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matt/opena
| Author | SHA1 | Date | |
|---|---|---|---|
| be3e51250e | |||
| 332acf6777 |
@ -741,12 +741,12 @@ optimized_attention = attention_basic
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if model_management.sage_attention_enabled():
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if model_management.sage_attention_enabled():
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logging.info("Using sage attention")
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logging.info("Using sage attention")
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optimized_attention = attention_sage
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optimized_attention = attention_sage
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elif model_management.flash_attention_enabled():
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logging.info("Using Flash Attention")
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optimized_attention = attention_flash
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elif model_management.xformers_enabled():
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elif model_management.xformers_enabled():
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logging.info("Using xformers attention")
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logging.info("Using xformers attention")
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optimized_attention = attention_xformers
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optimized_attention = attention_xformers
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elif model_management.flash_attention_enabled():
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logging.info("Using Flash Attention")
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optimized_attention = attention_flash
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elif model_management.pytorch_attention_enabled():
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elif model_management.pytorch_attention_enabled():
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logging.info("Using pytorch attention")
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logging.info("Using pytorch attention")
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optimized_attention = attention_pytorch
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optimized_attention = attention_pytorch
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@ -265,6 +265,7 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
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input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
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input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
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cond_shapes = collections.defaultdict(list)
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cond_shapes = collections.defaultdict(list)
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for tt in batch_amount:
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for tt in batch_amount:
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cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()}
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for k, v in to_run[tt][0].conditioning.items():
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for k, v in to_run[tt][0].conditioning.items():
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cond_shapes[k].append(v.size())
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cond_shapes[k].append(v.size())
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@ -1,4 +1,4 @@
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comfyui-frontend-package==1.44.19
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comfyui-frontend-package==1.43.18
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comfyui-workflow-templates==0.9.82
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comfyui-workflow-templates==0.9.82
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comfyui-embedded-docs==0.5.0
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comfyui-embedded-docs==0.5.0
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torch
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torch
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