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Set performance level (low, medium, high) instead of a Turbo field. The previous Turbo field is equivalent to 'Medium' performance now

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This commit is contained in:
cmdr2 2022-12-15 23:30:06 +05:30
parent fb075a0013
commit aa01fd058e
9 changed files with 67 additions and 33 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",
"turbo",
"performance_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 disabling the '<em>Turbo mode</em>' under '<em>Advanced Settings</em>'.<br/>
2. Try picking a lower performance level in the '<em>Performance Level</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,
turbo: turboField.checked,
performance_level: perfLevelField.value,
//render_device: undefined, // Set device affinity. Prefer this device, but wont activate.
use_stable_diffusion_model: stableDiffusionModelField.value,
use_vae_model: vaeModelField.value,

20
ui/media/js/parameters.js
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@ -94,12 +94,20 @@ var PARAMETERS = [
default: true,
},
{
id: "turbo",
type: ParameterType.checkbox,
label: "Turbo Mode",
note: "generates images faster, but uses an additional 1 GB of GPU memory",
id: "performance_level",
type: ParameterType.select,
label: "Performance Level",
note: "Faster performance requires more GPU memory<br/><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",
icon: "fa-forward",
default: true,
default: "high",
options: [
{value: "high", label: "High"},
{value: "medium", label: "Medium"},
{value: "low", label: "Low"}
],
},
{
id: "use_cpu",
@ -219,7 +227,7 @@ function initParameters() {
initParameters()
let turboField = document.querySelector('#turbo')
let perfLevelField = document.querySelector('#performance_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
turbo: bool = True
performance_level: str = "high" # 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_model_to_config(ckpt_model_name, vae_model_name, hypernetwork_model_name):
def save_to_config(ckpt_model_name, vae_model_name, hypernetwork_model_name, performance_level):
config = getConfig()
if 'model' not in config:
config['model'] = {}
@ -124,6 +124,8 @@ def save_model_to_config(ckpt_model_name, vae_model_name, hypernetwork_model_nam
if hypernetwork_model_name is None or hypernetwork_model_name == "":
del config['model']['hypernetwork']
config['performance_level'] = performance_level
setConfig(config)
def update_render_threads():

12
ui/sd_internal/device_manager.py
View File

@ -128,6 +128,18 @@ 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):
if device != 'cpu':
_, mem_total = torch.cuda.mem_get_info(device)
mem_total /= float(10**9)
if mem_total < 4.5:
return 'low'
elif mem_total < 6.5:
return 'medium'
return 'high'
def validate_device_id(device, log_prefix=''):
def is_valid():
if not isinstance(device, str):

46
ui/sd_internal/model_manager.py
View File

@ -3,7 +3,7 @@ import logging
import picklescan.scanner
import rich
from sd_internal import app, TaskData
from sd_internal import app, TaskData, device_manager
from diffusionkit import model_loader
from diffusionkit.types import Context
@ -25,6 +25,11 @@ DEFAULT_MODELS = {
'gfpgan': ['GFPGANv1.3'],
'realesrgan': ['RealESRGAN_x4plus'],
}
PERF_LEVEL_TO_VRAM_OPTIMIZATIONS = {
'low': {'KEEP_ENTIRE_MODEL_IN_CPU'},
'medium': {'KEEP_FS_AND_CS_IN_CPU', 'SET_ATTENTION_STEP_TO_4'},
'high': {},
}
known_models = {}
@ -37,8 +42,7 @@ def load_default_models(context: Context):
for model_type in KNOWN_MODEL_TYPES:
context.model_paths[model_type] = resolve_model_to_use(model_type=model_type)
# disable TURBO initially (this should be read from the config eventually)
context.vram_optimizations -= {'TURBO'}
set_vram_optimizations(context)
# load mandatory models
model_loader.load_model(context, 'stable-diffusion')
@ -94,16 +98,19 @@ def resolve_model_to_use(model_name:str=None, model_type:str=None):
return None
def reload_models_if_necessary(context: Context, task_data: TaskData):
model_paths_in_req = (
('stable-diffusion', task_data.use_stable_diffusion_model),
('vae', task_data.use_vae_model),
('hypernetwork', task_data.use_hypernetwork_model),
('gfpgan', task_data.use_face_correction),
('realesrgan', task_data.use_upscale),
)
model_paths_in_req = {
'stable-diffusion': task_data.use_stable_diffusion_model,
'vae': task_data.use_vae_model,
'hypernetwork': task_data.use_hypernetwork_model,
'gfpgan': task_data.use_face_correction,
'realesrgan': task_data.use_upscale,
}
models_to_reload = {model_type: path for model_type, path in model_paths_in_req.items() if context.model_paths.get(model_type) != path}
for model_type, model_path_in_req in model_paths_in_req:
if context.model_paths.get(model_type) != model_path_in_req:
if set_vram_optimizations(context): # reload SD
models_to_reload['stable-diffusion'] = model_paths_in_req['stable-diffusion']
for model_type, model_path_in_req in models_to_reload.items():
context.model_paths[model_type] = model_path_in_req
action_fn = model_loader.unload_model if context.model_paths[model_type] is None else model_loader.load_model
@ -117,11 +124,16 @@ def resolve_model_paths(task_data: TaskData):
if task_data.use_face_correction: task_data.use_face_correction = resolve_model_to_use(task_data.use_face_correction, 'gfpgan')
if task_data.use_upscale: task_data.use_upscale = resolve_model_to_use(task_data.use_upscale, 'gfpgan')
def set_vram_optimizations(context: Context, task_data: TaskData):
if task_data.turbo:
context.vram_optimizations.add('TURBO')
else:
context.vram_optimizations.remove('TURBO')
def set_vram_optimizations(context: Context):
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]
if vram_optimizations != context.vram_optimizations:
context.vram_optimizations = vram_optimizations
return True
return False
def make_model_folders():
for model_type in KNOWN_MODEL_TYPES:

2
ui/sd_internal/task_manager.py
View File

@ -281,7 +281,6 @@ def thread_render(device):
current_state = ServerStates.LoadingModel
model_manager.resolve_model_paths(task.task_data)
model_manager.set_vram_optimizations(renderer.context, task.task_data)
model_manager.reload_models_if_necessary(renderer.context, task.task_data)
current_state = ServerStates.Rendering
@ -342,6 +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),
}
# 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_model_to_config(task_data.use_stable_diffusion_model, task_data.use_vae_model, task_data.use_hypernetwork_model)
app.save_to_config(task_data.use_stable_diffusion_model, task_data.use_vae_model, task_data.use_hypernetwork_model, task_data.performance_level)
# enqueue the task
new_task = task_manager.render(render_req, task_data)