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refactor compile out of processing

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Vladimir Mandic
2024-02-19 18:05:15 -05:00
parent 47106883cc
commit 456af9abdb
2 changed files with 60 additions and 48 deletions
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modules/processing_diffusers.py
+12 -42
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@@ -8,7 +8,7 @@ import numpy as np
import torch
import torchvision.transforms.functional as TF
import diffusers
from modules import shared, devices, processing, sd_samplers, sd_models, images, errors, prompt_parser_diffusers, sd_hijack_hypertile, processing_correction, processing_vae
from modules import shared, devices, processing, sd_samplers, sd_models, images, errors, prompt_parser_diffusers, sd_hijack_hypertile, processing_correction, processing_vae, sd_models_compile
from modules.processing_helpers import resize_init_images, resize_hires, fix_prompts, calculate_base_steps, calculate_hires_steps, calculate_refiner_steps
from modules.onnx_impl import preprocess_pipeline as preprocess_onnx_pipeline, check_parameters_changed as olive_check_parameters_changed
@@ -214,7 +214,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
if hasattr(model, "decoder") and hasattr(model, "prior_prior") and 'prior_num_inference_steps' in possible:
steps = kwargs.pop("num_inference_steps", 20)
args["prior_num_inference_steps"] = steps
args["num_inference_steps"] = max(int(steps / 2), 1) # TODO: add another slider without overcrowding the UI
args["num_inference_steps"] = max(int(steps / 2), 1)
if hasattr(model, "decoder") and hasattr(model, "prior_prior") and 'prior_guidance_scale' in possible:
cfg_scale = kwargs.pop("guidance_scale", p.cfg_scale)
args["prior_guidance_scale"] = cfg_scale
@@ -309,41 +309,6 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
debug(f'Diffusers pipeline args: {args}')
return args
def recompile_model(hires=False): # recompile if a parameter changes
if shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
if shared.opts.cuda_compile_backend == "openvino_fx":
compile_height = p.height if not hires and hasattr(p, 'height') else p.hr_upscale_to_y
compile_width = p.width if not hires and hasattr(p, 'width') else p.hr_upscale_to_x
if (shared.compiled_model_state is None or
(not shared.compiled_model_state.first_pass
and (shared.compiled_model_state.height != compile_height
or shared.compiled_model_state.width != compile_width
or shared.compiled_model_state.batch_size != p.batch_size))):
shared.log.info("OpenVINO: Parameter change detected")
shared.log.info("OpenVINO: Recompiling base model")
sd_models.unload_model_weights(op='model')
sd_models.reload_model_weights(op='model')
if is_refiner_enabled():
shared.log.info("OpenVINO: Recompiling refiner")
sd_models.unload_model_weights(op='refiner')
sd_models.reload_model_weights(op='refiner')
shared.compiled_model_state.height = compile_height
shared.compiled_model_state.width = compile_width
shared.compiled_model_state.batch_size = p.batch_size
def openvino_post_compile(op="base"): # delete unet after OpenVINO compile
if shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "openvino_fx":
if shared.compiled_model_state.first_pass and op == "base":
shared.compiled_model_state.first_pass = False
if not shared.opts.openvino_disable_memory_cleanup and hasattr(shared.sd_model, "unet"):
shared.sd_model.unet.apply(sd_models.convert_to_faketensors)
devices.torch_gc(force=True)
if shared.compiled_model_state.first_pass_refiner and op == "refiner":
shared.compiled_model_state.first_pass_refiner = False
if not shared.opts.openvino_disable_memory_cleanup and hasattr(shared.sd_refiner, "unet"):
shared.sd_refiner.unet.apply(sd_models.convert_to_faketensors)
devices.torch_gc(force=True)
def update_sampler(sd_model, second_pass=False):
sampler_selection = p.hr_sampler_name if second_pass else p.sampler_name
if sd_model.__class__.__name__ in ['AmusedPipeline']:
@@ -405,7 +370,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
sd_models.move_model(shared.sd_model, devices.device)
# recompile if a parameter changes
recompile_model()
sd_models_compile.openvino_recompile_model(p, hires=False, refiner=False)
# pipeline type is set earlier in processing, but check for sanity
is_control = getattr(p, 'is_control', False) is True
@@ -444,10 +409,12 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
p.extra_generation_params["Sampler Eta"] = shared.opts.scheduler_eta
try:
t0 = time.time()
sd_models_compile.check_deepcache(enable=True)
output = shared.sd_model(**base_args) # pylint: disable=not-callable
if isinstance(output, dict):
output = SimpleNamespace(**output)
openvino_post_compile(op="base") # only executes on compiled vino models
sd_models_compile.openvino_post_compile(op="base") # only executes on compiled vino models
sd_models_compile.check_deepcache(enable=False)
if shared.cmd_opts.profile:
t1 = time.time()
shared.log.debug(f'Profile: pipeline call: {t1-t0:.2f}')
@@ -494,7 +461,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
output.images = resize_hires(p, latents=output.images)
if (latent_scale_mode is not None or p.hr_force) and p.denoising_strength > 0:
p.ops.append('hires')
recompile_model(hires=True)
sd_models_compile.openvino_recompile_model(p, hires=True, refiner=False)
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
if shared.sd_model.__class__.__name__ == "OnnxRawPipeline":
shared.sd_model = preprocess_onnx_pipeline(p)
@@ -518,10 +485,12 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
shared.state.job = 'hires'
shared.state.sampling_steps = hires_args['num_inference_steps']
try:
sd_models_compile.check_deepcache(enable=True)
output = shared.sd_model(**hires_args) # pylint: disable=not-callable
if isinstance(output, dict):
output = SimpleNamespace(**output)
openvino_post_compile(op="base")
sd_models_compile.check_deepcache(enable=False)
sd_models_compile.openvino_post_compile(op="base")
except AssertionError as e:
shared.log.info(e)
p.init_images = []
@@ -546,6 +515,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
sd_models.move_model(shared.sd_refiner, devices.device)
p.ops.append('refine')
p.is_refiner_pass = True
sd_models_compile.openvino_recompile_model(p, hires=False, refiner=True)
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
shared.sd_refiner = sd_models.set_diffuser_pipe(shared.sd_refiner, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
update_sampler(shared.sd_refiner, second_pass=True)
@@ -581,7 +551,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
refiner_output = shared.sd_refiner(**refiner_args) # pylint: disable=not-callable
if isinstance(refiner_output, dict):
refiner_output = SimpleNamespace(**refiner_output)
openvino_post_compile(op="refiner")
sd_models_compile.openvino_post_compile(op="refiner")
except AssertionError as e:
shared.log.info(e)