[LoRA] feat: lora support for SANA. (#10234)

* feat: lora support for SANA.

* make fix-copies

* rename test class.

* attention_kwargs -> cross_attention_kwargs.

* Revert "attention_kwargs -> cross_attention_kwargs."

This reverts commit 23433bf9bc.

* exhaust 119 max line limit

* sana lora fine-tuning script.

* readme

* add a note about the supported models.

* Apply suggestions from code review

Co-authored-by: Aryan <aryan@huggingface.co>

* style

* docs for attention_kwargs.

* remove lora_scale from pag pipeline.

* copy fix

---------

Co-authored-by: Aryan <aryan@huggingface.co>

examples/dreambooth/REAMDE_sana.md

@@ -0,0 +1,127 @@
+# DreamBooth training example for SANA
+
+[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject.
+
+The `train_dreambooth_lora_sana.py` script shows how to implement the training procedure with [LoRA](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) and adapt it for [SANA](https://arxiv.org/abs/2410.10629). 
+
+
+This will also allow us to push the trained model parameters to the Hugging Face Hub platform.
+
+## Running locally with PyTorch
+
+### Installing the dependencies
+
+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 as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
+
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install -e .
+```
+
+Then cd in the `examples/dreambooth` folder and run
+```bash
+pip install -r requirements_sana.txt
+```
+
+And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+
+Or for a default accelerate configuration without answering questions about your environment
+
+```bash
+accelerate config default
+```
+
+Or if your environment doesn't support an interactive shell (e.g., a notebook)
+
+```python
+from accelerate.utils import write_basic_config
+write_basic_config()
+```
+
+When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
+Note also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.14.0` installed in your environment.
+
+
+### Dog toy example
+
+Now let's get our dataset. For this example we will use some dog images: https://huggingface.co/datasets/diffusers/dog-example.
+
+Let's first download it locally:
+
+```python
+from huggingface_hub import snapshot_download
+
+local_dir = "./dog"
+snapshot_download(
+    "diffusers/dog-example",
+    local_dir=local_dir, repo_type="dataset",
+    ignore_patterns=".gitattributes",
+)
+```
+
+This will also allow us to push the trained LoRA parameters to the Hugging Face Hub platform.
+
+Now, we can launch training using:
+
+```bash
+export MODEL_NAME="Efficient-Large-Model/Sana_1600M_1024px_diffusers"
+export INSTANCE_DIR="dog"
+export OUTPUT_DIR="trained-sana-lora"
+
+accelerate launch train_dreambooth_lora_sana.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --mixed_precision="bf16" \
+  --instance_prompt="a photo of sks dog" \
+  --resolution=1024 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=4 \
+  --use_8bit_adam \
+  --learning_rate=1e-4 \
+  --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" \
+  --push_to_hub
+```
+
+For using `push_to_hub`, make you're logged into your Hugging Face account:
+
+```bash
+huggingface-cli login
+```
+
+To better track our training experiments, we're using the following flags in the command above:
+
+* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use it, be sure to install `wandb` with `pip install wandb`. Don't forget to call `wandb login <your_api_key>` before training if you haven't done it before.
+* `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
+
+## Notes
+
+Additionally, we welcome you to explore the following CLI arguments:
+
+* `--lora_layers`: The transformer modules to apply LoRA training on. Please specify the layers in a comma seperated. E.g. - "to_k,to_q,to_v" will result in lora training of attention layers only.
+* `--complex_human_instruction`: Instructions for complex human attention as shown in [here](https://github.com/NVlabs/Sana/blob/main/configs/sana_app_config/Sana_1600M_app.yaml#L55).
+* `--max_sequence_length`: Maximum sequence length to use for text embeddings.
+
+
+We provide several options for optimizing memory optimization:
+
+* `--offload`: When enabled, we will offload the text encoder and VAE to CPU, when they are not used.
+* `cache_latents`: When enabled, we will pre-compute the latents from the input images with the VAE and remove the VAE from memory once done.
+* `--use_8bit_adam`: When enabled, we will use the 8bit version of AdamW provided by the `bitsandbytes` library.
+
+Refer to the [official documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana) of the `SanaPipeline` to know more about the models available under the SANA family and their preferred dtypes during inference.

examples/dreambooth/requirements_sana.txt

@@ -0,0 +1,8 @@
+accelerate>=1.0.0
+torchvision
+transformers>=4.47.0
+ftfy
+tensorboard
+Jinja2
+peft>=0.14.0
+sentencepiece

src/diffusers/loaders/__init__.py

@@ -70,6 +70,7 @@ if is_torch_available():
             "FluxLoraLoaderMixin",
             "CogVideoXLoraLoaderMixin",
             "Mochi1LoraLoaderMixin",
+            "SanaLoraLoaderMixin",
         ]
         _import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
         _import_structure["ip_adapter"] = ["IPAdapterMixin"]
@@ -92,6 +93,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
                 LoraLoaderMixin,
                 LTXVideoLoraLoaderMixin,
                 Mochi1LoraLoaderMixin,
+                SanaLoraLoaderMixin,
                 SD3LoraLoaderMixin,
                 StableDiffusionLoraLoaderMixin,
                 StableDiffusionXLLoraLoaderMixin,

src/diffusers/loaders/lora_pipeline.py

@@ -3562,6 +3562,314 @@ class LTXVideoLoraLoaderMixin(LoraBaseMixin):
         super().unfuse_lora(components=components)
 
 
+class SanaLoraLoaderMixin(LoraBaseMixin):
+    r"""
+    Load LoRA layers into [`SanaTransformer2DModel`]. Specific to [`SanaPipeline`].
+    """
+
+    _lora_loadable_modules = ["transformer"]
+    transformer_name = TRANSFORMER_NAME
+
+    @classmethod
+    @validate_hf_hub_args
+    # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.lora_state_dict
+    def lora_state_dict(
+        cls,
+        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+        **kwargs,
+    ):
+        r"""
+        Return state dict for lora weights and the network alphas.
+
+        <Tip warning={true}>
+
+        We support loading A1111 formatted LoRA checkpoints in a limited capacity.
+
+        This function is experimental and might change in the future.
+
+        </Tip>
+
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                Can be either:
+
+                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
+                      the Hub.
+                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
+                      with [`ModelMixin.save_pretrained`].
+                    - A [torch state
+                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
+
+            cache_dir (`Union[str, os.PathLike]`, *optional*):
+                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
+                is not used.
+            force_download (`bool`, *optional*, defaults to `False`):
+                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
+                cached versions if they exist.
+
+            proxies (`Dict[str, str]`, *optional*):
+                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
+                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
+            local_files_only (`bool`, *optional*, defaults to `False`):
+                Whether to only load local model weights and configuration files or not. If set to `True`, the model
+                won't be downloaded from the Hub.
+            token (`str` or *bool*, *optional*):
+                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
+                `diffusers-cli login` (stored in `~/.huggingface`) is used.
+            revision (`str`, *optional*, defaults to `"main"`):
+                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
+                allowed by Git.
+            subfolder (`str`, *optional*, defaults to `""`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+
+        """
+        # Load the main state dict first which has the LoRA layers for either of
+        # transformer and text encoder or both.
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        token = kwargs.pop("token", None)
+        revision = kwargs.pop("revision", None)
+        subfolder = kwargs.pop("subfolder", None)
+        weight_name = kwargs.pop("weight_name", None)
+        use_safetensors = kwargs.pop("use_safetensors", None)
+
+        allow_pickle = False
+        if use_safetensors is None:
+            use_safetensors = True
+            allow_pickle = True
+
+        user_agent = {
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        }
+
+        state_dict = _fetch_state_dict(
+            pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
+            weight_name=weight_name,
+            use_safetensors=use_safetensors,
+            local_files_only=local_files_only,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            proxies=proxies,
+            token=token,
+            revision=revision,
+            subfolder=subfolder,
+            user_agent=user_agent,
+            allow_pickle=allow_pickle,
+        )
+
+        is_dora_scale_present = any("dora_scale" in k for k in state_dict)
+        if is_dora_scale_present:
+            warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
+            logger.warning(warn_msg)
+            state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
+
+        return state_dict
+
