GitLab

- Update torchvision version from0.21.1 to0.21.0 in Windows CUDA acceleration guides
- Update both English and Chinese versions of the documentation

- Remove accelerate package from requirements.txt
- This change ensures only necessary external dependencies are introduced

- Add accelerate package to support model training acceleration
- Update requirements.txt to include new dependency

master -> dev

Release 1.3.0

docs(readme): update release notes for version 1.3.0

docs(readme): update release notes for version 1.3.0 

- Remove duplicate entries for paddleocr2torch and thread safety
- Add new entry for real-time progress bar implementation
- Update mfr model to unimernet(2503)
- Extend torch version compatibility
- Enhance cuda support for various GPU models
- Improve parsing speed on MPS devices

- Update release notes in both English and Chinese README files
- Highlight major optimizations and improvements in version 1.3.0
- Clarify compatibility changes for torch, CUDA, and Python versions
- Emphasize performance improvements and parsing speed enhancements
- Mention specific bug fixes and parsing effect optimizations

Release 1.3.0

fix: support non-pdf file in batch mode

fix: convert image with pymupdf

fix: support non-pdf file in batch mode

feat(web_api): update configuration and remove unused code 

feat(web_api): update configuration and remove unused code

- Comment out PaddlePaddle GPU installation in Dockerfile
- Add OCR model download URL in download_models.py
- Update config version in magic-pdf.json
- Remove outdated information and simplify README.md
- Remove volume creation for PaddleOCR models in Dockerfile

docs(user_guide): update installation guide and CUDA support

docs(user_guide): update installation guide and CUDA support

- Update CUDA version requirements to 12.4 and higher
- Add support for CUDA 12.6 and CANN environments- Update Python version requirements to 3.10-3.12
- Remove paddlepaddle-gpu installation and related instructions
- Update magic-pdf installation command to use Aliyun mirror
- Add storage requirements and update memory requirements
- Update GPU hardware support list to include all GPUs with Tensor Cores
- Add support for Apple Silicon

docs(readme): update changelog and compatibility information 

docs(readme): update changelog and compatibility information

- Update changelog for version 1.3.0 release
- Clarify CUDA and GPU compatibility improvements
- Add information about batch processing speed improvements
- Update model download process and memory usage optimizations
- Include link to batch processing demo script

feat(model): add tqdm progress bar to model prediction loops

feat(model): add tqdm progress bar to model prediction loops

- Update table recognition logic to process each table individually
- Refactor layout detection to use tqdm for progress tracking
- Optimize OCR recognition by using a single tqdm wrapper
- Improve MFR prediction with a more accurate progress bar
- Simplify MFD prediction by removing unnecessary total calculation

- Comment out OCR timing measurement code to improve readability and performance
- Remove unnecessary logging of OCR processing time

- Remove unused imports and comments
- Increase MIN_BATCH_INFERENCE_SIZE from 100 to 200
- Comment out VRAM cleaning and logging in batch_analyze.py
- Simplify code in doc_analyze_by_custom_model.py- Add tqdm progress bar in pdf_parse_union_core_v2.py
- Enable tqdm in OCR processing

- Remove outdated comments in table-config examples
- Add tqdm to requirements in all Docker environments

- Add tqdm progress bar to batch prediction loops in multiple model modules
- Improve logging and error handling in batch analysis script
- Update table model initialization to use default sub-model if none specified
- Add tqdm dependency to requirements.txt

feat(model): update Chinese OCR detection model to PP-OCRv3 

feat(model): update Chinese OCR detection model to PP-OCRv3

- Replace ch_PP-OCRv4_det_infer.pth with ch_PP-OCRv3_det_infer.pth in models_config.yml
- Add new ch_PP-OCRv3_det_infer model configuration in arch_config.yaml

refactor(ocr): remove redundant code and improve code quality 

refactor(ocr): remove redundant code and improve code quality

- Remove unnecessary GPU checks and cuda() calls
- Consolidate tensor device placement using .to(self.device)
- Add warning suppression for cleaner output
- Refactor conditional logic for better readability

feat: add batch example

refactor(demo): simplify batch_demo.py and update demo.py