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Replaced print with logger

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This commit is contained in:
Yuta Hayashibe
2024-02-25 00:50:36 +09:00
parent e69d34103b
commit 5d5f39b6e6
5 changed files with 138 additions and 125 deletions
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194
gen_img.py
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@@ -61,6 +61,12 @@ from library.sdxl_original_unet import InferSdxlUNet2DConditionModel
from library.original_unet import FlashAttentionFunction from library.original_unet import FlashAttentionFunction
from networks.control_net_lllite import ControlNetLLLite from networks.control_net_lllite import ControlNetLLLite
from library.utils import GradualLatent, EulerAncestralDiscreteSchedulerGL from library.utils import GradualLatent, EulerAncestralDiscreteSchedulerGL
from library.utils import setup_logging, add_logging_arguments
setup_logging()
import logging
logger = logging.getLogger(__name__)
# scheduler: # scheduler:
SCHEDULER_LINEAR_START = 0.00085 SCHEDULER_LINEAR_START = 0.00085
@@ -82,12 +88,12 @@ CLIP_VISION_MODEL = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers, sdpa): def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers, sdpa):
if mem_eff_attn: if mem_eff_attn:
print("Enable memory efficient attention for U-Net") logger.info("Enable memory efficient attention for U-Net")
# これはDiffusersのU-Netではなく自前のU-Netなので置き換えなくても良い # これはDiffusersのU-Netではなく自前のU-Netなので置き換えなくても良い
unet.set_use_memory_efficient_attention(False, True) unet.set_use_memory_efficient_attention(False, True)
elif xformers: elif xformers:
print("Enable xformers for U-Net") logger.info("Enable xformers for U-Net")
try: try:
import xformers.ops import xformers.ops
except ImportError: except ImportError:
@@ -95,7 +101,7 @@ def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditio
unet.set_use_memory_efficient_attention(True, False) unet.set_use_memory_efficient_attention(True, False)
elif sdpa: elif sdpa:
print("Enable SDPA for U-Net") logger.info("Enable SDPA for U-Net")
unet.set_use_memory_efficient_attention(False, False) unet.set_use_memory_efficient_attention(False, False)
unet.set_use_sdpa(True) unet.set_use_sdpa(True)
@@ -112,7 +118,7 @@ def replace_vae_modules(vae: diffusers.models.AutoencoderKL, mem_eff_attn, xform
def replace_vae_attn_to_memory_efficient(): def replace_vae_attn_to_memory_efficient():
print("VAE Attention.forward has been replaced to FlashAttention (not xformers)") logger.info("VAE Attention.forward has been replaced to FlashAttention (not xformers)")
flash_func = FlashAttentionFunction flash_func = FlashAttentionFunction
def forward_flash_attn(self, hidden_states, **kwargs): def forward_flash_attn(self, hidden_states, **kwargs):
@@ -168,7 +174,7 @@ def replace_vae_attn_to_memory_efficient():
def replace_vae_attn_to_xformers(): def replace_vae_attn_to_xformers():
print("VAE: Attention.forward has been replaced to xformers") logger.info("VAE: Attention.forward has been replaced to xformers")
import xformers.ops import xformers.ops
def forward_xformers(self, hidden_states, **kwargs): def forward_xformers(self, hidden_states, **kwargs):
@@ -224,7 +230,7 @@ def replace_vae_attn_to_xformers():
def replace_vae_attn_to_sdpa(): def replace_vae_attn_to_sdpa():
print("VAE: Attention.forward has been replaced to sdpa") logger.info("VAE: Attention.forward has been replaced to sdpa")
def forward_sdpa(self, hidden_states, **kwargs): def forward_sdpa(self, hidden_states, **kwargs):
residual = hidden_states residual = hidden_states
@@ -386,10 +392,10 @@ class PipelineLike:
def set_gradual_latent(self, gradual_latent): def set_gradual_latent(self, gradual_latent):
if gradual_latent is None: if gradual_latent is None:
print("gradual_latent is disabled") logger.info("gradual_latent is disabled")
self.gradual_latent = None self.gradual_latent = None
else: else:
print(f"gradual_latent is enabled: {gradual_latent}") logger.info(f"gradual_latent is enabled: {gradual_latent}")
self.gradual_latent = gradual_latent # (ds_ratio, start_timesteps, every_n_steps, ratio_step) self.gradual_latent = gradual_latent # (ds_ratio, start_timesteps, every_n_steps, ratio_step)
@torch.no_grad() @torch.no_grad()
@@ -467,7 +473,7 @@ class PipelineLike:
do_classifier_free_guidance = guidance_scale > 1.0 do_classifier_free_guidance = guidance_scale > 1.0
if not do_classifier_free_guidance and negative_scale is not None: if not do_classifier_free_guidance and negative_scale is not None:
print(f"negative_scale is ignored if guidance scalle <= 1.0") logger.warning(f"negative_scale is ignored if guidance scalle <= 1.0")
negative_scale = None negative_scale = None
# get unconditional embeddings for classifier free guidance # get unconditional embeddings for classifier free guidance
@@ -576,7 +582,7 @@ class PipelineLike:
text_pool = text_pool[num_sub_prompts - 1 :: num_sub_prompts] # last subprompt text_pool = text_pool[num_sub_prompts - 1 :: num_sub_prompts] # last subprompt
if init_image is not None and self.clip_vision_model is not None: if init_image is not None and self.clip_vision_model is not None:
print(f"encode by clip_vision_model and apply clip_vision_strength={self.clip_vision_strength}") logger.info(f"encode by clip_vision_model and apply clip_vision_strength={self.clip_vision_strength}")
vision_input = self.clip_vision_processor(init_image, return_tensors="pt", device=self.device) vision_input = self.clip_vision_processor(init_image, return_tensors="pt", device=self.device)
pixel_values = vision_input["pixel_values"].to(self.device, dtype=text_embeddings.dtype) pixel_values = vision_input["pixel_values"].to(self.device, dtype=text_embeddings.dtype)
@@ -742,8 +748,8 @@ class PipelineLike:
enable_gradual_latent = False enable_gradual_latent = False
if self.gradual_latent: if self.gradual_latent:
if not hasattr(self.scheduler, "set_gradual_latent_params"): if not hasattr(self.scheduler, "set_gradual_latent_params"):
print("gradual_latent is not supported for this scheduler. Ignoring.") logger.warning("gradual_latent is not supported for this scheduler. Ignoring.")
print(self.scheduler.__class__.__name__) logger.warning(f"{self.scheduler.__class__.__name__}")
else: else:
enable_gradual_latent = True enable_gradual_latent = True
step_elapsed = 1000 step_elapsed = 1000
@@ -792,7 +798,7 @@ class PipelineLike:
if not enabled or ratio >= 1.0: if not enabled or ratio >= 1.0:
continue continue
if ratio < i / len(timesteps): if ratio < i / len(timesteps):
print(f"ControlNetLLLite {j} is disabled (ratio={ratio} at {i} / {len(timesteps)})") logger.info(f"ControlNetLLLite {j} is disabled (ratio={ratio} at {i} / {len(timesteps)})")
control_net.set_cond_image(None) control_net.set_cond_image(None)
each_control_net_enabled[j] = False each_control_net_enabled[j] = False
@@ -1013,7 +1019,7 @@ def get_prompts_with_weights(tokenizer: CLIPTokenizer, token_replacer, prompt: L
if word.strip() == "BREAK": if word.strip() == "BREAK":
# pad until next multiple of tokenizer's max token length # pad until next multiple of tokenizer's max token length
pad_len = tokenizer.model_max_length - (len(text_token) % tokenizer.model_max_length) pad_len = tokenizer.model_max_length - (len(text_token) % tokenizer.model_max_length)
print(f"BREAK pad_len: {pad_len}") logger.info(f"BREAK pad_len: {pad_len}")
for i in range(pad_len): for i in range(pad_len):
# v2のときEOSをつけるべきかどうかわからないぜ # v2のときEOSをつけるべきかどうかわからないぜ
# if i == 0: # if i == 0:
@@ -1043,7 +1049,7 @@ def get_prompts_with_weights(tokenizer: CLIPTokenizer, token_replacer, prompt: L
tokens.append(text_token) tokens.append(text_token)
weights.append(text_weight) weights.append(text_weight)
if truncated: if truncated:
print("warning: Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples") logger.warning("warning: Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
return tokens, weights return tokens, weights
@@ -1344,7 +1350,7 @@ def handle_dynamic_prompt_variants(prompt, repeat_count):
elif len(count_range) == 2: elif len(count_range) == 2:
count_range = [int(count_range[0]), int(count_range[1])] count_range = [int(count_range[0]), int(count_range[1])]
else: else:
print(f"invalid count range: {count_range}") logger.warning(f"invalid count range: {count_range}")
count_range = [1, 1] count_range = [1, 1]
if count_range[0] > count_range[1]: if count_range[0] > count_range[1]:
count_range = [count_range[1], count_range[0]] count_range = [count_range[1], count_range[0]]
@@ -1488,9 +1494,9 @@ def main(args):
# assert not highres_fix or args.image_path is None, f"highres_fix doesn't work with img2img / highres_fixはimg2imgと同時に使えません" # assert not highres_fix or args.image_path is None, f"highres_fix doesn't work with img2img / highres_fixはimg2imgと同時に使えません"
if args.v_parameterization and not args.v2: if args.v_parameterization and not args.v2:
print("v_parameterization should be with v2 / v1でv_parameterizationを使用することは想定されていません") logger.warning("v_parameterization should be with v2 / v1でv_parameterizationを使用することは想定されていません")
if args.v2 and args.clip_skip is not None: if args.v2 and args.clip_skip is not None:
print("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません") logger.warning("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません")
# モデルを読み込む # モデルを読み込む
if not os.path.exists(args.ckpt): # ファイルがないならパターンで探し、一つだけ該当すればそれを使う if not os.path.exists(args.ckpt): # ファイルがないならパターンで探し、一つだけ該当すればそれを使う
@@ -1510,7 +1516,7 @@ def main(args):
else: else:
# if `text_encoder_2` subdirectory exists, sdxl # if `text_encoder_2` subdirectory exists, sdxl
is_sdxl = os.path.isdir(os.path.join(name_or_path, "text_encoder_2")) is_sdxl = os.path.isdir(os.path.join(name_or_path, "text_encoder_2"))
print(f"SDXL: {is_sdxl}") logger.info(f"SDXL: {is_sdxl}")
if is_sdxl: if is_sdxl:
if args.clip_skip is None: if args.clip_skip is None:
@@ -1526,10 +1532,10 @@ def main(args):
args.clip_skip = 2 if args.v2 else 1 args.clip_skip = 2 if args.v2 else 1
if use_stable_diffusion_format: if use_stable_diffusion_format:
print("load StableDiffusion checkpoint") logger.info("load StableDiffusion checkpoint")
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.ckpt) text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.ckpt)
else: else:
print("load Diffusers pretrained models") logger.info("load Diffusers pretrained models")
loading_pipe = StableDiffusionPipeline.from_pretrained(args.ckpt, safety_checker=None, torch_dtype=dtype) loading_pipe = StableDiffusionPipeline.from_pretrained(args.ckpt, safety_checker=None, torch_dtype=dtype)
text_encoder = loading_pipe.text_encoder text_encoder = loading_pipe.text_encoder
vae = loading_pipe.vae vae = loading_pipe.vae
@@ -1553,7 +1559,7 @@ def main(args):
# VAEを読み込む # VAEを読み込む
if args.vae is not None: if args.vae is not None:
vae = model_util.load_vae(args.vae, dtype) vae = model_util.load_vae(args.vae, dtype)
print("additional VAE loaded") logger.info("additional VAE loaded")
# xformers、Hypernetwork対応 # xformers、Hypernetwork対応
if not args.diffusers_xformers: if not args.diffusers_xformers:
@@ -1562,7 +1568,7 @@ def main(args):
replace_vae_modules(vae, mem_eff, args.xformers, args.sdpa) replace_vae_modules(vae, mem_eff, args.xformers, args.sdpa)
# tokenizerを読み込む # tokenizerを読み込む
print("loading tokenizer") logger.info("loading tokenizer")
if is_sdxl: if is_sdxl:
tokenizer1, tokenizer2 = sdxl_train_util.load_tokenizers(args) tokenizer1, tokenizer2 = sdxl_train_util.load_tokenizers(args)
tokenizers = [tokenizer1, tokenizer2] tokenizers = [tokenizer1, tokenizer2]
@@ -1654,7 +1660,7 @@ def main(args):
noise = None noise = None
if noise == None: if noise == None:
print(f"unexpected noise request: {self.sampler_noise_index}, {shape}") logger.warning(f"unexpected noise request: {self.sampler_noise_index}, {shape}")
noise = torch.randn(shape, dtype=dtype, device=device, generator=generator) noise = torch.randn(shape, dtype=dtype, device=device, generator=generator)
self.sampler_noise_index += 1 self.sampler_noise_index += 1
@@ -1715,7 +1721,7 @@ def main(args):
vae_dtype = dtype vae_dtype = dtype
if args.no_half_vae: if args.no_half_vae:
print("set vae_dtype to float32") logger.info("set vae_dtype to float32")
vae_dtype = torch.float32 vae_dtype = torch.float32
vae.to(vae_dtype).to(device) vae.to(vae_dtype).to(device)
vae.eval() vae.eval()
@@ -1739,10 +1745,10 @@ def main(args):
network_merge = args.network_merge_n_models network_merge = args.network_merge_n_models
else: else:
network_merge = 0 network_merge = 0
print(f"network_merge: {network_merge}") logger.info(f"network_merge: {network_merge}")
for i, network_module in enumerate(args.network_module): for i, network_module in enumerate(args.network_module):
print("import network module:", network_module) logger.info("import network module: {network_module}")
imported_module = importlib.import_module(network_module) imported_module = importlib.import_module(network_module)
network_mul = 1.0 if args.network_mul is None or len(args.network_mul) <= i else args.network_mul[i] network_mul = 1.0 if args.network_mul is None or len(args.network_mul) <= i else args.network_mul[i]
@@ -1760,7 +1766,7 @@ def main(args):
raise ValueError("No weight. Weight is required.") raise ValueError("No weight. Weight is required.")
network_weight = args.network_weights[i] network_weight = args.network_weights[i]
print("load network weights from:", network_weight) logger.info(f"load network weights from: {network_weight}")
if model_util.is_safetensors(network_weight) and args.network_show_meta: if model_util.is_safetensors(network_weight) and args.network_show_meta:
from safetensors.torch import safe_open from safetensors.torch import safe_open
@@ -1768,7 +1774,7 @@ def main(args):
with safe_open(network_weight, framework="pt") as f: with safe_open(network_weight, framework="pt") as f:
metadata = f.metadata() metadata = f.metadata()
if metadata is not None: if metadata is not None:
print(f"metadata for: {network_weight}: {metadata}") logger.info(f"metadata for: {network_weight}: {metadata}")
network, weights_sd = imported_module.create_network_from_weights( network, weights_sd = imported_module.create_network_from_weights(
network_mul, network_weight, vae, text_encoders, unet, for_inference=True, **net_kwargs network_mul, network_weight, vae, text_encoders, unet, for_inference=True, **net_kwargs
@@ -1778,20 +1784,20 @@ def main(args):
mergeable = network.is_mergeable() mergeable = network.is_mergeable()
if network_merge and not mergeable: if network_merge and not mergeable:
print("network is not mergiable. ignore merge option.") logger.warning("network is not mergiable. ignore merge option.")
if not mergeable or i >= network_merge: if not mergeable or i >= network_merge:
# not merging # not merging
network.apply_to(text_encoders, unet) network.apply_to(text_encoders, unet)
info = network.load_state_dict(weights_sd, False) # network.load_weightsを使うようにするとよい info = network.load_state_dict(weights_sd, False) # network.load_weightsを使うようにするとよい
print(f"weights are loaded: {info}") logger.info(f"weights are loaded: {info}")
if args.opt_channels_last: if args.opt_channels_last:
network.to(memory_format=torch.channels_last) network.to(memory_format=torch.channels_last)
network.to(dtype).to(device) network.to(dtype).to(device)
if network_pre_calc: if network_pre_calc:
print("backup original weights") logger.info("backup original weights")
network.backup_weights() network.backup_weights()
networks.append(network) networks.append(network)
@@ -1805,7 +1811,7 @@ def main(args):
# upscalerの指定があれば取得する # upscalerの指定があれば取得する
upscaler = None upscaler = None
if args.highres_fix_upscaler: if args.highres_fix_upscaler:
print("import upscaler module:", args.highres_fix_upscaler) logger.info("import upscaler module: {args.highres_fix_upscaler}")
imported_module = importlib.import_module(args.highres_fix_upscaler) imported_module = importlib.import_module(args.highres_fix_upscaler)
us_kwargs = {} us_kwargs = {}
@@ -1814,7 +1820,7 @@ def main(args):
key, value = net_arg.split("=") key, value = net_arg.split("=")
us_kwargs[key] = value us_kwargs[key] = value
print("create upscaler") logger.info("create upscaler")
upscaler = imported_module.create_upscaler(**us_kwargs) upscaler = imported_module.create_upscaler(**us_kwargs)
upscaler.to(dtype).to(device) upscaler.to(dtype).to(device)
@@ -1833,7 +1839,7 @@ def main(args):
control_net_lllites: List[Tuple[ControlNetLLLite, float]] = [] control_net_lllites: List[Tuple[ControlNetLLLite, float]] = []
if args.control_net_lllite_models: if args.control_net_lllite_models:
for i, model_file in enumerate(args.control_net_lllite_models): for i, model_file in enumerate(args.control_net_lllite_models):
print(f"loading ControlNet-LLLite: {model_file}") logger.info(f"loading ControlNet-LLLite: {model_file}")
from safetensors.torch import load_file from safetensors.torch import load_file
@@ -1867,7 +1873,7 @@ def main(args):
), "ControlNet and ControlNet-LLLite cannot be used at the same time" ), "ControlNet and ControlNet-LLLite cannot be used at the same time"
if args.opt_channels_last: if args.opt_channels_last:
print(f"set optimizing: channels last") logger.info(f"set optimizing: channels last")
for text_encoder in text_encoders: for text_encoder in text_encoders:
text_encoder.to(memory_format=torch.channels_last) text_encoder.to(memory_format=torch.channels_last)
vae.to(memory_format=torch.channels_last) vae.to(memory_format=torch.channels_last)
@@ -1894,7 +1900,7 @@ def main(args):
) )
pipe.set_control_nets(control_nets) pipe.set_control_nets(control_nets)
pipe.set_control_net_lllites(control_net_lllites) pipe.set_control_net_lllites(control_net_lllites)
print("pipeline is ready.") logger.info("pipeline is ready.")
if args.diffusers_xformers: if args.diffusers_xformers:
pipe.enable_xformers_memory_efficient_attention() pipe.enable_xformers_memory_efficient_attention()
@@ -1965,7 +1971,7 @@ def main(args):
token_ids1 = tokenizers[0].convert_tokens_to_ids(token_strings) token_ids1 = tokenizers[0].convert_tokens_to_ids(token_strings)
token_ids2 = tokenizers[1].convert_tokens_to_ids(token_strings) if is_sdxl else None token_ids2 = tokenizers[1].convert_tokens_to_ids(token_strings) if is_sdxl else None
print(f"Textual Inversion embeddings `{token_string}` loaded. Tokens are added: {token_ids1} and {token_ids2}") logger.info(f"Textual Inversion embeddings `{token_string}` loaded. Tokens are added: {token_ids1} and {token_ids2}")
assert ( assert (
min(token_ids1) == token_ids1[0] and token_ids1[-1] == token_ids1[0] + len(token_ids1) - 1 min(token_ids1) == token_ids1[0] and token_ids1[-1] == token_ids1[0] + len(token_ids1) - 1
), f"token ids1 is not ordered" ), f"token ids1 is not ordered"
@@ -2002,7 +2008,7 @@ def main(args):
# promptを取得する # promptを取得する
prompt_list = None prompt_list = None
if args.from_file is not None: if args.from_file is not None:
print(f"reading prompts from {args.from_file}") logger.info(f"reading prompts from {args.from_file}")
with open(args.from_file, "r", encoding="utf-8") as f: with open(args.from_file, "r", encoding="utf-8") as f:
prompt_list = f.read().splitlines() prompt_list = f.read().splitlines()
prompt_list = [d for d in prompt_list if len(d.strip()) > 0 and d[0] != "#"] prompt_list = [d for d in prompt_list if len(d.strip()) > 0 and d[0] != "#"]
@@ -2019,7 +2025,7 @@ def main(args):
spec.loader.exec_module(module) spec.loader.exec_module(module)
return module return module
print(f"reading prompts from module: {args.from_module}") logger.info(f"reading prompts from module: {args.from_module}")
prompt_module = load_module_from_path("prompt_module", args.from_module) prompt_module = load_module_from_path("prompt_module", args.from_module)
prompter = prompt_module.get_prompter(args, pipe, networks) prompter = prompt_module.get_prompter(args, pipe, networks)
@@ -2050,7 +2056,7 @@ def main(args):
for p in paths: for p in paths:
image = Image.open(p) image = Image.open(p)
if image.mode != "RGB": if image.mode != "RGB":
print(f"convert image to RGB from {image.mode}: {p}") logger.info(f"convert image to RGB from {image.mode}: {p}")
image = image.convert("RGB") image = image.convert("RGB")
images.append(image) images.append(image)
@@ -2066,14 +2072,14 @@ def main(args):
return resized return resized
if args.image_path is not None: if args.image_path is not None:
print(f"load image for img2img: {args.image_path}") logger.info(f"load image for img2img: {args.image_path}")
init_images = load_images(args.image_path) init_images = load_images(args.image_path)
assert len(init_images) > 0, f"No image / 画像がありません: {args.image_path}" assert len(init_images) > 0, f"No image / 画像がありません: {args.image_path}"
print(f"loaded {len(init_images)} images for img2img") logger.info(f"loaded {len(init_images)} images for img2img")
# CLIP Vision # CLIP Vision
if args.clip_vision_strength is not None: if args.clip_vision_strength is not None:
print(f"load CLIP Vision model: {CLIP_VISION_MODEL}") logger.info(f"load CLIP Vision model: {CLIP_VISION_MODEL}")
vision_model = CLIPVisionModelWithProjection.from_pretrained(CLIP_VISION_MODEL, projection_dim=1280) vision_model = CLIPVisionModelWithProjection.from_pretrained(CLIP_VISION_MODEL, projection_dim=1280)
vision_model.to(device, dtype) vision_model.to(device, dtype)
processor = CLIPImageProcessor.from_pretrained(CLIP_VISION_MODEL) processor = CLIPImageProcessor.from_pretrained(CLIP_VISION_MODEL)
@@ -2081,22 +2087,22 @@ def main(args):
pipe.clip_vision_model = vision_model pipe.clip_vision_model = vision_model
pipe.clip_vision_processor = processor pipe.clip_vision_processor = processor
pipe.clip_vision_strength = args.clip_vision_strength pipe.clip_vision_strength = args.clip_vision_strength
print(f"CLIP Vision model loaded.") logger.info(f"CLIP Vision model loaded.")
else: else:
init_images = None init_images = None
if args.mask_path is not None: if args.mask_path is not None:
print(f"load mask for inpainting: {args.mask_path}") logger.info(f"load mask for inpainting: {args.mask_path}")
mask_images = load_images(args.mask_path) mask_images = load_images(args.mask_path)
assert len(mask_images) > 0, f"No mask image / マスク画像がありません: {args.image_path}" assert len(mask_images) > 0, f"No mask image / マスク画像がありません: {args.image_path}"
print(f"loaded {len(mask_images)} mask images for inpainting") logger.info(f"loaded {len(mask_images)} mask images for inpainting")
else: else:
mask_images = None mask_images = None
# promptがないとき、画像のPngInfoから取得する # promptがないとき、画像のPngInfoから取得する
if init_images is not None and prompter is None and not args.interactive: if init_images is not None and prompter is None and not args.interactive:
print("get prompts from images' metadata") logger.info("get prompts from images' metadata")
prompt_list = [] prompt_list = []
for img in init_images: for img in init_images:
if "prompt" in img.text: if "prompt" in img.text:
@@ -2127,17 +2133,17 @@ def main(args):
h = int(h * args.highres_fix_scale + 0.5) h = int(h * args.highres_fix_scale + 0.5)
if init_images is not None: if init_images is not None:
print(f"resize img2img source images to {w}*{h}") logger.info(f"resize img2img source images to {w}*{h}")
init_images = resize_images(init_images, (w, h)) init_images = resize_images(init_images, (w, h))
if mask_images is not None: if mask_images is not None:
print(f"resize img2img mask images to {w}*{h}") logger.info(f"resize img2img mask images to {w}*{h}")
mask_images = resize_images(mask_images, (w, h)) mask_images = resize_images(mask_images, (w, h))
regional_network = False regional_network = False
if networks and mask_images: if networks and mask_images:
# mask を領域情報として流用する、現在は一回のコマンド呼び出しで1枚だけ対応 # mask を領域情報として流用する、現在は一回のコマンド呼び出しで1枚だけ対応
regional_network = True regional_network = True
print("use mask as region") logger.info("use mask as region")
size = None size = None
for i, network in enumerate(networks): for i, network in enumerate(networks):
@@ -2162,14 +2168,14 @@ def main(args):
prev_image = None # for VGG16 guided prev_image = None # for VGG16 guided
if args.guide_image_path is not None: if args.guide_image_path is not None:
print(f"load image for ControlNet guidance: {args.guide_image_path}") logger.info(f"load image for ControlNet guidance: {args.guide_image_path}")
guide_images = [] guide_images = []
for p in args.guide_image_path: for p in args.guide_image_path:
guide_images.extend(load_images(p)) guide_images.extend(load_images(p))
print(f"loaded {len(guide_images)} guide images for guidance") logger.info(f"loaded {len(guide_images)} guide images for guidance")
if len(guide_images) == 0: if len(guide_images) == 0:
print( logger.warning(
f"No guide image, use previous generated image. / ガイド画像がありません。直前に生成した画像を使います: {args.image_path}" f"No guide image, use previous generated image. / ガイド画像がありません。直前に生成した画像を使います: {args.image_path}"
) )
guide_images = None guide_images = None
@@ -2200,7 +2206,7 @@ def main(args):
max_embeddings_multiples = 1 if args.max_embeddings_multiples is None else args.max_embeddings_multiples max_embeddings_multiples = 1 if args.max_embeddings_multiples is None else args.max_embeddings_multiples
for gen_iter in range(args.n_iter): for gen_iter in range(args.n_iter):
print(f"iteration {gen_iter+1}/{args.n_iter}") logger.info(f"iteration {gen_iter+1}/{args.n_iter}")
if args.iter_same_seed: if args.iter_same_seed:
iter_seed = seed_random.randint(0, 2**32 - 1) iter_seed = seed_random.randint(0, 2**32 - 1)
else: else:
@@ -2219,7 +2225,7 @@ def main(args):
# 1st stageのバッチを作成して呼び出す：サイズを小さくして呼び出す # 1st stageのバッチを作成して呼び出す：サイズを小さくして呼び出す
is_1st_latent = upscaler.support_latents() if upscaler else args.highres_fix_latents_upscaling is_1st_latent = upscaler.support_latents() if upscaler else args.highres_fix_latents_upscaling
print("process 1st stage") logger.info("process 1st stage")
batch_1st = [] batch_1st = []
for _, base, ext in batch: for _, base, ext in batch:
@@ -2264,7 +2270,7 @@ def main(args):
images_1st = process_batch(batch_1st, True, True) images_1st = process_batch(batch_1st, True, True)
# 2nd stageのバッチを作成して以下処理する # 2nd stageのバッチを作成して以下処理する
print("process 2nd stage") logger.info("process 2nd stage")
width_2nd, height_2nd = batch[0].ext.width, batch[0].ext.height width_2nd, height_2nd = batch[0].ext.width, batch[0].ext.height
if upscaler: if upscaler:
@@ -2437,7 +2443,7 @@ def main(args):
n.restore_weights() n.restore_weights()
for n in networks: for n in networks:
n.pre_calculation() n.pre_calculation()
print("pre-calculation... done") logger.info("pre-calculation... done")
images = pipe( images = pipe(
prompts, prompts,
@@ -2520,7 +2526,7 @@ def main(args):
cv2.waitKey() cv2.waitKey()
cv2.destroyAllWindows() cv2.destroyAllWindows()
except ImportError: except ImportError:
print( logger.warning(
"opencv-python is not installed, cannot preview / opencv-pythonがインストールされていないためプレビューできません" "opencv-python is not installed, cannot preview / opencv-pythonがインストールされていないためプレビューできません"
) )
@@ -2535,7 +2541,7 @@ def main(args):
# interactive # interactive
valid = False valid = False
while not valid: while not valid:
print("\nType prompt:") logger.info("\nType prompt:")
try: try:
raw_prompt = input() raw_prompt = input()
except EOFError: except EOFError:
@@ -2595,74 +2601,74 @@ def main(args):
prompt_args = raw_prompt.strip().split(" --") prompt_args = raw_prompt.strip().split(" --")
prompt = prompt_args[0] prompt = prompt_args[0]
length = len(prompter) if hasattr(prompter, "__len__") else 0 length = len(prompter) if hasattr(prompter, "__len__") else 0
print(f"prompt {prompt_index+1}/{length}: {prompt}") logger.info(f"prompt {prompt_index+1}/{length}: {prompt}")
for parg in prompt_args[1:]: for parg in prompt_args[1:]:
try: try:
m = re.match(r"w (\d+)", parg, re.IGNORECASE) m = re.match(r"w (\d+)", parg, re.IGNORECASE)
if m: if m:
width = int(m.group(1)) width = int(m.group(1))
print(f"width: {width}") logger.info(f"width: {width}")
continue continue
m = re.match(r"h (\d+)", parg, re.IGNORECASE) m = re.match(r"h (\d+)", parg, re.IGNORECASE)
if m: if m:
height = int(m.group(1)) height = int(m.group(1))
print(f"height: {height}") logger.info(f"height: {height}")
continue continue
m = re.match(r"ow (\d+)", parg, re.IGNORECASE) m = re.match(r"ow (\d+)", parg, re.IGNORECASE)
if m: if m:
original_width = int(m.group(1)) original_width = int(m.group(1))
print(f"original width: {original_width}") logger.info(f"original width: {original_width}")
continue continue
m = re.match(r"oh (\d+)", parg, re.IGNORECASE) m = re.match(r"oh (\d+)", parg, re.IGNORECASE)
if m: if m:
original_height = int(m.group(1)) original_height = int(m.group(1))
print(f"original height: {original_height}") logger.info(f"original height: {original_height}")
continue continue
m = re.match(r"nw (\d+)", parg, re.IGNORECASE) m = re.match(r"nw (\d+)", parg, re.IGNORECASE)
if m: if m:
original_width_negative = int(m.group(1)) original_width_negative = int(m.group(1))
print(f"original width negative: {original_width_negative}") logger.info(f"original width negative: {original_width_negative}")
continue continue
m = re.match(r"nh (\d+)", parg, re.IGNORECASE) m = re.match(r"nh (\d+)", parg, re.IGNORECASE)
if m: if m:
original_height_negative = int(m.group(1)) original_height_negative = int(m.group(1))
print(f"original height negative: {original_height_negative}") logger.info(f"original height negative: {original_height_negative}")
continue continue
m = re.match(r"ct (\d+)", parg, re.IGNORECASE) m = re.match(r"ct (\d+)", parg, re.IGNORECASE)
if m: if m:
crop_top = int(m.group(1)) crop_top = int(m.group(1))
print(f"crop top: {crop_top}") logger.info(f"crop top: {crop_top}")
continue continue
m = re.match(r"cl (\d+)", parg, re.IGNORECASE) m = re.match(r"cl (\d+)", parg, re.IGNORECASE)
if m: if m:
crop_left = int(m.group(1)) crop_left = int(m.group(1))
print(f"crop left: {crop_left}") logger.info(f"crop left: {crop_left}")
continue continue
m = re.match(r"s (\d+)", parg, re.IGNORECASE) m = re.match(r"s (\d+)", parg, re.IGNORECASE)
if m: # steps if m: # steps
steps = max(1, min(1000, int(m.group(1)))) steps = max(1, min(1000, int(m.group(1))))
print(f"steps: {steps}") logger.info(f"steps: {steps}")
continue continue
m = re.match(r"d ([\d,]+)", parg, re.IGNORECASE) m = re.match(r"d ([\d,]+)", parg, re.IGNORECASE)
if m: # seed if m: # seed
seeds = [int(d) for d in m.group(1).split(",")] seeds = [int(d) for d in m.group(1).split(",")]
print(f"seeds: {seeds}") logger.info(f"seeds: {seeds}")
continue continue
m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE)
if m: # scale if m: # scale
scale = float(m.group(1)) scale = float(m.group(1))
print(f"scale: {scale}") logger.info(f"scale: {scale}")
continue continue
m = re.match(r"nl ([\d\.]+|none|None)", parg, re.IGNORECASE) m = re.match(r"nl ([\d\.]+|none|None)", parg, re.IGNORECASE)
@@ -2671,25 +2677,25 @@ def main(args):
negative_scale = None negative_scale = None
else: else:
negative_scale = float(m.group(1)) negative_scale = float(m.group(1))
print(f"negative scale: {negative_scale}") logger.info(f"negative scale: {negative_scale}")
continue continue
m = re.match(r"t ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"t ([\d\.]+)", parg, re.IGNORECASE)
if m: # strength if m: # strength
strength = float(m.group(1)) strength = float(m.group(1))
print(f"strength: {strength}") logger.info(f"strength: {strength}")
continue continue
