initial draft

.github/workflows/mirror_community_pipeline.yml

@@ -79,14 +79,14 @@ jobs:
 
       # Check secret is set
       - name: whoami
-        run: hf auth whoami
+        run: huggingface-cli whoami
         env:
             HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}
 
       # Push to HF! (under subfolder based on checkout ref)
       # https://huggingface.co/datasets/diffusers/community-pipelines-mirror
       - name: Mirror community pipeline to HF
-        run: hf upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
+        run: huggingface-cli upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
         env:
             PATH_IN_REPO: ${{ env.PATH_IN_REPO }}
             HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}

benchmarks/README.md

@@ -31,7 +31,7 @@ pip install -r requirements.txt
 We need to be authenticated to access some of the checkpoints used during benchmarking:
 
 ```sh
-hf auth login
+huggingface-cli login
 ```
 
 We use an L40 GPU with 128GB RAM to run the benchmark CI. As such, the benchmarks are configured to run on NVIDIA GPUs. So, make sure you have access to a similar machine (or modify the benchmarking scripts accordingly).

docs/source/en/_toctree.yml

@@ -179,7 +179,7 @@
   isExpanded: false
   sections:
   - local: quantization/overview
-    title: Getting started
+    title: Getting Started
   - local: quantization/bitsandbytes
     title: bitsandbytes
   - local: quantization/gguf

docs/source/en/api/configuration.md

@@ -16,7 +16,7 @@ Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from
 
 <Tip>
 
-To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
+To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.
 
 </Tip>
 

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md

@@ -31,7 +31,7 @@ _As the model is gated, before using it with diffusers you first need to go to t
 Use the command below to log in:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 <Tip>

docs/source/en/api/quantization.md

@@ -27,19 +27,19 @@ Learn how to quantize models in the [Quantization](../quantization/overview) gui
 
 ## BitsAndBytesConfig
 
-[[autodoc]] quantizers.quantization_config.BitsAndBytesConfig
+[[autodoc]] BitsAndBytesConfig
 
 ## GGUFQuantizationConfig
 
-[[autodoc]] quantizers.quantization_config.GGUFQuantizationConfig
+[[autodoc]] GGUFQuantizationConfig
 
 ## QuantoConfig
 
-[[autodoc]] quantizers.quantization_config.QuantoConfig
+[[autodoc]] QuantoConfig
 
 ## TorchAoConfig
 
-[[autodoc]] quantizers.quantization_config.TorchAoConfig
+[[autodoc]] TorchAoConfig
 
 ## DiffusersQuantizer
 

docs/source/en/index.md

@@ -12,24 +12,37 @@ specific language governing permissions and limitations under the License.
 
 <p align="center">
     <br>
-    <img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400" style="border: none;"/>
+    <img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400"/>
     <br>
 </p>
 
 # Diffusers
 
-Diffusers is a library of state-of-the-art pretrained diffusion models for generating videos, images, and audio.
+🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or want to train your own diffusion model, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](conceptual/philosophy#usability-over-performance), [simple over easy](conceptual/philosophy#simple-over-easy), and [customizability over abstractions](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).
 
-The library revolves around the [`DiffusionPipeline`], an API designed for:
+The library has three main components:
 
-- easy inference with only a few lines of code
-- flexibility to mix-and-match pipeline components (models, schedulers)
-- loading and using adapters like LoRA
+- State-of-the-art diffusion pipelines for inference with just a few lines of code. There are many pipelines in 🤗 Diffusers, check out the table in the pipeline [overview](api/pipelines/overview) for a complete list of available pipelines and the task they solve.
+- Interchangeable [noise schedulers](api/schedulers/overview) for balancing trade-offs between generation speed and quality.
+- Pretrained [models](api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
 
-Diffusers also comes with optimizations - such as offloading and quantization - to ensure even the largest models are accessible on memory-constrained devices. If memory is not an issue, Diffusers supports torch.compile to boost inference speed.
-
-Get started right away with a Diffusers model on the [Hub](https://huggingface.co/models?library=diffusers&sort=trending) today!
-
-## Learn
-
-If you're a beginner, we recommend starting with the [Hugging Face Diffusion Models Course](https://huggingface.co/learn/diffusion-course/unit0/1). You'll learn the theory behind diffusion models, and learn how to use the Diffusers library to generate images, fine-tune your own models, and more.
+<div class="mt-10">
+  <div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-2 md:gap-y-4 md:gap-x-5">
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./tutorials/tutorial_overview"
+      ><div class="w-full text-center bg-gradient-to-br from-blue-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Tutorials</div>
+      <p class="text-gray-700">Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. We recommend starting here if you're using 🤗 Diffusers for the first time!</p>
+    </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./using-diffusers/loading_overview"
+      ><div class="w-full text-center bg-gradient-to-br from-indigo-400 to-indigo-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">How-to guides</div>
+      <p class="text-gray-700">Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques.</p>
+    </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./conceptual/philosophy"
+      ><div class="w-full text-center bg-gradient-to-br from-pink-400 to-pink-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Conceptual guides</div>
+      <p class="text-gray-700">Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.</p>
+   </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./api/models/overview"
+      ><div class="w-full text-center bg-gradient-to-br from-purple-400 to-purple-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Reference</div>
+      <p class="text-gray-700">Technical descriptions of how 🤗 Diffusers classes and methods work.</p>
+    </a>
+  </div>
+</div>

docs/source/en/quantization/overview.md

@@ -11,7 +11,7 @@ specific language governing permissions and limitations under the License.
 
