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Update accel_sdxl_gen_img.py

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DKnight54
2025-01-25 11:56:04 +08:00
committed by GitHub
parent ce9dfda59f
commit c710432f0a
1 changed files with 7 additions and 10 deletions
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17
accel_sdxl_gen_img.py
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@@ -20,7 +20,7 @@ import diffusers
import numpy as np
import torch
from library.device_utils import init_ipex, clean_memory, get_preferred_device
from library.device_utils import init_ipex, clean_memory, get_preferred_device, clean_memory_on_device
init_ipex()
import torchvision
@@ -1499,11 +1499,13 @@ def main(args):
args.ckpt = files[0]
#device = get_preferred_device()
logger.info(f"preferred device: {device}")
clean_memory_on_device(device)
model_dtype = sdxl_train_util.match_mixed_precision(args, dtype)
(_, text_encoder1, text_encoder2, vae, unet, _, _) = sdxl_train_util._load_target_model(
args.ckpt, args.vae, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, dtype, device if args.lowram else "cpu", model_dtype
)
unet: InferSdxlUNet2DConditionModel = InferSdxlUNet2DConditionModel(unet)
distributed_state.wait_for_everyone()
# xformers、Hypernetwork対応
if not args.diffusers_xformers:
@@ -1828,6 +1830,7 @@ def main(args):
)
pipe.set_control_nets(control_nets)
logger.info(f"pipeline on {device} is ready.")
distributed_state.wait_for_everyone()
if args.diffusers_xformers:
pipe.enable_xformers_memory_efficient_attention()
@@ -2834,12 +2837,10 @@ def main(args):
if (i+1) % args.batch_size == 0:
batch_data_split.append(batch_index.copy())
batch_index.clear()
if distributed_state.is_main_process:
logger.info(f"batch_data_split: {batch_data_split}")
with torch.no_grad():
with distributed_state.split_between_processes(batch_data_split) as batch_list:
logger.info(f"Loading batch of {len(batch_list[0])} prompts onto device {distributed_state.device}:")
logger.info(f"batch_list: {batch_list}")
logger.info(f"batch_list:")
for i in range(len(batch_list[0])):
logger.info(f"Prompt {i+1}: {batch_list[0][i].base.prompt}")
prev_image = process_batch(batch_list[0], highres_fix)[0]
@@ -2851,7 +2852,6 @@ def main(args):
if len(batch_data) == args.batch_size*distributed_state.num_processes:
if distributed_state.is_main_process:
logger.info(f"Collected {len(batch_data)} prompts for {distributed_state.num_processes} devices.")
logger.info(f"{batch_data}")
batch_data_split = [] #[batch_data[i:i+3] for i in range(0, len(my_list), 3)]
batch_index = []
test_batch_data_split = []
@@ -2861,23 +2861,20 @@ def main(args):
batch_index.append(batch_data[i])
test_batch_index.append(batch_data[i])
if (i+1) % args.batch_size == 0:
logger.info(f"Loading {batch_index}")
batch_data_split.append(batch_index.copy())
batch_index.clear()
if (i+1) % 4 == 0:
logger.info(f"Loading {test_batch_index}")
test_batch_data_split.append(test_batch_index.copy())
test_batch_index.clear()
logger.info(f"batch_data_split: {batch_data_split}")
for i in range(len(test_batch_data_split)):
logger.info(f"test_batch_data_split[{i}]: {test_batch_data_split[i]}")
logger.info(f"test_batch_data_split[{i}]:")
for j in range(len(test_batch_data_split[i])):
logger.info(f"Prompt {j}: {test_batch_data_split[i][j].base.prompt}")
with torch.no_grad():
with distributed_state.split_between_processes(batch_data_split) as batch_list:
logger.info(f"Loading batch of {len(batch_list[0])} prompts onto device {distributed_state.device}:")
logger.info(f"batch_list: {batch_list}")
logger.info(f"batch_list:")
for i in range(len(batch_list[0])):
logger.info(f"Prompt {i+1}: {batch_list[0][i].base.prompt}")
prev_image = process_batch(batch_list[0], highres_fix)[0]