+    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.load_lora_weights
+    def load_lora_weights(
+        self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
+    ):
+        """
+        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
+        `self.text_encoder`. All kwargs are forwarded to `self.lora_state_dict`. See
+        [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.
+        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer`] for more details on how the state
+        dict is loaded into `self.transformer`.
+
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
+            adapter_name (`str`, *optional*):
+                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+                `default_{i}` where i is the total number of adapters being loaded.
+            low_cpu_mem_usage (`bool`, *optional*):
+                Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
+                weights.
+            kwargs (`dict`, *optional*):
+                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT_LORA)
+        if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
+            raise ValueError(
+                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
+            )
+
+        # if a dict is passed, copy it instead of modifying it inplace
+        if isinstance(pretrained_model_name_or_path_or_dict, dict):
+            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
+
+        # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
+        state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
+
+        is_correct_format = all("lora" in key for key in state_dict.keys())
+        if not is_correct_format:
+            raise ValueError("Invalid LoRA checkpoint.")
+
+        self.load_lora_into_transformer(
+            state_dict,
+            transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
+            adapter_name=adapter_name,
+            _pipeline=self,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+        )
+
+    @classmethod
+    # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->SanaTransformer2DModel
+    def load_lora_into_transformer(
+        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False
+    ):
+        """
+        This will load the LoRA layers specified in `state_dict` into `transformer`.
+
+        Parameters:
+            state_dict (`dict`):
+                A standard state dict containing the lora layer parameters. The keys can either be indexed directly
+                into the unet or prefixed with an additional `unet` which can be used to distinguish between text
+                encoder lora layers.
+            transformer (`SanaTransformer2DModel`):
+                The Transformer model to load the LoRA layers into.
+            adapter_name (`str`, *optional*):
+                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+                `default_{i}` where i is the total number of adapters being loaded.
+            low_cpu_mem_usage (`bool`, *optional*):
+                Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
+                weights.
+        """
+        if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
+            raise ValueError(
+                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
+            )
+
+        # Load the layers corresponding to transformer.
+        logger.info(f"Loading {cls.transformer_name}.")
+        transformer.load_lora_adapter(
+            state_dict,
+            network_alphas=None,
+            adapter_name=adapter_name,
+            _pipeline=_pipeline,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+        )
+
+    @classmethod
+    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.save_lora_weights
+    def save_lora_weights(
+        cls,
+        save_directory: Union[str, os.PathLike],
+        transformer_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+        is_main_process: bool = True,
+        weight_name: str = None,
+        save_function: Callable = None,
+        safe_serialization: bool = True,
+    ):
+        r"""
+        Save the LoRA parameters corresponding to the UNet and text encoder.
+
+        Arguments:
+            save_directory (`str` or `os.PathLike`):
+                Directory to save LoRA parameters to. Will be created if it doesn't exist.
+            transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
+                State dict of the LoRA layers corresponding to the `transformer`.
+            is_main_process (`bool`, *optional*, defaults to `True`):
+                Whether the process calling this is the main process or not. Useful during distributed training and you
+                need to call this function on all processes. In this case, set `is_main_process=True` only on the main
+                process to avoid race conditions.
+            save_function (`Callable`):
+                The function to use to save the state dictionary. Useful during distributed training when you need to
