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Merge branch 'main' into refactor/rename-performance-options

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Lincoln Stein 2023-08-21 19:47:55 -04:00 committed by GitHub
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4 changed files with 141 additions and 59 deletions
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3
invokeai/app/invocations/baseinvocation.py
View File

@ -71,6 +71,9 @@ class FieldDescriptions:
safe_mode = "Whether or not to use safe mode"
scribble_mode = "Whether or not to use scribble mode"
scale_factor = "The factor by which to scale"
blend_alpha = (
"Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
)
num_1 = "The first number"
num_2 = "The second number"
mask = "The mask to use for the operation"

2
invokeai/app/invocations/compel.py
View File

@ -233,7 +233,7 @@ class SDXLPromptInvocationBase:
dtype_for_device_getter=torch_dtype,
truncate_long_prompts=True, # TODO:
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, # TODO: clip skip
requires_pooled=True,
requires_pooled=get_pooled,
)
conjunction = Compel.parse_prompt_string(prompt)

79
invokeai/app/invocations/latent.py
View File

@ -4,6 +4,7 @@ from contextlib import ExitStack
from typing import List, Literal, Optional, Union
import einops
import numpy as np
import torch
import torchvision.transforms as T
from diffusers.image_processor import VaeImageProcessor
@ -720,3 +721,81 @@ class ImageToLatentsInvocation(BaseInvocation):
latents = latents.to("cpu")
context.services.latents.save(name, latents)
return build_latents_output(latents_name=name, latents=latents, seed=None)
@title("Blend Latents")
@tags("latents", "blend")
class BlendLatentsInvocation(BaseInvocation):
"""Blend two latents using a given alpha. Latents must have same size."""
type: Literal["lblend"] = "lblend"
# Inputs
latents_a: LatentsField = InputField(
description=FieldDescriptions.latents,
input=Input.Connection,
)
latents_b: LatentsField = InputField(
description=FieldDescriptions.latents,
input=Input.Connection,
)
alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents_a = context.services.latents.get(self.latents_a.latents_name)
latents_b = context.services.latents.get(self.latents_b.latents_name)
if latents_a.shape != latents_b.shape:
raise "Latents to blend must be the same size."
# TODO:
device = choose_torch_device()
def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
"""
Spherical linear interpolation
Args:
t (float/np.ndarray): Float value between 0.0 and 1.0
v0 (np.ndarray): Starting vector
v1 (np.ndarray): Final vector
DOT_THRESHOLD (float): Threshold for considering the two vectors as
colineal. Not recommended to alter this.
Returns:
v2 (np.ndarray): Interpolation vector between v0 and v1
"""
inputs_are_torch = False
if not isinstance(v0, np.ndarray):
inputs_are_torch = True
v0 = v0.detach().cpu().numpy()
if not isinstance(v1, np.ndarray):
inputs_are_torch = True
v1 = v1.detach().cpu().numpy()
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
if np.abs(dot) > DOT_THRESHOLD:
v2 = (1 - t) * v0 + t * v1
else:
theta_0 = np.arccos(dot)
sin_theta_0 = np.sin(theta_0)
theta_t = theta_0 * t
sin_theta_t = np.sin(theta_t)
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
s1 = sin_theta_t / sin_theta_0
v2 = s0 * v0 + s1 * v1
if inputs_are_torch:
v2 = torch.from_numpy(v2).to(device)
return v2
# blend
blended_latents = slerp(self.alpha, latents_a, latents_b)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
blended_latents = blended_latents.to("cpu")
torch.cuda.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
# context.services.latents.set(name, resized_latents)
context.services.latents.save(name, blended_latents)
return build_latents_output(latents_name=name, latents=blended_latents)

116
invokeai/app/services/invocation_stats.py
View File

@ -49,9 +49,36 @@ from invokeai.backend.model_management.model_cache import CacheStats
GIG = 1073741824
@dataclass
class NodeStats:
"""Class for tracking execution stats of an invocation node"""
calls: int = 0
time_used: float = 0.0 # seconds
max_vram: float = 0.0 # GB
cache_hits: int = 0
cache_misses: int = 0
cache_high_watermark: int = 0
@dataclass
class NodeLog:
"""Class for tracking node usage"""
# {node_type => NodeStats}
nodes: Dict[str, NodeStats] = field(default_factory=dict)
class InvocationStatsServiceBase(ABC):
"Abstract base class for recording node memory/time performance statistics"
graph_execution_manager: ItemStorageABC["GraphExecutionState"]
# {graph_id => NodeLog}
_stats: Dict[str, NodeLog]
_cache_stats: Dict[str, CacheStats]
ram_used: float
ram_changed: float
@abstractmethod
def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]):
"""
@ -94,8 +121,6 @@ class InvocationStatsServiceBase(ABC):
invocation_type: str,
time_used: float,
vram_used: float,
ram_used: float,
ram_changed: float,
):
"""
Add timing information on execution of a node. Usually
@ -104,8 +129,6 @@ class InvocationStatsServiceBase(ABC):
:param invocation_type: String literal type of the node
:param time_used: Time used by node's exection (sec)
:param vram_used: Maximum VRAM used during exection (GB)
:param ram_used: Current RAM available (GB)
:param ram_changed: Change in RAM usage over course of the run (GB)
"""
pass
@ -116,25 +139,19 @@ class InvocationStatsServiceBase(ABC):
"""
pass
@abstractmethod
def update_mem_stats(
self,
ram_used: float,
ram_changed: float,
):
"""
Update the collector with RAM memory usage info.
@dataclass
class NodeStats:
"""Class for tracking execution stats of an invocation node"""
calls: int = 0
time_used: float = 0.0 # seconds
max_vram: float = 0.0 # GB
cache_hits: int = 0
cache_misses: int = 0
cache_high_watermark: int = 0
@dataclass
class NodeLog:
"""Class for tracking node usage"""
# {node_type => NodeStats}
nodes: Dict[str, NodeStats] = field(default_factory=dict)
:param ram_used: How much RAM is currently in use.
:param ram_changed: How much RAM changed since last generation.
"""
pass
class InvocationStatsService(InvocationStatsServiceBase):
@ -152,12 +169,12 @@ class InvocationStatsService(InvocationStatsServiceBase):
class StatsContext:
"""Context manager for collecting statistics."""
invocation: BaseInvocation = None
collector: "InvocationStatsServiceBase" = None
graph_id: str = None
start_time: int = 0
ram_used: int = 0
model_manager: ModelManagerService = None
invocation: BaseInvocation
collector: "InvocationStatsServiceBase"
graph_id: str
start_time: float
ram_used: int
model_manager: ModelManagerService
def __init__(
self,
@ -170,7 +187,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
self.invocation = invocation
self.collector = collector
self.graph_id = graph_id
self.start_time = 0
self.start_time = 0.0
self.ram_used = 0
self.model_manager = model_manager
@ -191,7 +208,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
)
self.collector.update_invocation_stats(
graph_id=self.graph_id,
invocation_type=self.invocation.type,
invocation_type=self.invocation.type, # type: ignore - `type` is not on the `BaseInvocation` model, but *is* on all invocations
time_used=time.time() - self.start_time,
vram_used=torch.cuda.max_memory_allocated() / GIG if torch.cuda.is_available() else 0.0,
)
@ -202,11 +219,6 @@ class InvocationStatsService(InvocationStatsServiceBase):
graph_execution_state_id: str,
model_manager: ModelManagerService,
) -> StatsContext:
"""
Return a context object that will capture the statistics.
:param invocation: BaseInvocation object from the current graph.
:param graph_execution_state: GraphExecutionState object from the current session.
"""
if not self._stats.get(graph_execution_state_id): # first time we're seeing this
self._stats[graph_execution_state_id] = NodeLog()
self._cache_stats[graph_execution_state_id] = CacheStats()
@ -217,7 +229,6 @@ class InvocationStatsService(InvocationStatsServiceBase):
self._stats = {}
def reset_stats(self, graph_execution_id: str):
"""Zero the statistics for the indicated graph."""
try:
self._stats.pop(graph_execution_id)
except KeyError:
@ -228,12 +239,6 @@ class InvocationStatsService(InvocationStatsServiceBase):
ram_used: float,
ram_changed: float,
):
"""
Update the collector with RAM memory usage info.
:param ram_used: How much RAM is currently in use.
:param ram_changed: How much RAM changed since last generation.
"""
self.ram_used = ram_used
self.ram_changed = ram_changed
@ -244,16 +249,6 @@ class InvocationStatsService(InvocationStatsServiceBase):
time_used: float,
vram_used: float,
):
"""
Add timing information on execution of a node. Usually
used internally.
:param graph_id: ID of the graph that is currently executing
:param invocation_type: String literal type of the node
:param time_used: Time used by node's exection (sec)
:param vram_used: Maximum VRAM used during exection (GB)
:param ram_used: Current RAM available (GB)
:param ram_changed: Change in RAM usage over course of the run (GB)
"""
if not self._stats[graph_id].nodes.get(invocation_type):
self._stats[graph_id].nodes[invocation_type] = NodeStats()
stats = self._stats[graph_id].nodes[invocation_type]
@ -262,14 +257,15 @@ class InvocationStatsService(InvocationStatsServiceBase):
stats.max_vram = max(stats.max_vram, vram_used)
def log_stats(self):
"""
Send the statistics to the system logger at the info level.
Stats will only be printed when the execution of the graph
is complete.
"""
completed = set()
errored = set()
for graph_id, node_log in self._stats.items():
current_graph_state = self.graph_execution_manager.get(graph_id)
try:
current_graph_state = self.graph_execution_manager.get(graph_id)
except Exception:
errored.add(graph_id)
continue
if not current_graph_state.is_complete():
continue
@ -302,3 +298,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
for graph_id in completed:
del self._stats[graph_id]
del self._cache_stats[graph_id]
for graph_id in errored:
del self._stats[graph_id]
del self._cache_stats[graph_id]