78 lines
3.6 KiB
Markdown
78 lines
3.6 KiB
Markdown
# Documentation
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- Class name: ModelMergeSDXLTransformers
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- Category: model_merging
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI_experiments
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ModelMergeSDXLTransformers节点旨在将多个模型融合成一个统一的结构，利用基于变换器的架构。它的目的是协调各个模型的功能，通过复杂的融合过程提高整体性能。
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# Input types
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## Required
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- model1
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- ‘model1’参数是要合并的第一个模型，对组合模型的初始结构至关重要。它为整合过程设定了基础，显著影响最终模型的能力。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- model2
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- ‘model2’参数代表要合并的第二个模型，在合并模型的最终配置中起着关键作用。它与‘model1’的整合对于实现期望的性能提升至关重要。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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## Optional
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- input_blocks
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- ‘input_blocks’参数是定义模型内初始变换块的参数集合。每个块都是可调的，有助于模型有效处理输入数据。
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- Comfy dtype: COMBO[FLOAT]
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- Python dtype: Dict[str, Union[float, Dict[str, float]]]
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- middle_block
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- ‘middle_block’参数包括一组位于模型输入和输出部分之间的变换块。它在进一步完善模型的处理能力方面起着重要作用。
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- Comfy dtype: COMBO[FLOAT]
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- Python dtype: Dict[str, Dict[str, float]]
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- output_blocks
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- ‘output_blocks’参数由构成模型最后阶段的变换块组成。这些块在决定模型输出质量和整体预测能力方面至关重要。
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- Comfy dtype: COMBO[FLOAT]
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- Python dtype: Dict[str, Union[float, Dict[str, float]]]
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- out
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- ‘out’参数用于调整模型的最终输出层。它对于微调模型的预测以满足特定要求很重要。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- merged_model
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- ‘merged_model’输出代表了合并过程中产生的集成模型。它包含了原始模型的组合能力和学习到的特征。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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# Usage tips
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- Infra type: GPU
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# Source code
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```
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class ModelMergeSDXLTransformers(comfy_extras.nodes_model_merging.ModelMergeBlocks):
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@classmethod
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def INPUT_TYPES(s):
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arg_dict = {'model1': ('MODEL',), 'model2': ('MODEL',)}
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argument = ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01})
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arg_dict['time_embed.'] = argument
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arg_dict['label_emb.'] = argument
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transformers = {4: 2, 5: 2, 7: 10, 8: 10}
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for i in range(9):
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arg_dict['input_blocks.{}.0.'.format(i)] = argument
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if i in transformers:
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arg_dict['input_blocks.{}.1.'.format(i)] = argument
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for j in range(transformers[i]):
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arg_dict['input_blocks.{}.1.transformer_blocks.{}.'.format(i, j)] = argument
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for i in range(3):
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arg_dict['middle_block.{}.'.format(i)] = argument
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if i == 1:
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for j in range(10):
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arg_dict['middle_block.{}.transformer_blocks.{}.'.format(i, j)] = argument
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transformers = {3: 2, 4: 2, 5: 2, 6: 10, 7: 10, 8: 10}
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for i in range(9):
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arg_dict['output_blocks.{}.0.'.format(i)] = argument
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t = 8 - i
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if t in transformers:
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arg_dict['output_blocks.{}.1.'.format(i)] = argument
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for j in range(transformers[t]):
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arg_dict['output_blocks.{}.1.transformer_blocks.{}.'.format(i, j)] = argument
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arg_dict['out.'] = argument
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return {'required': arg_dict}
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``` |