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@@ -84,18 +84,68 @@ Development is based on Python 3.10, should you encounter problems with other Py
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#### 1. Install Magic-PDF
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```bash
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+# If you only need the basic features (without built-in model parsing functionality)
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pip install magic-pdf
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+# or
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+# For complete parsing capabilities (including high-precision model parsing)
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+pip install magic-pdf[full-cpu]
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+
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+# Additionally, you will need to install the dependency detectron2.
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+# For detectron2, compile it yourself as per https://github.com/facebookresearch/detectron2/issues/5114
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+# Or use our precompiled wheel
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+
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+# windows
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+pip install https://github.com/opendatalab/MinerU/raw/master/assets/whl/detectron2-0.6-cp310-cp310-win_amd64.whl
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+
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+# linux
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+pip install https://github.com/opendatalab/MinerU/raw/master/assets/whl/detectron2-0.6-cp310-cp310-linux_x86_64.whl
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+
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+# macOS(Intel)
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+pip install https://github.com/opendatalab/MinerU/raw/master/assets/whl/detectron2-0.6-cp310-cp310-macosx_10_9_universal2.whl
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+
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+# macOS(M1/M2/M3)
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+pip install https://github.com/opendatalab/MinerU/raw/master/assets/whl/detectron2-0.6-cp310-cp310-macosx_11_0_arm64.whl
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+
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```
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-#### 2. Usage via Command Line
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-###### simple
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+#### 2. Downloading model weights files
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+
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+For detailed references, please see below[how_to_download_models](docs/how_to_download_models.md)
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+
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+After downloading the model weights, move the 'models' directory to a directory on a larger disk space, preferably an SSD.
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+
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+
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+#### 3. Copy the Configuration File and Make Configurations
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```bash
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+# Copy the configuration file to the root directory
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cp magic-pdf.template.json ~/magic-pdf.json
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-magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
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+```
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+In magic-pdf.json, configure "models-dir" to point to the directory where the model weights files are located.
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+
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+```json
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+{
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+ "models-dir": "/tmp/models"
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+}
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+```
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+
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+
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+#### 4. Usage via Command Line
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+
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+###### simple
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+
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+```bash
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+magic-pdf pdf-command --pdf "pdf_path" --inside_model true
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```
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After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".
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+You can find the corresponding xxx_model.json file in the markdown directory.
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+If you intend to do secondary development on the post-processing pipeline, you can use the command:
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+```bash
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+magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
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+```
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+In this way, you won't need to re-run the model data, making debugging more convenient.
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+
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###### more
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@@ -103,7 +153,35 @@ After the program has finished, you can find the generated markdown files under
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magic-pdf --help
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```
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-#### 3. Usage via Api
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+
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+#### 5. Acceleration Using CUDA or MPS
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+
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+##### CUDA
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+
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+You need to install the corresponding PyTorch version according to your CUDA version.
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+```bash
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+# When using the GPU solution, you need to reinstall PyTorch for the corresponding CUDA version. This example installs the CUDA 11.8 version.
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+pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
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+```
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+Also, you need to modify the value of "device-mode" in the configuration file magic-pdf.json.
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+```json
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+{
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+ "device-mode":"cuda"
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+}
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+```
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+
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+##### MPS
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+
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+For macOS users with M-series chip devices, you can use MPS for inference acceleration.
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+You also need to modify the value of "device-mode" in the configuration file magic-pdf.json.
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+
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+```json
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+{
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+ "device-mode":"mps"
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+}
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+```
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+
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+#### 6. Usage via Api
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###### Local
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```python
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