Merge branch 'master' into dev/deepdanbooru

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Greendayle 2022-10-08 18:28:22 +02:00 committed by GitHub
commit 0ec80f0125
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6 changed files with 23 additions and 6 deletions

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@ -127,7 +127,7 @@ if not is_installed("gfpgan"):
if not is_installed("clip"):
run_pip(f"install {clip_package}", "clip")
if not is_installed("xformers") and xformers:
if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
if platform.system() == "Windows":
run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
elif platform.system() == "Linux":

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@ -13,13 +13,14 @@ import lark
schedule_parser = lark.Lark(r"""
!start: (prompt | /[][():]/+)*
prompt: (emphasized | scheduled | plain | WHITESPACE)*
prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
!emphasized: "(" prompt ")"
| "(" prompt ":" prompt ")"
| "[" prompt "]"
scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
alternate: "[" prompt ("|" prompt)+ "]"
WHITESPACE: /\s+/
plain: /([^\\\[\]():]|\\.)+/
plain: /([^\\\[\]():|]|\\.)+/
%import common.SIGNED_NUMBER -> NUMBER
""")
@ -59,6 +60,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
tree.children[-1] *= steps
tree.children[-1] = min(steps, int(tree.children[-1]))
l.append(tree.children[-1])
def alternate(self, tree):
l.extend(range(1, steps+1))
CollectSteps().visit(tree)
return sorted(set(l))
@ -67,6 +70,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
def scheduled(self, args):
before, after, _, when = args
yield before or () if step <= when else after
def alternate(self, args):
yield next(args[(step - 1)%len(args)])
def start(self, args):
def flatten(x):
if type(x) == str:

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@ -22,12 +22,16 @@ def apply_optimizations():
undo_optimizations()
ldm.modules.diffusionmodules.model.nonlinearity = silu
if cmd_opts.xformers and shared.xformers_available and not torch.version.hip:
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)):
print("Applying xformers cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
elif cmd_opts.opt_split_attention_v1:
print("Applying v1 cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
print("Applying cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward

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@ -211,6 +211,7 @@ def cross_attention_attnblock_forward(self, x):
return h3
def xformers_attnblock_forward(self, x):
try:
h_ = x
h_ = self.norm(h_)
q1 = self.q(h_).contiguous()
@ -218,4 +219,6 @@ def xformers_attnblock_forward(self, x):
v = self.v(h_).contiguous()
out = xformers.ops.memory_efficient_attention(q1, k1, v)
out = self.proj_out(out)
return x+out
return x + out
except NotImplementedError:
return cross_attention_attnblock_forward(self, x)

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@ -44,6 +44,7 @@ parser.add_argument("--scunet-models-path", type=str, help="Path to directory wi
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")

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@ -5,6 +5,8 @@ import importlib
import signal
import threading
from fastapi.middleware.gzip import GZipMiddleware
from modules.paths import script_path
from modules import devices, sd_samplers
@ -93,7 +95,7 @@ def webui():
demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
demo.launch(
app,local_url,share_url = demo.launch(
share=cmd_opts.share,
server_name="0.0.0.0" if cmd_opts.listen else None,
server_port=cmd_opts.port,
@ -102,6 +104,8 @@ def webui():
inbrowser=cmd_opts.autolaunch,
prevent_thread_lock=True
)
app.add_middleware(GZipMiddleware,minimum_size=1000)
while 1:
time.sleep(0.5)