 -->
 
-# Getting started
+# Quantization
 
 Quantization focuses on representing data with fewer bits while also trying to preserve the precision of the original data. This often means converting a data type to represent the same information with fewer bits. For example, if your model weights are stored as 32-bit floating points and they're quantized to 16-bit floating points, this halves the model size which makes it easier to store and reduces memory usage. Lower precision can also speedup inference because it takes less time to perform calculations with fewer bits.
 
@@ -19,25 +19,19 @@ Diffusers supports multiple quantization backends to make large diffusion models
 
 ## Pipeline-level quantization
 
-There are two ways to use [`~quantizers.PipelineQuantizationConfig`] depending on how much customization you want to apply to the quantization configuration. 
+There are two ways you can use [`~quantizers.PipelineQuantizationConfig`] depending on the level of control you want over the quantization specifications of each model in the pipeline.
 
-- for basic use cases, define the `quant_backend`, `quant_kwargs`, and `components_to_quantize` arguments
-- for granular quantization control, define a `quant_mapping` that provides the quantization configuration for individual model components
+- for more basic and simple use cases, you only need to define the `quant_backend`, `quant_kwargs`, and `components_to_quantize`
+- for more granular quantization control, provide a `quant_mapping` that provides the quantization specifications for the individual model components
 
-### Basic quantization
+### Simple quantization
 
 Initialize [`~quantizers.PipelineQuantizationConfig`] with the following parameters.
 
 - `quant_backend` specifies which quantization backend to use. Currently supported backends include: `bitsandbytes_4bit`, `bitsandbytes_8bit`, `gguf`, `quanto`, and `torchao`.
-- `quant_kwargs` specifies the quantization arguments to use.
-
-> [!TIP]
-> These `quant_kwargs` arguments are different for each backend. Refer to the [Quantization API](../api/quantization) docs to view the arguments for each backend.
-
+- `quant_kwargs` contains the specific quantization arguments to use.
 - `components_to_quantize` specifies which components of the pipeline to quantize. Typically, you should quantize the most compute intensive components like the transformer. The text encoder is another component to consider quantizing if a pipeline has more than one such as [`FluxPipeline`]. The example below quantizes the T5 text encoder in [`FluxPipeline`] while keeping the CLIP model intact.
 
-The example below loads the bitsandbytes backend with the following arguments from [`~quantizers.quantization_config.BitsAndBytesConfig`], `load_in_4bit`, `bnb_4bit_quant_type`, and `bnb_4bit_compute_dtype`.
-
 ```py
 import torch
 from diffusers import DiffusionPipeline
@@ -62,13 +56,13 @@ pipe = DiffusionPipeline.from_pretrained(
 image = pipe("photo of a cute dog").images[0]
 ```
 
-### Advanced quantization
+### quant_mapping
 
-The `quant_mapping` argument provides more options for how to quantize each individual component in a pipeline, like combining different quantization backends.
+The `quant_mapping` argument provides more flexible options for how to quantize each individual component in a pipeline, like combining different quantization backends.
 
 Initialize [`~quantizers.PipelineQuantizationConfig`] and pass a `quant_mapping` to it. The `quant_mapping` allows you to specify the quantization options for each component in the pipeline such as the transformer and text encoder.
 
-The example below uses two quantization backends, [`~quantizers.quantization_config.QuantoConfig`] and [`transformers.BitsAndBytesConfig`], for the transformer and text encoder.
+The example below uses two quantization backends, [`~quantizers.QuantoConfig`] and [`transformers.BitsAndBytesConfig`], for the transformer and text encoder.
 
 ```py
 import torch
@@ -91,7 +85,7 @@ pipeline_quant_config = PipelineQuantizationConfig(
 There is a separate bitsandbytes backend in [Transformers](https://huggingface.co/docs/transformers/main_classes/quantization#transformers.BitsAndBytesConfig). You need to import and use [`transformers.BitsAndBytesConfig`] for components that come from Transformers. For example, `text_encoder_2` in [`FluxPipeline`] is a [`~transformers.T5EncoderModel`] from Transformers so you need to use [`transformers.BitsAndBytesConfig`] instead of [`diffusers.BitsAndBytesConfig`].
 
 > [!TIP]
-> Use the [basic quantization](#basic-quantization) method above if you don't want to manage these distinct imports or aren't sure where each pipeline component comes from.
+> Use the [simple quantization](#simple-quantization) method above if you don't want to manage these distinct imports or aren't sure where each pipeline component comes from.
 
 ```py
 import torch
@@ -135,4 +129,4 @@ Check out the resources below to learn more about quantization.
 
