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Merge pull request #10 from openvinotoolkit/openvino_custom_scripts

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Openvino custom scripts
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Yamini Nimmagadda 2023-08-14 14:12:02 -07:00 committed by GitHub
commit 7633ef5ab2
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1 changed files with 21 additions and 22 deletions
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scripts/openvino_accelerate.py
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@ -22,6 +22,7 @@ from modules.processing import (
)
from modules.sd_models import CheckpointInfo
from modules.shared import opts, state
from modules.ui_common import create_refresh_button
from PIL import Image, ImageOps
from pathlib import Path
@ -310,14 +311,14 @@ def set_scheduler(sd_model, sampler_name):
return sd_model.scheduler
def get_diffusers_sd_model(local_config, model_config, sampler_name, enable_caching, openvino_device, mode):
def get_diffusers_sd_model(model_config, sampler_name, enable_caching, openvino_device, mode):
if (model_state.recompile == 1):
torch._dynamo.reset()
openvino_clear_caches()
curr_dir_path = os.getcwd()
checkpoint_name = shared.opts.sd_model_checkpoint.split(" ")[0]
checkpoint_path = os.path.join(curr_dir_path, 'models', 'Stable-diffusion', checkpoint_name)
if local_config:
if model_config != "None":
local_config_file = os.path.join(curr_dir_path, 'configs', model_config)
sd_model = StableDiffusionPipeline.from_single_file(checkpoint_path, local_config_file=local_config_file, load_safety_checker=False)
else:
@ -434,7 +435,7 @@ def init_new(self, all_prompts, all_seeds, all_subseeds):
raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
def process_images_openvino(p: StableDiffusionProcessing, local_config, model_config, sampler_name, enable_caching, openvino_device, mode) -> Processed:
def process_images_openvino(p: StableDiffusionProcessing, model_config, sampler_name, enable_caching, openvino_device, mode) -> Processed:
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
if (mode == 0 and p.enable_hr):
@ -514,7 +515,7 @@ def process_images_openvino(p: StableDiffusionProcessing, local_config, model_co
model_state.mode = mode
model_state.model_hash = shared.sd_model.sd_model_hash
shared.sd_diffusers_model = get_diffusers_sd_model(local_config, model_config, sampler_name, enable_caching, openvino_device, mode)
shared.sd_diffusers_model = get_diffusers_sd_model(model_config, sampler_name, enable_caching, openvino_device, mode)
shared.sd_diffusers_model.scheduler = set_scheduler(shared.sd_diffusers_model, sampler_name)
extra_network_data = p.parse_extra_network_prompts()
@ -705,15 +706,20 @@ class Script(scripts.Script):
def ui(self, is_img2img):
core = Core()
config_dir_list = os.listdir(os.path.join(os.getcwd(), 'configs'))
config_list = []
for file in config_dir_list:
if file.endswith('.yaml'):
config_list.append(file)
def get_config_list():
config_dir_list = os.listdir(os.path.join(os.getcwd(), 'configs'))
config_list = []
config_list.append("None")
for file in config_dir_list:
if file.endswith('.yaml'):
config_list.append(file)
return config_list
with gr.Row():
model_config = gr.Dropdown(label="Select a local config for the model from the configs directory of the webui root", choices=get_config_list(), value="None", visible=True)
create_refresh_button(model_config, get_config_list, lambda: {"choices": get_config_list()},"refresh_model_config")
local_config = gr.Checkbox(label="Use a local inference config file", value=False)
model_config = gr.Dropdown(label="Select a config for the model (Below config files are listed from the configs directory of the WebUI root)", choices=config_list, value="v1-inference.yaml", visible=False)
openvino_device = gr.Dropdown(label="Select a device", choices=list(core.available_devices), value=model_state.device)
override_sampler = gr.Checkbox(label="Override the sampling selection from the main UI (Recommended as only below sampling methods have been validated for OpenVINO)", value=True)
sampler_name = gr.Radio(label="Select a sampling method", choices=["Euler a", "Euler", "LMS", "Heun", "DPM++ 2M", "LMS Karras", "DPM++ 2M Karras", "DDIM", "PLMS"], value="Euler a")
@ -731,13 +737,6 @@ class Script(scripts.Script):
So it's normal for the first inference after a settings change to be slower, while subsequent inferences use the optimized compiled model and run faster.
""")
def local_config_change(choice):
if choice:
return gr.update(visible=True)
else:
return gr.update(visible=False)
local_config.change(local_config_change, local_config, model_config)
def device_change(choice):
if (model_state.device == choice):
return gr.update(value="Device selected is " + choice, visible=True)
@ -747,9 +746,9 @@ class Script(scripts.Script):
return gr.update(value="Device changed to " + choice + ". Model will be re-compiled", visible=True)
openvino_device.change(device_change, openvino_device, warmup_status)
return [local_config, model_config, openvino_device, override_sampler, sampler_name, enable_caching]
return [model_config, openvino_device, override_sampler, sampler_name, enable_caching]
def run(self, p, local_config, model_config, openvino_device, override_sampler, sampler_name, enable_caching):
def run(self, p, model_config, openvino_device, override_sampler, sampler_name, enable_caching):
model_state.partition_id = 0
os.environ["OPENVINO_TORCH_BACKEND_DEVICE"] = str(openvino_device)
@ -767,14 +766,14 @@ class Script(scripts.Script):
mode = 0
if self.is_txt2img:
mode = 0
processed = process_images_openvino(p, local_config, model_config, p.sampler_name, enable_caching, openvino_device, mode)
processed = process_images_openvino(p, model_config, p.sampler_name, enable_caching, openvino_device, mode)
else:
if p.image_mask is None:
mode = 1
else:
mode = 2
p.init = functools.partial(init_new, p)
processed = process_images_openvino(p, local_config, model_config, p.sampler_name, enable_caching, openvino_device, mode)
processed = process_images_openvino(p, model_config, p.sampler_name, enable_caching, openvino_device, mode)
return processed