m = re.match(r"n (.+)", parg, re.IGNORECASE) m = re.match(r"n (.+)", parg, re.IGNORECASE)
if m: # negative prompt if m: # negative prompt
negative_prompt = m.group(1) negative_prompt = m.group(1)
print(f"negative prompt: {negative_prompt}") logger.info(f"negative prompt: {negative_prompt}")
continue continue
m = re.match(r"c (.+)", parg, re.IGNORECASE) m = re.match(r"c (.+)", parg, re.IGNORECASE)
if m: # clip prompt if m: # clip prompt
clip_prompt = m.group(1) clip_prompt = m.group(1)
print(f"clip prompt: {clip_prompt}") logger.info(f"clip prompt: {clip_prompt}")
continue continue
m = re.match(r"am ([\d\.\-,]+)", parg, re.IGNORECASE) m = re.match(r"am ([\d\.\-,]+)", parg, re.IGNORECASE)
@@ -2697,89 +2703,89 @@ def main(args):
network_muls = [float(v) for v in m.group(1).split(",")] network_muls = [float(v) for v in m.group(1).split(",")]
while len(network_muls) < len(networks): while len(network_muls) < len(networks):
network_muls.append(network_muls[-1]) network_muls.append(network_muls[-1])
print(f"network mul: {network_muls}") logger.info(f"network mul: {network_muls}")
continue continue
# Deep Shrink # Deep Shrink
m = re.match(r"dsd1 ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"dsd1 ([\d\.]+)", parg, re.IGNORECASE)
if m: # deep shrink depth 1 if m: # deep shrink depth 1
ds_depth_1 = int(m.group(1)) ds_depth_1 = int(m.group(1))
print(f"deep shrink depth 1: {ds_depth_1}") logger.info(f"deep shrink depth 1: {ds_depth_1}")
continue continue
m = re.match(r"dst1 ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"dst1 ([\d\.]+)", parg, re.IGNORECASE)
if m: # deep shrink timesteps 1 if m: # deep shrink timesteps 1
ds_timesteps_1 = int(m.group(1)) ds_timesteps_1 = int(m.group(1))
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
print(f"deep shrink timesteps 1: {ds_timesteps_1}") logger.info(f"deep shrink timesteps 1: {ds_timesteps_1}")
continue continue
m = re.match(r"dsd2 ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"dsd2 ([\d\.]+)", parg, re.IGNORECASE)
if m: # deep shrink depth 2 if m: # deep shrink depth 2
ds_depth_2 = int(m.group(1)) ds_depth_2 = int(m.group(1))
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
print(f"deep shrink depth 2: {ds_depth_2}") logger.info(f"deep shrink depth 2: {ds_depth_2}")
continue continue
m = re.match(r"dst2 ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"dst2 ([\d\.]+)", parg, re.IGNORECASE)
if m: # deep shrink timesteps 2 if m: # deep shrink timesteps 2
ds_timesteps_2 = int(m.group(1)) ds_timesteps_2 = int(m.group(1))
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
print(f"deep shrink timesteps 2: {ds_timesteps_2}") logger.info(f"deep shrink timesteps 2: {ds_timesteps_2}")
continue continue
m = re.match(r"dsr ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"dsr ([\d\.]+)", parg, re.IGNORECASE)
if m: # deep shrink ratio if m: # deep shrink ratio
ds_ratio = float(m.group(1)) ds_ratio = float(m.group(1))
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
print(f"deep shrink ratio: {ds_ratio}") logger.info(f"deep shrink ratio: {ds_ratio}")
continue continue
# Gradual Latent # Gradual Latent
m = re.match(r"glt ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glt ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent timesteps if m: # gradual latent timesteps
gl_timesteps = int(m.group(1)) gl_timesteps = int(m.group(1))
print(f"gradual latent timesteps: {gl_timesteps}") logger.info(f"gradual latent timesteps: {gl_timesteps}")
continue continue
m = re.match(r"glr ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glr ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent ratio if m: # gradual latent ratio
gl_ratio = float(m.group(1)) gl_ratio = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent ratio: {ds_ratio}") logger.info(f"gradual latent ratio: {ds_ratio}")
continue continue
m = re.match(r"gle ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"gle ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent every n steps if m: # gradual latent every n steps
gl_every_n_steps = int(m.group(1)) gl_every_n_steps = int(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent every n steps: {gl_every_n_steps}") logger.info(f"gradual latent every n steps: {gl_every_n_steps}")
continue continue
m = re.match(r"gls ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"gls ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent ratio step if m: # gradual latent ratio step
gl_ratio_step = float(m.group(1)) gl_ratio_step = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent ratio step: {gl_ratio_step}") logger.info(f"gradual latent ratio step: {gl_ratio_step}")
continue continue
m = re.match(r"glsn ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glsn ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent s noise if m: # gradual latent s noise
gl_s_noise = float(m.group(1)) gl_s_noise = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent s noise: {gl_s_noise}") logger.info(f"gradual latent s noise: {gl_s_noise}")
continue continue
m = re.match(r"glus ([\d\.\-,]+)", parg, re.IGNORECASE) m = re.match(r"glus ([\d\.\-,]+)", parg, re.IGNORECASE)
if m: # gradual latent unsharp params if m: # gradual latent unsharp params
gl_unsharp_params = m.group(1) gl_unsharp_params = m.group(1)
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent unsharp params: {gl_unsharp_params}") logger.info(f"gradual latent unsharp params: {gl_unsharp_params}")
continue continue
except ValueError as ex: except ValueError as ex:
print(f"Exception in parsing / 解析エラー: {parg}") logger.error(f"Exception in parsing / 解析エラー: {parg}")
print(ex) logger.error(f"{ex}")
# override Deep Shrink # override Deep Shrink
if ds_depth_1 is not None: if ds_depth_1 is not None:
@@ -2825,7 +2831,7 @@ def main(args):
if seed is None: if seed is None:
seed = seed_random.randint(0, 2**32 - 1) seed = seed_random.randint(0, 2**32 - 1)
if args.interactive: if args.interactive:
print(f"seed: {seed}") logger.info(f"seed: {seed}")
# prepare init image, guide image and mask # prepare init image, guide image and mask
init_image = mask_image = guide_image = None init_image = mask_image = guide_image = None
@@ -2841,7 +2847,7 @@ def main(args):
width = width - width % 32 width = width - width % 32
height = height - height % 32 height = height - height % 32
if width != init_image.size[0] or height != init_image.size[1]: if width != init_image.size[0] or height != init_image.size[1]:
print( logger.warning(
f"img2img image size is not divisible by 32 so aspect ratio is changed / img2imgの画像サイズが32で割り切れないためリサイズされます。画像が歪みます" f"img2img image size is not divisible by 32 so aspect ratio is changed / img2imgの画像サイズが32で割り切れないためリサイズされます。画像が歪みます"
) )
@@ -2903,12 +2909,14 @@ def main(args):
process_batch(batch_data, highres_fix) process_batch(batch_data, highres_fix)
batch_data.clear() batch_data.clear()
print("done!") logger.info("done!")
