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sayakpaulsayakpaul
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‎docs/source/en/api/activations.md‎

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@@ -25,3 +25,12 @@ Customized activation functions for supporting various models in 🤗 Diffusers.
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## ApproximateGELU
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[[autodoc]] models.activations.ApproximateGELU
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## SwiGLU
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[[autodoc]] models.activations.SwiGLU
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## FP32SiLU
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[[autodoc]] models.activations.FP32SiLU

‎docs/source/en/api/attnprocessor.md‎

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@@ -67,3 +67,76 @@ An attention processor is a class for applying different types of attention mech
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## JointAttnProcessor2_0
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[[autodoc]] models.attention_processor.JointAttnProcessor2_0
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## JointAttnProcessor2_0
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[[autodoc]] models.attention_processor.PAGJointAttnProcessor2_0
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## PAGCFGJointAttnProcessor2_0
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[[autodoc]] models.attention_processor.PAGCFGJointAttnProcessor2_0
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## FusedJointAttnProcessor2_0
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[[autodoc]] models.attention_processor.FusedJointAttnProcessor2_0
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## AllegroAttnProcessor2_0
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[[autodoc]] models.attention_processor.AllegroAttnProcessor2_0
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## AuraFlowAttnProcessor2_0
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[[autodoc]] models.attention_processor.AuraFlowAttnProcessor2_0
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## MochiVaeAttnProcessor2_0
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[[autodoc]] models.attention_processor.MochiVaeAttnProcessor2_0
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## PAGCFGIdentitySelfAttnProcessor2_0
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[[autodoc]] models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0
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## FusedAuraFlowAttnProcessor2_0
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[[autodoc]] models.attention_processor.FusedAuraFlowAttnProcessor2_0
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## FusedFluxAttnProcessor2_0
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[[autodoc]] models.attention_processor.FusedFluxAttnProcessor2_0
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## SanaMultiscaleAttnProcessor2_0
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[[autodoc]] models.attention_processor.SanaMultiscaleAttnProcessor2_0
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## PAGHunyuanAttnProcessor2_0
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[[autodoc]] models.attention_processor.PAGHunyuanAttnProcessor2_0
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## HunyuanAttnProcessor2_0
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[[autodoc]] models.attention_processor.HunyuanAttnProcessor2_0
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## FluxAttnProcessor2_0
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[[autodoc]] models.attention_processor.FluxAttnProcessor2_0
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## PAGIdentitySelfAttnProcessor2_0
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[[autodoc]] models.attention_processor.PAGIdentitySelfAttnProcessor2_0
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## FusedCogVideoXAttnProcessor2_0
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[[autodoc]] models.attention_processor.FusedCogVideoXAttnProcessor2_0
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## MochiAttnProcessor2_0
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[[autodoc]] models.attention_processor.MochiAttnProcessor2_0
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## StableAudioAttnProcessor2_0
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[[autodoc]] models.attention_processor.StableAudioAttnProcessor2_0
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## XLAFlashAttnProcessor2_0
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[[autodoc]] models.attention_processor.XLAFlashAttnProcessor2_0
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## FusedHunyuanAttnProcessor2_0
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[[autodoc]] models.attention_processor.FusedHunyuanAttnProcessor2_0
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## IPAdapterXFormersAttnProcessor
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[[autodoc]] models.attention_processor.IPAdapterXFormersAttnProcessor
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## LuminaAttnProcessor2_0
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[[autodoc]] models.attention_processor.LuminaAttnProcessor2_0
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## PAGCFGHunyuanAttnProcessor2_0
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[[autodoc]] models.attention_processor.PAGCFGHunyuanAttnProcessor2_0
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## FluxSingleAttnProcessor2_0
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[[autodoc]] models.attention_processor.FluxSingleAttnProcessor2_0
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## CogVideoXAttnProcessor2_0
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[[autodoc]] models.attention_processor.CogVideoXAttnProcessor2_0

‎docs/source/en/api/normalization.md‎

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@@ -41,3 +41,28 @@ Customized normalization layers for supporting various models in 🤗 Diffusers.
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## GlobalResponseNorm
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[[autodoc]] models.normalization.GlobalResponseNorm
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## LuminaLayerNormContinuous
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[[autodoc]] models.normalization.LuminaLayerNormContinuous
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## SD35AdaLayerNormZeroX
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[[autodoc]] models.normalization.SD35AdaLayerNormZeroX
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## AdaLayerNormZeroSingle
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[[autodoc]] models.normalization.AdaLayerNormZeroSingle
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## LuminaRMSNormZero
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[[autodoc]] models.normalization.LuminaRMSNormZero
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## LpNorm
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[[autodoc]] models.normalization.LpNorm
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## CogView3PlusAdaLayerNormZeroTextImage
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[[autodoc]] models.normalization.CogView3PlusAdaLayerNormZeroTextImage
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## CogVideoXLayerNormZero
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[[autodoc]] models.normalization.CogVideoXLayerNormZero
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## MochiRMSNormZero
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[[autodoc]] models.normalization.MochiRMSNormZero

‎utils/check_support_list.py‎

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@@ -41,11 +41,15 @@ def check_attention_processors():
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processor_classes = re.findall(r"class\s+(\w+Processor(?:\d*_?\d*))[(:]", doctext)
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processor_classes = [proc for proc in processor_classes if "LoRA" not in proc and proc != "Attention"]
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undocumented_attn_processors = set()
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for processor in processor_classes:
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if processor not in documented_attention_processors:
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raise ValueError(
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f"{processor} should be in listed in the attention processor documentation but is not. Please update the documentation."
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)
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undocumented_attn_processors.add(processor)
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if undocumented_attn_processors:
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raise ValueError(
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f"The following attention processors should be in listed in the attention processor documentation but are not: {list(undocumented_attn_processors)}. Please update the documentation."
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)
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def check_image_processors():
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doctext = f.read()
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processor_classes = re.findall(r"class\s+(\w+Processor(?:\d*_?\d*))[(:]", doctext)
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undocumented_img_processors = set()
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for processor in processor_classes:
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if processor not in documented_image_processors:
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undocumented_img_processors.add(processor)
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raise ValueError(
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f"{processor} should be in listed in the image processor documentation but is not. Please update the documentation."
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f"The following image processors should be in listed in the image processor documentation but are not: {list(undocumented_img_processors)}. Please update the documentation."
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)
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doctext = f.read()
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activation_classes = re.findall(r"class\s+(\w+)\s*\(.*?nn\.Module.*?\):", doctext)
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undocumented_activations = set()
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for activation in activation_classes:
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if activation not in documented_activations:
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raise ValueError(
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f"{activation} should be in listed in the activations documentation but is not. Please update the documentation."
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)
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undocumented_activations.add(activation)
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if undocumented_activations:
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raise ValueError(
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f"The following activations should be in listed in the activations documentation but are not: {list(undocumented_activations)}. Please update the documentation."
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)
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def check_normalizations():
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# LayerNorm is an exception because adding doc for is confusing.
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normalization_classes = [norm for norm in normalization_classes if norm != "LayerNorm"]
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undocumented_norms = set()
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for norm in normalization_classes:
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if norm not in documented_normalizations:
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raise ValueError(
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f"{norm} should be in listed in the normalizations documentation but is not. Please update the documentation."
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)
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undocumented_norms.add(norm)
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if undocumented_norms:
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raise ValueError(
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f"The following norms should be in listed in the normalizations documentation but are not: {list(undocumented_norms)}. Please update the documentation."
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)
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if __name__ == "__main__":

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