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Merge pull request #294 from EliasOenal/master

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Fixes for mps/Metal: use of seeds, img2img, CodeFormer
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AUTOMATIC1111 2022-09-12 19:58:06 +03:00 committed by GitHub
commit 11e03b9abd
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3 changed files with 35 additions and 11 deletions
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8
modules/codeformer_model.py
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@ -47,6 +47,8 @@ def setup_codeformer():
def __init__(self):
self.net = None
self.face_helper = None
if shared.device.type == 'mps': # CodeFormer currently does not support mps backend
shared.device_codeformer = torch.device('cpu')
def create_models(self):
@ -54,13 +56,13 @@ def setup_codeformer():
self.net.to(shared.device)
return self.net, self.face_helper
net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(shared.device)
net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(shared.device_codeformer)
ckpt_path = load_file_from_url(url=pretrain_model_url, model_dir=os.path.join(path, 'weights/CodeFormer'), progress=True)
checkpoint = torch.load(ckpt_path)['params_ema']
net.load_state_dict(checkpoint)
net.eval()
face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=shared.device)
face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=shared.device_codeformer)
self.net = net
self.face_helper = face_helper
@ -82,7 +84,7 @@ def setup_codeformer():
for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to(shared.device)
cropped_face_t = cropped_face_t.unsqueeze(0).to(shared.device_codeformer)
try:
with torch.no_grad():

36
modules/processing.py
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@ -103,18 +103,33 @@ 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]
torch.manual_seed(subseed)
subnoise = torch.randn(noise_shape, device=shared.device)
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;
# 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.
torch.manual_seed(seed)
noise = torch.randn(noise_shape, device=shared.device)
# 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)
if subnoise is not None:
#noise = subnoise * subseed_strength + noise * (1 - subseed_strength)
@ -124,9 +139,11 @@ 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)
torch.manual_seed(seed)
x = torch.randn(shape, device=shared.device)
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
dy = (shape[1] - noise_shape[1]) // 2
w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
@ -465,7 +482,10 @@ 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]))
latmask = np.moveaxis(np.array(latmask, dtype=np.float64), 2, 0) / 255
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 = latmask[0]
latmask = np.around(latmask)
latmask = np.tile(latmask[None], (4, 1, 1))

2
modules/shared.py
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@ -49,6 +49,8 @@ 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