+                replace `torch.save` with another method. Can be configured with the environment variable
+                `DIFFUSERS_SAVE_MODE`.
+            safe_serialization (`bool`, *optional*, defaults to `True`):
+                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
+        """
+        state_dict = {}
+
+        if not transformer_lora_layers:
+            raise ValueError("You must pass `transformer_lora_layers`.")
+
+        if transformer_lora_layers:
+            state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))
+
+        # Save the model
+        cls.write_lora_layers(
+            state_dict=state_dict,
+            save_directory=save_directory,
+            is_main_process=is_main_process,
+            weight_name=weight_name,
+            save_function=save_function,
+            safe_serialization=safe_serialization,
+        )
+
+    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.fuse_lora with unet->transformer
+    def fuse_lora(
+        self,
+        components: List[str] = ["transformer", "text_encoder"],
+        lora_scale: float = 1.0,
+        safe_fusing: bool = False,
+        adapter_names: Optional[List[str]] = None,
+        **kwargs,
+    ):
+        r"""
+        Fuses the LoRA parameters into the original parameters of the corresponding blocks.
+
+        <Tip warning={true}>
+
+        This is an experimental API.
+
+        </Tip>
+
+        Args:
+            components: (`List[str]`): List of LoRA-injectable components to fuse the LoRAs into.
+            lora_scale (`float`, defaults to 1.0):
+                Controls how much to influence the outputs with the LoRA parameters.
+            safe_fusing (`bool`, defaults to `False`):
+                Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
+            adapter_names (`List[str]`, *optional*):
+                Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.fuse_lora(lora_scale=0.7)
+        ```
+        """
+        super().fuse_lora(
+            components=components, lora_scale=lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names
+        )
+
+    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.unfuse_lora with unet->transformer
+    def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder"], **kwargs):
+        r"""
+        Reverses the effect of
+        [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora).
+
+        <Tip warning={true}>
+
+        This is an experimental API.
+
+        </Tip>
+
+        Args:
+            components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
+            unfuse_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
+            unfuse_text_encoder (`bool`, defaults to `True`):
+                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
+                LoRA parameters then it won't have any effect.
+        """
+        super().unfuse_lora(components=components)
+
+
 class LoraLoaderMixin(StableDiffusionLoraLoaderMixin):
     def __init__(self, *args, **kwargs):
         deprecation_message = "LoraLoaderMixin is deprecated and this will be removed in a future version. Please use `StableDiffusionLoraLoaderMixin`, instead."

src/diffusers/loaders/peft.py

@@ -54,6 +54,7 @@ _SET_ADAPTER_SCALE_FN_MAPPING = {
     "CogVideoXTransformer3DModel": lambda model_cls, weights: weights,
     "MochiTransformer3DModel": lambda model_cls, weights: weights,
     "LTXVideoTransformer3DModel": lambda model_cls, weights: weights,
+    "SanaTransformer2DModel": lambda model_cls, weights: weights,
 }
 
 

src/diffusers/models/transformers/sana_transformer.py

@@ -18,7 +18,8 @@ import torch
 from torch import nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
-from ...utils import is_torch_version, logging
+from ...loaders import PeftAdapterMixin
+from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
 from ..attention_processor import (
     Attention,
     AttentionProcessor,
@@ -180,7 +181,7 @@ class SanaTransformerBlock(nn.Module):
         return hidden_states
 
 
-class SanaTransformer2DModel(ModelMixin, ConfigMixin):
+class SanaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
     r"""
     A 2D Transformer model introduced in [Sana](https://huggingface.co/papers/2410.10629) family of models.
 
@@ -363,8 +364,24 @@ class SanaTransformer2DModel(ModelMixin, ConfigMixin):
         timestep: torch.LongTensor,
         encoder_attention_mask: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
     ) -> Union[Tuple[torch.Tensor, ...], Transformer2DModelOutput]:
+        if attention_kwargs is not None:
+            attention_kwargs = attention_kwargs.copy()
+            lora_scale = attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
         # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
         #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
         #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
@@ -460,6 +477,11 @@ class SanaTransformer2DModel(ModelMixin, ConfigMixin):
         hidden_states = hidden_states.permute(0, 5, 1, 3, 2, 4)
         output = hidden_states.reshape(batch_size, -1, post_patch_height * p, post_patch_width * p)
 
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
         if not return_dict:
             return (output,)
+
         return Transformer2DModelOutput(sample=output)

src/diffusers/pipelines/pag/pipeline_pag_sana.py

@@ -170,7 +170,6 @@ class SanaPAGPipeline(DiffusionPipeline, PAGMixin):
             pag_attn_processors=(PAGCFGSanaLinearAttnProcessor2_0(), PAGIdentitySanaLinearAttnProcessor2_0()),
         )
 
-    # Copied from diffusers.pipelines.sana.pipeline_sana.SanaPipeline.encode_prompt
     def encode_prompt(
         self,
         prompt: Union[str, List[str]],

src/diffusers/pipelines/sana/pipeline_sana.py

@@ -16,21 +16,25 @@ import html
 import inspect
 import re
 import urllib.parse as ul
-from typing import Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PixArtImageProcessor
+from ...loaders import SanaLoraLoaderMixin
 from ...models import AutoencoderDC, SanaTransformer2DModel
 from ...schedulers import DPMSolverMultistepScheduler
 from ...utils import (
     BACKENDS_MAPPING,
+    USE_PEFT_BACKEND,
     is_bs4_available,
     is_ftfy_available,
     logging,
     replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
 )
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline
@@ -130,7 +134,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class SanaPipeline(DiffusionPipeline):
+class SanaPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
     r"""
     Pipeline for text-to-image generation using [Sana](https://huggingface.co/papers/2410.10629).
     """
@@ -177,6 +181,7 @@ class SanaPipeline(DiffusionPipeline):
         clean_caption: bool = False,
         max_sequence_length: int = 300,
         complex_human_instruction: Optional[List[str]] = None,
+        lora_scale: Optional[float] = None,
     ):
         r"""
         Encodes the prompt into text encoder hidden states.
@@ -210,6 +215,15 @@ class SanaPipeline(DiffusionPipeline):
         if device is None:
             device = self._execution_device
 
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, SanaLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None and USE_PEFT_BACKEND:
+                scale_lora_layers(self.text_encoder, lora_scale)
+
         if prompt is not None and isinstance(prompt, str):
             batch_size = 1
         elif prompt is not None and isinstance(prompt, list):
@@ -305,6 +319,11 @@ class SanaPipeline(DiffusionPipeline):
             negative_prompt_embeds = None
             negative_prompt_attention_mask = None
 
+        if self.text_encoder is not None:
+            if isinstance(self, SanaLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
         return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
@@ -554,6 +573,10 @@ class SanaPipeline(DiffusionPipeline):
     def guidance_scale(self):
         return self._guidance_scale
 
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
     @property
     def do_classifier_free_guidance(self):
         return self._guidance_scale > 1.0
@@ -590,6 +613,7 @@ class SanaPipeline(DiffusionPipeline):
         return_dict: bool = True,
         clean_caption: bool = True,
         use_resolution_binning: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 300,
@@ -662,6 +686,10 @@ class SanaPipeline(DiffusionPipeline):
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
+            attention_kwargs:
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
             clean_caption (`bool`, *optional*, defaults to `True`):
                 Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
                 be installed. If the dependencies are not installed, the embeddings will be created from the raw
@@ -722,6 +750,7 @@ class SanaPipeline(DiffusionPipeline):
         )
 
         self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
         self._interrupt = False
 
         # 2. Default height and width to transformer
@@ -733,6 +762,7 @@ class SanaPipeline(DiffusionPipeline):
             batch_size = prompt_embeds.shape[0]
 
         device = self._execution_device
+        lora_scale = self.attention_kwargs.get("scale", None) if self.attention_kwargs is not None else None
 
         # 3. Encode input prompt
         (
@@ -753,6 +783,7 @@ class SanaPipeline(DiffusionPipeline):
             clean_caption=clean_caption,
             max_sequence_length=max_sequence_length,
             complex_human_instruction=complex_human_instruction,
+            lora_scale=lora_scale,
         )
         if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
@@ -801,6 +832,7 @@ class SanaPipeline(DiffusionPipeline):
                     encoder_attention_mask=prompt_attention_mask,
                     timestep=timestep,
                     return_dict=False,
+                    attention_kwargs=self.attention_kwargs,
                 )[0]
                 noise_pred = noise_pred.float()
 

tests/lora/test_lora_layers_sana.py