def setup_parser() -> argparse.ArgumentParser: def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
add_logging_arguments(parser)
parser.add_argument( parser.add_argument(
"--sdxl", action="store_true", help="load Stable Diffusion XL model / Stable Diffusion XLのモデルを読み込む" "--sdxl", action="store_true", help="load Stable Diffusion XL model / Stable Diffusion XLのモデルを読み込む"
) )

22
gen_img_diffusers.py
View File

@@ -489,10 +489,10 @@ class PipelineLike:
def set_gradual_latent(self, gradual_latent): def set_gradual_latent(self, gradual_latent):
if gradual_latent is None: if gradual_latent is None:
print("gradual_latent is disabled") logger.info("gradual_latent is disabled")
self.gradual_latent = None self.gradual_latent = None
else: else:
print(f"gradual_latent is enabled: {gradual_latent}") logger.info(f"gradual_latent is enabled: {gradual_latent}")
self.gradual_latent = gradual_latent # (ds_ratio, start_timesteps, every_n_steps, ratio_step) self.gradual_latent = gradual_latent # (ds_ratio, start_timesteps, every_n_steps, ratio_step)
# region xformersとか使う部分：独自に書き換えるので関係なし # region xformersとか使う部分：独自に書き換えるので関係なし
@@ -971,8 +971,8 @@ class PipelineLike:
enable_gradual_latent = False enable_gradual_latent = False
if self.gradual_latent: if self.gradual_latent:
if not hasattr(self.scheduler, "set_gradual_latent_params"): if not hasattr(self.scheduler, "set_gradual_latent_params"):
print("gradual_latent is not supported for this scheduler. Ignoring.") logger.info("gradual_latent is not supported for this scheduler. Ignoring.")
print(self.scheduler.__class__.__name__) logger.info(f'{self.scheduler.__class__.__name__}')
else: else:
enable_gradual_latent = True enable_gradual_latent = True
step_elapsed = 1000 step_elapsed = 1000
@@ -3314,42 +3314,42 @@ def main(args):
m = re.match(r"glt ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glt ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent timesteps if m: # gradual latent timesteps
gl_timesteps = int(m.group(1)) gl_timesteps = int(m.group(1))
print(f"gradual latent timesteps: {gl_timesteps}") logger.info(f"gradual latent timesteps: {gl_timesteps}")
continue continue
m = re.match(r"glr ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glr ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent ratio if m: # gradual latent ratio
gl_ratio = float(m.group(1)) gl_ratio = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent ratio: {ds_ratio}") logger.info(f"gradual latent ratio: {ds_ratio}")
continue continue
m = re.match(r"gle ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"gle ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent every n steps if m: # gradual latent every n steps
gl_every_n_steps = int(m.group(1)) gl_every_n_steps = int(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent every n steps: {gl_every_n_steps}") logger.info(f"gradual latent every n steps: {gl_every_n_steps}")
continue continue
m = re.match(r"gls ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"gls ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent ratio step if m: # gradual latent ratio step
gl_ratio_step = float(m.group(1)) gl_ratio_step = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent ratio step: {gl_ratio_step}") logger.info(f"gradual latent ratio step: {gl_ratio_step}")
continue continue
m = re.match(r"glsn ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glsn ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent s noise if m: # gradual latent s noise
gl_s_noise = float(m.group(1)) gl_s_noise = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent s noise: {gl_s_noise}") logger.info(f"gradual latent s noise: {gl_s_noise}")
continue continue
m = re.match(r"glus ([\d\.\-,]+)", parg, re.IGNORECASE) m = re.match(r"glus ([\d\.\-,]+)", parg, re.IGNORECASE)
if m: # gradual latent unsharp params if m: # gradual latent unsharp params
gl_unsharp_params = m.group(1) gl_unsharp_params = m.group(1)
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent unsharp params: {gl_unsharp_params}") logger.info(f"gradual latent unsharp params: {gl_unsharp_params}")
continue continue
except ValueError as ex: except ValueError as ex:
@@ -3369,7 +3369,7 @@ def main(args):
if gl_unsharp_params is not None: if gl_unsharp_params is not None:
unsharp_params = gl_unsharp_params.split(",") unsharp_params = gl_unsharp_params.split(",")
us_ksize, us_sigma, us_strength = [float(v) for v in unsharp_params[:3]] us_ksize, us_sigma, us_strength = [float(v) for v in unsharp_params[:3]]
print(unsharp_params) logger.info(f'{unsharp_params}')
us_target_x = True if len(unsharp_params) < 4 else bool(int(unsharp_params[3])) us_target_x = True if len(unsharp_params) < 4 else bool(int(unsharp_params[3]))
us_ksize = int(us_ksize) us_ksize = int(us_ksize)
else: else:

11
library/device_utils.py
View File

@@ -3,6 +3,11 @@ import gc
import torch import torch
from .utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)
try: try:
HAS_CUDA = torch.cuda.is_available() HAS_CUDA = torch.cuda.is_available()
except Exception: except Exception:
@@ -59,7 +64,7 @@ def get_preferred_device() -> torch.device:
device = torch.device("mps") device = torch.device("mps")
else: else:
device = torch.device("cpu") device = torch.device("cpu")
print(f"get_preferred_device() -> {device}") logger.info(f"get_preferred_device() -> {device}")
return device return device
@@ -77,8 +82,8 @@ def init_ipex():
is_initialized, error_message = ipex_init() is_initialized, error_message = ipex_init()
if not is_initialized: if not is_initialized:
print("failed to initialize ipex:", error_message) logger.error("failed to initialize ipex: {error_message}")
else: else:
return return
except Exception as e: except Exception as e:
print("failed to initialize ipex:", e) logger.error("failed to initialize ipex: {e}")

4
networks/dylora.py
View File

@@ -327,10 +327,10 @@ class DyLoRANetwork(torch.nn.Module):
for i, text_encoder in enumerate(text_encoders): for i, text_encoder in enumerate(text_encoders):
if len(text_encoders) > 1: if len(text_encoders) > 1:
index = i + 1 index = i + 1
print(f"create LoRA for Text Encoder {index}") logger.info(f"create LoRA for Text Encoder {index}")
else: else:
index = None index = None
print(f"create LoRA for Text Encoder") logger.info("create LoRA for Text Encoder")
