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Merge pull request #15815 from AUTOMATIC1111/torch-float64-or-float32

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fix soft inpainting on mps and xpu, torch_utils.float64
This commit is contained in:
AUTOMATIC1111
2024-06-08 11:07:29 +03:00
committed by GitHub
parent 6450d24afe f015b94176
commit b4723bb8c1
3 changed files with 16 additions and 7 deletions
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5
modules/sd_samplers_timesteps_impl.py
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@@ -5,13 +5,14 @@ import numpy as np
from modules import shared
from modules.models.diffusion.uni_pc import uni_pc
from modules.torch_utils import float64
@torch.no_grad()
def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
@@ -43,7 +44,7 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
extra_args = {} if extra_args is None else extra_args

9
modules/torch_utils.py
View File

@@ -1,6 +1,7 @@
from __future__ import annotations
import torch.nn
import torch
def get_param(model) -> torch.nn.Parameter:
@@ -15,3 +16,11 @@ def get_param(model) -> torch.nn.Parameter:
return param
raise ValueError(f"No parameters found in model {model!r}")
def float64(t: torch.Tensor):
"""return torch.float64 if device is not mps or xpu, else return torch.float32"""
match t.device.type:
case 'mps', 'xpu':
return torch.float32
return torch.float64