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