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To optimize the runtime of this program, we can leverage some of PyTorch's functions for better performance. Specifically, we can use `torch.rsqrt` and `torch.mean` wisely to optimize the normalization calculation. This can be beneficial from a performance perspective since certain operations might be optimized internally. Here is an optimized version of the code. ### Explanation. - `torch.mean(x * x, dim=self.dim, keepdim=True)`: Calculating the mean of the squared values directly. - `torch.rsqrt(mean_square)`: Using `torch.rsqrt` to compute the reciprocal of the square root. This can be more efficient than computing the square root and then taking the reciprocal separately. - `x * torch.rsqrt(mean_square)`: Multiplying `x` by the reciprocal square root we computed above. This reformulation can lead to improved performance because it reduces the number of operations by specifically leveraging PyTorch's optimized backend operations. |
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| .. | ||
| cldm | ||
| comfy_types | ||
| extra_samplers | ||
| image_encoders | ||
| k_diffusion | ||
| ldm | ||
| sd1_tokenizer | ||
| t2i_adapter | ||
| taesd | ||
| text_encoders | ||
| checkpoint_pickle.py | ||
| cli_args.py | ||
| clip_config_bigg.json | ||
| clip_model.py | ||
| clip_vision_config_g.json | ||
| clip_vision_config_h.json | ||
| clip_vision_config_vitl_336_llava.json | ||
| clip_vision_config_vitl_336.json | ||
| clip_vision_config_vitl.json | ||
| clip_vision_siglip_384.json | ||
| clip_vision_siglip_512.json | ||
| clip_vision.py | ||
| conds.py | ||
| controlnet.py | ||
| diffusers_convert.py | ||
| diffusers_load.py | ||
| float.py | ||
| gligen.py | ||
| hooks.py | ||
| latent_formats.py | ||
| lora_convert.py | ||
| lora.py | ||
| model_base.py | ||
| model_detection.py | ||
| model_management.py | ||
| model_patcher.py | ||
| model_sampling.py | ||
| ops.py | ||
| options.py | ||
| patcher_extension.py | ||
| rmsnorm.py | ||
| sample.py | ||
| sampler_helpers.py | ||
| samplers.py | ||
| sd1_clip_config.json | ||
| sd1_clip.py | ||
| sd.py | ||
| sdxl_clip.py | ||
| supported_models_base.py | ||
| supported_models.py | ||
| utils.py | ||