text_encoder_loras = create_modules(False, text_encoder, DyLoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE) text_encoder_loras = create_modules(False, text_encoder, DyLoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
self.text_encoder_loras.extend(text_encoder_loras) self.text_encoder_loras.extend(text_encoder_loras)

32
sdxl_gen_img.py
View File

@@ -380,10 +380,10 @@ class PipelineLike:
def set_gradual_latent(self, gradual_latent): def set_gradual_latent(self, gradual_latent):
if gradual_latent is None: if gradual_latent is None:
print("gradual_latent is disabled") logger.info("gradual_latent is disabled")
self.gradual_latent = None self.gradual_latent = None
else: else:
print(f"gradual_latent is enabled: {gradual_latent}") logger.info(f"gradual_latent is enabled: {gradual_latent}")
self.gradual_latent = gradual_latent # (ds_ratio, start_timesteps, every_n_steps, ratio_step) self.gradual_latent = gradual_latent # (ds_ratio, start_timesteps, every_n_steps, ratio_step)
@torch.no_grad() @torch.no_grad()
@@ -789,8 +789,8 @@ class PipelineLike:
enable_gradual_latent = False enable_gradual_latent = False
if self.gradual_latent: if self.gradual_latent:
if not hasattr(self.scheduler, "set_gradual_latent_params"): if not hasattr(self.scheduler, "set_gradual_latent_params"):
print("gradual_latent is not supported for this scheduler. Ignoring.") logger.info("gradual_latent is not supported for this scheduler. Ignoring.")
print(self.scheduler.__class__.__name__) logger.info(f'{self.scheduler.__class__.__name__}')
else: else:
enable_gradual_latent = True enable_gradual_latent = True
step_elapsed = 1000 step_elapsed = 1000
@@ -2614,84 +2614,84 @@ def main(args):
m = re.match(r"glt ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glt ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent timesteps if m: # gradual latent timesteps
gl_timesteps = int(m.group(1)) gl_timesteps = int(m.group(1))
print(f"gradual latent timesteps: {gl_timesteps}") logger.info(f"gradual latent timesteps: {gl_timesteps}")
continue continue
m = re.match(r"glr ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glr ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent ratio if m: # gradual latent ratio
gl_ratio = float(m.group(1)) gl_ratio = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent ratio: {ds_ratio}") logger.info(f"gradual latent ratio: {ds_ratio}")
continue continue
m = re.match(r"gle ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"gle ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent every n steps if m: # gradual latent every n steps
gl_every_n_steps = int(m.group(1)) gl_every_n_steps = int(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent every n steps: {gl_every_n_steps}") logger.info(f"gradual latent every n steps: {gl_every_n_steps}")
continue continue
m = re.match(r"gls ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"gls ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent ratio step if m: # gradual latent ratio step
gl_ratio_step = float(m.group(1)) gl_ratio_step = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent ratio step: {gl_ratio_step}") logger.info(f"gradual latent ratio step: {gl_ratio_step}")
continue continue
m = re.match(r"glsn ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glsn ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent s noise if m: # gradual latent s noise
gl_s_noise = float(m.group(1)) gl_s_noise = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent s noise: {gl_s_noise}") logger.info(f"gradual latent s noise: {gl_s_noise}")
continue continue
m = re.match(r"glus ([\d\.\-,]+)", parg, re.IGNORECASE) m = re.match(r"glus ([\d\.\-,]+)", parg, re.IGNORECASE)
if m: # gradual latent unsharp params if m: # gradual latent unsharp params
gl_unsharp_params = m.group(1) gl_unsharp_params = m.group(1)
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent unsharp params: {gl_unsharp_params}") logger.info(f"gradual latent unsharp params: {gl_unsharp_params}")
continue continue
# Gradual Latent # Gradual Latent
m = re.match(r"glt ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glt ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent timesteps if m: # gradual latent timesteps
gl_timesteps = int(m.group(1)) gl_timesteps = int(m.group(1))
print(f"gradual latent timesteps: {gl_timesteps}") logger.info(f"gradual latent timesteps: {gl_timesteps}")
continue continue
m = re.match(r"glr ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glr ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent ratio if m: # gradual latent ratio
gl_ratio = float(m.group(1)) gl_ratio = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent ratio: {ds_ratio}") logger.info(f"gradual latent ratio: {ds_ratio}")
continue continue
m = re.match(r"gle ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"gle ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent every n steps if m: # gradual latent every n steps
gl_every_n_steps = int(m.group(1)) gl_every_n_steps = int(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent every n steps: {gl_every_n_steps}") logger.info(f"gradual latent every n steps: {gl_every_n_steps}")
continue continue
m = re.match(r"gls ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"gls ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent ratio step if m: # gradual latent ratio step
gl_ratio_step = float(m.group(1)) gl_ratio_step = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent ratio step: {gl_ratio_step}") logger.info(f"gradual latent ratio step: {gl_ratio_step}")
continue continue
m = re.match(r"glsn ([\d\.]+)", parg, re.IGNORECASE) m = re.match(r"glsn ([\d\.]+)", parg, re.IGNORECASE)
if m: # gradual latent s noise if m: # gradual latent s noise
gl_s_noise = float(m.group(1)) gl_s_noise = float(m.group(1))
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent s noise: {gl_s_noise}") logger.info(f"gradual latent s noise: {gl_s_noise}")
continue continue
m = re.match(r"glus ([\d\.\-,]+)", parg, re.IGNORECASE) m = re.match(r"glus ([\d\.\-,]+)", parg, re.IGNORECASE)
if m: # gradual latent unsharp params if m: # gradual latent unsharp params
gl_unsharp_params = m.group(1) gl_unsharp_params = m.group(1)
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
print(f"gradual latent unsharp params: {gl_unsharp_params}") logger.info(f"gradual latent unsharp params: {gl_unsharp_params}")
continue continue
except ValueError as ex: except ValueError as ex: