text stringlengths 41 89.8k | type stringclasses 1
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|---|---|---|---|---|---|---|---|
class FrozenDict(OrderedDict):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
for key, value in self.items():
setattr(self, key, value)
self.__frozen = True
def __delitem__(self, *args, **kwargs):
raise Exception(f"You cannot use ``__delitem... | class_definition | 1,471 | 2,729 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/configuration_utils.py | null | 0 |
class ConfigMixin:
r"""
Base class for all configuration classes. All configuration parameters are stored under `self.config`. Also
provides the [`~ConfigMixin.from_config`] and [`~ConfigMixin.save_config`] methods for loading, downloading, and
saving classes that inherit from [`ConfigMixin`].
Clas... | class_definition | 2,732 | 29,807 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/configuration_utils.py | null | 1 |
class LegacyConfigMixin(ConfigMixin):
r"""
A subclass of `ConfigMixin` to resolve class mapping from legacy classes (like `Transformer2DModel`) to more
pipeline-specific classes (like `DiTTransformer2DModel`).
"""
@classmethod
def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = Non... | class_definition | 33,715 | 34,386 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/configuration_utils.py | null | 2 |
class VaeImageProcessor(ConfigMixin):
"""
Image processor for VAE.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
`height` and `width` arguments from [`image... | class_definition | 2,722 | 32,473 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/image_processor.py | null | 3 |
class VaeImageProcessorLDM3D(VaeImageProcessor):
"""
Image processor for VAE LDM3D.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
vae_scale_factor (`int`, *optional*, defa... | class_definition | 32,476 | 44,360 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/image_processor.py | null | 4 |
class IPAdapterMaskProcessor(VaeImageProcessor):
"""
Image processor for IP Adapter image masks.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
vae_scale_factor (`int`, *op... | class_definition | 44,363 | 48,691 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/image_processor.py | null | 5 |
class PixArtImageProcessor(VaeImageProcessor):
"""
Image processor for PixArt image resize and crop.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
`height` ... | class_definition | 48,694 | 52,715 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/image_processor.py | null | 6 |
class VideoProcessor(VaeImageProcessor):
r"""Simple video processor."""
def preprocess_video(self, video, height: Optional[int] = None, width: Optional[int] = None) -> torch.Tensor:
r"""
Preprocesses input video(s).
Args:
video (`List[PIL.Image]`, `List[List[PIL.Image]]`, `... | class_definition | 800 | 5,401 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/video_processor.py | null | 7 |
class SchedulerType(Enum):
LINEAR = "linear"
COSINE = "cosine"
COSINE_WITH_RESTARTS = "cosine_with_restarts"
POLYNOMIAL = "polynomial"
CONSTANT = "constant"
CONSTANT_WITH_WARMUP = "constant_with_warmup"
PIECEWISE_CONSTANT = "piecewise_constant" | class_definition | 875 | 1,147 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/optimization.py | null | 8 |
class EMAModel:
"""
Exponential Moving Average of models weights
"""
def __init__(
self,
parameters: Iterable[torch.nn.Parameter],
decay: float = 0.9999,
min_decay: float = 0.0,
update_after_step: int = 0,
use_ema_warmup: bool = False,
inv_gamma: ... | class_definition | 11,693 | 26,213 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/training_utils.py | null | 9 |
class PipelineCallback(ConfigMixin):
"""
Base class for all the official callbacks used in a pipeline. This class provides a structure for implementing
custom callbacks and ensures that all callbacks have a consistent interface.
Please implement the following:
`tensor_inputs`: This should retur... | class_definition | 134 | 1,945 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 10 |
class MultiPipelineCallbacks:
"""
This class is designed to handle multiple pipeline callbacks. It accepts a list of PipelineCallback objects and
provides a unified interface for calling all of them.
"""
def __init__(self, callbacks: List[PipelineCallback]):
self.callbacks = callbacks
... | class_definition | 1,948 | 2,790 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 11 |
class SDCFGCutoffCallback(PipelineCallback):
"""
Callback function for Stable Diffusion Pipelines. After certain number of steps (set by `cutoff_step_ratio` or
`cutoff_step_index`), this callback will disable the CFG.
Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute ... | class_definition | 2,793 | 3,989 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 12 |
class SDXLCFGCutoffCallback(PipelineCallback):
"""
Callback function for the base Stable Diffusion XL Pipelines. After certain number of steps (set by
`cutoff_step_ratio` or `cutoff_step_index`), this callback will disable the CFG.
Note: This callback mutates the pipeline by changing the `_guidance_sca... | class_definition | 3,992 | 5,768 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 13 |
class SDXLControlnetCFGCutoffCallback(PipelineCallback):
"""
Callback function for the Controlnet Stable Diffusion XL Pipelines. After certain number of steps (set by
`cutoff_step_ratio` or `cutoff_step_index`), this callback will disable the CFG.
Note: This callback mutates the pipeline by changing th... | class_definition | 5,771 | 7,759 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 14 |
class IPAdapterScaleCutoffCallback(PipelineCallback):
"""
Callback function for any pipeline that inherits `IPAdapterMixin`. After certain number of steps (set by
`cutoff_step_ratio` or `cutoff_step_index`), this callback will set the IP Adapter scale to `0.0`.
Note: This callback mutates the IP Adapte... | class_definition | 7,762 | 8,748 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 15 |
class ValueGuidedRLPipeline(DiffusionPipeline):
r"""
Pipeline for value-guided sampling from a diffusion model trained to predict sequences of states.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, s... | class_definition | 843 | 6,032 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/experimental/rl/value_guided_sampling.py | null | 16 |
class QuantizationMethod(str, Enum):
BITS_AND_BYTES = "bitsandbytes"
GGUF = "gguf"
TORCHAO = "torchao" | class_definition | 1,208 | 1,322 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/quantization_config.py | null | 17 |
class QuantizationConfigMixin:
"""
Mixin class for quantization config
"""
quant_method: QuantizationMethod
_exclude_attributes_at_init = []
@classmethod
def from_dict(cls, config_dict, return_unused_kwargs=False, **kwargs):
"""
Instantiates a [`QuantizationConfigMixin`] fr... | class_definition | 1,336 | 5,659 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/quantization_config.py | null | 18 |
class BitsAndBytesConfig(QuantizationConfigMixin):
"""
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `bitsandbytes`.
This replaces `load_in_8bit` or `load_in_4bit`therefore both options are mutually exclusive.
Currently... | class_definition | 5,673 | 17,200 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/quantization_config.py | null | 19 |
class GGUFQuantizationConfig(QuantizationConfigMixin):
"""This is a config class for GGUF Quantization techniques.
Args:
compute_dtype: (`torch.dtype`, defaults to `torch.float32`):
This sets the computational type which might be different than the input type. For example, inputs might be
... | class_definition | 17,214 | 18,046 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/quantization_config.py | null | 20 |
class TorchAoConfig(QuantizationConfigMixin):
"""This is a config class for torchao quantization/sparsity techniques.
Args:
quant_type (`str`):
The type of quantization we want to use, currently supporting:
- **Integer quantization:**
- Full function name... | class_definition | 18,060 | 30,174 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/quantization_config.py | null | 21 |
class DiffusersQuantizer(ABC):
"""
Abstract class of the HuggingFace quantizer. Supports for now quantizing HF diffusers models for inference and/or
quantization. This class is used only for diffusers.models.modeling_utils.ModelMixin.from_pretrained and cannot be
easily used outside the scope of that me... | class_definition | 1,076 | 9,564 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/base.py | null | 22 |
class DiffusersAutoQuantizer:
"""
The auto diffusers quantizer class that takes care of automatically instantiating to the correct
`DiffusersQuantizer` given the `QuantizationConfig`.
