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use heuristic to select RAM cache size during headless install; blackified

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This commit is contained in:
Lincoln Stein 2023-09-25 19:18:58 -04:00 committed by Kent Keirsey
parent 0c97a1e7e7
commit d59e534cad
10 changed files with 39 additions and 40 deletions
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4
installer/lib/installer.py
View File

@ -344,12 +344,12 @@ class InvokeAiInstance:
auto_install = True
sys.argv = new_argv
import requests # to catch download exceptions
import messages
import requests # to catch download exceptions
auto_install = auto_install or messages.user_wants_auto_configuration()
if auto_install:
sys.argv.append('--yes')
sys.argv.append("--yes")
else:
messages.introduction()

22
installer/lib/messages.py
View File

@ -7,7 +7,7 @@ import os
import platform
from pathlib import Path
from prompt_toolkit import prompt, HTML
from prompt_toolkit import HTML, prompt
from prompt_toolkit.completion import PathCompleter
from prompt_toolkit.validation import Validator
from rich import box, print
@ -97,13 +97,17 @@ def user_wants_auto_configuration() -> bool:
padding=(1, 1),
)
)
choice = prompt(HTML("Choose <b>&lt;a&gt;</b>utomatic or <b>&lt;m&gt;</b>anual configuration [a/m] (a): "),
validator=Validator.from_callable(
lambda n: n=='' or n.startswith(('a', 'A', 'm', 'M')),
error_message="Please select 'a' or 'm'"
),
) or 'a'
return choice.lower().startswith('a')
choice = (
prompt(
HTML("Choose <b>&lt;a&gt;</b>utomatic or <b>&lt;m&gt;</b>anual configuration [a/m] (a): "),
validator=Validator.from_callable(
lambda n: n == "" or n.startswith(("a", "A", "m", "M")), error_message="Please select 'a' or 'm'"
),
)
or "a"
)
return choice.lower().startswith("a")
def dest_path(dest=None) -> Path:
"""
@ -209,7 +213,7 @@ def graphical_accelerator():
"cpu",
)
idk = (
"I'm not sure what to choose",
"I'm not sure what to choose",
"idk",
)

4
invokeai/app/invocations/math.py
View File

@ -199,7 +199,7 @@ class IntegerMathInvocation(BaseInvocation):
elif self.operation == "DIV":
return IntegerOutput(value=int(self.a / self.b))
elif self.operation == "EXP":
return IntegerOutput(value=self.a**self.b)
return IntegerOutput(value=self.a ** self.b)
elif self.operation == "MOD":
return IntegerOutput(value=self.a % self.b)
elif self.operation == "ABS":
@ -273,7 +273,7 @@ class FloatMathInvocation(BaseInvocation):
elif self.operation == "DIV":
return FloatOutput(value=self.a / self.b)
elif self.operation == "EXP":
return FloatOutput(value=self.a**self.b)
return FloatOutput(value=self.a ** self.b)
elif self.operation == "SQRT":
return FloatOutput(value=np.sqrt(self.a))
elif self.operation == "ABS":

14
invokeai/backend/install/invokeai_configure.py
View File

@ -70,7 +70,6 @@ def get_literal_fields(field) -> list[Any]:
config = InvokeAIAppConfig.get_config()
Model_dir = "models"
Default_config_file = config.model_conf_path
SD_Configs = config.legacy_conf_path
@ -458,7 +457,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
)
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Model RAM cache size (GB). Make this at least large enough to hold a single full model.",
name="Model RAM cache size (GB). Make this at least large enough to hold a single full model (2GB for SD-1, 6GB for SDXL).",
begin_entry_at=0,
editable=False,
color="CONTROL",
@ -651,8 +650,19 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
return editApp.new_opts()
def default_ramcache() -> float:
"""Run a heuristic for the default RAM cache based on installed RAM."""
# Note that on my 64 GB machine, psutil.virtual_memory().total gives 62 GB,
# So we adjust everthing down a bit.
return (
15.0 if MAX_RAM >= 60 else 7.5 if MAX_RAM >= 30 else 4 if MAX_RAM >= 14 else 2.1
) # 2.1 is just large enough for sd 1.5 ;-)
def default_startup_options(init_file: Path) -> Namespace:
opts = InvokeAIAppConfig.get_config()
opts.ram = default_ramcache()
return opts

4
invokeai/backend/ip_adapter/resampler.py
View File

@ -33,7 +33,7 @@ def reshape_tensor(x, heads):
class PerceiverAttention(nn.Module):
def __init__(self, *, dim, dim_head=64, heads=8):
super().__init__()
self.scale = dim_head**-0.5
self.scale = dim_head ** -0.5
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head * heads
@ -91,7 +91,7 @@ class Resampler(nn.Module):
):
super().__init__()
self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim ** 0.5)
self.proj_in = nn.Linear(embedding_dim, dim)

2
invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
View File

@ -261,7 +261,7 @@ class InvokeAICrossAttentionMixin:
if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
return self.einsum_lowest_level(q, k, v, None, None, None)
else:
slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
slice_size = math.floor(2 ** 30 / (q.shape[0] * q.shape[1]))
return self.einsum_op_slice_dim1(q, k, v, slice_size)
def einsum_op_mps_v2(self, q, k, v):

20
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -175,10 +175,7 @@ class InvokeAIDiffuserComponent:
dim=0,
),
}
(
encoder_hidden_states,
encoder_attention_mask,
) = self._concat_conditionings_for_batch(
(encoder_hidden_states, encoder_attention_mask,) = self._concat_conditionings_for_batch(
conditioning_data.unconditioned_embeddings.embeds,
conditioning_data.text_embeddings.embeds,
)
@ -240,10 +237,7 @@ class InvokeAIDiffuserComponent:
wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
if wants_cross_attention_control:
(
unconditioned_next_x,
conditioned_next_x,
) = self._apply_cross_attention_controlled_conditioning(
(unconditioned_next_x, conditioned_next_x,) = self._apply_cross_attention_controlled_conditioning(
sample,
timestep,
conditioning_data,
@ -251,10 +245,7 @@ class InvokeAIDiffuserComponent:
**kwargs,
)
elif self.sequential_guidance:
(
unconditioned_next_x,
conditioned_next_x,
) = self._apply_standard_conditioning_sequentially(
(unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning_sequentially(
sample,
timestep,
conditioning_data,
@ -262,10 +253,7 @@ class InvokeAIDiffuserComponent:
)
else:
(
unconditioned_next_x,
conditioned_next_x,
) = self._apply_standard_conditioning(
(unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning(
sample,
timestep,
conditioning_data,

5
invokeai/backend/training/textual_inversion_training.py
View File

@ -470,10 +470,7 @@ class TextualInversionDataset(Dataset):
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
(
h,
w,
) = (
(h, w,) = (
img.shape[0],
img.shape[1],
)

2
invokeai/backend/util/mps_fixes.py
View File

@ -203,7 +203,7 @@ class ChunkedSlicedAttnProcessor:
if attn.upcast_attention:
out_item_size = 4
chunk_size = 2**29
chunk_size = 2 ** 29
out_size = query.shape[1] * key.shape[1] * out_item_size
chunks_count = min(query.shape[1], math.ceil((out_size - 1) / chunk_size))

2
invokeai/backend/util/util.py
View File

@ -207,7 +207,7 @@ def parallel_data_prefetch(
return gather_res
def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3):
def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t ** 5 - 15 * t ** 4 + 10 * t ** 3):
delta = (res[0] / shape[0], res[1] / shape[1])
d = (shape[0] // res[0], shape[1] // res[1])