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-rw-r--r--modules/launch_utils.py3
-rw-r--r--modules/mac_specific.py3
-rw-r--r--modules/sd_hijack_optimizations.py13
-rw-r--r--modules/sd_samplers_timesteps_impl.py4
-rw-r--r--modules/sub_quadratic_attention.py4
-rw-r--r--webui-macos-env.sh2
6 files changed, 17 insertions, 12 deletions
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 94b2322a..449a8755 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -173,6 +173,9 @@ def git_clone(url, dir, name, commithash=None):
if current_hash == commithash:
return
+ if run_git(dir, name, 'config --get remote.origin.url', None, f"Couldn't determine {name}'s origin URL", live=False).strip() != url:
+ run_git(dir, name, f'remote set-url origin "{url}"', None, f"Failed to set {name}'s origin URL", live=False)
+
run_git(dir, name, 'fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}", autofix=False)
run_git(dir, name, f'checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
diff --git a/modules/mac_specific.py b/modules/mac_specific.py
index bce527cc..89256c5b 100644
--- a/modules/mac_specific.py
+++ b/modules/mac_specific.py
@@ -52,9 +52,6 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs):
if has_mps:
- # MPS fix for randn in torchsde
- CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps')
-
if platform.mac_ver()[0].startswith("13.2."):
# MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760)
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 0e810eec..7f9e328d 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,6 +1,7 @@
from __future__ import annotations
import math
import psutil
+import platform
import torch
from torch import einsum
@@ -94,7 +95,10 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem):
class SdOptimizationSubQuad(SdOptimization):
name = "sub-quadratic"
cmd_opt = "opt_sub_quad_attention"
- priority = 10
+
+ @property
+ def priority(self):
+ return 1000 if shared.device.type == 'mps' else 10
def apply(self):
ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
@@ -120,7 +124,7 @@ class SdOptimizationInvokeAI(SdOptimization):
@property
def priority(self):
- return 1000 if not torch.cuda.is_available() else 10
+ return 1000 if shared.device.type != 'mps' and not torch.cuda.is_available() else 10
def apply(self):
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
@@ -427,7 +431,10 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
if chunk_threshold is None:
- chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
+ if q.device.type == 'mps':
+ chunk_threshold_bytes = 268435456 * (2 if platform.processor() == 'i386' else bytes_per_token)
+ else:
+ chunk_threshold_bytes = int(get_available_vram() * 0.7)
elif chunk_threshold == 0:
chunk_threshold_bytes = None
else:
diff --git a/modules/sd_samplers_timesteps_impl.py b/modules/sd_samplers_timesteps_impl.py
index 48d7e649..d32e3521 100644
--- a/modules/sd_samplers_timesteps_impl.py
+++ b/modules/sd_samplers_timesteps_impl.py
@@ -11,7 +11,7 @@ from modules.models.diffusion.uni_pc import uni_pc
def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
- alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64)
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
@@ -42,7 +42,7 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
- alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64)
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
extra_args = {} if extra_args is None else extra_args
diff --git a/modules/sub_quadratic_attention.py b/modules/sub_quadratic_attention.py
index 497568eb..ae4ee4bb 100644
--- a/modules/sub_quadratic_attention.py
+++ b/modules/sub_quadratic_attention.py
@@ -58,7 +58,7 @@ def _summarize_chunk(
scale: float,
) -> AttnChunk:
attn_weights = torch.baddbmm(
- torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+ torch.zeros(1, 1, 1, device=query.device, dtype=query.dtype),
query,
key.transpose(1,2),
alpha=scale,
@@ -121,7 +121,7 @@ def _get_attention_scores_no_kv_chunking(
scale: float,
) -> Tensor:
attn_scores = torch.baddbmm(
- torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+ torch.zeros(1, 1, 1, device=query.device, dtype=query.dtype),
query,
key.transpose(1,2),
alpha=scale,
diff --git a/webui-macos-env.sh b/webui-macos-env.sh
index 6354e73b..24bc5c42 100644
--- a/webui-macos-env.sh
+++ b/webui-macos-env.sh
@@ -12,8 +12,6 @@ fi
export install_dir="$HOME"
export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
export TORCH_COMMAND="pip install torch==2.0.1 torchvision==0.15.2"
-export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
-export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71"
export PYTORCH_ENABLE_MPS_FALLBACK=1
####################################################################
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-20 09:33:22 +0000