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changes for #294

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AUTOMATIC 2022-09-12 20:09:32 +03:00
parent 11e03b9abd
commit c7e0e28ccd
3 changed files with 22 additions and 32 deletions
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17
modules/devices.py
View File

@ -31,3 +31,20 @@ def enable_tf32():
errors.run(enable_tf32, "Enabling TF32")
device = get_optimal_device()
device_codeformer = cpu if has_mps else device
def randn(seed, shape):
# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
if device.type == 'mps':
generator = torch.Generator(device=cpu)
generator.manual_seed(seed)
noise = torch.randn(shape, generator=generator, device=cpu).to(device)
return noise
torch.manual_seed(seed)
return torch.randn(shape, device=device)

35
modules/processing.py
View File

@ -103,33 +103,17 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
for i, seed in enumerate(seeds):
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':
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]
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)
subnoise = devices.randn(subseed, noise_shape)
# randn results depend on device; gpu and cpu get different results for same seed;
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
# but the original script had it like this, so I do not dare change it for now because
# 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.
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)
noise = devices.randn(seed, noise_shape)
if subnoise is not None:
#noise = subnoise * subseed_strength + noise * (1 - subseed_strength)
@ -137,14 +121,8 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
if noise_shape != shape:
#noise = torch.nn.functional.interpolate(noise.unsqueeze(1), size=shape[1:], mode="bilinear").squeeze()
# noise_shape = (64, 80)
# shape = (64, 72)
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:
x = torch.randn(shape, device=shared.device)
dx = (shape[2] - noise_shape[2]) // 2 # -4
x = devices.randn(seed, shape)
dx = (shape[2] - noise_shape[2]) // 2
dy = (shape[1] - noise_shape[1]) // 2
w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
@ -482,10 +460,7 @@ 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
precision = np.float32
latmask = np.moveaxis(np.array(latmask, dtype=precision), 2, 0) / 255
latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
latmask = latmask[0]
latmask = np.around(latmask)
latmask = np.tile(latmask[None], (4, 1, 1))

2
modules/shared.py
View File

@ -49,8 +49,6 @@ parser.add_argument("--opt-channelslast", action='store_true', help="change memo
cmd_opts = parser.parse_args()
device = get_optimal_device()
device_codeformer = device
device_seed_type = device
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram