Gemma/SmolVLM - Extract save_tags_to_file() into tagger.py from deepbooru and waifudiffusion Documentation and clarity: - Document deepseek global monkey-patches (LlamaFlashAttention2, attrdict) - Document Florence task="task" as intentional design choice - Add vendored-code comment to joytag.py - Document openclip direct .to() usage vs sd_models.move_model - Comment model.eval() calls that are required (trust_remote_code, custom loaders) vs removed where redundant (standard from_pretrained) API robustness: - Add HTTP 422 error response for VQA caption error strings in API endpoints (post_vqa, _dispatch_vlm)