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granular model move

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2024-10-14 13:21:45 -04:00
parent cdcca50fb4
commit e80e8c8e14
1 changed files with 25 additions and 2 deletions
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modules/processing_diffusers.py
+25 -2
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@@ -4,7 +4,7 @@ import time
import numpy as np
import torch
import torchvision.transforms.functional as TF
from modules import shared, devices, processing, sd_models, errors, sd_hijack_hypertile, processing_vae, sd_models_compile, hidiffusion, timer
from modules import shared, devices, processing, sd_models, errors, sd_hijack_hypertile, processing_vae, sd_models_compile, hidiffusion, timer, modelstats
from modules.processing_helpers import resize_hires, calculate_base_steps, calculate_hires_steps, calculate_refiner_steps, save_intermediate, update_sampler, is_txt2img, is_refiner_enabled
from modules.processing_args import set_pipeline_args
from modules.onnx_impl import preprocess_pipeline as preprocess_onnx_pipeline, check_parameters_changed as olive_check_parameters_changed
@@ -84,6 +84,10 @@ def process_base(p: processing.StableDiffusionProcessing):
t0 = time.time()
sd_models_compile.check_deepcache(enable=True)
sd_models.move_model(shared.sd_model, devices.device)
if hasattr(shared.sd_model, 'unet'):
sd_models.move_model(shared.sd_model.unet, devices.device)
if hasattr(shared.sd_model, 'transformer'):
sd_models.move_model(shared.sd_model.transformer, devices.device)
hidiffusion.apply(p, shared.sd_model_type)
# if 'image' in base_args:
# base_args['image'] = set_latents(p)
@@ -120,8 +124,9 @@ def process_base(p: processing.StableDiffusionProcessing):
errors.display(e, 'Processing')
except RuntimeError as e:
shared.state.interrupted = True
shared.log.error(f'Processing: args={base_args} {e}')
shared.log.error(f'Processing: step=base args={base_args} {e}')
errors.display(e, 'Processing')
modelstats.analyze()
if hasattr(shared.sd_model, 'embedding_db') and len(shared.sd_model.embedding_db.embeddings_used) > 0: # register used embeddings
p.extra_generation_params['Embeddings'] = ', '.join(shared.sd_model.embedding_db.embeddings_used)
@@ -183,6 +188,10 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
output.images = processing_vae.vae_decode(latents=output.images, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.hr_upscale_to_x, height=p.hr_upscale_to_y) # controlnet cannnot deal with latent input
p.task_args['image'] = output.images # replace so hires uses new output
sd_models.move_model(shared.sd_model, devices.device)
if hasattr(shared.sd_model, 'unet'):
sd_models.move_model(shared.sd_model.unet, devices.device)
if hasattr(shared.sd_model, 'transformer'):
sd_models.move_model(shared.sd_model.transformer, devices.device)
orig_denoise = p.denoising_strength
p.denoising_strength = getattr(p, 'hr_denoising_strength', p.denoising_strength)
update_sampler(p, shared.sd_model, second_pass=True)
@@ -215,6 +224,11 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
sd_models_compile.openvino_post_compile(op="base")
except AssertionError as e:
shared.log.info(e)
except RuntimeError as e:
shared.state.interrupted = True
shared.log.error(f'Processing step=hires: args={hires_args} {e}')
errors.display(e, 'Processing')
modelstats.analyze()
p.denoising_strength = orig_denoise
shared.state.job = prev_job
shared.state.nextjob()
@@ -241,6 +255,10 @@ def process_refine(p: processing.StableDiffusionProcessing, output):
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
if shared.opts.diffusers_move_refiner:
sd_models.move_model(shared.sd_refiner, devices.device)
if hasattr(shared.sd_refiner, 'unet'):
sd_models.move_model(shared.sd_model.unet, devices.device)
if hasattr(shared.sd_refiner, 'transformer'):
sd_models.move_model(shared.sd_model.transformer, devices.device)
p.ops.append('refine')
p.is_refiner_pass = True
sd_models_compile.openvino_recompile_model(p, hires=False, refiner=True)
@@ -287,6 +305,11 @@ def process_refine(p: processing.StableDiffusionProcessing, output):
sd_models_compile.openvino_post_compile(op="refiner")
except AssertionError as e:
shared.log.info(e)
except RuntimeError as e:
shared.state.interrupted = True
shared.log.error(f'Processing step=refine: args={refiner_args} {e}')
errors.display(e, 'Processing')
modelstats.analyze()
""" # TODO decode using refiner
if not shared.state.interrupted and not shared.state.skipped: