DiffSynth-Studio 2.0 major update

docs/en/Training/Differential_LoRA.md

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+# Differential LoRA Training
+
+Differential LoRA training is a special form of LoRA training designed to enable models to learn differences between images.
+
+## Training Approach
+
+We were unable to identify the original proposer of differential LoRA training, as this technique has been circulating in the open-source community for a long time.
+
+Assume we have two similar-content images: Image 1 and Image 2. For example, both images contain a car, but Image 1 has fewer details while Image 2 has more details. In differential LoRA training, we perform two-step training:
+
+* Train LoRA 1 using Image 1 as training data with [standard supervised training](/docs/en/Training/Supervised_Fine_Tuning.md)
+* Train LoRA 2 using Image 2 as training data, after integrating LoRA 1 into the base model, with [standard supervised training](/docs/en/Training/Supervised_Fine_Tuning.md)
+
+In the first training step, since there is only one training image, the LoRA model easily overfits. Therefore, after training, LoRA 1 will cause the model to generate Image 1 without hesitation, regardless of the random seed. In the second training step, the LoRA model overfits again. Thus, after training, with the combined effect of LoRA 1 and LoRA 2, the model will generate Image 2 without hesitation. In short:
+
+* LoRA 1 = Generate Image 1
+* LoRA 1 + LoRA 2 = Generate Image 2
+
+At this point, discarding LoRA 1 and using only LoRA 2, the model will understand the difference between Image 1 and Image 2, making the generated content tend toward "less like Image 1, more like Image 2."
+
+Single training data can ensure the model overfits to the training data, but lacks stability. To improve stability, we can train with multiple image pairs and average the trained LoRA 2 models to obtain a more stable LoRA.
+
+Using this training approach, some functionally unique LoRA models can be trained. For example, using ugly and beautiful image pairs to train LoRAs that enhance image aesthetics; using low-detail and high-detail image pairs to train LoRAs that increase image detail.
+
+## Model Effects
+
+We have trained several aesthetic enhancement LoRAs using differential LoRA training techniques. You can visit the corresponding model pages to view the generation effects.
+
+* [DiffSynth-Studio/Qwen-Image-LoRA-ArtAug-v1](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-LoRA-ArtAug-v1)
+* [DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1)
+
+## Using Differential LoRA Training in the Training Framework
+
+The first step of training is identical to ordinary LoRA training. In the second step's training command, fill in the path of the first step's LoRA model file through the `--preset_lora_path` parameter, and set `--preset_lora_model` to the same parameters as `lora_base_model` to load LoRA 1 into the base model.
+
+## Framework Design Concept
+
+In the training framework, the model pointed to by `--preset_lora_path` is loaded in the `switch_pipe_to_training_mode` of `DiffusionTrainingModule`.