 - The Transformers quantization [Overview](https://huggingface.co/docs/transformers/quantization/overview#when-to-use-what) provides an overview of the pros and cons of different quantization backends.
 
-- Read the [Exploring Quantization Backends in Diffusers](https://huggingface.co/blog/diffusers-quantization) blog post for a brief introduction to each quantization backend, how to choose a backend, and combining quantization with other memory optimizations.
+- Read the [Exploring Quantization Backends in Diffusers](https://huggingface.co/blog/diffusers-quantization) blog post for a brief introduction to each quantization backend, how to choose a backend, and combining quantization with other memory optimizations.

docs/source/en/training/cogvideox.md

@@ -145,10 +145,10 @@ When running `accelerate config`, if you use torch.compile, there can be dramati
 If you would like to push your model to the Hub after training is completed with a neat model card, make sure you're logged in:
 
 ```bash
-hf auth login
+huggingface-cli login
 
 # Alternatively, you could upload your model manually using:
-# hf upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
+# huggingface-cli upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
 ```
 
 Make sure your data is prepared as described in [Data Preparation](#data-preparation). When ready, you can begin training!

docs/source/en/training/create_dataset.md

@@ -67,7 +67,7 @@ dataset = load_dataset(
 Then use the [`~datasets.Dataset.push_to_hub`] method to upload the dataset to the Hub:
 
 ```python
-# assuming you have ran the hf auth login command in a terminal
+# assuming you have ran the huggingface-cli login command in a terminal
 dataset.push_to_hub("name_of_your_dataset")
 
 # if you want to push to a private repo, simply pass private=True:

docs/source/en/tutorials/basic_training.md

@@ -42,7 +42,7 @@ We encourage you to share your model with the community, and in order to do that
 Or login in from the terminal:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 Since the model checkpoints are quite large, install [Git-LFS](https://git-lfs.com/) to version these large files:

docs/source/en/tutorials/using_peft_for_inference.md

@@ -319,19 +319,6 @@ If you expect to varied resolutions during inference with this feature, then mak
 
 There are still scenarios where recompulation is unavoidable, such as when the hotswapped LoRA targets more layers than the initial adapter. Try to load the LoRA that targets the most layers *first*. For more details about this limitation, refer to the PEFT [hotswapping](https://huggingface.co/docs/peft/main/en/package_reference/hotswap#peft.utils.hotswap.hotswap_adapter) docs.
 
-<details>
-<summary>Technical details of hotswapping</summary>
-
-The [`~loaders.lora_base.LoraBaseMixin.enable_lora_hotswap`] method converts the LoRA scaling factor from floats to torch.tensors and pads the shape of the weights to the largest required shape to avoid reassigning the whole attribute when the data in the weights are replaced.
-
-This is why the `max_rank` argument is important. The results are unchanged even when the values are padded with zeros. Computation may be slower though depending on the padding size.
-
-Since no new LoRA attributes are added, each subsequent LoRA is only allowed to target the same layers, or subset of layers, the first LoRA targets. Choosing the LoRA loading order is important because if the LoRAs target disjoint layers, you may end up creating a dummy LoRA that targets the union of all target layers.
-
-For more implementation details, take a look at the [`hotswap.py`](https://github.com/huggingface/peft/blob/92d65cafa51c829484ad3d95cf71d09de57ff066/src/peft/utils/hotswap.py) file.
-
-</details>
-
 ## Merge
 
 The weights from each LoRA can be merged together to produce a blend of multiple existing styles. There are several methods for merging LoRAs, each of which differ in *how* the weights are merged (may affect generation quality).
@@ -686,6 +673,4 @@ Browse the [LoRA Studio](https://lorastudio.co/models) for different LoRAs to us
 	height="450"
 ></iframe>
 
-You can find additional LoRAs in the [FLUX LoRA the Explorer](https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer) and [LoRA the Explorer](https://huggingface.co/spaces/multimodalart/LoraTheExplorer) Spaces.
-
-Check out the [Fast LoRA inference for Flux with Diffusers and PEFT](https://huggingface.co/blog/lora-fast) blog post to learn how to optimize LoRA inference with methods like FlashAttention-3 and fp8 quantization.
+You can find additional LoRAs in the [FLUX LoRA the Explorer](https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer) and [LoRA the Explorer](https://huggingface.co/spaces/multimodalart/LoraTheExplorer) Spaces.

docs/source/ko/optimization/mps.md

@@ -37,7 +37,7 @@ Diffusers는 Stable Diffusion 추론을 위해 PyTorch `mps`를 사용해 Apple
 
 
 ```python
-# `hf auth login`에 로그인되어 있음을 확인
+# `huggingface-cli login`에 로그인되어 있음을 확인
 from diffusers import DiffusionPipeline
 
 pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")

