[Bump version] 0.13.0dev0 & Deprecate `predict_epsilon` (#2109)

* [Bump version] 0.13

* Bump model up

* up

examples/community/imagic_stable_diffusion.py

@@ -185,7 +185,7 @@ class ImagicStableDiffusionPipeline(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
         message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs)
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
         image = init_image or image
 
         accelerator = Accelerator(

examples/community/lpw_stable_diffusion.py

@@ -759,7 +759,7 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
         message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs)
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
         image = init_image or image
 
         # 0. Default height and width to unet

examples/community/lpw_stable_diffusion_onnx.py

@@ -745,7 +745,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
             (nsfw) content, according to the `safety_checker`.
         """
         message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs)
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
         image = init_image or image
 
         # 0. Default height and width to unet

examples/dreambooth/train_dreambooth.py

@@ -46,7 +46,7 @@ from transformers import AutoTokenizer, PretrainedConfig
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.12.0")
+check_min_version("0.13.0.dev0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_flax.py

@@ -36,7 +36,7 @@ from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel,
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.12.0")
+check_min_version("0.13.0.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 transformers import AutoTokenizer, PretrainedConfig
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.12.0")
+check_min_version("0.13.0.dev0")
 
 logger = get_logger(__name__)
 

examples/text_to_image/train_text_to_image.py

@@ -45,7 +45,7 @@ from transformers import CLIPTextModel, CLIPTokenizer
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.12.0")
+check_min_version("0.13.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/text_to_image/train_text_to_image_flax.py

@@ -34,7 +34,7 @@ from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel,
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.12.0")
+check_min_version("0.13.0.dev0")
 
 logger = logging.getLogger(__name__)
 

examples/text_to_image/train_text_to_image_lora.py

@@ -47,7 +47,7 @@ from transformers import CLIPTextModel, CLIPTokenizer
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.12.0")
+check_min_version("0.13.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/textual_inversion/textual_inversion.py

@@ -68,7 +68,7 @@ else:
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.12.0")
+check_min_version("0.13.0.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("0.12.0")
+check_min_version("0.13.0.dev0")
 
 logger = logging.getLogger(__name__)
 

examples/unconditional_image_generation/train_unconditional.py

@@ -33,7 +33,7 @@ from tqdm.auto import tqdm
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.12.0")
+check_min_version("0.13.0.dev0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/unconditional_image_generation/train_unconditional_ort.py

@@ -30,7 +30,7 @@ from tqdm.auto import tqdm
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.12.0")
+check_min_version("0.13.0.dev0")
 
 logger = get_logger(__name__)
 

setup.py

@@ -219,7 +219,7 @@ install_requires = [
 
 setup(
     name="diffusers",
-    version="0.12.0",  # 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="0.13.0.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__ = "0.12.0"
+__version__ = "0.13.0.dev0"
 
 from .configuration_utils import ConfigMixin
 from .utils import (

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -606,7 +606,7 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
         message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs)
+        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

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -17,8 +17,7 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 
-from ...configuration_utils import FrozenDict
-from ...utils import deprecate, randn_tensor
+from ...utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
@@ -46,7 +45,6 @@ class DDPMPipeline(DiffusionPipeline):
         num_inference_steps: int = 1000,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
-        **kwargs,
     ) -> Union[ImagePipelineOutput, Tuple]:
         r"""
         Args:
@@ -68,30 +66,6 @@ class DDPMPipeline(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 make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-
-        if predict_epsilon is not None:
-            new_config = dict(self.scheduler.config)
-            new_config["prediction_type"] = "epsilon" if predict_epsilon else "sample"
-            self.scheduler._internal_dict = FrozenDict(new_config)
-
-        if generator is not None and generator.device.type != self.device.type and self.device.type != "mps":
-            message = (
-                f"The `generator` device is `{generator.device}` and does not match the pipeline "
-                f"device `{self.device}`, so the `generator` will be ignored. "
-                f'Please use `torch.Generator(device="{self.device}")` instead.'
-            )
-            deprecate(
-                "generator.device == 'cpu'",
-                "0.13.0",
-                message,
-            )
-            generator = None
-
         # Sample gaussian noise to begin loop
         if isinstance(self.unet.sample_size, int):
             image_shape = (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size)

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py

@@ -102,7 +102,7 @@ class LDMSuperResolutionPipeline(DiffusionPipeline):
             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", "0.13.0", message, take_from=kwargs)
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
         image = init_image or image
 
         if isinstance(image, PIL.Image.Image):

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py

@@ -623,7 +623,7 @@ class CycleDiffusionPipeline(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
         message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs)
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
         image = init_image or image
 
         # 1. Check inputs

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -311,7 +311,7 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
         message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs)
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
         image = init_image or image
 
         if isinstance(prompt, str):