update readme

README.md

@@ -17,6 +17,7 @@
 [![Unit Tests](https://github.com/google/maxtext/actions/workflows/UnitTests.yml/badge.svg)](https://github.com/google/maxdiffusion/actions/workflows/UnitTests.yml)
 
 # What's new?
+- **`2024/12/12`**: Load multiple LoRAs for inference.
 - **`2024/10/22`**: LoRA support for Hyper SDXL.
 - **`2024/8/1`**: Orbax is the new default checkpointer. You can still use `pipeline.save_pretrained` after training to save in diffusers format.
 - **`2024/7/20`**: Dreambooth training for Stable Diffusion 1.x,2.x is now supported.
@@ -33,6 +34,7 @@ MaxDiffusion supports
 * Stable Diffusion XL (training and inference).
 * Stable Diffusion Lightning (inference).
 * Hyper-SD XL LoRA loading (inference).
+* Load Multiple LoRA (inference).
 * ControlNet inference (Stable Diffusion 1.4 & SDXL).
 * Dreambooth training support for Stable Diffusion 1.x,2.x.
 
@@ -45,6 +47,7 @@ MaxDiffusion supports
       * [Dreambooth](#dreambooth)
     * [Inference](#inference)
       * [Hyper-SD XL LoRA](#hyper-sdxl-lora)
+      * [Load Multiple LoRA](#load-multiple-lora)
       * [SDXL Lightning](#sdxl-lightning)
       * [ControlNet](#controlnet)
 * [Comparison To Alternatives](#comparison-to-alternatives)
@@ -139,6 +142,14 @@ To generate images, run the following command:
   python src/maxdiffusion/generate_sdxl.py src/maxdiffusion/configs/base_xl.yml run_name="test-lora" output_dir=/tmp/ jax_cache_dir=/tmp/cache_dir/ num_inference_steps=2 do_classifier_free_guidance=False prompt="a photograph of a cat wearing a hat riding a skateboard in a park." per_device_batch_size=1 pretrained_model_name_or_path="Lykon/AAM_XL_AnimeMix" from_pt=True revision=main diffusion_scheduler_config='{"_class_name" : "FlaxDDIMScheduler", "timestep_spacing" : "trailing"}' lora_config='{"lora_model_name_or_path" : ["ByteDance/Hyper-SD"], "weight_name" : ["Hyper-SDXL-2steps-lora.safetensors"], "adapter_name" : ["hyper-sdxl"], "scale": [0.7], "from_pt": ["true"]}'
   ```
 
+  ## Load Multiple LoRA
+
+    Supports loading multiple LoRAs for inference. Both from local or from HuggingFace hub.
+
+    ```bash
+    python src/maxdiffusion/generate_sdxl.py src/maxdiffusion/configs/base_xl.yml run_name="test-lora" output_dir=/tmp/tmp/ jax_cache_dir=/tmp/cache_dir/ num_inference_steps=30 do_classifier_free_guidance=True prompt="ultra detailed diagram blueprint of a papercut Sitting MaineCoon cat, wide canvas, ampereart, electrical diagram, bl3uprint, papercut" per_device_batch_size=1 diffusion_scheduler_config='{"_class_name" : "FlaxDDIMScheduler", "timestep_spacing" : "trailing"}' lora_config='{"lora_model_name_or_path" : ["/home/jfacevedo/blueprintify-sd-xl-10.safetensors","TheLastBen/Papercut_SDXL"], "weight_name" : ["/home/jfacevedo/blueprintify-sd-xl-10.safetensors","papercut.safetensors"], "adapter_name" : ["blueprint","papercut"], "scale": [0.8, 0.7], "from_pt": ["true", "true"]}'
+    ```
+
   ## SDXL Lightning
 
   Single and Multi host inference is supported with sharding annotations: