[LoRA] add LoRA support to HiDream and fine-tuning script (#11281)

* initial commit * initial commit * initial commit * initial commit * initial commit * initial commit * Update examples/dreambooth/train_dreambooth_lora_hidream.py Co-authored-by: Bagheera <59658056+bghira@users.noreply.github.com> * move prompt embeds, pooled embeds outside * Update examples/dreambooth/train_dreambooth_lora_hidream.py Co-authored-by: hlky <hlky@hlky.ac> * Update examples/dreambooth/train_dreambooth_lora_hidream.py Co-authored-by: hlky <hlky@hlky.ac> * fix import * fix import and tokenizer 4, text encoder 4 loading * te * prompt embeds * fix naming * shapes * initial commit to add HiDreamImageLoraLoaderMixin * fix init * add tests * loader * fix model input * add code example to readme * fix default max length of text encoders * prints * nullify training cond in unpatchify for temp fix to incompatible shaping of transformer output during training * smol fix * unpatchify * unpatchify * fix validation * flip pred and loss * fix shift!!! * revert unpatchify changes (for now) * smol fix * Apply style fixes * workaround moe training * workaround moe training * remove prints * to reduce some memory, keep vae in `weight_dtype` same as we have for flux (as it's the same vae) bbd0c161b5/examples/dreambooth/train_dreambooth_lora_flux.py (L1207) * refactor to align with HiDream refactor * refactor to align with HiDream refactor * refactor to align with HiDream refactor * add support for cpu offloading of text encoders * Apply style fixes * adjust lr and rank for train example * fix copies * Apply style fixes * update README * update README * update README * fix license * keep prompt2,3,4 as None in validation * remove reverse ode comment * Update examples/dreambooth/train_dreambooth_lora_hidream.py Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> * Update examples/dreambooth/train_dreambooth_lora_hidream.py Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> * vae offload change * fix text encoder offloading * Apply style fixes * cleaner to_kwargs * fix module name in copied from * add requirements * fix offloading * fix offloading * fix offloading * update transformers version in reqs * try AutoTokenizer * try AutoTokenizer * Apply style fixes * empty commit * Delete tests/lora/test_lora_layers_hidream.py * change tokenizer_4 to load with AutoTokenizer as well * make text_encoder_four and tokenizer_four configurable * save model card * save model card * revert T5 * fix test * remove non diffusers lumina2 conversion --------- Co-authored-by: Bagheera <59658056+bghira@users.noreply.github.com> Co-authored-by: hlky <hlky@hlky.ac> Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
2026-01-27 17:22:53 +03:00 · 2025-04-22 11:44:02 +03:00
parent 6ab62c7431
commit e30d3bf544
10 changed files with 2437 additions and 7 deletions
--- a/docs/source/en/api/loaders/lora.md
+++ b/docs/source/en/api/loaders/lora.md
@@ -28,6 +28,7 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
 - [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
+- [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.

 <Tip>
@@ -91,6 +92,10 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse

 [[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin

+## HiDreamImageLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
+
 ## LoraBaseMixin

