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class OnnxStableDiffusionUpscalePipeline(DiffusionPipeline): vae: OnnxRuntimeModel text_encoder: OnnxRuntimeModel tokenizer: CLIPTokenizer unet: OnnxRuntimeModel low_res_scheduler: DDPMScheduler scheduler: KarrasDiffusionSchedulers safety_checker: OnnxRuntimeModel feature_extractor: CLIP...
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class StableUnCLIPImg2ImgPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin ): """ Pipeline for text-guided image-to-image generation using stable unCLIP. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for t...
class_definition
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class StableDiffusionInpaintPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, FromSingleFileMixin, ): r""" Pipeline for text-guided image inpainting using Stable Diffusion. This model inherits from [`Diffu...
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class I2VGenXLPipelineOutput(BaseOutput): r""" Output class for image-to-video pipeline. 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 den...
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class I2VGenXLPipeline( DiffusionPipeline, StableDiffusionMixin, ): r""" Pipeline for image-to-video generation as proposed in [I2VGenXL](https://i2vgen-xl.github.io/). This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all...
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class DiTPipeline(DiffusionPipeline): r""" Pipeline for image generation based on a Transformer backbone instead of a UNet. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particu...
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/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py
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class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler image_noising_scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_c...
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class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler image_noising_scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safet...
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class IFWatermarker(ModelMixin, ConfigMixin): def __init__(self): super().__init__() self.register_buffer("watermark_image", torch.zeros((62, 62, 4))) self.watermark_image_as_pil = None def apply_watermark(self, images: List[PIL.Image.Image], sample_size=None): # Copied from ht...
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class IFSafetyChecker(PreTrainedModel): config_class = CLIPConfig _no_split_modules = ["CLIPEncoderLayer"] def __init__(self, config: CLIPConfig): super().__init__(config) self.vision_model = CLIPVisionModelWithProjection(config.vision_config) self.p_head = nn.Linear(config.visio...
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class IFPipelineOutput(BaseOutput): r""" Output class for Stable Diffusion 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...
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class IFPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker: Optional[IFSafetyChecker] watermarker: Optional[IFWa...
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class IFImg2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker: Optional[IFSafetyChecker] watermarker: Option...
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class IFSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler image_noising_scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker:...
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class IFInpaintingPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker: Optional[IFSafetyChecker] watermarker: Opt...
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class HunyuanDiTControlNetPipeline(DiffusionPipeline): r""" Pipeline for English/Chinese-to-image generation using HunyuanDiT. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or ...
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class StableDiffusion3Pipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin): r""" Args: transformer ([`SD3Transformer2DModel`]): Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. scheduler ([`FlowMatchEulerDiscreteSch...
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class StableDiffusion3PipelineOutput(BaseOutput): """ Output class for Stable Diffusion 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)`. P...
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class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin): r""" Args: transformer ([`SD3Transformer2DModel`]): Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. scheduler ([`FlowMatchEulerDisc...
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class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin): r""" Args: transformer ([`SD3Transformer2DModel`]): Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. scheduler ([`FlowMatchEulerDisc...
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class StableDiffusionXLWatermarker: def __init__(self): self.watermark = WATERMARK_BITS self.encoder = WatermarkEncoder() self.encoder.set_watermark("bits", self.watermark) def apply_watermark(self, images: torch.Tensor): # can't encode images that are smaller than 256 ...
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class StableDiffusionXLPipelineOutput(BaseOutput): """ Output class for Stable Diffusion 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)`. ...
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class FlaxStableDiffusionXLPipelineOutput(BaseOutput): """ Output class for Flax Stable Diffusion XL pipelines. Args: images (`np.ndarray`) Array of shape `(batch_size, height, width, num_channels)` with images from the diffusion pipeline. """ images...
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class FlaxStableDiffusionXLPipeline(FlaxDiffusionPipeline): def __init__( self, text_encoder: FlaxCLIPTextModel, text_encoder_2: FlaxCLIPTextModel, vae: FlaxAutoencoderKL, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: FlaxUNet2DConditionModel, ...
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class StableDiffusionXLInpaintPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin, IPAdapterMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL. This model inherits from [`Di...
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class StableDiffusionXLPipeline( DiffusionPipeline, StableDiffusionMixin, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin, IPAdapterMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL. This model inherits from [`Diffusion...
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class StableDiffusionXLImg2ImgPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, IPAdapterMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL. This model inherits from [`Di...