docs/source/ko/training/create_dataset.md

@@ -75,7 +75,7 @@ dataset = load_dataset(
 [push_to_hub(https://huggingface.co/docs/datasets/v2.13.1/en/package_reference/main_classes#datasets.Dataset.push_to_hub) 을 사용해서 Hub에 데이터셋을 업로드 합니다:
 
 ```python
-# 터미널에서 hf auth login 커맨드를 이미 실행했다고 가정합니다
+# 터미널에서 huggingface-cli login 커맨드를 이미 실행했다고 가정합니다
 dataset.push_to_hub("name_of_your_dataset")
 
 # 개인 repo로 push 하고 싶다면, `private=True` 을 추가하세요:

docs/source/ko/training/lora.md

@@ -39,7 +39,7 @@ specific language governing permissions and limitations under the License.
 모델을 저장하거나 커뮤니티와 공유하려면 Hugging Face 계정에 로그인하세요(아직 계정이 없는 경우 [생성](https://huggingface.co/join)하세요):
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 ## Text-to-image

docs/source/ko/tutorials/basic_training.md

@@ -42,7 +42,7 @@ Unconditional 이미지 생성은 학습에 사용된 데이터셋과 유사한
 또는 터미널로 로그인할 수 있습니다:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 모델 체크포인트가 상당히 크기 때문에 [Git-LFS](https://git-lfs.com/)에서 대용량 파일의 버전 관리를 할 수 있습니다.

docs/source/ko/using-diffusers/other-formats.md

@@ -42,7 +42,7 @@ Stable Diffusion 모델들은 학습 및 저장된 프레임워크와 다운로
 시작하기 전에 스크립트를 실행할 🤗 Diffusers의 로컬 클론(clone)이 있는지 확인하고 Hugging Face 계정에 로그인하여 pull request를 열고 변환된 모델을 허브에 푸시할 수 있도록 하세요.
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 스크립트를 사용하려면:

examples/advanced_diffusion_training/README.md

@@ -69,7 +69,7 @@ Note also that we use PEFT library as backend for LoRA training, make sure to ha
 
 Lastly, we recommend logging into your HF account so that your trained LoRA is automatically uploaded to the hub:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 This command will prompt you for a token. Copy-paste yours from your [settings/tokens](https://huggingface.co/settings/tokens),and press Enter.
 

examples/advanced_diffusion_training/README_flux.md

@@ -67,7 +67,7 @@ Note also that we use PEFT library as backend for LoRA training, make sure to ha
 
 Lastly, we recommend logging into your HF account so that your trained LoRA is automatically uploaded to the hub:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 This command will prompt you for a token. Copy-paste yours from your [settings/tokens](https://huggingface.co/settings/tokens),and press Enter.
 

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -1,24 +1,3 @@
-# /// script
-# requires-python = ">=3.10"
-# dependencies = [
-#   "torch",
-#   "torchvision",
-#   "diffusers @ git+https://github.com/huggingface/diffusers.git@main",
-#   "transformers",
-#   "accelerate",
-#   "peft",
-#   "safetensors",
-#   "huggingface_hub",
-#   "datasets",
-#   "Pillow",
-#   "tqdm",
-#   "bitsandbytes",
-#   "sentencepiece",
-#   "protobuf",
-#   "prodigyopt",
-# ]
-# ///
-
 #!/usr/bin/env python
 # coding=utf-8
 # Copyright 2025 The HuggingFace Inc. team. All rights reserved.
@@ -34,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 
-# /// script
-# dependencies = [
-#     "diffusers @ git+https://github.com/huggingface/diffusers.git",
-#     "torch>=2.0.0",
-#     "accelerate>=0.31.0",
-#     "transformers>=4.41.2",
-#     "ftfy",
-#     "tensorboard",
-#     "Jinja2",
-#     "peft>=0.11.1",
-#     "sentencepiece",
-# ]
-# ///
-
 import argparse
 import copy
 import itertools
@@ -1092,7 +1057,7 @@ class DreamBoothDataset(Dataset):
         if interpolation is None:
             raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
         train_resize = transforms.Resize(size, interpolation=interpolation)
-        train_crop = transforms.CenterCrop(size) if args.center_crop else transforms.RandomCrop(size)
+        train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)
         train_flip = transforms.RandomHorizontalFlip(p=1.0)
         train_transforms = transforms.Compose(
             [
@@ -1136,7 +1101,7 @@ class DreamBoothDataset(Dataset):
         self.image_transforms = transforms.Compose(
             [
                 transforms.Resize(size, interpolation=interpolation),
-                transforms.CenterCrop(size) if args.center_crop else transforms.RandomCrop(size),
+                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
                 transforms.ToTensor(),
                 transforms.Normalize([0.5], [0.5]),
             ]
@@ -1356,7 +1321,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py

@@ -13,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 
-# /// script
-# dependencies = [
-#     "diffusers @ git+https://github.com/huggingface/diffusers.git",
-#     "torch>=2.0.0",
-#     "accelerate>=0.31.0",
-#     "transformers>=4.41.2",
-#     "ftfy",
-#     "tensorboard",
-#     "Jinja2",
-#     "peft>=0.11.1",
-#     "sentencepiece",
-# ]
-# ///
-
 import argparse
 import gc
 import hashlib
@@ -1064,7 +1050,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -13,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 
-# /// script
-# dependencies = [
-#     "diffusers @ git+https://github.com/huggingface/diffusers.git",
-#     "torch>=2.0.0",
-#     "accelerate>=0.31.0",
-#     "transformers>=4.41.2",
-#     "ftfy",
-#     "tensorboard",
-#     "Jinja2",
-#     "peft>=0.11.1",
-#     "sentencepiece",
-# ]
-# ///
-
 import argparse
 import gc
 import itertools
@@ -1306,7 +1292,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if args.do_edm_style_training and args.snr_gamma is not None:

examples/cogvideo/README.md

@@ -125,10 +125,10 @@ When running `accelerate config`, if we specify torch compile mode to True there
 If you would like to push your model to the HF Hub after training is completed with a neat model card, make sure you're logged in:
 
