Add SD3 DreamBooth

ppdiffusers/examples/dreambooth/README_sd3.md

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+# DreamBooth训练示例：Stable Diffusion 3 (SD3)
+
+[DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://arxiv.org/abs/2208.12242) 是一种用于个性化文本到图像模型的方法，只需要主题的少量图像（3~5张）即可。
+
+`train_dreambooth_sd3.py` 脚本展示了如何进行DreamBooth全参数微调[Stable Diffusion 3](https://huggingface.co/papers/2403.03206)， `train_dreambooth_lora_sd3.py` 脚本中展示了如何进行DreamBooth LoRA微调。
+
+
+> [!NOTE]  
+> Stable Diffusion 3遵循 [Stability Community 开源协议](https://stability.ai/license)。
+> Community License: Free for research, non-commercial, and commercial use for organisations or individuals with less than $1M annual revenue. You only need a paid Enterprise license if your yearly revenues exceed USD$1M and you use Stability AI models in commercial products or services. Read more: https://stability.ai/license
+
+
+## DreamBooth微调
+
+### 安装依赖
+
+在运行脚本之前，请确保安装了库的训练依赖项：
+
+```bash
+pip install -r requirements_sd3.txt
+```
+
+
+
+### 示例
+首先需要获取示例数据集。在这个示例中，我们将使用一些狗的图像：https://paddlenlp.bj.bcebos.com/models/community/westfish/develop-sdxl/dog.zip 。
+
+解压数据集``unzip dog.zip``后，使用以下命令启动训练：
+
+```bash
+export MODEL_NAME="stabilityai/stable-diffusion-3-medium-diffusers"
+export INSTANCE_DIR="dog"
+export OUTPUT_DIR="trained-sd3"
+wandb offline
+```
+
+```bash
+python train_dreambooth_sd3.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --mixed_precision="fp16" \
+  --instance_prompt="a photo of sks dog" \
+  --resolution=1024 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=4 \
+  --learning_rate=1e-4 \
+  --report_to="wandb" \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=50 \
+  --validation_prompt="A photo of sks dog in a bucket" \
+  --validation_epochs=25 \
+  --seed="0" \
+  --checkpointing_steps=250
+```
+
+fp16训练需要显存67000MiB，为了更好地跟踪我们的训练实验，我们在上面的命令中使用了以下标志：
+
+* `report_to="wandb"` 将确保在 Weights and Biases 上跟踪训练运行。要使用它，请确保安装 `wandb`，使用 `pip install wandb`。
+* `validation_prompt` 和 `validation_epochs` 允许脚本进行几次验证推理运行。这可以让我们定性地检查训练是否按预期进行。
+
+
+### 推理
+训练完成后，我们可以通过以下python脚本执行推理：
+```python
+from ppdiffusers import StableDiffusion3Pipeline
+from ppdiffusers import (
+    AutoencoderKL,
+    StableDiffusion3Pipeline,
+    SD3Transformer2DModel,
+)
+import paddle
+
+transformer_path = "your-checkpoint/transformer"
+
+pipe = StableDiffusion3Pipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3-medium-diffusers", paddle_dtype=paddle.float16
+)
+transformer = SD3Transformer2DModel.from_pretrained(transformer_path)
+
+image = pipe("A picture of a sks dog in a bucket", num_inference_steps=25).images[0]
+image.save("sks_dog_dreambooth_finetune.png")
+```
+
+
+
+## LoRA + DreamBooth
+
+[LoRA](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) 是一种流行的节省参数的微调技术，允许您以极少的可学习参数实现全微调的性能。
+
+要使用 LoRA 进行 DreamBooth，运行：
+
+```bash
+export MODEL_NAME="stabilityai/stable-diffusion-3-medium-diffusers"
+export INSTANCE_DIR="dog"
+export OUTPUT_DIR="trained-sd3-lora"
+export USE_PEFT_BACKEND=True
+wandb offline
+
+python train_dreambooth_lora_sd3.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --mixed_precision="fp16" \
+  --instance_prompt="a photo of sks dog" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=4 \
+  --learning_rate=5e-5 \
+  --report_to="wandb" \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=500 \
+  --validation_prompt="A photo of sks dog in a bucket" \
+  --validation_epochs=25 \
+  --seed="0" \
+  --checkpointing_steps=250
+```
+
+fp16训练需要显存47000MiB，。训练完成后，我们可以通过以下python脚本执行推理：
+```python
+from ppdiffusers import StableDiffusion3Pipeline
+from ppdiffusers import (
+    AutoencoderKL,
+    StableDiffusion3Pipeline,
+    SD3Transformer2DModel,
+)
+import paddle
+
+pipe = StableDiffusion3Pipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3-medium-diffusers", paddle_dtype=paddle.float16
+)
+pipeline.load_lora_weights('your-lora-checkpoint')
+
+image = pipe("A picture of a sks dog in a bucket", num_inference_steps=25).images[0]
+image.save("sks_dog_dreambooth_lora.png")
+```

ppdiffusers/examples/dreambooth/requirements_sd3.txt

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+Pillow
+wandb