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class StableDiffusionXLInstructPix2PixPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, ): r""" Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion XL. This ...
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class HunyuanDiTPipeline(DiffusionPipeline): r""" Pipeline for English/Chinese-to-image generation using HunyuanDiT. 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, ru...
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class PixArtAlphaPipeline(DiffusionPipeline): r""" Pipeline for text-to-image generation using PixArt-Alpha. 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...
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class PixArtSigmaPipeline(DiffusionPipeline): r""" Pipeline for text-to-image generation using PixArt-Sigma. """ bad_punct_regex = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + r"\)" + r"\(" + r"\]" + r"\[" + r"\}" + r"\{" + r"\|" ...
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class DDPMPipeline(DiffusionPipeline): r""" Pipeline for image generation. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Parameters: unet ...
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class ModelWrapper: def __init__(self, model, alphas_cumprod): self.model = model self.alphas_cumprod = alphas_cumprod def apply_model(self, *args, **kwargs): if len(args) == 3: encoder_hidden_states = args[-1] args = args[:2] if kwargs.get("cond", None) ...
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class StableDiffusionKDiffusionPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin ): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the ...
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class ModelWrapper: def __init__(self, model, alphas_cumprod): self.model = model self.alphas_cumprod = alphas_cumprod def apply_model(self, *args, **kwargs): if len(args) == 3: encoder_hidden_states = args[-1] args = args[:2] if kwargs.get("cond", None) ...
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class StableDiffusionXLKDiffusionPipeline( DiffusionPipeline, StableDiffusionMixin, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin, IPAdapterMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL and k-diffusion. This model...
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class UnCLIPImageVariationPipeline(DiffusionPipeline): """ Pipeline to generate image variations from an input image using UnCLIP. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a ...
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class UnCLIPTextProjModel(ModelMixin, ConfigMixin): """ Utility class for CLIP embeddings. Used to combine the image and text embeddings into a format usable by the decoder. For more details, see the original paper: https://arxiv.org/abs/2204.06125 section 2.1 """ @register_to_config def _...
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class UnCLIPPipeline(DiffusionPipeline): """ Pipeline for text-to-image generation using unCLIP. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Arg...
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class AllegroPipelineOutput(BaseOutput): r""" Output class for Allegro 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 i...
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class AllegroPipeline(DiffusionPipeline): r""" Pipeline for text-to-video generation using Allegro. 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 partic...
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class KolorsPAGPipeline( DiffusionPipeline, StableDiffusionMixin, StableDiffusionXLLoraLoaderMixin, IPAdapterMixin, PAGMixin ): r""" Pipeline for text-to-image generation using Kolors. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the lib...
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class StableDiffusionXLControlNetPAGImg2ImgPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin, IPAdapterMixin, PAGMixin, ): r""" Pipeline for image-to-image generation using Stable Diffusion XL with Co...
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class HunyuanDiTPAGPipeline(DiffusionPipeline, PAGMixin): r""" Pipeline for English/Chinese-to-image generation using HunyuanDiT and [Perturbed Attention Guidance](https://huggingface.co/docs/diffusers/en/using-diffusers/pag). This model inherits from [`DiffusionPipeline`]. Check the superclass documen...
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class StableDiffusionControlNetPAGPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin, PAGMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidan...
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class StableDiffusion3PAGPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, PAGMixin): r""" [PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for text-to-image generation using Stable Diffusion 3. Args: transformer ([`SD3Transformer2DModel`]): ...
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class StableDiffusionPAGPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin, PAGMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [...
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class AnimateDiffPAGPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, FreeInitMixin, AnimateDiffFreeNoiseMixin, PAGMixin, ): r""" Pipeline for text-to-video generation using [AnimateDiff](https://hu...
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class StableDiffusion3PAGImg2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, PAGMixin): r""" [PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for image-to-image generation using Stable Diffusion 3. Args: transformer ([`SD3Transformer2DMod...
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class StableDiffusionControlNetPAGInpaintPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin, PAGMixin, ): r""" Pipeline for image inpainting using Stable Diffusion with ControlNet guidanc...
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class StableDiffusionXLControlNetPAGPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin, PAGMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet...
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class StableDiffusionXLPAGInpaintPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin, IPAdapterMixin, PAGMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL. This model i...
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class StableDiffusionPAGInpaintPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin, PAGMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits...