 ```
-hf auth login
+huggingface-cli login
 
 # Alternatively, you could upload your model manually using:
-# hf upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
+# huggingface-cli upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
 ```
 
 Make sure your data is prepared as described in [Data Preparation](#data-preparation). When ready, you can begin training!

examples/cogvideo/train_cogvideox_image_to_video_lora.py

@@ -962,7 +962,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/cogvideo/train_cogvideox_lora.py

@@ -984,7 +984,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/cogview4-control/README.md

@@ -10,7 +10,7 @@ To incorporate additional condition latents, we expand the input features of Cog
 > As the model is gated, before using it with diffusers you first need to go to the [CogView4 Hugging Face page](https://huggingface.co/THUDM/CogView4-6B), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 The example command below shows how to launch fine-tuning for pose conditions. The dataset ([`raulc0399/open_pose_controlnet`](https://huggingface.co/datasets/raulc0399/open_pose_controlnet)) being used here already has the pose conditions of the original images, so we don't have to compute them.

examples/cogview4-control/train_control_cogview4.py

@@ -705,7 +705,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_out_dir = Path(args.output_dir, args.logging_dir)

examples/community/README.md

@@ -3129,7 +3129,7 @@ from io import BytesIO
 from diffusers import DiffusionPipeline
 
 # load the pipeline
-# make sure you're logged in with `hf auth login`
+# make sure you're logged in with `huggingface-cli login`
 model_id_or_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 # can also be used with dreamlike-art/dreamlike-photoreal-2.0
 pipe = DiffusionPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16, custom_pipeline="pipeline_fabric").to("cuda")

examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py

@@ -877,7 +877,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/consistency_distillation/train_lcm_distill_lora_sdxl.py

@@ -709,7 +709,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py

@@ -872,7 +872,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/consistency_distillation/train_lcm_distill_sd_wds.py

@@ -842,7 +842,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/consistency_distillation/train_lcm_distill_sdxl_wds.py

@@ -882,7 +882,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/controlnet/README.md

@@ -359,7 +359,7 @@ wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
 We encourage you to store or share your model with the community. To use huggingface hub, please login to your Hugging Face account, or ([create one](https://huggingface.co/docs/diffusers/main/en/training/hf.co/join) if you don’t have one already):
 
 ```sh
-hf auth login
+huggingface-cli login
 ```
 
 Make sure you have the `MODEL_DIR`,`OUTPUT_DIR` and `HUB_MODEL_ID` environment variables set. The `OUTPUT_DIR` and `HUB_MODEL_ID` variables specify where to save the model to on the Hub:

examples/controlnet/README_flux.md

@@ -22,7 +22,7 @@ Here is a gpu memory consumption for reference, tested on a single A100 with 80G
 
 > **Gated access**
 >
-> As the model is gated, before using it with diffusers you first need to go to the [FLUX.1 [dev] Hugging Face page](https://huggingface.co/black-forest-labs/FLUX.1-dev), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in: `hf auth login`
+> As the model is gated, before using it with diffusers you first need to go to the [FLUX.1 [dev] Hugging Face page](https://huggingface.co/black-forest-labs/FLUX.1-dev), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in: `huggingface-cli login`
 
 
 ## Running locally with PyTorch
@@ -88,7 +88,7 @@ wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
 wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
 ```
 
-Then run `hf auth login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.
+Then run `huggingface-cli login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.
 
 we can define the num_layers, num_single_layers, which determines the size of the control(default values are num_layers=4, num_single_layers=10)
 

examples/controlnet/README_sd3.md

@@ -56,7 +56,7 @@ First download the SD3 model from [Hugging Face Hub](https://huggingface.co/stab
 > As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers) or [Stable Diffusion 3.5 Large Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 This will also allow us to push the trained model parameters to the Hugging Face Hub platform.

examples/controlnet/README_sdxl.md

@@ -58,7 +58,7 @@ wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
 wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
 ```
 
-Then run `hf auth login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.
+Then run `huggingface-cli login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.
 