"""
@classmethod
def from_dict(cls, quantization_config_dict: Dict):
quant_method = quantization_confi... | class_definition | 1,524 | 5,675 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/auto.py | null | 23 |
class GGUFQuantizer(DiffusersQuantizer):
use_keep_in_fp32_modules = True
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
self.compute_dtype = quantization_config.compute_dtype
self.pre_quantized = quantization_config.pre_quantized
... | class_definition | 677 | 5,753 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/gguf/gguf_quantizer.py | null | 24 |
class GGUFParameter(torch.nn.Parameter):
def __new__(cls, data, requires_grad=False, quant_type=None):
data = data if data is not None else torch.empty(0)
self = torch.Tensor._make_subclass(cls, data, requires_grad)
self.quant_type = quant_type
return self
def as_tensor(self):
... | class_definition | 13,739 | 15,334 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/gguf/utils.py | null | 25 |
class GGUFLinear(nn.Linear):
def __init__(
self,
in_features,
out_features,
bias=False,
compute_dtype=None,
device=None,
) -> None:
super().__init__(in_features, out_features, bias, device)
self.compute_dtype = compute_dtype
def forward(self, ... | class_definition | 15,337 | 15,937 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/gguf/utils.py | null | 26 |
class TorchAoHfQuantizer(DiffusersQuantizer):
r"""
Diffusers Quantizer for TorchAO: https://github.com/pytorch/ao/.
"""
requires_calibration = False
required_packages = ["torchao"]
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
d... | class_definition | 2,926 | 12,820 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/torchao/torchao_quantizer.py | null | 27 |
class BnB4BitDiffusersQuantizer(DiffusersQuantizer):
"""
4-bit quantization from bitsandbytes.py quantization method:
before loading: converts transformer layers into Linear4bit during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear4bit into ... | class_definition | 1,257 | 14,257 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/bitsandbytes/bnb_quantizer.py | null | 28 |
class BnB8BitDiffusersQuantizer(DiffusersQuantizer):
"""
8-bit quantization from bitsandbytes quantization method:
before loading: converts transformer layers into Linear8bitLt during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear8bitLt into... | class_definition | 14,260 | 25,821 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/bitsandbytes/bnb_quantizer.py | null | 29 |
class FlaxImagePipelineOutput(BaseOutput):
"""
Output class for image pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
"""
images... | class_definition | 2,892 | 3,254 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_flax_utils.py | null | 30 |
class FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin):
r"""
Base class for Flax-based pipelines.
[`FlaxDiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and
provides methods for loading, downloading and saving models. It also includes methods to:
... | class_definition | 3,257 | 27,130 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_flax_utils.py | null | 31 |
class AutoPipelineForText2Image(ConfigMixin):
r"""
[`AutoPipelineForText2Image`] is a generic pipeline class that instantiates a text-to-image pipeline class. The
specific underlying pipeline class is automatically selected from either the
[`~AutoPipelineForText2Image.from_pretrained`] or [`~AutoPipeli... | class_definition | 10,531 | 25,964 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py | null | 32 |
class AutoPipelineForImage2Image(ConfigMixin):
r"""
[`AutoPipelineForImage2Image`] is a generic pipeline class that instantiates an image-to-image pipeline class. The
specific underlying pipeline class is automatically selected from either the
[`~AutoPipelineForImage2Image.from_pretrained`] or [`~AutoP... | class_definition | 25,967 | 42,073 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py | null | 33 |
class AutoPipelineForInpainting(ConfigMixin):
r"""
[`AutoPipelineForInpainting`] is a generic pipeline class that instantiates an inpainting pipeline class. The
specific underlying pipeline class is automatically selected from either the
[`~AutoPipelineForInpainting.from_pretrained`] or [`~AutoPipeline... | class_definition | 42,076 | 58,024 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py | null | 34 |
class FreeInitMixin:
r"""Mixin class for FreeInit."""
def enable_free_init(
self,
num_iters: int = 3,
use_fast_sampling: bool = False,
method: str = "butterworth",
order: int = 4,
spatial_stop_frequency: float = 0.25,
temporal_stop_frequency: float = 0.25... | class_definition | 737 | 7,690 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_init_utils.py | null | 35 |
class OnnxRuntimeModel:
def __init__(self, model=None, **kwargs):
logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.")
self.model = model
self.model_save_dir = kwargs.get("model_save_dir", None)
self.latest_model_name = kwargs.get("latest_model_... | class_definition | 1,473 | 8,328 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/onnx_utils.py | null | 36 |
class ImagePipelineOutput(BaseOutput):
"""
Output class for image pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
"""
images: Un... | class_definition | 3,101 | 3,459 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py | null | 37 |
class AudioPipelineOutput(BaseOutput):
"""
Output class for audio pipelines.
