mirror of
https://github.com/kohya-ss/sd-scripts.git
synced 2026-04-08 22:35:09 +00:00
Merge branch 'dev' into gradual_latent_hires_fix
This commit is contained in:
Kohya S
@@ -105,6 +105,10 @@ from library.original_unet import FlashAttentionFunction
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from library.utils import GradualLatent, EulerAncestralDiscreteSchedulerGL
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from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
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from library.utils import setup_logging
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setup_logging()
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import logging
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logger = logging.getLogger(__name__)
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# scheduler:
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SCHEDULER_LINEAR_START = 0.00085
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@@ -140,12 +144,12 @@ USE_CUTOUTS = False
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def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers, sdpa):
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if mem_eff_attn:
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print("Enable memory efficient attention for U-Net")
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logger.info("Enable memory efficient attention for U-Net")
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# 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
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unet.set_use_memory_efficient_attention(False, True)
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elif xformers:
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print("Enable xformers for U-Net")
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logger.info("Enable xformers for U-Net")
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try:
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import xformers.ops
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except ImportError:
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@@ -153,7 +157,7 @@ def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditio
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unet.set_use_memory_efficient_attention(True, False)
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elif sdpa:
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print("Enable SDPA for U-Net")
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logger.info("Enable SDPA for U-Net")
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unet.set_use_memory_efficient_attention(False, False)
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unet.set_use_sdpa(True)
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@@ -169,7 +173,7 @@ def replace_vae_modules(vae: diffusers.models.AutoencoderKL, mem_eff_attn, xform
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def replace_vae_attn_to_memory_efficient():
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print("VAE Attention.forward has been replaced to FlashAttention (not xformers)")
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logger.info("VAE Attention.forward has been replaced to FlashAttention (not xformers)")
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flash_func = FlashAttentionFunction
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def forward_flash_attn(self, hidden_states, **kwargs):
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@@ -225,7 +229,7 @@ def replace_vae_attn_to_memory_efficient():
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def replace_vae_attn_to_xformers():
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print("VAE: Attention.forward has been replaced to xformers")
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logger.info("VAE: Attention.forward has been replaced to xformers")
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import xformers.ops
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def forward_xformers(self, hidden_states, **kwargs):
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@@ -281,7 +285,7 @@ def replace_vae_attn_to_xformers():
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def replace_vae_attn_to_sdpa():
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print("VAE: Attention.forward has been replaced to sdpa")
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logger.info("VAE: Attention.forward has been replaced to sdpa")
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def forward_sdpa(self, hidden_states, **kwargs):
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residual = hidden_states
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@@ -695,7 +699,7 @@ class PipelineLike:
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do_classifier_free_guidance = guidance_scale > 1.0
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if not do_classifier_free_guidance and negative_scale is not None:
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print(f"negative_scale is ignored if guidance scalle <= 1.0")
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logger.warning(f"negative_scale is ignored if guidance scalle <= 1.0")
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negative_scale = None
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# get unconditional embeddings for classifier free guidance
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@@ -777,11 +781,11 @@ class PipelineLike:
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clip_text_input = prompt_tokens
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if clip_text_input.shape[1] > self.tokenizer.model_max_length:
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# TODO 75文字を超えたら警告を出す？
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print("trim text input", clip_text_input.shape)
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logger.info(f"trim text input {clip_text_input.shape}")
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clip_text_input = torch.cat(
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[clip_text_input[:, : self.tokenizer.model_max_length - 1], clip_text_input[:, -1].unsqueeze(1)], dim=1
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)
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print("trimmed", clip_text_input.shape)
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logger.info(f"trimmed {clip_text_input.shape}")
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for i, clip_prompt in enumerate(clip_prompts):
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if clip_prompt is not None: # clip_promptがあれば上書きする
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@@ -1752,7 +1756,7 @@ def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length:
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if word.strip() == "BREAK":
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# pad until next multiple of tokenizer's max token length
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pad_len = pipe.tokenizer.model_max_length - (len(text_token) % pipe.tokenizer.model_max_length)
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print(f"BREAK pad_len: {pad_len}")
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logger.info(f"BREAK pad_len: {pad_len}")
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for i in range(pad_len):
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# v2のときEOSをつけるべきかどうかわからないぜ
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# if i == 0:
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@@ -1782,7 +1786,7 @@ def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length:
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tokens.append(text_token)
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weights.append(text_weight)
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if truncated:
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print("warning: Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
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logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
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return tokens, weights
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@@ -2094,7 +2098,7 @@ def handle_dynamic_prompt_variants(prompt, repeat_count):
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elif len(count_range) == 2:
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count_range = [int(count_range[0]), int(count_range[1])]
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else:
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print(f"invalid count range: {count_range}")
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logger.warning(f"invalid count range: {count_range}")
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count_range = [1, 1]
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if count_range[0] > count_range[1]:
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count_range = [count_range[1], count_range[0]]
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@@ -2164,7 +2168,7 @@ def handle_dynamic_prompt_variants(prompt, repeat_count):
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# def load_clip_l14_336(dtype):
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# print(f"loading CLIP: {CLIP_ID_L14_336}")
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# logger.info(f"loading CLIP: {CLIP_ID_L14_336}")
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# text_encoder = CLIPTextModel.from_pretrained(CLIP_ID_L14_336, torch_dtype=dtype)
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# return text_encoder
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@@ -2212,9 +2216,9 @@ def main(args):
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# 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"
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if args.v_parameterization and not args.v2:
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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")
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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")
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if args.v2 and args.clip_skip is not None:
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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")
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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")
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# モデルを読み込む
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if not os.path.isfile(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
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@@ -2224,10 +2228,10 @@ def main(args):
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use_stable_diffusion_format = os.path.isfile(args.ckpt)
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if use_stable_diffusion_format:
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print("load StableDiffusion checkpoint")
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logger.info("load StableDiffusion checkpoint")
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text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.ckpt)
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else:
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print("load Diffusers pretrained models")
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logger.info("load Diffusers pretrained models")
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loading_pipe = StableDiffusionPipeline.from_pretrained(args.ckpt, safety_checker=None, torch_dtype=dtype)
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text_encoder = loading_pipe.text_encoder
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vae = loading_pipe.vae
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@@ -2250,21 +2254,21 @@ def main(args):
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# VAEを読み込む
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if args.vae is not None:
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vae = model_util.load_vae(args.vae, dtype)
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print("additional VAE loaded")
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logger.info("additional VAE loaded")
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# # 置換するCLIPを読み込む
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# if args.replace_clip_l14_336:
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# text_encoder = load_clip_l14_336(dtype)
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# print(f"large clip {CLIP_ID_L14_336} is loaded")
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# logger.info(f"large clip {CLIP_ID_L14_336} is loaded")
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if args.clip_guidance_scale > 0.0 or args.clip_image_guidance_scale:
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print("prepare clip model")
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logger.info("prepare clip model")
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clip_model = CLIPModel.from_pretrained(CLIP_MODEL_PATH, torch_dtype=dtype)
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else:
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clip_model = None
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if args.vgg16_guidance_scale > 0.0:
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print("prepare resnet model")
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logger.info("prepare resnet model")
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vgg16_model = torchvision.models.vgg16(torchvision.models.VGG16_Weights.IMAGENET1K_V1)
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else:
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vgg16_model = None
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@@ -2276,7 +2280,7 @@ def main(args):
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replace_vae_modules(vae, mem_eff, args.xformers, args.sdpa)
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# tokenizerを読み込む
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print("loading tokenizer")
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logger.info("loading tokenizer")
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if use_stable_diffusion_format:
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tokenizer = train_util.load_tokenizer(args)
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@@ -2335,7 +2339,7 @@ def main(args):
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self.sampler_noises = noises
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def randn(self, shape, device=None, dtype=None, layout=None, generator=None):
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# print("replacing", shape, len(self.sampler_noises), self.sampler_noise_index)
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# logger.info(f"replacing {shape} {len(self.sampler_noises)} {self.sampler_noise_index}")
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if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises):
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noise = self.sampler_noises[self.sampler_noise_index]
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if shape != noise.shape:
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@@ -2344,7 +2348,7 @@ def main(args):
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noise = None
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if noise == None:
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print(f"unexpected noise request: {self.sampler_noise_index}, {shape}")
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logger.warning(f"unexpected noise request: {self.sampler_noise_index}, {shape}")
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noise = torch.randn(shape, dtype=dtype, device=device, generator=generator)
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self.sampler_noise_index += 1
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@@ -2375,7 +2379,7 @@ def main(args):
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# clip_sample=Trueにする
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if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False:
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print("set clip_sample to True")
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logger.info("set clip_sample to True")
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scheduler.config.clip_sample = True
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# deviceを決定する
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@@ -2432,7 +2436,7 @@ def main(args):
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network_merge = 0
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for i, network_module in enumerate(args.network_module):
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print("import network module:", network_module)
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logger.info(f"import network module: {network_module}")
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imported_module = importlib.import_module(network_module)
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network_mul = 1.0 if args.network_mul is None or len(args.network_mul) <= i else args.network_mul[i]
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@@ -2450,7 +2454,7 @@ def main(args):
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raise ValueError("No weight. Weight is required.")
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network_weight = args.network_weights[i]
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print("load network weights from:", network_weight)
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logger.info(f"load network weights from: {network_weight}")
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if model_util.is_safetensors(network_weight) and args.network_show_meta:
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from safetensors.torch import safe_open
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@@ -2458,7 +2462,7 @@ def main(args):
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with safe_open(network_weight, framework="pt") as f:
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metadata = f.metadata()
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if metadata is not None:
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print(f"metadata for: {network_weight}: {metadata}")
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logger.info(f"metadata for: {network_weight}: {metadata}")
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network, weights_sd = imported_module.create_network_from_weights(
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network_mul, network_weight, vae, text_encoder, unet, for_inference=True, **net_kwargs
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@@ -2468,20 +2472,20 @@ def main(args):
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mergeable = network.is_mergeable()
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if network_merge and not mergeable:
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print("network is not mergiable. ignore merge option.")
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logger.warning("network is not mergiable. ignore merge option.")
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if not mergeable or i >= network_merge:
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# not merging
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network.apply_to(text_encoder, unet)
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info = network.load_state_dict(weights_sd, False) # network.load_weightsを使うようにするとよい
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print(f"weights are loaded: {info}")
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logger.info(f"weights are loaded: {info}")
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if args.opt_channels_last:
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network.to(memory_format=torch.channels_last)
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network.to(dtype).to(device)
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if network_pre_calc:
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print("backup original weights")
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logger.info("backup original weights")
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network.backup_weights()
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networks.append(network)
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@@ -2495,7 +2499,7 @@ def main(args):
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# upscalerの指定があれば取得する
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upscaler = None
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if args.highres_fix_upscaler:
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print("import upscaler module:", args.highres_fix_upscaler)
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logger.info(f"import upscaler module {args.highres_fix_upscaler}")
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imported_module = importlib.import_module(args.highres_fix_upscaler)
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us_kwargs = {}
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@@ -2504,7 +2508,7 @@ def main(args):
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key, value = net_arg.split("=")
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us_kwargs[key] = value
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print("create upscaler")
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logger.info("create upscaler")
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upscaler = imported_module.create_upscaler(**us_kwargs)
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upscaler.to(dtype).to(device)
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@@ -2521,7 +2525,7 @@ def main(args):
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control_nets.append(ControlNetInfo(ctrl_unet, ctrl_net, prep, weight, ratio))
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if args.opt_channels_last:
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print(f"set optimizing: channels last")
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logger.info(f"set optimizing: channels last")
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text_encoder.to(memory_format=torch.channels_last)
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vae.to(memory_format=torch.channels_last)
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unet.to(memory_format=torch.channels_last)
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@@ -2553,7 +2557,7 @@ def main(args):
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args.vgg16_guidance_layer,
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)
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pipe.set_control_nets(control_nets)
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print("pipeline is ready.")
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logger.info("pipeline is ready.")
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if args.diffusers_xformers:
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pipe.enable_xformers_memory_efficient_attention()
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@@ -2619,7 +2623,7 @@ def main(args):
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), f"tokenizer has same word to token string (filename). please rename the file / 指定した名前（ファイル名）のトークンが既に存在します。ファイルをリネームしてください: {embeds_file}"
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token_ids = tokenizer.convert_tokens_to_ids(token_strings)
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print(f"Textual Inversion embeddings `{token_string}` loaded. Tokens are added: {token_ids}")
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logger.info(f"Textual Inversion embeddings `{token_string}` loaded. Tokens are added: {token_ids}")
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assert (
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min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1
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), f"token ids is not ordered"
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@@ -2678,7 +2682,7 @@ def main(args):
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), f"tokenizer has same word to token string (filename). please rename the file / 指定した名前（ファイル名）のトークンが既に存在します。ファイルをリネームしてください: {embeds_file}"
|
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|
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token_ids = tokenizer.convert_tokens_to_ids(token_strings)
|
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print(f"XTI embeddings `{token_string}` loaded. Tokens are added: {token_ids}")
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logger.info(f"XTI embeddings `{token_string}` loaded. Tokens are added: {token_ids}")
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# if num_vectors_per_token > 1:
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pipe.add_token_replacement(token_ids[0], token_ids)
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@@ -2703,7 +2707,7 @@ def main(args):
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|
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# promptを取得する
|
||||
if args.from_file is not None:
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||||
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:
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prompt_list = f.read().splitlines()
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||||
prompt_list = [d for d in prompt_list if len(d.strip()) > 0 and d[0] != "#"]
|
||||
@@ -2732,7 +2736,7 @@ def main(args):
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for p in paths:
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image = Image.open(p)
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if image.mode != "RGB":
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print(f"convert image to RGB from {image.mode}: {p}")
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logger.info(f"convert image to RGB from {image.mode}: {p}")
|
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image = image.convert("RGB")
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images.append(image)
|
||||
|
||||
@@ -2748,24 +2752,24 @@ def main(args):
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return resized
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||||
|
||||
if args.image_path is not None:
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||||
print(f"load image for img2img: {args.image_path}")
|
||||
logger.info(f"load image for img2img: {args.image_path}")
|
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init_images = load_images(args.image_path)
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||||
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")
|
||||
else:
|
||||
init_images = 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)
|
||||
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:
|
||||
mask_images = None
|
||||
|
||||
# promptがないとき、画像のPngInfoから取得する
|
||||
if init_images is not None and len(prompt_list) == 0 and not args.interactive:
|
||||
print("get prompts from images' meta data")
|
||||
logger.info("get prompts from images' meta data")
|
||||
for img in init_images:
|
||||
if "prompt" in img.text:
|
||||
prompt = img.text["prompt"]
|
||||
@@ -2794,17 +2798,17 @@ def main(args):
|
||||
h = int(h * args.highres_fix_scale + 0.5)
|
||||
|
||||
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))
|
||||
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))
|
||||
|
||||
regional_network = False
|
||||
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
|
||||
regional_network = True
|
||||
print("use mask as region")
|
||||
logger.info("use mask as region")
|
||||
|
||||
size = None
|
||||
for i, network in enumerate(networks):
|
||||
@@ -2829,14 +2833,14 @@ def main(args):
|
||||
|
||||
prev_image = None # for VGG16 guided
|
||||
if args.guide_image_path is not None:
|
||||
print(f"load image for CLIP/VGG16/ControlNet guidance: {args.guide_image_path}")
|
||||
logger.info(f"load image for CLIP/VGG16/ControlNet guidance: {args.guide_image_path}")
|
||||
guide_images = []
|
||||
for p in args.guide_image_path:
|
||||
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:
|
||||
print(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}")
|
||||
logger.info(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
|
||||
else:
|
||||
guide_images = None
|
||||
@@ -2862,7 +2866,7 @@ def main(args):
|
||||
max_embeddings_multiples = 1 if args.max_embeddings_multiples is None else args.max_embeddings_multiples
|
||||
|
||||
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}")
|
||||
iter_seed = random.randint(0, 0x7FFFFFFF)
|
||||
|
||||
# shuffle prompt list
|
||||
@@ -2878,7 +2882,7 @@ 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
|
||||
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 = []
|
||||
for _, base, ext in batch:
|
||||
width_1st = int(ext.width * args.highres_fix_scale + 0.5)
|
||||
@@ -2904,7 +2908,7 @@ def main(args):
|
||||
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
|
||||
print("process 2nd stage")
|
||||
logger.info("process 2nd stage")
|
||||
width_2nd, height_2nd = batch[0].ext.width, batch[0].ext.height
|
||||
|
||||
if upscaler:
|
||||
@@ -3061,7 +3065,7 @@ def main(args):
|
||||
n.restore_weights()
|
||||
for n in networks:
|
||||
n.pre_calculation()
|
||||
print("pre-calculation... done")
|
||||
logger.info("pre-calculation... done")
|
||||
|
||||
images = pipe(
|
||||
prompts,
|
||||
@@ -3130,7 +3134,7 @@ def main(args):
|
||||
cv2.waitKey()
|
||||
cv2.destroyAllWindows()
|
||||
except ImportError:
|
||||
print("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")
|
||||
logger.info("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")
|
||||
|
||||
return images
|
||||
|
||||
@@ -3143,7 +3147,8 @@ def main(args):
|
||||
# interactive
|
||||
valid = False
|
||||
while not valid:
|
||||
print("\nType prompt:")
|
||||
logger.info("")
|
||||
logger.info("Type prompt:")
|
||||
try:
|
||||
raw_prompt = input()
|
||||
except EOFError:
|
||||
@@ -3194,38 +3199,38 @@ def main(args):
|
||||
|
||||
prompt_args = raw_prompt.strip().split(" --")
|
||||
prompt = prompt_args[0]
|
||||
print(f"prompt {prompt_index+1}/{len(prompt_list)}: {prompt}")
|
||||
logger.info(f"prompt {prompt_index+1}/{len(prompt_list)}: {prompt}")
|
||||
|
||||
for parg in prompt_args[1:]:
|
||||
try:
|
||||
m = re.match(r"w (\d+)", parg, re.IGNORECASE)
|
||||
if m:
|
||||
width = int(m.group(1))
|
||||
print(f"width: {width}")
|
||||
logger.info(f"width: {width}")
|
||||
continue
|
||||
|
||||
m = re.match(r"h (\d+)", parg, re.IGNORECASE)
|
||||
if m:
|
||||
height = int(m.group(1))
|
||||
print(f"height: {height}")
|
||||
logger.info(f"height: {height}")
|
||||
continue
|
||||
|
||||
m = re.match(r"s (\d+)", parg, re.IGNORECASE)
|
||||
if m: # steps
|
||||
steps = max(1, min(1000, int(m.group(1))))
|
||||
print(f"steps: {steps}")
|
||||
logger.info(f"steps: {steps}")
|
||||
continue
|
||||
|
||||
m = re.match(r"d ([\d,]+)", parg, re.IGNORECASE)
|
||||
if m: # seed
|
||||
seeds = [int(d) for d in m.group(1).split(",")]
|
||||
print(f"seeds: {seeds}")
|
||||
logger.info(f"seeds: {seeds}")
|
||||
continue
|
||||
|
||||
m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # scale
|
||||
scale = float(m.group(1))
|
||||
print(f"scale: {scale}")
|
||||
logger.info(f"scale: {scale}")
|
||||
continue
|
||||
|
||||
m = re.match(r"nl ([\d\.]+|none|None)", parg, re.IGNORECASE)
|
||||
@@ -3234,25 +3239,25 @@ def main(args):
|
||||
negative_scale = None
|
||||
else:
|
||||
negative_scale = float(m.group(1))
|
||||
print(f"negative scale: {negative_scale}")
|
||||
logger.info(f"negative scale: {negative_scale}")
|
||||
continue
|
||||
|
||||
m = re.match(r"t ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # strength
|
||||
strength = float(m.group(1))
|
||||
print(f"strength: {strength}")
|
||||
logger.info(f"strength: {strength}")
|
||||
continue
|
||||
|
||||
m = re.match(r"n (.+)", parg, re.IGNORECASE)
|
||||
if m: # negative prompt
|
||||
negative_prompt = m.group(1)
|
||||
print(f"negative prompt: {negative_prompt}")
|
||||
logger.info(f"negative prompt: {negative_prompt}")
|
||||
continue
|
||||
|
||||
m = re.match(r"c (.+)", parg, re.IGNORECASE)
|
||||
if m: # clip prompt
|
||||
clip_prompt = m.group(1)
|
||||
print(f"clip prompt: {clip_prompt}")
|
||||
logger.info(f"clip prompt: {clip_prompt}")
|
||||
continue
|
||||
|
||||
m = re.match(r"am ([\d\.\-,]+)", parg, re.IGNORECASE)
|
||||
@@ -3260,42 +3265,42 @@ def main(args):
|
||||
network_muls = [float(v) for v in m.group(1).split(",")]
|
||||
while len(network_muls) < len(networks):
|
||||
network_muls.append(network_muls[-1])
|
||||
print(f"network mul: {network_muls}")
|
||||
logger.info(f"network mul: {network_muls}")
|
||||
continue
|
||||
|
||||
# Deep Shrink
|
||||
m = re.match(r"dsd1 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink depth 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
|
||||
|
||||
m = re.match(r"dst1 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink timesteps 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
|
||||
print(f"deep shrink timesteps 1: {ds_timesteps_1}")
|
||||
logger.info(f"deep shrink timesteps 1: {ds_timesteps_1}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dsd2 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink depth 2
|
||||
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
|
||||
print(f"deep shrink depth 2: {ds_depth_2}")
|
||||
logger.info(f"deep shrink depth 2: {ds_depth_2}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dst2 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink timesteps 2
|
||||
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
|
||||
print(f"deep shrink timesteps 2: {ds_timesteps_2}")
|
||||
logger.info(f"deep shrink timesteps 2: {ds_timesteps_2}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dsr ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink ratio
|
||||
ds_ratio = float(m.group(1))
|
||||
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}")
|
||||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||||
logger.info(f"deep shrink ratio: {ds_ratio}")
|
||||
continue
|
||||
|
||||
# Gradual Latent
|
||||
@@ -3341,8 +3346,8 @@ def main(args):
|
||||
continue
|
||||
|
||||
except ValueError as ex:
|
||||
print(f"Exception in parsing / 解析エラー: {parg}")
|
||||
print(ex)
|
||||
logger.info(f"Exception in parsing / 解析エラー: {parg}")
|
||||
logger.info(ex)
|
||||
|
||||
# override Deep Shrink
|
||||
if ds_depth_1 is not None:
|
||||
@@ -3385,7 +3390,7 @@ def main(args):
|
||||
if len(predefined_seeds) > 0:
|
||||
seed = predefined_seeds.pop(0)
|
||||
else:
|
||||
print("predefined seeds are exhausted")
|
||||
logger.info("predefined seeds are exhausted")
|
||||
seed = None
|
||||
elif args.iter_same_seed:
|
||||
seed = iter_seed
|
||||
@@ -3395,7 +3400,7 @@ def main(args):
|
||||
if seed is None:
|
||||
seed = random.randint(0, 0x7FFFFFFF)
|
||||
if args.interactive:
|
||||
print(f"seed: {seed}")
|
||||
logger.info(f"seed: {seed}")
|
||||
|
||||
# prepare init image, guide image and mask
|
||||
init_image = mask_image = guide_image = None
|
||||
@@ -3411,7 +3416,7 @@ def main(args):
|
||||
width = width - width % 32
|
||||
height = height - height % 32
|
||||
if width != init_image.size[0] or height != init_image.size[1]:
|
||||
print(
|
||||
logger.info(
|
||||
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"
|
||||
)
|
||||
|
||||
@@ -3427,9 +3432,9 @@ def main(args):
|
||||
guide_image = guide_images[global_step % len(guide_images)]
|
||||
elif args.clip_image_guidance_scale > 0 or args.vgg16_guidance_scale > 0:
|
||||
if prev_image is None:
|
||||
print("Generate 1st image without guide image.")
|
||||
logger.info("Generate 1st image without guide image.")
|
||||
else:
|
||||
print("Use previous image as guide image.")
|
||||
logger.info("Use previous image as guide image.")
|
||||
guide_image = prev_image
|
||||
|
||||
if regional_network:
|
||||
@@ -3473,7 +3478,7 @@ def main(args):
|
||||
process_batch(batch_data, highres_fix)
|
||||
batch_data.clear()
|
||||
|
||||
print("done!")
|
||||
logger.info("done!")
|
||||
|
||||
|
||||
def setup_parser() -> argparse.ArgumentParser:
|
||||
|
||||
Reference in New Issue
Block a user