Post release 0.14

examples/community/imagic_stable_diffusion.py

@@ -22,7 +22,7 @@ from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from diffusers.utils import deprecate, logging
+from diffusers.utils import logging
 
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
@@ -184,10 +184,6 @@ class ImagicStableDiffusionPipeline(DiffusionPipeline):
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.
         """
-        message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
-        image = init_image or image
-
         accelerator = Accelerator(
             gradient_accumulation_steps=1,
             mixed_precision="fp16",
@@ -346,7 +342,6 @@ class ImagicStableDiffusionPipeline(DiffusionPipeline):
         return_dict: bool = True,
         guidance_scale: float = 7.5,
         eta: float = 0.0,
-        **kwargs,
     ):
         r"""
         Function invoked when calling the pipeline for generation.

examples/community/lpw_stable_diffusion.py

@@ -12,7 +12,7 @@ import diffusers
 from diffusers import SchedulerMixin, StableDiffusionPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
-from diffusers.utils import deprecate, logging
+from diffusers.utils import logging
 
 
 try:
@@ -252,7 +252,6 @@ def get_weighted_text_embeddings(
     no_boseos_middle: Optional[bool] = False,
     skip_parsing: Optional[bool] = False,
     skip_weighting: Optional[bool] = False,
-    **kwargs,
 ):
     r"""
     Prompts can be assigned with local weights using brackets. For example,
@@ -682,7 +681,6 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         is_cancelled_callback: Optional[Callable[[], bool]] = None,
         callback_steps: int = 1,
-        **kwargs,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -758,10 +756,6 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.
         """
-        message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
-        image = init_image or image
-
         # 0. Default height and width to unet
         height = height or self.unet.config.sample_size * self.vae_scale_factor
         width = width or self.unet.config.sample_size * self.vae_scale_factor
@@ -884,7 +878,6 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         is_cancelled_callback: Optional[Callable[[], bool]] = None,
         callback_steps: int = 1,
-        **kwargs,
     ):
         r"""
         Function for text-to-image generation.
@@ -960,7 +953,6 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
             callback=callback,
             is_cancelled_callback=is_cancelled_callback,
             callback_steps=callback_steps,
-            **kwargs,
         )
 
     def img2img(
@@ -980,7 +972,6 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         is_cancelled_callback: Optional[Callable[[], bool]] = None,
         callback_steps: int = 1,
-        **kwargs,
     ):
         r"""
         Function for image-to-image generation.
@@ -1056,7 +1047,6 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
             callback=callback,
             is_cancelled_callback=is_cancelled_callback,
             callback_steps=callback_steps,
-            **kwargs,
         )
 
     def inpaint(
@@ -1077,7 +1067,6 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         is_cancelled_callback: Optional[Callable[[], bool]] = None,
         callback_steps: int = 1,
-        **kwargs,
     ):
         r"""
         Function for inpaint.
@@ -1158,5 +1147,4 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
             callback=callback,
             is_cancelled_callback=is_cancelled_callback,
             callback_steps=callback_steps,
-            **kwargs,
         )

examples/community/lpw_stable_diffusion_onnx.py

@@ -11,7 +11,7 @@ from transformers import CLIPFeatureExtractor, CLIPTokenizer
 import diffusers
 from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, SchedulerMixin
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.utils import deprecate, logging
+from diffusers.utils import logging
 
 
 try:
@@ -744,10 +744,6 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.
         """
-        message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
-        image = init_image or image
-
         # 0. Default height and width to unet
         height = height or self.unet.config.sample_size * self.vae_scale_factor
         width = width or self.unet.config.sample_size * self.vae_scale_factor

examples/dreambooth/train_dreambooth.py

@@ -47,7 +47,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0")
+check_min_version("0.14.0.dev0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_flax.py

@@ -36,7 +36,7 @@ from diffusers.utils import check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0")
+check_min_version("0.14.0.dev0")
 