Args:
audios (`np.ndarray`)
List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`.
"""
audios: np.ndarray | class_definition | 3,473 | 3,742 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py | null | 38 |
class DiffusionPipeline(ConfigMixin, PushToHubMixin):
r"""
Base class for all pipelines.
[`DiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and
provides methods for loading, downloading and saving models. It also includes methods to:
- move... | class_definition | 3,745 | 93,613 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py | null | 39 |
class StableDiffusionMixin:
r"""
Helper for DiffusionPipeline with vae and unet.(mainly for LDM such as stable diffusion)
"""
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute deco... | class_definition | 93,616 | 98,430 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py | null | 40 |
class SplitInferenceModule(nn.Module):
r"""
A wrapper module class that splits inputs along a specified dimension before performing a forward pass.
This module is useful when you need to perform inference on large tensors in a memory-efficient way by breaking
them into smaller chunks, processing each c... | class_definition | 1,277 | 6,502 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py | null | 41 |
class AnimateDiffFreeNoiseMixin:
r"""Mixin class for [FreeNoise](https://arxiv.org/abs/2310.15169)."""
def _enable_free_noise_in_block(self, block: Union[CrossAttnDownBlockMotion, DownBlockMotion, UpBlockMotion]):
r"""Helper function to enable FreeNoise in transformer blocks."""
for motion_mod... | class_definition | 6,505 | 29,681 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py | null | 42 |
class StableDiffusionGLIGENPipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the... | class_definition | 4,078 | 43,386 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py | null | 43 |
class StableDiffusionGLIGENTextImagePipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic me... | class_definition | 6,195 | 51,977 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py | null | 44 |
class StableVideoDiffusionPipelineOutput(BaseOutput):
r"""
Output class for Stable Video Diffusion pipeline.
Args:
frames (`[List[List[PIL.Image.Image]]`, `np.ndarray`, `torch.Tensor`]):
List of denoised PIL images of length `batch_size` or numpy array or torch tensor of shape `(batch_s... | class_definition | 5,752 | 6,213 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py | null | 45 |
class StableVideoDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline to generate video from an input image using Stable Video Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running... | class_definition | 6,216 | 29,021 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py | null | 46 |
class PIAPipelineOutput(BaseOutput):
r"""
Output class for PIAPipeline.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
Nested list of length `batch_size` with denoised PIL image sequences of length `num_frames`, NumPy array of
shape `(batch_size... | class_definition | 4,608 | 5,135 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py | null | 47 |
class PIAPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
FromSingleFileMixin,
FreeInitMixin,
):
r"""
Pipeline for text-to-video generation.
This model inherits from [`DiffusionPipeline`]. Check the s... | class_definition | 5,138 | 46,411 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py | null | 48 |
class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
r"""
The mochi pipeline for text-to-video generation.
Reference: https://github.com/genmoai/models
Args:
transformer ([`MochiTransformer3DModel`]):
Conditional Transformer architecture to denoise the encoded video laten... | class_definition | 6,336 | 35,210 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py | null | 49 |
class MochiPipelineOutput(BaseOutput):
r"""
Output class for Mochi pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised PIL image... | class_definition | 101 | 608 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_output.py | null | 50 |
class KolorsImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin, StableDiffusionXLLoraLoaderMixin, IPAdapterMixin):
r"""
Pipeline for text-to-image generation using Kolors.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library impleme... | class_definition | 6,495 | 65,830 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py | null | 51 |
class KolorsPipeline(DiffusionPipeline, StableDiffusionMixin, StableDiffusionXLLoraLoaderMixin, IPAdapterMixin):
r"""
Pipeline for text-to-image generation using Kolors.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for... | class_definition | 5,455 | 55,976 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors.py | null | 52 |
class KolorsPipelineOutput(BaseOutput):
"""
Output class for Kolors pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy a... | class_definition | 148 | 589 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_output.py | null | 53 |
class ChatGLMConfig(PretrainedConfig):
model_type = "chatglm"
def __init__(
self,
num_layers=28,
padded_vocab_size=65024,
hidden_size=4096,
ffn_hidden_size=13696,
kv_channels=128,
num_attention_heads=32,
seq_length=2048,
hidden_dropout=0.0... | class_definition | 1,037 | 3,327 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 54 |
class RMSNorm(torch.nn.Module):
def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
super().__init__()
self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype))
self.eps = eps
def forward(self, hidden_states: torch.Tensor)... | class_definition | 3,330 | 3,909 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 55 |
class CoreAttention(torch.nn.Module):
def __init__(self, config: ChatGLMConfig, layer_number):
super(CoreAttention, self).__init__()
self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
if self.ap... | class_definition | 4,031 | 10,104 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 56 |
class SelfAttention(torch.nn.Module):
"""Parallel self-attention layer abstract class.
Self-attention layer takes input with size [s, b, h] and returns output of the same size.
"""
def __init__(self, config: ChatGLMConfig, layer_number, device=None):
super(SelfAttention, self).__init__()
... | class_definition | 11,779 | 17,834 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 57 |
class MLP(torch.nn.Module):
"""MLP.
MLP will take the input with h hidden state, project it to 4*h hidden dimension, perform nonlinear transformation,
and project the state back into h hidden dimension.
"""
def __init__(self, config: ChatGLMConfig, device=None):
super(MLP, self).__init__()... | class_definition | 17,837 | 19,182 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 58 |
class GLMBlock(torch.nn.Module):
"""A single transformer layer.
Transformer layer takes input with size [s, b, h] and returns an output of the same size.
"""
def __init__(self, config: ChatGLMConfig, layer_number, device=None):
super(GLMBlock, self).__init__()
self.layer_number = layer... | class_definition | 19,185 | 21,803 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 59 |
class GLMTransformer(torch.nn.Module):
"""Transformer class."""
def __init__(self, config: ChatGLMConfig, device=None):
super(GLMTransformer, self).__init__()
self.fp32_residual_connection = config.fp32_residual_connection
self.post_layer_norm = config.post_layer_norm
# Number... | class_definition | 21,806 | 24,692 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 60 |
class ChatGLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
is_parallelizable = False
supports_gradient_checkpointing = True
config_class = ChatGLMConfig
base_model_prefix... | class_definition | 24,695 | 26,533 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 61 |
class Embedding(torch.nn.Module):
"""Language model embeddings."""
def __init__(self, config: ChatGLMConfig, device=None):
super(Embedding, self).__init__()
self.hidden_size = config.hidden_size
# Word embeddings (parallel).
self.word_embeddings = nn.Embedding(
conf... | class_definition | 26,610 | 27,582 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 62 |
class RotaryEmbedding(nn.Module):
def __init__(self, dim, original_impl=False, device=None, dtype=None):
super().__init__()
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim))
self.register_buffer("inv_freq", inv_freq)
self.dim = dim
sel... | class_definition | 27,585 | 29,355 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 63 |
class PrefixEncoder(torch.nn.Module):
"""
The torch.nn model to encode the prefix Input shape: (batch-size, prefix-length) Output shape: (batch-size,
prefix-length, 2*layers*hidden)
"""
def __init__(self, config: ChatGLMConfig):
super().__init__()
self.prefix_projection = config.pre... | class_definition | 29,358 | 30,645 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 64 |
class ChatGLMModel(ChatGLMPreTrainedModel):
def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
super().__init__(config)
if empty_init:
init_method = skip_init
else:
init_method = default_init
init_kwargs = {}
if device is not None... | class_definition | 30,648 | 35,794 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/text_encoder.py | null | 65 |
class SPTokenizer:
def __init__(self, model_path: str):
# reload tokenizer
assert os.path.isfile(model_path), model_path
self.sp_model = SentencePieceProcessor(model_file=model_path)
# BOS / EOS token IDs
self.n_words: int = self.sp_model.vocab_size()
self.bos_id: in... | class_definition | 952 | 4,128 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py | null | 66 |
