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Post release 0.14

This commit is contained in:
Patrick von Platen 2023-02-17 23:57:46 +02:00
parent b2c1e0d6d4
commit 3231712b7d
20 changed files with 15 additions and 66 deletions
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7
examples/community/imagic_stable_diffusion.py
View File

@ -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.

14
examples/community/lpw_stable_diffusion.py
View File

@ -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,
)

6
examples/community/lpw_stable_diffusion_onnx.py
View File

@ -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

2
examples/dreambooth/train_dreambooth.py
View File

@ -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__)

2
examples/dreambooth/train_dreambooth_flax.py
View File

@ -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"))

2
examples/dreambooth/train_dreambooth_lora.py
View File

@ -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__)

2
examples/text_to_image/train_text_to_image.py
View File

@ -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")

2
examples/text_to_image/train_text_to_image_flax.py
View File

@ -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__)

2
examples/text_to_image/train_text_to_image_lora.py
View File

@ -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")

2
examples/textual_inversion/textual_inversion.py
View File

@ -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__)

2
examples/textual_inversion/textual_inversion_flax.py
View File

@ -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__)

2
examples/unconditional_image_generation/train_unconditional.py
View File

@ -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")

2
setup.py
View File

@ -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",

2
src/diffusers/__init__.py
View File

@ -1,4 +1,4 @@
__version__ = "0.13.0"
__version__ = "0.14.0.dev0"
from .configuration_utils import ConfigMixin
from .utils import (

7
src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py
View File

@ -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):

5
src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py
View File

@ -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)

5
src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py
View File

@ -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):

5
src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py
View File

@ -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):

5
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
View File

@ -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)

5
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py
View File

@ -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)