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class SanaPAGPipeline(DiffusionPipeline, PAGMixin): r""" Pipeline for text-to-image generation using [Sana](https://huggingface.co/papers/2410.10629). This pipeline supports the use of [Perturbed Attention Guidance (PAG)](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag). """ #...
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class StableDiffusionPAGImg2ImgPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, FromSingleFileMixin, PAGMixin, ): r""" Pipeline for text-guided image-to-image generation using Stable Diffusion. This m...
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class PAGMixin: r"""Mixin class for [Pertubed Attention Guidance](https://arxiv.org/abs/2403.17377v1).""" def _set_pag_attn_processor(self, pag_applied_layers, do_classifier_free_guidance): r""" Set the attention processor for the PAG layers. """ pag_attn_processors = self._pag_...
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class PixArtSigmaPAGPipeline(DiffusionPipeline, PAGMixin): r""" [PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for text-to-image generation using PixArt-Sigma. """ bad_punct_regex = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + r"\)" + r"\...
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class StableDiffusionXLPAGPipeline( DiffusionPipeline, StableDiffusionMixin, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin, IPAdapterMixin, PAGMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL. This model inherits...
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class StableDiffusionXLPAGImg2ImgPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, IPAdapterMixin, PAGMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL. This model i...
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class StableDiffusionXLControlNetXSPipeline( DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet-XS guidance. This model inherits from [`DiffusionPipeli...
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class StableDiffusionControlNetXSPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, FromSingleFileMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion with ControlNet-XS guidance. This model inherits fro...
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class PaintByExamplePipeline(DiffusionPipeline, StableDiffusionMixin): r""" <Tip warning={true}> 🧪 This is an experimental feature! </Tip> Pipeline for image-guided image inpainting using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation fo...
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class PaintByExampleImageEncoder(CLIPPreTrainedModel): def __init__(self, config, proj_size=None): super().__init__(config) self.proj_size = proj_size or getattr(config, "projection_dim", 768) self.model = CLIPVisionModel(config) self.mapper = PaintByExampleMapper(config) se...
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class PaintByExampleMapper(nn.Module): def __init__(self, config): super().__init__() num_layers = (config.num_hidden_layers + 1) // 5 hid_size = config.hidden_size num_heads = 1 self.blocks = nn.ModuleList( [ BasicTransformerBlock(hid_size, num_he...
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class RePaintPipeline(DiffusionPipeline): r""" Pipeline for image inpainting using RePaint. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Paramete...
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class KarrasVePipeline(DiffusionPipeline): r""" Pipeline for unconditional image generation. Parameters: unet ([`UNet2DModel`]): A `UNet2DModel` to denoise the encoded image. scheduler ([`KarrasVeScheduler`]): A scheduler to be used in combination with `unet` to deno...
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class AltDiffusionImg2ImgPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, FromSingleFileMixin, ): r""" Pipeline for text-guided image-to-image generation using Alt Diffusion. This model inherits from [`Di...
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class AltDiffusionPipelineOutput(BaseOutput): """ Output class for Alt Diffusion 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)`. ...
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class AltDiffusionPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin, ): r""" Pipeline for text-to-image generation using Alt Diffusion. This model inherits from [`DiffusionPipeline`]. C...
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class TransformationModelOutput(ModelOutput): """ Base class for text model's outputs that also contains a pooling of the last hidden states. Args: text_embeds (`torch.Tensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): ...
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class RobertaSeriesConfig(XLMRobertaConfig): def __init__( self, pad_token_id=1, bos_token_id=0, eos_token_id=2, project_dim=512, pooler_fn="cls", learn_encoder=False, use_attention_mask=True, **kwargs, ): super().__init__(pad_token...
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class RobertaSeriesModelWithTransformation(RobertaPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler", r"logit_scale"] _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] base_model_prefix = "roberta" config_class = RobertaSeriesConfig def __init__(self, c...
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class AudioDiffusionPipeline(DiffusionPipeline): """ Pipeline for audio diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Parameters: ...
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class Mel(ConfigMixin, SchedulerMixin): """ Parameters: x_res (`int`): x resolution of spectrogram (time). y_res (`int`): y resolution of spectrogram (frequency bins). sample_rate (`int`): Sample rate of audio. n_fft (`int`): Number...
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class LDMPipeline(DiffusionPipeline): r""" Pipeline for unconditional image generation using latent diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, e...
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class SpectrogramNotesEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin): @register_to_config def __init__( self, max_length: int, vocab_size: int, d_model: int, dropout_rate: float, num_layers: int, num_heads: int, d_kv: int, d_ff: int, ...
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class SpectrogramContEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin): @register_to_config def __init__( self, input_dims: int, targets_context_length: int, d_model: int, dropout_rate: float, num_layers: int, num_heads: int, d_kv: int, d_...
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class SpectrogramDiffusionPipeline(DiffusionPipeline): r""" Pipeline for unconditional audio generation. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). ...
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class NoteRepresentationConfig: """Configuration note representations.""" onsets_only: bool include_ties: bool
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class NoteEventData: pitch: int velocity: Optional[int] = None program: Optional[int] = None is_drum: Optional[bool] = None instrument: Optional[int] = None
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class NoteEncodingState: """Encoding state for note transcription, keeping track of active pitches.""" # velocity bin for active pitches and programs active_pitches: MutableMapping[Tuple[int, int], int] = dataclasses.field(default_factory=dict)
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class EventRange: type: str min_value: int max_value: int
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class Event: type: str value: int
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class Tokenizer: def __init__(self, regular_ids: int): # The special tokens: 0=PAD, 1=EOS, and 2=UNK self._num_special_tokens = 3 self._num_regular_tokens = regular_ids def encode(self, token_ids): encoded = [] for token_id in token_ids: if not 0 <= token_id ...
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class Codec: """Encode and decode events. Useful for declaring what certain ranges of a vocabulary should be used for. This is intended to be used from Python before encoding or after decoding with GenericTokenVocabulary. This class is more lightweight and does not include things like EOS or UNK token ...
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class ProgramGranularity: # both tokens_map_fn and program_map_fn should be idempotent tokens_map_fn: Callable[[Sequence[int], Codec], Sequence[int]] program_map_fn: Callable[[int], int]
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class MidiProcessor: def __init__(self): self.codec = Codec( max_shift_steps=DEFAULT_MAX_SHIFT_SECONDS * DEFAULT_STEPS_PER_SECOND, steps_per_second=DEFAULT_STEPS_PER_SECOND, event_ranges=[ EventRange("pitch", note_seq.MIN_MIDI_PITCH, note_seq.MAX_MIDI_PITC...
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class LearnedClassifierFreeSamplingEmbeddings(ModelMixin, ConfigMixin): """ Utility class for storing learned text embeddings for classifier free sampling """ @register_to_config def __init__(self, learnable: bool, hidden_size: Optional[int] = None, length: Optional[int] = None): super().__...
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class VQDiffusionPipeline(DiffusionPipeline): r""" Pipeline for text-to-image generation using VQ Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc...
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class Pix2PixInversionPipelineOutput(BaseOutput, TextualInversionLoaderMixin): """ Output class for Stable Diffusion pipelines. Args: latents (`torch.Tensor`) inverted latents tensor images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of leng...
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class Pix2PixZeroL2Loss: def __init__(self): self.loss = 0.0 def compute_loss(self, predictions, targets): self.loss += ((predictions - targets) ** 2).sum((1, 2)).mean(0)
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class Pix2PixZeroAttnProcessor: """An attention processor class to store the attention weights. In Pix2Pix Zero, it happens during computations in the cross-attention blocks.""" def __init__(self, is_pix2pix_zero=False): self.is_pix2pix_zero = is_pix2pix_zero if self.is_pix2pix_zero: ...
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class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline, StableDiffusionMixin): r""" Pipeline for pixel-level image editing using Pix2Pix Zero. Based on Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements...
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class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin): r""" Pipeline for text-guided image to image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implem...
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class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline): r""" Pipeline for text-guided image inpainting using Stable Diffusion. This is a *legacy feature* for Onnx pipelines to provide compatibility with StableDiffusionInpaintPipelineLegacy and may be removed in the future. This model inherit...
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class StableDiffusionParadigmsPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, FromSingleFileMixin, ): r""" Pipeline for text-to-image generation using a parallelized version of Stable Diffusion. This model inherits from [`D...
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class StableDiffusionInpaintPipelineLegacy( DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, FromSingleFileMixin ): r""" Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*. This model inherits from [`DiffusionPipeline`]. Ch...
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class StableDiffusionModelEditingPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin ): r""" Pipeline for text-to-image model editing. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods ...
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class ScoreSdeVePipeline(DiffusionPipeline): r""" Pipeline for unconditional image generation. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Param...
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class PNDMPipeline(DiffusionPipeline): r""" Pipeline for unconditional image generation. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Parameters:...
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