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Refactored Metal/mps fixes.

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Elias Oenal
2022-09-12 16:32:44 +02:00
parent 5dc05c0d0d
commit b7f95869b4
3 changed files with 27 additions and 38 deletions
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42
modules/processing.py
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@@ -1,6 +1,3 @@
# Metal backend fixes written and placed
# into the public domain by Elias Oenal <sd@eliasoenal.com>
import contextlib
import json
import math
@@ -109,17 +106,19 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
# Pytorch currently doesn't handle seeting randomness correctly when the metal backend is used.
generator = torch
if shared.device.type == 'mps':
g = torch.Generator(device='cpu')
shared.device_seed_type = 'cpu'
generator = torch.Generator(device=shared.device_seed_type)
subnoise = None
if subseeds is not None:
subseed = 0 if i >= len(subseeds) else subseeds[i]
if shared.device.type == 'mps':
g.manual_seed(subseed)
subnoise = torch.randn(noise_shape, generator=g, device='cpu').to('mps')
else: # cpu or cuda
torch.manual_seed(subseed)
generator.manual_seed(subseed)
if shared.device.type != shared.device_seed_type:
subnoise = torch.randn(noise_shape, generator=generator, device=shared.device_seed_type).to(shared.device)
else:
subnoise = torch.randn(noise_shape, device=shared.device)
# randn results depend on device; gpu and cpu get different results for same seed;
@@ -128,12 +127,11 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
# it will break everyone's seeds.
# When using the mps backend falling back to the cpu device is needed, since mps currently
# does not implement seeding properly.
if shared.device.type == 'mps':
g.manual_seed(seed)
noise = torch.randn(noise_shape, generator=g, device='cpu').to('mps')
else: # cpu or cuda
torch.manual_seed(seed)
x = torch.randn(shape, device=shared.device)
generator.manual_seed(seed)
if shared.device.type != shared.device_seed_type:
noise = torch.randn(noise_shape, generator=generator, device=shared.device_seed_type).to(shared.device)
else:
noise = torch.randn(noise_shape, device=shared.device)
if subnoise is not None:
#noise = subnoise * subseed_strength + noise * (1 - subseed_strength)
@@ -143,12 +141,10 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
#noise = torch.nn.functional.interpolate(noise.unsqueeze(1), size=shape[1:], mode="bilinear").squeeze()
# noise_shape = (64, 80)
# shape = (64, 72)
if shared.device.type == 'mps':
g.manual_seed(seed)
x = torch.randn(shape, generator=g, device='cpu').to('mps')
generator.manual_seed(seed)
if shared.device.type != shared.device_seed_type:
x = torch.randn(shape, generator=generator, device=shared.device_seed_type).to(shared.device)
else:
torch.manual_seed(seed)
x = torch.randn(shape, device=shared.device)
dx = (shape[2] - noise_shape[2]) // 2 # -4
dy = (shape[1] - noise_shape[1]) // 2
@@ -484,10 +480,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
if self.image_mask is not None:
init_mask = latent_mask
latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
precision = np.float64
if shared.device.type == 'mps': # mps backend does not support float64
latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
else:
latmask = np.moveaxis(np.array(latmask, dtype=np.float64), 2, 0) / 255
precision = np.float32
latmask = np.moveaxis(np.array(latmask, dtype=precision), 2, 0) / 255
latmask = latmask[0]
latmask = np.around(latmask)
latmask = np.tile(latmask[None], (4, 1, 1))