diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/devices.py | 15 | ||||
| -rw-r--r-- | modules/hypernetworks/hypernetwork.py | 2 | ||||
| -rw-r--r-- | modules/interrogate.py | 3 | ||||
| -rw-r--r-- | modules/sd_hijack.py | 6 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 22 | ||||
| -rw-r--r-- | modules/swinir_model.py | 6 | ||||
| -rw-r--r-- | modules/textual_inversion/dataset.py | 4 | ||||
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 2 |
8 files changed, 29 insertions, 31 deletions
diff --git a/modules/devices.py b/modules/devices.py index f00079c6..046460fa 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -66,24 +66,15 @@ dtype_vae = torch.float16 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) + if device.type == 'mps': + return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) def randn_without_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) - noise = torch.randn(shape, generator=generator, device=cpu).to(device) - return noise - + return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 8466887f..eb5ae372 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -495,7 +495,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, if shared.state.interrupted:
break
- with torch.autocast("cuda"):
+ with devices.autocast():
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
if tag_drop_out != 0 or shuffle_tags:
shared.sd_model.cond_stage_model.to(devices.device)
diff --git a/modules/interrogate.py b/modules/interrogate.py index 9769aa34..40c6b082 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -148,8 +148,7 @@ class InterrogateModels: clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
- precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
- with torch.no_grad(), precision_scope("cuda"):
+ with torch.no_grad(), devices.autocast():
image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
image_features /= image_features.norm(dim=-1, keepdim=True)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index eef6efd2..95a17093 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -183,11 +183,7 @@ def register_buffer(self, name, attr): if type(attr) == torch.Tensor:
if attr.device != devices.device:
-
- if devices.has_mps():
- attr = attr.to(device="mps", dtype=torch.float32)
- else:
- attr = attr.to(devices.device)
+ attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None))
setattr(self, name, attr)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 5fefb227..4c123d3b 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -6,6 +6,7 @@ import tqdm from PIL import Image
import inspect
import k_diffusion.sampling
+import torchsde._brownian.brownian_interval
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms
from modules import prompt_parser, devices, processing, images
@@ -364,7 +365,23 @@ class TorchHijack: if noise.shape == x.shape:
return noise
- return torch.randn_like(x)
+ if x.device.type == 'mps':
+ return torch.randn_like(x, device=devices.cpu).to(x.device)
+ else:
+ return torch.randn_like(x)
+
+
+# MPS fix for randn in torchsde
+def torchsde_randn(size, dtype, device, seed):
+ if device.type == 'mps':
+ generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+ return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+ else:
+ generator = torch.Generator(device).manual_seed(int(seed))
+ return torch.randn(size, dtype=dtype, device=device, generator=generator)
+
+
+torchsde._brownian.brownian_interval._randn = torchsde_randn
class KDiffusionSampler:
@@ -415,8 +432,7 @@ class KDiffusionSampler: self.model_wrap.step = 0
self.eta = p.eta or opts.eta_ancestral
- if self.sampler_noises is not None:
- k_diffusion.sampling.torch = TorchHijack(self.sampler_noises)
+ k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
extra_params_kwargs = {}
for param_name in self.extra_params:
diff --git a/modules/swinir_model.py b/modules/swinir_model.py index facd262d..483eabd4 100644 --- a/modules/swinir_model.py +++ b/modules/swinir_model.py @@ -13,10 +13,6 @@ from modules.swinir_model_arch import SwinIR as net from modules.swinir_model_arch_v2 import Swin2SR as net2 from modules.upscaler import Upscaler, UpscalerData -precision_scope = ( - torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext -) - class UpscalerSwinIR(Upscaler): def __init__(self, dirname): @@ -112,7 +108,7 @@ def upscale( img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() img = img.unsqueeze(0).to(devices.device_swinir) - with torch.no_grad(), precision_scope("cuda"): + with torch.no_grad(), devices.autocast(): _, _, h_old, w_old = img.size() h_pad = (h_old // window_size + 1) * window_size - h_old w_pad = (w_old // window_size + 1) * window_size - w_old diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index e5725f33..2dc64c3c 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -82,7 +82,7 @@ class PersonalizedBase(Dataset): torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
latent_sample = None
- with torch.autocast("cuda"):
+ with devices.autocast():
latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
@@ -101,7 +101,7 @@ class PersonalizedBase(Dataset): entry.cond_text = self.create_text(filename_text)
if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
- with torch.autocast("cuda"):
+ with devices.autocast():
entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
self.dataset.append(entry)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 4eb75cb5..daf8d1b8 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -316,7 +316,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ if shared.state.interrupted:
break
- with torch.autocast("cuda"):
+ with devices.autocast():
# c = stack_conds(batch.cond).to(devices.device)
# mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
# print(mask)
|