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Change the performance field to GPU Memory Usage instead, and use the 'balanced' profile by default, since it's just 5% slower than 'high', and uses nearly 50% less VRAM

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This commit is contained in:
cmdr2 2022-12-16 11:34:49 +05:30
parent aa01fd058e
commit 7982a9ae25
9 changed files with 36 additions and 21 deletions
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2
ui/media/js/auto-save.js
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@ -36,7 +36,7 @@ const SETTINGS_IDS_LIST = [
"save_to_disk",
"diskPath",
"sound_toggle",
"performance_level",
"vram_usage_level",
"confirm_dangerous_actions",
"metadata_output_format",
"auto_save_settings",

4
ui/media/js/main.js
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@ -602,7 +602,7 @@ function onTaskCompleted(task, reqBody, instance, outputContainer, stepUpdate) {
<b>Suggestions</b>:
<br/>
1. If you have set an initial image, please try reducing its dimension to ${MAX_INIT_IMAGE_DIMENSION}x${MAX_INIT_IMAGE_DIMENSION} or smaller.<br/>
2. Try picking a lower performance level in the '<em>Performance Level</em>' setting (in the '<em>Settings</em>' tab).<br/>
2. Try picking a lower level in the '<em>GPU Memory Usage</em>' setting (in the '<em>Settings</em>' tab).<br/>
3. Try generating a smaller image.<br/>`
}
} else {
@ -887,7 +887,7 @@ function getCurrentUserRequest() {
width: parseInt(widthField.value),
height: parseInt(heightField.value),
// allow_nsfw: allowNSFWField.checked,
performance_level: perfLevelField.value,
vram_usage_level: vramUsageLevelField.value,
//render_device: undefined, // Set device affinity. Prefer this device, but wont activate.
use_stable_diffusion_model: stableDiffusionModelField.value,
use_vae_model: vaeModelField.value,

14
ui/media/js/parameters.js
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@ -94,18 +94,18 @@ var PARAMETERS = [
default: true,
},
{
id: "performance_level",
id: "vram_usage_level",
type: ParameterType.select,
label: "Performance Level",
label: "GPU Memory Usage",
note: "Faster performance requires more GPU memory<br/><br/>" +
"<b>Balanced:</b> almost as fast as High, significantly lower GPU memory usage<br/>" +
"<b>High:</b> fastest, maximum GPU memory usage</br>" +
"<b>Medium:</b> decent speed, uses 1 GB more memory than Low<br/>" +
"<b>Low:</b> slowest, for GPUs with 4 GB (or less) memory",
"<b>Low:</b> slowest, force-used for GPUs with 4 GB (or less) memory",
icon: "fa-forward",
default: "high",
default: "balanced",
options: [
{value: "balanced", label: "Balanced"},
{value: "high", label: "High"},
{value: "medium", label: "Medium"},
{value: "low", label: "Low"}
],
},
@ -227,7 +227,7 @@ function initParameters() {
initParameters()
let perfLevelField = document.querySelector('#performance_level')
let vramUsageLevelField = document.querySelector('#vram_usage_level')
let useCPUField = document.querySelector('#use_cpu')
let autoPickGPUsField = document.querySelector('#auto_pick_gpus')
let useGPUsField = document.querySelector('#use_gpus')

2
ui/sd_internal/__init__.py
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@ -6,7 +6,7 @@ class TaskData(BaseModel):
request_id: str = None
session_id: str = "session"
save_to_disk_path: str = None
performance_level: str = "high" # or "low" or "medium"
vram_usage_level: str = "balanced" # or "low" or "medium"
use_face_correction: str = None # or "GFPGANv1.3"
use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B"

4
ui/sd_internal/app.py
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@ -110,7 +110,7 @@ def setConfig(config):
except:
log.error(traceback.format_exc())
def save_to_config(ckpt_model_name, vae_model_name, hypernetwork_model_name, performance_level):
def save_to_config(ckpt_model_name, vae_model_name, hypernetwork_model_name, vram_usage_level):
config = getConfig()
if 'model' not in config:
config['model'] = {}
@ -124,7 +124,7 @@ def save_to_config(ckpt_model_name, vae_model_name, hypernetwork_model_name, per
if hypernetwork_model_name is None or hypernetwork_model_name == "":
del config['model']['hypernetwork']
config['performance_level'] = performance_level
config['vram_usage_level'] = vram_usage_level
setConfig(config)

4
ui/sd_internal/device_manager.py
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@ -128,7 +128,7 @@ def needs_to_force_full_precision(context):
device_name = context.device_name.lower()
return (('nvidia' in device_name or 'geforce' in device_name) and (' 1660' in device_name or ' 1650' in device_name)) or ('Quadro T2000' in device_name)
def get_max_perf_level(device):
def get_max_vram_usage_level(device):
if device != 'cpu':
_, mem_total = torch.cuda.mem_get_info(device)
mem_total /= float(10**9)
@ -136,7 +136,7 @@ def get_max_perf_level(device):
if mem_total < 4.5:
return 'low'
elif mem_total < 6.5:
return 'medium'
return 'balanced'
return 'high'

23
ui/sd_internal/model_manager.py
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@ -25,9 +25,9 @@ DEFAULT_MODELS = {
'gfpgan': ['GFPGANv1.3'],
'realesrgan': ['RealESRGAN_x4plus'],
}
PERF_LEVEL_TO_VRAM_OPTIMIZATIONS = {
VRAM_USAGE_LEVEL_TO_OPTIMIZATIONS = {
'balanced': {'KEEP_FS_AND_CS_IN_CPU', 'SET_ATTENTION_STEP_TO_4'},
'low': {'KEEP_ENTIRE_MODEL_IN_CPU'},
'medium': {'KEEP_FS_AND_CS_IN_CPU', 'SET_ATTENTION_STEP_TO_4'},
'high': {},
}
@ -125,9 +125,24 @@ def resolve_model_paths(task_data: TaskData):
if task_data.use_upscale: task_data.use_upscale = resolve_model_to_use(task_data.use_upscale, 'gfpgan')
def set_vram_optimizations(context: Context):
def is_greater(a, b): # is a > b?
if a == "low": # b will be "low", "balanced" or "high"
return False
elif a == "balanced" and b != "low": # b will be "balanced" or "high"
return False
return True
config = app.getConfig()
perf_level = config.get('performance_level', device_manager.get_max_perf_level(context.device))
vram_optimizations = PERF_LEVEL_TO_VRAM_OPTIMIZATIONS[perf_level]
max_usage_level = device_manager.get_max_vram_usage_level(context.device)
vram_usage_level = config.get('vram_usage_level', 'balanced')
if is_greater(vram_usage_level, max_usage_level):
log.error(f'Requested GPU Memory Usage level ({vram_usage_level}) is higher than what is ' + \
f'possible ({max_usage_level}) on this device ({context.device}). Using "{max_usage_level}" instead')
vram_usage_level = max_usage_level
vram_optimizations = VRAM_USAGE_LEVEL_TO_OPTIMIZATIONS[vram_usage_level]
if vram_optimizations != context.vram_optimizations:
context.vram_optimizations = vram_optimizations

2
ui/sd_internal/task_manager.py
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@ -341,7 +341,7 @@ def get_devices():
'name': torch.cuda.get_device_name(device),
'mem_free': mem_free,
'mem_total': mem_total,
'max_perf_level': device_manager.get_max_perf_level(device),
'max_vram_usage_level': device_manager.get_max_vram_usage_level(device),
}
# list the compatible devices

2
ui/server.py
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@ -134,7 +134,7 @@ def render(req: dict):
render_req.init_image_mask = req.get('mask') # hack: will rename this in the HTTP API in a future revision
app.save_to_config(task_data.use_stable_diffusion_model, task_data.use_vae_model, task_data.use_hypernetwork_model, task_data.performance_level)
app.save_to_config(task_data.use_stable_diffusion_model, task_data.use_vae_model, task_data.use_hypernetwork_model, task_data.vram_usage_level)
# enqueue the task
new_task = task_manager.render(render_req, task_data)