diff options
| -rw-r--r-- | modules/codeformer_model.py | 8 | ||||
| -rw-r--r-- | modules/processing.py | 36 | ||||
| -rw-r--r-- | modules/shared.py | 2 |
3 files changed, 35 insertions, 11 deletions
diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index fd1da692..6cd29c83 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -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():
diff --git a/modules/processing.py b/modules/processing.py index 568f6098..1e6745cc 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -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))
diff --git a/modules/shared.py b/modules/shared.py index ea1c879b..9002141a 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -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
|