class ChatGLMTokenizer(PreTrainedTokenizer):
vocab_files_names = {"vocab_file": "tokenizer.model"}
model_input_names = ["input_ids", "attention_mask", "position_ids"]
def __init__(
self,
vocab_file,
padding_side="left",
clean_up_tokenization_spaces=False,
encode_spe... | class_definition | 4,131 | 13,427 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/tokenizer.py | null | 67 |
class AttentionStore:
@staticmethod
def get_empty_store():
return {"down": [], "mid": [], "up": []}
def __call__(self, attn, is_cross: bool, place_in_unet: str):
if self.cur_att_layer >= 0 and is_cross:
if attn.shape[1] == np.prod(self.attn_res):
self.step_store[... | class_definition | 2,929 | 4,789 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py | null | 68 |
class AttendExciteAttnProcessor:
def __init__(self, attnstore, place_in_unet):
super().__init__()
self.attnstore = attnstore
self.place_in_unet = place_in_unet
def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
batch_size, sequen... | class_definition | 4,792 | 6,260 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py | null | 69 |
class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion and Attend-and-Excite.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic met... | class_definition | 6,263 | 49,042 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py | null | 70 |
class GaussianSmoothing(torch.nn.Module):
"""
Arguments:
Apply gaussian smoothing on a 1d, 2d or 3d tensor. Filtering is performed seperately for each channel in the input
using a depthwise convolution.
channels (int, sequence): Number of channels of the input tensors. Output will
ha... | class_definition | 49,045 | 51,459 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py | null | 71 |
class KandinskyImg2ImgPipeline(DiffusionPipeline):
"""
Pipeline for image-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running o... | class_definition | 3,216 | 22,005 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py | null | 72 |
class KandinskyPriorPipelineOutput(BaseOutput):
"""
Output class for KandinskyPriorPipeline.
Args:
image_embeds (`torch.Tensor`)
clip image embeddings for text prompt
negative_image_embeds (`List[PIL.Image.Image]` or `np.ndarray`)
clip image embeddings for unconditio... | class_definition | 3,773 | 4,221 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py | null | 73 |
class KandinskyPriorPipeline(DiffusionPipeline):
"""
Pipeline for generating image prior for Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a par... | class_definition | 4,224 | 24,019 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py | null | 74 |
class KandinskyInpaintPipeline(DiffusionPipeline):
"""
Pipeline for text-guided image inpainting using Kandinsky2.1
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, run... | class_definition | 9,239 | 28,723 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py | null | 75 |
class KandinskyPipeline(DiffusionPipeline):
"""
Pipeline for text-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a part... | class_definition | 2,473 | 17,900 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py | null | 76 |
class MCLIPConfig(XLMRobertaConfig):
model_type = "M-CLIP"
def __init__(self, transformerDimSize=1024, imageDimSize=768, **kwargs):
self.transformerDimensions = transformerDimSize
self.numDims = imageDimSize
super().__init__(**kwargs) | class_definition | 91 | 358 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/text_encoder.py | null | 77 |
class MultilingualCLIP(PreTrainedModel):
config_class = MCLIPConfig
def __init__(self, config, *args, **kwargs):
super().__init__(config, *args, **kwargs)
self.transformer = XLMRobertaModel(config)
self.LinearTransformation = torch.nn.Linear(
in_features=config.transformerDi... | class_definition | 361 | 1,021 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/text_encoder.py | null | 78 |
class KandinskyCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for text-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, ... | class_definition | 3,805 | 14,587 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py | null | 79 |
class KandinskyImg2ImgCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for image-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or ... | class_definition | 14,590 | 26,939 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py | null | 80 |
class KandinskyInpaintCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running... | class_definition | 26,942 | 39,546 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py | null | 81 |
class StableDiffusion3ControlNetPipeline(
DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin
):
r"""
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatch... | class_definition | 6,067 | 62,584 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py | null | 82 |
class StableDiffusion3ControlNetInpaintingPipeline(
DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin
):
r"""
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([... | class_definition | 7,071 | 63,574 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py | null | 83 |
class StableDiffusionXLControlNetUnionPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
... | class_definition | 7,065 | 77,638 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl.py | null | 84 |
class MultiControlNetModel(MultiControlNetModel):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `MultiControlNetModel` from `diffusers.pipelines.controlnet.multicontrolnet` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.multi... | class_definition | 153 | 683 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/multicontrolnet.py | null | 85 |
class StableDiffusionXLControlNetPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
T... | class_definition | 7,369 | 82,634 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py | null | 86 |
class StableDiffusionControlNetInpaintPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
):
r"""
Pipeline for image inpainting using Stable Diffusion with ControlNet guidance.
This mode... | class_definition | 4,886 | 76,438 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py | null | 87 |
class StableDiffusionControlNetPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
This mod... | class_definition | 7,017 | 69,209 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet.py | null | 88 |
class StableDiffusionXLControlNetImg2ImgPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
FromSingleFileMixin,
IPAdapterMixin,
):
r"""
Pipeline for image-to-image generation using Stable Diffusion XL with ControlNet guidance... | class_definition | 6,129 | 87,120 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py | null | 89 |
class StableDiffusionControlNetImg2ImgPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
):
r"""
Pipeline for image-to-image generation using Stable Diffusion with ControlNet guidance.
... | class_definition | 5,348 | 67,467 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py | null | 90 |
class StableDiffusionXLControlNetUnionImg2ImgPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
FromSingleFileMixin,
IPAdapterMixin,
):
r"""
Pipeline for image-to-image generation using Stable Diffusion XL with ControlNet gui... | class_definition | 6,821 | 84,172 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py | null | 91 |
class BlipDiffusionControlNetPipeline(DiffusionPipeline):
"""
Pipeline for Canny Edge based Controlled subject-driven generation using Blip Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines ... | class_definition | 3,311 | 17,584 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py | null | 92 |
class StableDiffusionXLControlNetInpaintPipeline(
DiffusionPipeline,
StableDiffusionMixin,
StableDiffusionXLLoraLoaderMixin,
FromSingleFileMixin,
IPAdapterMixin,
TextualInversionLoaderMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits... | class_definition | 6,480 | 95,146 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py | null | 93 |
class StableDiffusionXLControlNetUnionInpaintPipeline(
DiffusionPipeline,
StableDiffusionMixin,
StableDiffusionXLLoraLoaderMixin,
FromSingleFileMixin,
IPAdapterMixin,
TextualInversionLoaderMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inh... | class_definition | 6,216 | 91,284 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_inpaint_sd_xl.py | null | 94 |
class FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline):
r"""
Flax-based pipeline for text-to-image generation using Stable Diffusion with ControlNet Guidance.
This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all p... | class_definition | 4,267 | 21,326 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.py | null | 95 |
class LTXPipelineOutput(BaseOutput):
r"""
Output class for LTX pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised PIL image seq... | class_definition | 101 | 604 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_output.py | null | 96 |
class LTXPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixin):
r"""
Pipeline for text-to-video generation.
Reference: https://github.com/Lightricks/LTX-Video
Args:
transformer ([`LTXVideoTransformer3DModel`]):
Conditional Transformer architecture to denoise the... | class_definition | 6,258 | 38,281 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx.py | null | 97 |
class LTXImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixin):
r"""
Pipeline for image-to-video generation.
Reference: https://github.com/Lightricks/LTX-Video
Args:
transformer ([`LTXVideoTransformer3DModel`]):
Conditional Transformer architecture t... | class_definition | 7,201 | 42,930 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py | null | 98 |
class LeditsAttentionStore:
@staticmethod
def get_empty_store():
return {"down_cross": [], "mid_cross": [], "up_cross": [], "down_self": [], "mid_self": [], "up_self": []}
def __call__(self, attn, is_cross: bool, place_in_unet: str, editing_prompts, PnP=False):
# attn.shape = batch_size * h... | class_definition | 3,219 | 6,701 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py | null | 99 |
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