From 2da73b7073dc520ee480dee8ff911b9aa83ff70a Mon Sep 17 00:00:00 2001 From: Simon Lui <502929+simonlui@users.noreply.github.com> Date: Sat, 2 Sep 2023 20:07:52 -0700 Subject: [PATCH] Revert changes in comfy/ldm/modules/diffusionmodules/util.py, which is unused. --- comfy/ldm/modules/diffusionmodules/util.py | 24 +++++++--------------- 1 file changed, 7 insertions(+), 17 deletions(-) diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py index 9d07d93..d890c80 100644 --- a/comfy/ldm/modules/diffusionmodules/util.py +++ b/comfy/ldm/modules/diffusionmodules/util.py @@ -15,7 +15,6 @@ import torch.nn as nn import numpy as np from einops import repeat -from comfy import model_management from comfy.ldm.util import instantiate_from_config import comfy.ops @@ -140,22 +139,13 @@ class CheckpointFunction(torch.autograd.Function): @staticmethod def backward(ctx, *output_grads): ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] - if model_management.is_nvidia(): - with torch.enable_grad(), \ - torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): - # Fixes a bug where the first op in run_function modifies the - # Tensor storage in place, which is not allowed for detach()'d - # Tensors. - shallow_copies = [x.view_as(x) for x in ctx.input_tensors] - output_tensors = ctx.run_function(*shallow_copies) - elif model_management.is_intel_xpu(): - with torch.enable_grad(), \ - torch.xpu.amp.autocast(**ctx.gpu_autocast_kwargs): - # Fixes a bug where the first op in run_function modifies the - # Tensor storage in place, which is not allowed for detach()'d - # Tensors. - shallow_copies = [x.view_as(x) for x in ctx.input_tensors] - output_tensors = ctx.run_function(*shallow_copies) + with torch.enable_grad(), \ + torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) input_grads = torch.autograd.grad( output_tensors, ctx.input_tensors + ctx.input_params,