 ```bash
 export MODEL_DIR="stabilityai/stable-diffusion-xl-base-1.0"

examples/controlnet/train_controlnet.py

@@ -734,7 +734,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/controlnet/train_controlnet_flax.py

@@ -665,7 +665,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging.basicConfig(

examples/controlnet/train_controlnet_flux.py

@@ -814,7 +814,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_out_dir = Path(args.output_dir, args.logging_dir)

examples/controlnet/train_controlnet_sd3.py

@@ -928,7 +928,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/controlnet/train_controlnet_sdxl.py

@@ -829,7 +829,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/custom_diffusion/train_custom_diffusion.py

@@ -663,7 +663,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/dreambooth/README.md

@@ -330,7 +330,7 @@ For this example we want to directly store the trained LoRA embeddings on the Hu
 we need to be logged in and add the `--push_to_hub` flag.
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 Now we can start training!

examples/dreambooth/README_flux.md

@@ -19,7 +19,7 @@ The `train_dreambooth_flux.py` script shows how to implement the training proced
 > As the model is gated, before using it with diffusers you first need to go to the [FLUX.1 [dev] Hugging Face page](https://huggingface.co/black-forest-labs/FLUX.1-dev), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 This will also allow us to push the trained model parameters to the Hugging Face Hub platform.

examples/dreambooth/README_hidream.md

@@ -95,7 +95,7 @@ accelerate launch train_dreambooth_lora_hidream.py \
 For using `push_to_hub`, make you're logged into your Hugging Face account:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 To better track our training experiments, we're using the following flags in the command above:

examples/dreambooth/README_lumina2.md

@@ -101,7 +101,7 @@ accelerate launch train_dreambooth_lora_lumina2.py \
 For using `push_to_hub`, make you're logged into your Hugging Face account:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 To better track our training experiments, we're using the following flags in the command above:

examples/dreambooth/README_sana.md

@@ -101,7 +101,7 @@ accelerate launch train_dreambooth_lora_sana.py \
 For using `push_to_hub`, make you're logged into your Hugging Face account:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 To better track our training experiments, we're using the following flags in the command above:

examples/dreambooth/README_sd3.md

@@ -8,7 +8,7 @@ The `train_dreambooth_sd3.py` script shows how to implement the training procedu
 > As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 This will also allow us to push the trained model parameters to the Hugging Face Hub platform.

examples/dreambooth/train_dreambooth.py

@@ -807,7 +807,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/dreambooth/train_dreambooth_flux.py

@@ -13,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 
-# /// script
-# dependencies = [
-#     "diffusers @ git+https://github.com/huggingface/diffusers.git",
-#     "torch>=2.0.0",
-#     "accelerate>=0.31.0",
-#     "transformers>=4.41.2",
-#     "ftfy",
-#     "tensorboard",
-#     "Jinja2",
-#     "peft>=0.11.1",
-#     "sentencepiece",
-# ]
-# ///
-
 import argparse
 import copy
 import gc
@@ -1027,7 +1013,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/dreambooth/train_dreambooth_lora.py

@@ -756,7 +756,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/dreambooth/train_dreambooth_lora_flux.py

@@ -13,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 
-# /// script
-# dependencies = [
-#     "diffusers @ git+https://github.com/huggingface/diffusers.git",
-#     "torch>=2.0.0",
-#     "accelerate>=0.31.0",
-#     "transformers>=4.41.2",
-#     "ftfy",
-#     "tensorboard",
-#     "Jinja2",
-#     "peft>=0.11.1",
-#     "sentencepiece",
-# ]
-# ///
-
 import argparse
 import copy
 import itertools
@@ -1065,7 +1051,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/dreambooth/train_dreambooth_lora_flux_kontext.py

@@ -1199,7 +1199,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/dreambooth/train_dreambooth_lora_hidream.py

@@ -936,7 +936,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/dreambooth/train_dreambooth_lora_lumina2.py

@@ -859,7 +859,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/dreambooth/train_dreambooth_lora_sana.py

@@ -13,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 
-# /// script
-# dependencies = [
-#     "diffusers @ git+https://github.com/huggingface/diffusers.git",
-#     "torch>=2.0.0",
-#     "accelerate>=1.0.0",
-#     "transformers>=4.47.0",
-#     "ftfy",
-#     "tensorboard",
-#     "Jinja2",
-#     "peft>=0.14.0",
-#     "sentencepiece",
-# ]
-# ///
-
 import argparse
 import copy
 import itertools
@@ -866,7 +852,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/dreambooth/train_dreambooth_lora_sd3.py

@@ -1063,7 +1063,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -983,7 +983,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if args.do_edm_style_training and args.snr_gamma is not None:

examples/dreambooth/train_dreambooth_sd3.py

@@ -988,7 +988,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/flux-control/README.md

@@ -13,7 +13,7 @@ To incorporate additional condition latents, we expand the input features of Flu
 > As the model is gated, before using it with diffusers you first need to go to the [FLUX.1 [dev] Hugging Face page](https://huggingface.co/black-forest-labs/FLUX.1-dev), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 The example command below shows how to launch fine-tuning for pose conditions. The dataset ([`raulc0399/open_pose_controlnet`](https://huggingface.co/datasets/raulc0399/open_pose_controlnet)) being used here already has the pose conditions of the original images, so we don't have to compute them.

