No more use_auth_token=True

README.md

@@ -78,7 +78,7 @@ You need to accept the model license before downloading or using the Stable Diff
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -114,7 +114,6 @@ pipe = StableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4", 
     revision="fp16", 
     torch_dtype=torch.float16,
-    use_auth_token=True
 )
 pipe = pipe.to("cuda")
 
@@ -140,7 +139,6 @@ pipe = StableDiffusionPipeline.from_pretrained(
     revision="fp16", 
     torch_dtype=torch.float16,
     scheduler=lms,
-    use_auth_token=True
 )
 pipe = pipe.to("cuda")
 
@@ -169,10 +167,9 @@ pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
     model_id_or_path,
     revision="fp16", 
     torch_dtype=torch.float16,
-    use_auth_token=True
 )
 # or download via git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
-# and pass `model_id_or_path="./stable-diffusion-v1-4"` without having to use `use_auth_token=True`.
+# and pass `model_id_or_path="./stable-diffusion-v1-4"`.
 pipe = pipe.to(device)
 
 # let's download an initial image
@@ -219,10 +216,9 @@ pipe = StableDiffusionInpaintPipeline.from_pretrained(
     model_id_or_path,
     revision="fp16", 
     torch_dtype=torch.float16,
-    use_auth_token=True
 )
 # or download via git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
-# and pass `model_id_or_path="./stable-diffusion-v1-4"` without having to use `use_auth_token=True`.
+# and pass `model_id_or_path="./stable-diffusion-v1-4"`.
 pipe = pipe.to(device)
 
 prompt = "a cat sitting on a bench"

docs/source/api/pipelines/overview.mdx

@@ -101,7 +101,7 @@ logic including pre-processing, an unrolled diffusion loop, and post-processing
 from torch import autocast
 from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -126,7 +126,7 @@ from diffusers import StableDiffusionImg2ImgPipeline
 # load the pipeline
 device = "cuda"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=True
+    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
 ).to(device)
 
 # let's download an initial image
@@ -177,7 +177,7 @@ mask_image = download_image(mask_url).resize((512, 512))
 
 device = "cuda"
 pipe = StableDiffusionInpaintPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=True
+    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
 ).to(device)
 
 prompt = "a cat sitting on a bench"

docs/source/optimization/fp16.mdx

@@ -56,7 +56,7 @@ If you use a CUDA GPU, you can take advantage of `torch.autocast` to perform inf
 from torch import autocast
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -75,7 +75,6 @@ pipe = StableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
     revision="fp16",
     torch_dtype=torch.float16,
-    use_auth_token=True
 )
 ```
 
@@ -97,7 +96,6 @@ pipe = StableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
     revision="fp16",
     torch_dtype=torch.float16,
-    use_auth_token=True
 )
 pipe = pipe.to("cuda")
 
@@ -152,8 +150,6 @@ def generate_inputs():
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
-    # scheduler=scheduler,
-    use_auth_token=True,
     revision="fp16",
     torch_dtype=torch.float16,
 ).to("cuda")
@@ -218,8 +214,6 @@ class UNet2DConditionOutput:
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
-    # scheduler=scheduler,
-    use_auth_token=True,
     revision="fp16",
     torch_dtype=torch.float16,
 ).to("cuda")

docs/source/optimization/mps.mdx

@@ -31,7 +31,7 @@ We recommend to "prime" the pipeline using an additional one-time pass through i
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
 pipe = pipe.to("mps")
 
 prompt = "a photo of an astronaut riding a horse on mars"

docs/source/optimization/onnx.mdx

@@ -31,7 +31,6 @@ pipe = StableDiffusionOnnxPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
     revision="onnx",
     provider="CUDAExecutionProvider",
-    use_auth_token=True,
 )
 
 prompt = "a photo of an astronaut riding a horse on mars"

docs/source/using-diffusers/img2img.mdx

@@ -25,7 +25,7 @@ from diffusers import StableDiffusionImg2ImgPipeline
 # load the pipeline
 device = "cuda"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=True
+    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
 ).to(device)
 
 # let's download an initial image

docs/source/using-diffusers/inpaint.mdx

@@ -37,7 +37,7 @@ mask_image = download_image(mask_url).resize((512, 512))
 
 device = "cuda"
 pipe = StableDiffusionInpaintPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=True
+    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
 ).to(device)
 
 prompt = "a cat sitting on a bench"

examples/unconditional_image_generation/train_unconditional.py

@@ -83,7 +83,6 @@ def main(args):
             args.dataset_name,
             args.dataset_config_name,
             cache_dir=args.cache_dir,
-            use_auth_token=True if args.use_auth_token else None,
             split="train",
         )
     else:
@@ -222,7 +221,6 @@ def transforms(examples):
     parser.add_argument("--ema_power", type=float, default=3 / 4)
     parser.add_argument("--ema_max_decay", type=float, default=0.9999)
     parser.add_argument("--push_to_hub", action="store_true")
-    parser.add_argument("--use_auth_token", action="store_true")
     parser.add_argument("--hub_token", type=str, default=None)
     parser.add_argument("--hub_model_id", type=str, default=None)
     parser.add_argument("--hub_private_repo", action="store_true")

scripts/convert_stable_diffusion_checkpoint_to_onnx.py

@@ -70,7 +70,7 @@ def onnx_export(
 
 @torch.no_grad()
 def convert_models(model_path: str, output_path: str, opset: int):
-    pipeline = StableDiffusionPipeline.from_pretrained(model_path, use_auth_token=True)
+    pipeline = StableDiffusionPipeline.from_pretrained(model_path)
     output_path = Path(output_path)
 
     # TEXT ENCODER

setup.py

@@ -86,7 +86,7 @@
     "flake8>=3.8.3",
     "flax>=0.4.1",
     "hf-doc-builder>=0.3.0",
-    "huggingface-hub>=0.9.1",
+    "huggingface-hub>=0.10.0",
     "importlib_metadata",
     "isort>=5.5.4",
     "jax>=0.2.8,!=0.3.2,<=0.3.6",

src/diffusers/configuration_utils.py

@@ -145,7 +145,8 @@ def from_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], ret
 
         <Tip>
 
-        Passing `use_auth_token=True`` is required when you want to use a private model.
+         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
+         models](https://huggingface.co/docs/hub/models-gated#gated-models).
 
         </Tip>
 
@@ -238,7 +239,7 @@ def get_config_dict(
                     f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier"
                     " listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a"
                     " token having permission to this repo with `use_auth_token` or log in with `huggingface-cli"
-                    " login` and pass `use_auth_token=True`."
+                    " login`."
                 )
             except RevisionNotFoundError:
                 raise EnvironmentError(

src/diffusers/dependency_versions_table.py

@@ -10,7 +10,7 @@
     "flake8": "flake8>=3.8.3",
     "flax": "flax>=0.4.1",
     "hf-doc-builder": "hf-doc-builder>=0.3.0",
-    "huggingface-hub": "huggingface-hub>=0.9.1",
+    "huggingface-hub": "huggingface-hub>=0.10.0",
     "importlib_metadata": "importlib_metadata",
     "isort": "isort>=5.5.4",
     "jax": "jax>=0.2.8,!=0.3.2,<=0.3.6",

src/diffusers/dynamic_modules_utils.py

@@ -198,7 +198,7 @@ def get_cached_module_file(
 
     <Tip>
 
-    Passing `use_auth_token=True` is required when you want to use a private model.
+    Passing `` is required when you want to use a private model.
 
     </Tip>
 
@@ -306,7 +306,7 @@ def get_class_from_dynamic_module(
 
     <Tip>
 
-    Passing `use_auth_token=True` is required when you want to use a private model.
+    Passing `` is required when you want to use a private model.
 
     </Tip>
 

src/diffusers/modeling_flax_utils.py

@@ -357,7 +357,7 @@ def from_pretrained(
                     f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
                     "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
                     "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
-                    "login` and pass `use_auth_token=True`."
+                    "login`."
                 )
             except RevisionNotFoundError:
                 raise EnvironmentError(

src/diffusers/modeling_utils.py

@@ -270,7 +270,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
         <Tip>
 
-        Passing `use_auth_token=True`` is required when you want to use a private model.
+         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
+         models](https://huggingface.co/docs/hub/models-gated#gated-models).
 
         </Tip>
 
@@ -338,7 +339,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
                     "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
                     "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
-                    "login` and pass `use_auth_token=True`."
+                    "login`."
                 )
             except RevisionNotFoundError:
                 raise EnvironmentError(

src/diffusers/pipeline_flax_utils.py

@@ -249,8 +249,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
         <Tip>
 
-        Passing `use_auth_token=True`` is required when you want to use a private model, *e.g.*
-        `"CompVis/stable-diffusion-v1-4"`
+         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
+         models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"CompVis/stable-diffusion-v1-4"`
 
         </Tip>
 
@@ -272,15 +272,13 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         >>> # Download pipeline that requires an authorization token
         >>> # For more information on access tokens, please refer to this section
         >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
-        >>> pipeline = FlaxDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
+        >>> pipeline = FlaxDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
 
         >>> # Download pipeline, but overwrite scheduler
         >>> from diffusers import LMSDiscreteScheduler
 
         >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
-        >>> pipeline = FlaxDiffusionPipeline.from_pretrained(
-        ...     "CompVis/stable-diffusion-v1-4", scheduler=scheduler, use_auth_token=True
-        ... )
+        >>> pipeline = FlaxDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler)
         ```
         """
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)

src/diffusers/pipeline_utils.py

@@ -240,8 +240,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
         <Tip>
 
-        Passing `use_auth_token=True`` is required when you want to use a private model, *e.g.*
-        `"CompVis/stable-diffusion-v1-4"`
+         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
+         models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"CompVis/stable-diffusion-v1-4"`
 
         </Tip>
 
@@ -263,15 +263,13 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         >>> # Download pipeline that requires an authorization token
         >>> # For more information on access tokens, please refer to this section
         >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
-        >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
+        >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
 
         >>> # Download pipeline, but overwrite scheduler
         >>> from diffusers import LMSDiscreteScheduler
 
         >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
-        >>> pipeline = DiffusionPipeline.from_pretrained(
-        ...     "CompVis/stable-diffusion-v1-4", scheduler=scheduler, use_auth_token=True
-        ... )
+        >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler)
         ```
         """
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)

src/diffusers/pipelines/README.md

@@ -88,7 +88,7 @@ logic including pre-processing, an unrolled diffusion loop, and post-processing
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -114,7 +114,6 @@ pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
     revision="fp16", 
     torch_dtype=torch.float16,
-    use_auth_token=True
 ).to(device)
 
 # let's download an initial image
@@ -164,7 +163,6 @@ pipe = StableDiffusionInpaintPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
     revision="fp16", 
     torch_dtype=torch.float16,
-    use_auth_token=True
 ).to(device)
 
 prompt = "a cat sitting on a bench"

src/diffusers/pipelines/stable_diffusion/README.md

@@ -61,7 +61,7 @@ pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-4")
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -81,7 +81,6 @@ scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="sca
 pipe = StableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4", 
     scheduler=scheduler,
-    use_auth_token=True
 ).to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -105,7 +104,6 @@ lms = LMSDiscreteScheduler(
 pipe = StableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4", 
     scheduler=lms,
-    use_auth_token=True
 ).to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"