wangbo/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-02-10 13:32:36 +08:00
Code Issues Actions 14 Packages Projects Releases Wiki Activity

Merge branch 'comfyanonymous:master' into refactor/execution

Browse Source
This commit is contained in:
Dr.Lt.Data 2023-07-20 01:21:32 +09:00 committed by GitHub
commit 948f9d59a1
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 4 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
comfy/cli_args.py
View File

@ -42,7 +42,7 @@ parser.add_argument("--auto-launch", action="store_true", help="Automatically la
parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.") parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
cm_group = parser.add_mutually_exclusive_group() cm_group = parser.add_mutually_exclusive_group()
cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).") cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Enable cudaMallocAsync.") cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.") parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.")

4
comfy/k_diffusion/external.py
View File

@ -91,7 +91,9 @@ class DiscreteSchedule(nn.Module):
return log_sigma.exp() return log_sigma.exp()
def predict_eps_discrete_timestep(self, input, t, **kwargs): def predict_eps_discrete_timestep(self, input, t, **kwargs):
sigma = self.t_to_sigma(t.round()) if t.dtype != torch.int64 and t.dtype != torch.int32:
t = t.round()
sigma = self.t_to_sigma(t)
input = input * ((utils.append_dims(sigma, input.ndim) ** 2 + 1.0) ** 0.5) input = input * ((utils.append_dims(sigma, input.ndim) ** 2 + 1.0) ** 0.5)
return (input - self(input, sigma, **kwargs)) / utils.append_dims(sigma, input.ndim) return (input - self(input, sigma, **kwargs)) / utils.append_dims(sigma, input.ndim)