It appears that the following code fixed the issue:
gO_load = torch.load("OutputModels/8x8_Gen_FINAL.pt")
dO_load = torch.load("OutputModels/8x8_Disc_FINAL.pt")

netG_state_dict = netG.state_dict()
filtered_g_dict={}
filtered_g_dict['module.c0.weight']=gO_load['module.c0.weight']
filtered_g_dict['module.b0.weight']=gO_load['module.b0.weight']
filtered_g_dict['module.b0.bias']=gO_load['module.b0.bias']
filtered_g_dict['module.b0.running_mean']=gO_load['module.b0.running_mean']
filtered_g_dict['module.b0.running_var']=gO_load['module.b0.running_var']
filtered_g_dict['module.b0.num_batches_tracked']=gO_load['module.b0.num_batches_tracked']

netG_state_dict.update(filtered_g_dict)
netG.load_state_dict(netG_state_dict)

for param in netG.module.c0.parameters(): param.requires_grad=False
for param in netG.module.b0.parameters(): param.requires_grad=False
for param in netG.module.c1.parameters(): param.requires_grad=True
for param in netG.module.b1.parameters(): param.requires_grad=True
for param in netG.module.c8.parameters(): param.requires_grad=True
optimizerG = optim.Adam(filter(lambda p: p.requires_grad, netG.parameters()), lr=lr*1.25, betas=(beta1, 0.999))


netD_state_dict = netD.state_dict()
filtered_d_dict={}
filtered_d_dict['module.c9.weight']=dO_load['module.c9.weight']

netD_state_dict.update(filtered_d_dict)
netD.load_state_dict(netD_state_dict)

for param in netD.module.c9.parameters(): param.requires_grad=False

optimizerD = optim.Adam(filter(lambda p: p.requires_grad, netD.parameters()), lr=lr, betas=(beta1, 0.999))

Can someone confirm that this is the right way to handle this? Thanks!