[Wuerstchen] text to image training script

examples/wuerstchen/text_to_image/README.md

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+# Würstchen text-to-image fine-tuning
+
+## Running locally with PyTorch
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+**Important**
+
+To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date. To do this, execute the following steps in a new virtual environment:
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install .
+```
+
+Then cd into the example folder and run
+```bash
+cd examples/wuerstchen/text_to_image
+pip install -r requirements.txt
+```
+
+And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+For this example we want to directly store the trained LoRA embeddings on the Hub, so we need to be logged in and add the `--push_to_hub` flag to the training script. To log in, run:
+```bash
+huggingface-cli login
+```
+
+## Prior training
+
+You can fine-tune the Würstchen prior model with the `train_text_to_image_prior.py` script. Note that we currently support `--gradient_checkpointing` for prior model fine-tuning so you can use it for more GPU memory constrained setups.
+
+<br>
+
+<!-- accelerate_snippet_start -->
+```bash
+export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+
+accelerate launch  train_text_to_image_prior.py \
+  --mixed_precision="fp16" \
+  --dataset_name=$DATASET_NAME \
+  --resolution=768 \
+  --train_batch_size=4 \
+  --gradient_accumulation_steps=4 \
+  --gradient_checkpointing \
+  --dataloader_num_workers=4 \
+  --max_train_steps=15000 \
+  --learning_rate=1e-05 \
+  --max_grad_norm=1 \
+  --checkpoints_total_limit=3 \
+  --lr_scheduler="constant" --lr_warmup_steps=0 \
+  --validation_prompts="A robot pokemon, 4k photo" \
+  --report_to="wandb" \
+  --push_to_hub \
+  --output_dir="wuerstchen-prior-pokemon-model"
+```
+<!-- accelerate_snippet_end -->
+
+## Training with LoRA
+
+Low-Rank Adaption of Large Language Models (or LoRA) was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.
+
+In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:
+
+- Previous pretrained weights are kept frozen so that the model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).
+- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.
+- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.
+
+
+### Prior Training
+
+First, you need to set up your development environment as explained in the [installation](#Running-locally-with-PyTorch) section. Make sure to set the `DATASET_NAME` environment variable. Here, we will use the [Pokemon captions dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).  
+
+```bash
+export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+
+accelerate launch train_text_to_image_prior_lora.py \
+  --mixed_precision="fp16" \
+  --dataset_name=$DATASET_NAME --caption_column="text" \
+  --resolution=768 \
+  --train_batch_size=8 \
+  --num_train_epochs=100 --checkpointing_steps=5000 \
+  --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
+  --seed=42 \
+  --rank=4 \
+  --validation_prompt="cute dragon creature" \
+  --report_to="wandb" \
+  --push_to_hub \
+  --output_dir="wuerstchen-prior-pokemon-lora"
+```

examples/wuerstchen/text_to_image/modeling_efficient_net_encoder.py

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+import torch.nn as nn
+from torchvision.models import efficientnet_v2_l, efficientnet_v2_s
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+
+
+class EfficientNetEncoder(ModelMixin, ConfigMixin):
+    @register_to_config
+    def __init__(self, c_latent=16, c_cond=1280, effnet="efficientnet_v2_s"):
+        super().__init__()
+
+        if effnet == "efficientnet_v2_s":
+            self.backbone = efficientnet_v2_s(weights="DEFAULT").features
+        else:
+            self.backbone = efficientnet_v2_l(weights="DEFAULT").features
+        self.mapper = nn.Sequential(
+            nn.Conv2d(c_cond, c_latent, kernel_size=1, bias=False),
+            nn.BatchNorm2d(c_latent),  # then normalize them to have mean 0 and std 1
+        )
+
+    def forward(self, x):
+        return self.mapper(self.backbone(x))

examples/wuerstchen/text_to_image/requirements.txt

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+accelerate>=0.16.0
+torchvision
+transformers>=4.25.1
+wandb
+huggingface-cli
+bitsandbytes
+deepspeed