@@ -0,0 +1,138 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import sys
+import unittest
+
+import torch
+from transformers import Gemma2ForCausalLM, GemmaTokenizer
+
+from diffusers import AutoencoderDC, FlowMatchEulerDiscreteScheduler, SanaPipeline, SanaTransformer2DModel
+from diffusers.utils.testing_utils import floats_tensor, require_peft_backend
+
+
+sys.path.append(".")
+
+from utils import PeftLoraLoaderMixinTests  # noqa: E402
+
+
+@require_peft_backend
+class SanaLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
+    pipeline_class = SanaPipeline
+    scheduler_cls = FlowMatchEulerDiscreteScheduler(shift=7.0)
+    scheduler_kwargs = {}
+    scheduler_classes = [FlowMatchEulerDiscreteScheduler]
+    transformer_kwargs = {
+        "patch_size": 1,
+        "in_channels": 4,
+        "out_channels": 4,
+        "num_layers": 1,
+        "num_attention_heads": 2,
+        "attention_head_dim": 4,
+        "num_cross_attention_heads": 2,
+        "cross_attention_head_dim": 4,
+        "cross_attention_dim": 8,
+        "caption_channels": 8,
+        "sample_size": 32,
+    }
+    transformer_cls = SanaTransformer2DModel
+    vae_kwargs = {
+        "in_channels": 3,
+        "latent_channels": 4,
+        "attention_head_dim": 2,
+        "encoder_block_types": (
+            "ResBlock",
+            "EfficientViTBlock",
+        ),
+        "decoder_block_types": (
+            "ResBlock",
+            "EfficientViTBlock",
+        ),
+        "encoder_block_out_channels": (8, 8),
+        "decoder_block_out_channels": (8, 8),
+        "encoder_qkv_multiscales": ((), (5,)),
+        "decoder_qkv_multiscales": ((), (5,)),
+        "encoder_layers_per_block": (1, 1),
+        "decoder_layers_per_block": [1, 1],
+        "downsample_block_type": "conv",
+        "upsample_block_type": "interpolate",
+        "decoder_norm_types": "rms_norm",
+        "decoder_act_fns": "silu",
+        "scaling_factor": 0.41407,
+    }
+    vae_cls = AutoencoderDC
+    tokenizer_cls, tokenizer_id = GemmaTokenizer, "hf-internal-testing/dummy-gemma"
+    text_encoder_cls, text_encoder_id = Gemma2ForCausalLM, "hf-internal-testing/dummy-gemma-for-diffusers"
+
+    @property
+    def output_shape(self):
+        return (1, 32, 32, 3)
+
+    def get_dummy_inputs(self, with_generator=True):
+        batch_size = 1
+        sequence_length = 16
+        num_channels = 4
+        sizes = (32, 32)
+
+        generator = torch.manual_seed(0)
+        noise = floats_tensor((batch_size, num_channels) + sizes)
+        input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
+
+        pipeline_inputs = {
+            "prompt": "",
+            "negative_prompt": "",
+            "num_inference_steps": 4,
+            "guidance_scale": 4.5,
+            "height": 32,
+            "width": 32,
+            "max_sequence_length": sequence_length,
+            "output_type": "np",
+            "complex_human_instruction": None,
+        }
+        if with_generator:
+            pipeline_inputs.update({"generator": generator})
+
+        return noise, input_ids, pipeline_inputs
+
+    @unittest.skip("Not supported in Sana.")
+    def test_modify_padding_mode(self):
+        pass
+
+    @unittest.skip("Not supported in Mochi.")
+    def test_simple_inference_with_text_denoiser_block_scale(self):
+        pass
+
+    @unittest.skip("Not supported in Mochi.")
+    def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
+        pass
+
+    @unittest.skip("Text encoder LoRA is not supported in Mochi.")
+    def test_simple_inference_with_partial_text_lora(self):
+        pass
+
+    @unittest.skip("Text encoder LoRA is not supported in Mochi.")
+    def test_simple_inference_with_text_lora(self):
+        pass
+
+    @unittest.skip("Text encoder LoRA is not supported in Mochi.")
+    def test_simple_inference_with_text_lora_and_scale(self):
+        pass
+
+    @unittest.skip("Text encoder LoRA is not supported in Mochi.")
+    def test_simple_inference_with_text_lora_fused(self):
+        pass
+
+    @unittest.skip("Text encoder LoRA is not supported in Mochi.")
+    def test_simple_inference_with_text_lora_save_load(self):
+        pass

tests/lora/utils.py

@@ -1545,7 +1545,12 @@ class PeftLoraLoaderMixinTests:
                         "adapter-1"
                     ].weight += float("inf")
                 else:
-                    pipe.transformer.transformer_blocks[0].attn.to_q.lora_A["adapter-1"].weight += float("inf")
+                    named_modules = [name for name, _ in pipe.transformer.named_modules()]
+                    has_attn1 = any("attn1" in name for name in named_modules)
+                    if has_attn1:
+                        pipe.transformer.transformer_blocks[0].attn1.to_q.lora_A["adapter-1"].weight += float("inf")
+                    else:
+                        pipe.transformer.transformer_blocks[0].attn.to_q.lora_A["adapter-1"].weight += float("inf")
 
             # with `safe_fusing=True` we should see an Error
             with self.assertRaises(ValueError):