examples/flux-control/train_control_flux.py

@@ -697,7 +697,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_out_dir = Path(args.output_dir, args.logging_dir)

examples/flux-control/train_control_lora_flux.py

@@ -725,7 +725,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
     if args.use_lora_bias and args.gaussian_init_lora:
         raise ValueError("`gaussian` LoRA init scheme isn't supported when `use_lora_bias` is True.")

examples/instruct_pix2pix/train_instruct_pix2pix.py

@@ -430,7 +430,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if args.non_ema_revision is not None:

examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py

@@ -483,7 +483,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if args.non_ema_revision is not None:

examples/kandinsky2_2/text_to_image/README.md

@@ -41,7 +41,7 @@ For all our examples, we will directly store the trained weights on the Hub, so
 Run the following command to authenticate your token
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 We also use [Weights and Biases](https://docs.wandb.ai/quickstart) logging by default, because it is really useful to monitor the training progress by regularly generating sample images during training. To install wandb, run

examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py

@@ -444,7 +444,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = os.path.join(args.output_dir, args.logging_dir)

examples/kandinsky2_2/text_to_image/train_text_to_image_lora_decoder.py

@@ -330,7 +330,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/kandinsky2_2/text_to_image/train_text_to_image_lora_prior.py

@@ -342,7 +342,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py

@@ -445,7 +445,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = os.path.join(args.output_dir, args.logging_dir)

examples/model_search/pipeline_easy.py

@@ -1249,7 +1249,7 @@ class EasyPipelineForText2Image(AutoPipelineForText2Image):
         <Tip>
 
         To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login`.
+        `huggingface-cli login`.
 
         </Tip>
 
@@ -1358,7 +1358,7 @@ class EasyPipelineForText2Image(AutoPipelineForText2Image):
         <Tip>
 
         To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login`.
+        `huggingface-cli login`.
 
         </Tip>
 
@@ -1507,7 +1507,7 @@ class EasyPipelineForImage2Image(AutoPipelineForImage2Image):
         <Tip>
 
         To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login`.
+        `huggingface-cli login`.
 
         </Tip>
 
@@ -1617,7 +1617,7 @@ class EasyPipelineForImage2Image(AutoPipelineForImage2Image):
         <Tip>
 
         To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login`.
+        `huggingface-cli login`.
 
         </Tip>
 
@@ -1766,7 +1766,7 @@ class EasyPipelineForInpainting(AutoPipelineForInpainting):
         <Tip>
 
         To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login
+        `huggingface-cli login`.
 
         </Tip>
 
@@ -1875,7 +1875,7 @@ class EasyPipelineForInpainting(AutoPipelineForInpainting):
         <Tip>
 
         To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login
+        `huggingface-cli login`.
 
         </Tip>
 

examples/research_projects/autoencoderkl/train_autoencoderkl.py

@@ -568,7 +568,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/research_projects/consistency_training/train_cm_ct_unconditional.py

@@ -789,7 +789,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)

examples/research_projects/controlnet/train_controlnet_webdataset.py

@@ -899,7 +899,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/research_projects/diffusion_dpo/train_diffusion_dpo.py

@@ -470,7 +470,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/research_projects/diffusion_dpo/train_diffusion_dpo_sdxl.py

@@ -512,7 +512,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora.py

@@ -502,7 +502,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora_wds.py

@@ -609,7 +609,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/research_projects/flux_lora_quantization/README.md

@@ -39,7 +39,7 @@ python compute_embeddings.py
 It should create a file named `embeddings.parquet`. We're then ready to launch training. First, authenticate so that you can access the Flux.1 Dev model: 
 
 ```bash
-hf auth login
+huggingface-cli
 ```
 
 Then launch:

examples/research_projects/flux_lora_quantization/train_dreambooth_lora_flux_miniature.py

@@ -587,7 +587,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":

examples/research_projects/gligen/README.md

@@ -47,11 +47,11 @@ pip install git+https://github.com/xinyu1205/recognize-anything.git --no-deps
 Download the pre-trained model:
 
 ```bash
-hf download --resume-download xinyu1205/recognize_anything_model ram_swin_large_14m.pth
-hf download --resume-download IDEA-Research/grounding-dino-base
-hf download --resume-download Salesforce/blip2-flan-t5-xxl
-hf download --resume-download clip-vit-large-patch14
-hf download --resume-download masterful/gligen-1-4-generation-text-box
+huggingface-cli download --resume-download xinyu1205/recognize_anything_model ram_swin_large_14m.pth
+huggingface-cli download --resume-download IDEA-Research/grounding-dino-base
+huggingface-cli download --resume-download Salesforce/blip2-flan-t5-xxl
+huggingface-cli download --resume-download clip-vit-large-patch14
+huggingface-cli download --resume-download masterful/gligen-1-4-generation-text-box
 ```
 
 Make the training data on 8 GPUs:
@@ -66,7 +66,7 @@ torchrun --master_port 17673 --nproc_per_node=8 make_datasets.py \
 You can download the COCO training data from
 
 ```bash
-hf download --resume-download Hzzone/GLIGEN_COCO coco_train2017.pth
+huggingface-cli download --resume-download Hzzone/GLIGEN_COCO coco_train2017.pth
 ```
 