 # Cache compiled models across invocations of this script.
 cc.initialize_cache(os.path.expanduser("~/.cache/jax/compilation_cache"))

examples/dreambooth/train_dreambooth_lora.py

@@ -54,7 +54,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0")
+check_min_version("0.14.0.dev0")
 
 logger = get_logger(__name__)
 

examples/text_to_image/train_text_to_image.py

@@ -47,7 +47,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0")
+check_min_version("0.14.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/text_to_image/train_text_to_image_flax.py

@@ -34,7 +34,7 @@ from diffusers.utils import check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0")
+check_min_version("0.14.0.dev0")
 
 logger = logging.getLogger(__name__)
 

examples/text_to_image/train_text_to_image_lora.py

@@ -48,7 +48,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0")
+check_min_version("0.14.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/textual_inversion/textual_inversion.py

@@ -74,7 +74,7 @@ else:
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0")
+check_min_version("0.14.0.dev0")
 
 logger = get_logger(__name__)
 

examples/textual_inversion/textual_inversion_flax.py

@@ -57,7 +57,7 @@ else:
 # ------------------------------------------------------------------------------
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0")
+check_min_version("0.14.0.dev0")
 
 logger = logging.getLogger(__name__)
 

examples/unconditional_image_generation/train_unconditional.py

@@ -27,7 +27,7 @@ from diffusers.utils import check_min_version, is_tensorboard_available, is_wand
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0")
+check_min_version("0.14.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

setup.py

@@ -219,7 +219,7 @@ install_requires = [
 
 setup(
     name="diffusers",
-    version="0.13.0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="0.14.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     description="Diffusers",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

src/diffusers/__init__.py

-__version__ = "0.13.0"
+__version__ = "0.14.0.dev0"
 
 from .configuration_utils import ConfigMixin
 from .utils import (

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py

@@ -15,7 +15,7 @@ from ...schedulers import (
     LMSDiscreteScheduler,
     PNDMScheduler,
 )
-from ...utils import PIL_INTERPOLATION, deprecate, randn_tensor
+from ...utils import PIL_INTERPOLATION, randn_tensor
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
@@ -72,7 +72,6 @@ class LDMSuperResolutionPipeline(DiffusionPipeline):
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
-        **kwargs,
     ) -> Union[Tuple, ImagePipelineOutput]:
         r"""
         Args:
@@ -100,10 +99,6 @@ class LDMSuperResolutionPipeline(DiffusionPipeline):
             [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is
             True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
         """
-        message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
-        image = init_image or image
-
         if isinstance(image, PIL.Image.Image):
             batch_size = 1
         elif isinstance(image, torch.Tensor):

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py

@@ -582,7 +582,6 @@ class CycleDiffusionPipeline(DiffusionPipeline):
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         callback_steps: int = 1,
-        **kwargs,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -646,10 +645,6 @@ class CycleDiffusionPipeline(DiffusionPipeline):
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.
         """
-        message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
-        image = init_image or image
-
         # 1. Check inputs
         self.check_inputs(prompt, strength, callback_steps)
 

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -253,7 +253,6 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
         callback_steps: int = 1,
-        **kwargs,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -309,10 +308,6 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.
         """
-        message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
-        image = init_image or image
-
         if isinstance(prompt, str):
             batch_size = 1
         elif isinstance(prompt, list):

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py

@@ -240,7 +240,6 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
         callback_steps: int = 1,
-        **kwargs,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -301,10 +300,6 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.
         """
-        message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
-        image = init_image or image
-
         if isinstance(prompt, str):
             batch_size = 1
         elif isinstance(prompt, list):

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -572,7 +572,6 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         callback_steps: int = 1,
-        **kwargs,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -639,10 +638,6 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.
         """
-        message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
-        image = init_image or image
-
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py

@@ -530,7 +530,6 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         callback_steps: int = 1,
-        **kwargs,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -603,10 +602,6 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.
         """
-        message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
-        image = init_image or image
-
         # 1. Check inputs
         self.check_inputs(prompt, strength, callback_steps)