 [[autodoc]] loaders.lora_base.LoraBaseMixin
--- a/examples/dreambooth/README_hidream.md
+++ b/examples/dreambooth/README_hidream.md
@@ -0,0 +1,133 @@
+# DreamBooth training example for HiDream Image
+
+[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_hidream.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 [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/). 
+
+
+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:
+> [!NOTE]
+> The following training configuration prioritizes lower memory consumption by using gradient checkpointing, 
+> 8-bit Adam optimizer, latent caching, offloading, no validation. 
+> Additionally, when provided with 'instance_prompt' only and no 'caption_column' (used for custom prompts for each image)
+> text embeddings are pre-computed to save memory.
+
+```bash
+export MODEL_NAME="HiDream-ai/HiDream-I1-Dev"
+export INSTANCE_DIR="dog"
+export OUTPUT_DIR="trained-hidream-lora"
+
+accelerate launch train_dreambooth_lora_hidream.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 \
+  --rank=16 \
+  --learning_rate=2e-4 \
+  --report_to="wandb" \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=1000 \
+  --cache_latents \
+  --gradient_checkpointing \
+  --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.
+* `--rank`: The rank of the LoRA layers. The higher the rank, the more parameters are trained. The default is 16.
+
+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.
+* `--instance_prompt` and no `--caption_column`: when only an instance prompt is provided, we will pre-compute the text embeddings and remove the text encoders from memory once done.
+
+Refer to the [official documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/) of the `HiDreamImagePipeline` to know more about the model.
--- a/examples/dreambooth/requirements_hidream.txt
+++ b/examples/dreambooth/requirements_hidream.txt
@@ -0,0 +1,8 @@
+accelerate>=1.4.0
+torchvision
+transformers>=4.50.0
+ftfy
+tensorboard
+Jinja2
+peft>=0.14.0
+sentencepiece
--- a/examples/dreambooth/test_dreambooth_lora_hidream.py
+++ b/examples/dreambooth/test_dreambooth_lora_hidream.py
@@ -0,0 +1,220 @@
+# 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 logging
+import os
+import sys
+import tempfile
+
+import safetensors
+
+
+sys.path.append("..")
+from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
+
+
+logging.basicConfig(level=logging.DEBUG)
+
+logger = logging.getLogger()
+stream_handler = logging.StreamHandler(sys.stdout)
+logger.addHandler(stream_handler)
+
+
+class DreamBoothLoRAHiDreamImage(ExamplesTestsAccelerate):
+    instance_data_dir = "docs/source/en/imgs"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-hidream-i1-pipe"
+    text_encoder_4_path = "hf-internal-testing/tiny-random-LlamaForCausalLM"
+    tokenizer_4_path = "hf-internal-testing/tiny-random-LlamaForCausalLM"
+    script_path = "examples/dreambooth/train_dreambooth_lora_hidream.py"
+    transformer_layer_type = "double_stream_blocks.0.block.attn1.to_k"
+
+    def test_dreambooth_lora_hidream(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                {self.script_path}
+                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
+                --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
+                --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
+                --instance_data_dir {self.instance_data_dir}
+                --resolution 32
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                --max_sequence_length 16
+                """.split()
+
+            test_args.extend(["--instance_prompt", ""])
+            run_command(self._launch_args + test_args)
+            # save_pretrained smoke test
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
+
+            # make sure the state_dict has the correct naming in the parameters.
+            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
+            is_lora = all("lora" in k for k in lora_state_dict.keys())
+            self.assertTrue(is_lora)
+
+            # when not training the text encoder, all the parameters in the state dict should start
+            # with `"transformer"` in their names.
+            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
+            self.assertTrue(starts_with_transformer)
+
+    def test_dreambooth_lora_latent_caching(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                {self.script_path}
+                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
+                --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
+                --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
+                --instance_data_dir {self.instance_data_dir}
+                --resolution 32
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --cache_latents
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                --max_sequence_length 16
+                """.split()
+
+            test_args.extend(["--instance_prompt", ""])
+            run_command(self._launch_args + test_args)
+            # save_pretrained smoke test
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
+
+            # make sure the state_dict has the correct naming in the parameters.
+            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
+            is_lora = all("lora" in k for k in lora_state_dict.keys())
+            self.assertTrue(is_lora)
+
+            # when not training the text encoder, all the parameters in the state dict should start
+            # with `"transformer"` in their names.
+            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
+            self.assertTrue(starts_with_transformer)
+
+    def test_dreambooth_lora_layers(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                {self.script_path}
+                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
+                --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
+                --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
+                --instance_data_dir {self.instance_data_dir}
+                --resolution 32
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --cache_latents
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lora_layers {self.transformer_layer_type}
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                --max_sequence_length 16
+                """.split()
+
+            test_args.extend(["--instance_prompt", ""])
+            run_command(self._launch_args + test_args)
+            # save_pretrained smoke test
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
+
+            # make sure the state_dict has the correct naming in the parameters.
+            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
+            is_lora = all("lora" in k for k in lora_state_dict.keys())
+            self.assertTrue(is_lora)
+
+            # when not training the text encoder, all the parameters in the state dict should start
+            # with `"transformer"` in their names. In this test, we only params of
+            # `self.transformer_layer_type` should be in the state dict.
+            starts_with_transformer = all(self.transformer_layer_type in key for key in lora_state_dict)
+            self.assertTrue(starts_with_transformer)
+
+    def test_dreambooth_lora_hidream_checkpointing_checkpoints_total_limit(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+            {self.script_path}
+            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
+            --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
+            --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
+            --instance_data_dir={self.instance_data_dir}
+            --output_dir={tmpdir}
+            --resolution=32
+            --train_batch_size=1
+            --gradient_accumulation_steps=1
+            --max_train_steps=6
+            --checkpoints_total_limit=2
+            --checkpointing_steps=2
+            --max_sequence_length 16
+            """.split()
+
+            test_args.extend(["--instance_prompt", ""])
+            run_command(self._launch_args + test_args)
+
+            self.assertEqual(
+                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
+                {"checkpoint-4", "checkpoint-6"},
+            )
+
+    def test_dreambooth_lora_hidream_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+            {self.script_path}
+            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
+            --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
+            --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
+            --instance_data_dir={self.instance_data_dir}
+            --output_dir={tmpdir}
+            --resolution=32
+            --train_batch_size=1
+            --gradient_accumulation_steps=1
+            --max_train_steps=4
+            --checkpointing_steps=2
+            --max_sequence_length 16
+            """.split()
+
+            test_args.extend(["--instance_prompt", ""])
+            run_command(self._launch_args + test_args)
+
+            self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-2", "checkpoint-4"})
+
+            resume_run_args = f"""
+            {self.script_path}
+            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
+            --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
+            --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
+            --instance_data_dir={self.instance_data_dir}
+            --output_dir={tmpdir}
+            --resolution=32
+            --train_batch_size=1
+            --gradient_accumulation_steps=1
+            --max_train_steps=8
+            --checkpointing_steps=2
+            --resume_from_checkpoint=checkpoint-4
+            --checkpoints_total_limit=2
+            --max_sequence_length 16
+            """.split()
+
+            resume_run_args.extend(["--instance_prompt", ""])
+            run_command(self._launch_args + resume_run_args)
+
+            self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-6", "checkpoint-8"})
--- a/examples/dreambooth/train_dreambooth_lora_hidream.py
+++ b/examples/dreambooth/train_dreambooth_lora_hidream.py
--- a/src/diffusers/loaders/init.py
+++ b/src/diffusers/loaders/init.py
@@ -77,6 +77,7 @@ if is_torch_available():
            "SanaLoraLoaderMixin",
            "Lumina2LoraLoaderMixin",
            "WanLoraLoaderMixin",
+            "HiDreamImageLoraLoaderMixin",
        ]
        _import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
        _import_structure["ip_adapter"] = [
@@ -108,6 +109,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
                CogVideoXLoraLoaderMixin,
                CogView4LoraLoaderMixin,
                FluxLoraLoaderMixin,
+                HiDreamImageLoraLoaderMixin,
                HunyuanVideoLoraLoaderMixin,
                LoraLoaderMixin,
                LTXVideoLoraLoaderMixin,
--- a/src/diffusers/loaders/lora_pipeline.py
+++ b/src/diffusers/loaders/lora_pipeline.py
@@ -5360,6 +5360,325 @@ class CogView4LoraLoaderMixin(LoraBaseMixin):
        super().unfuse_lora(components=components, **kwargs)


+class HiDreamImageLoraLoaderMixin(LoraBaseMixin):
+    r"""
+    Load LoRA layers into [`HiDreamImageTransformer2DModel`]. Specific to [`HiDreamImagePipeline`].
+    """
+
+    _lora_loadable_modules = ["transformer"]
+    transformer_name = TRANSFORMER_NAME
+
+    @classmethod
+    @validate_hf_hub_args
+    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.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: Optional[str] = None,
+        hotswap: bool = False,
+        **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.
+            hotswap (`bool`, *optional*):
+                See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_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,
+            hotswap=hotswap,
+        )
+
+    @classmethod
+    # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->HiDreamImageTransformer2DModel
+    def load_lora_into_transformer(
+        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False, hotswap: bool = 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 (`HiDreamImageTransformer2DModel`):
+                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.
+            hotswap (`bool`, *optional*):
+                See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_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,
+            hotswap=hotswap,
+        )
+
+    @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.SanaLoraLoaderMixin.fuse_lora
+    def fuse_lora(
+        self,
+        components: List[str] = ["transformer"],
+        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,
+            **kwargs,
+        )
+
+    # Copied from diffusers.loaders.lora_pipeline.SanaLoraLoaderMixin.unfuse_lora
+    def unfuse_lora(self, components: List[str] = ["transformer"], **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.
+        """
+        super().unfuse_lora(components=components, **kwargs)
+
+
 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."
--- a/src/diffusers/loaders/peft.py
+++ b/src/diffusers/loaders/peft.py
@@ -56,6 +56,7 @@ _SET_ADAPTER_SCALE_FN_MAPPING = {
    "Lumina2Transformer2DModel": lambda model_cls, weights: weights,
    "WanTransformer3DModel": lambda model_cls, weights: weights,
    "CogView4Transformer2DModel": lambda model_cls, weights: weights,
+    "HiDreamImageTransformer2DModel": lambda model_cls, weights: weights,
 }


--- a/src/diffusers/models/transformers/transformer_hidream_image.py
+++ b/src/diffusers/models/transformers/transformer_hidream_image.py
@@ -275,7 +275,14 @@ class HiDreamAttnProcessor:

 # Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py
 class MoEGate(nn.Module):
-    def __init__(self, embed_dim, num_routed_experts=4, num_activated_experts=2, aux_loss_alpha=0.01):
+    def __init__(
+        self,
+        embed_dim,
+        num_routed_experts=4,
+        num_activated_experts=2,
+        aux_loss_alpha=0.01,
+        _force_inference_output=False,
+    ):
        super().__init__()
        self.top_k = num_activated_experts
        self.n_routed_experts = num_routed_experts
@@ -289,9 +296,10 @@ class MoEGate(nn.Module):
        self.gating_dim = embed_dim
        self.weight = nn.Parameter(torch.randn(self.n_routed_experts, self.gating_dim) / embed_dim**0.5)

+        self._force_inference_output = _force_inference_output
+
    def forward(self, hidden_states):
        bsz, seq_len, h = hidden_states.shape
-        # print(bsz, seq_len, h)
        ### compute gating score
        hidden_states = hidden_states.view(-1, h)
        logits = F.linear(hidden_states, self.weight, None)
@@ -309,7 +317,7 @@ class MoEGate(nn.Module):
            topk_weight = topk_weight / denominator

        ### expert-level computation auxiliary loss
-        if self.training and self.alpha > 0.0:
+        if self.training and self.alpha > 0.0 and not self._force_inference_output:
            scores_for_aux = scores
            aux_topk = self.top_k
            # always compute aux loss based on the naive greedy topk method
@@ -341,14 +349,19 @@ class MOEFeedForwardSwiGLU(nn.Module):
        hidden_dim: int,
        num_routed_experts: int,
        num_activated_experts: int,
+        _force_inference_output: bool = False,
    ):
        super().__init__()
        self.shared_experts = HiDreamImageFeedForwardSwiGLU(dim, hidden_dim // 2)
        self.experts = nn.ModuleList(
            [HiDreamImageFeedForwardSwiGLU(dim, hidden_dim) for i in range(num_routed_experts)]
        )
+        self._force_inference_output = _force_inference_output
        self.gate = MoEGate(
-            embed_dim=dim, num_routed_experts=num_routed_experts, num_activated_experts=num_activated_experts
+            embed_dim=dim,
+            num_routed_experts=num_routed_experts,
+            num_activated_experts=num_activated_experts,
+            _force_inference_output=_force_inference_output,
        )
        self.num_activated_experts = num_activated_experts

@@ -359,7 +372,7 @@ class MOEFeedForwardSwiGLU(nn.Module):
        topk_idx, topk_weight, aux_loss = self.gate(x)
        x = x.view(-1, x.shape[-1])
        flat_topk_idx = topk_idx.view(-1)
-        if self.training:
+        if self.training and not self._force_inference_output:
            x = x.repeat_interleave(self.num_activated_experts, dim=0)
            y = torch.empty_like(x, dtype=wtype)
            for i, expert in enumerate(self.experts):
@@ -413,6 +426,7 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
        attention_head_dim: int,
        num_routed_experts: int = 4,
        num_activated_experts: int = 2,
+        _force_inference_output: bool = False,
    ):
        super().__init__()
        self.num_attention_heads = num_attention_heads
@@ -436,6 +450,7 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
                hidden_dim=4 * dim,
                num_routed_experts=num_routed_experts,
                num_activated_experts=num_activated_experts,
+                _force_inference_output=_force_inference_output,
            )
        else:
            self.ff_i = HiDreamImageFeedForwardSwiGLU(dim=dim, hidden_dim=4 * dim)
@@ -480,6 +495,7 @@ class HiDreamImageTransformerBlock(nn.Module):
        attention_head_dim: int,
        num_routed_experts: int = 4,
        num_activated_experts: int = 2,
+        _force_inference_output: bool = False,
    ):
        super().__init__()
        self.num_attention_heads = num_attention_heads
@@ -504,6 +520,7 @@ class HiDreamImageTransformerBlock(nn.Module):
                hidden_dim=4 * dim,
                num_routed_experts=num_routed_experts,
                num_activated_experts=num_activated_experts,
+                _force_inference_output=_force_inference_output,
            )
        else:
            self.ff_i = HiDreamImageFeedForwardSwiGLU(dim=dim, hidden_dim=4 * dim)
@@ -606,6 +623,7 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
        axes_dims_rope: Tuple[int, int] = (32, 32),
        max_resolution: Tuple[int, int] = (128, 128),
        llama_layers: List[int] = None,
+        force_inference_output: bool = False,
    ):
        super().__init__()
        self.out_channels = out_channels or in_channels
@@ -629,6 +647,7 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
                        attention_head_dim=attention_head_dim,
                        num_routed_experts=num_routed_experts,
                        num_activated_experts=num_activated_experts,
+                        _force_inference_output=force_inference_output,
                    )
                )
                for _ in range(num_layers)
@@ -644,6 +663,7 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
                        attention_head_dim=attention_head_dim,
                        num_routed_experts=num_routed_experts,
                        num_activated_experts=num_activated_experts,
+                        _force_inference_output=force_inference_output,
                    )
                )
                for _ in range(num_single_layers)
@@ -662,7 +682,7 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
        self.gradient_checkpointing = False

    def unpatchify(self, x: torch.Tensor, img_sizes: List[Tuple[int, int]], is_training: bool) -> List[torch.Tensor]:
-        if is_training:
+        if is_training and not self.config.force_inference_output:
            B, S, F = x.shape
            C = F // (self.config.patch_size * self.config.patch_size)
            x = (
--- a/src/diffusers/pipelines/hidream_image/pipeline_hidream_image.py
+++ b/src/diffusers/pipelines/hidream_image/pipeline_hidream_image.py
@@ -13,6 +13,7 @@ from transformers import (
 )

 from ...image_processor import VaeImageProcessor
+from ...loaders import HiDreamImageLoraLoaderMixin
 from ...models import AutoencoderKL, HiDreamImageTransformer2DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler, UniPCMultistepScheduler
 from ...utils import deprecate, is_torch_xla_available, logging, replace_example_docstring
@@ -142,7 +143,7 @@ def retrieve_timesteps(
    return timesteps, num_inference_steps


-class HiDreamImagePipeline(DiffusionPipeline):
+class HiDreamImagePipeline(DiffusionPipeline, HiDreamImageLoraLoaderMixin):
    model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->text_encoder_4->transformer->vae"
    _callback_tensor_inputs = ["latents", "prompt_embeds_t5", "prompt_embeds_llama3", "pooled_prompt_embeds"]