 It's in the format of
@@ -125,7 +125,7 @@ Note that although the pre-trained GLIGEN model has been loaded, the parameters
 The trained model can be downloaded from
 
 ```bash
-hf download --resume-download Hzzone/GLIGEN_COCO config.json diffusion_pytorch_model.safetensors
+huggingface-cli download --resume-download Hzzone/GLIGEN_COCO config.json diffusion_pytorch_model.safetensors
 ```
 
 You can run `demo.ipynb` to visualize the generated images.

examples/research_projects/instructpix2pix_lora/train_instruct_pix2pix_lora.py

@@ -488,7 +488,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if args.non_ema_revision is not None:

examples/research_projects/intel_opts/textual_inversion/textual_inversion_bf16.py

@@ -366,7 +366,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = os.path.join(args.output_dir, args.logging_dir)

examples/research_projects/lora/README.md

@@ -34,7 +34,7 @@ For this example we want to directly store the trained LoRA embeddings on the Hu
 we need to be logged in and add the `--push_to_hub` flag.
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 Now we can start training!

examples/research_projects/lora/train_text_to_image_lora.py

@@ -396,7 +396,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = os.path.join(args.output_dir, args.logging_dir)

examples/research_projects/multi_subject_dreambooth/train_multi_subject_dreambooth.py

@@ -684,7 +684,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/research_projects/multi_token_textual_inversion/README.md

@@ -60,7 +60,7 @@ You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need
 Run the following command to authenticate your token
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 If you have already cloned the repo, then you won't need to go through these steps.

examples/research_projects/multi_token_textual_inversion/textual_inversion.py

@@ -551,7 +551,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = os.path.join(args.output_dir, args.logging_dir)

examples/research_projects/multi_token_textual_inversion/textual_inversion_flax.py

@@ -153,7 +153,7 @@ def parse_args():
         "--use_auth_token",
         action="store_true",
         help=(
-            "Will use the token generated when running `hf auth login` (necessary to use this script with"
+            "Will use the token generated when running `huggingface-cli login` (necessary to use this script with"
             " private models)."
         ),
     )

examples/research_projects/onnxruntime/text_to_image/README.md

@@ -41,7 +41,7 @@ You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need
 Run the following command to authenticate your token
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 If you have already cloned the repo, then you won't need to go through these steps.

examples/research_projects/onnxruntime/text_to_image/train_text_to_image.py

@@ -415,7 +415,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if args.non_ema_revision is not None:

examples/research_projects/onnxruntime/textual_inversion/README.md

@@ -46,7 +46,7 @@ You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need
 Run the following command to authenticate your token
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 If you have already cloned the repo, then you won't need to go through these steps.

examples/research_projects/onnxruntime/textual_inversion/textual_inversion.py

@@ -566,7 +566,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = os.path.join(args.output_dir, args.logging_dir)

examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py

@@ -280,7 +280,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = os.path.join(args.output_dir, args.logging_dir)

examples/research_projects/pixart/train_pixart_controlnet_hf.py

@@ -562,7 +562,7 @@ def main():
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/research_projects/pytorch_xla/inference/flux/README.md

@@ -40,7 +40,7 @@ cd examples/research_projects/pytorch_xla/inference/flux/
 As the model is gated, before using it with diffusers you first need to go to the [FLUX.1 [dev] Hugging Face page](https://huggingface.co/black-forest-labs/FLUX.1-dev), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 Then run:

examples/research_projects/pytorch_xla/training/text_to_image/README.md

@@ -80,7 +80,7 @@ pip3 install .'
 Run the following command to authenticate your token.
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 This script only trains the unet part of the network. The VAE and text encoder

examples/research_projects/realfill/train_realfill.py

@@ -535,7 +535,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     logging_dir = Path(args.output_dir, args.logging_dir)

examples/research_projects/sana/README.md

@@ -19,7 +19,7 @@ mkdir -p $your_local_path # Create the directory if it doesn't exist
 Download the SANA Sprint teacher model from Hugging Face Hub. The script uses the 1.6B parameter model.
 
 ```bash
-hf download Efficient-Large-Model/SANA_Sprint_1.6B_1024px_teacher_diffusers --local-dir $your_local_path/SANA_Sprint_1.6B_1024px_teacher_diffusers
+huggingface-cli download Efficient-Large-Model/SANA_Sprint_1.6B_1024px_teacher_diffusers --local-dir $your_local_path/SANA_Sprint_1.6B_1024px_teacher_diffusers
 ```
 
 *(Optional: You can also download the 0.6B model by replacing the model name: `Efficient-Large-Model/Sana_Sprint_0.6B_1024px_teacher_diffusers`)*

examples/research_projects/sana/train_sana_sprint_diffusers.py

@@ -940,7 +940,7 @@ def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
             "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
         )
 
     if torch.backends.mps.is_available() and args.mixed_precision == "bf16":