add option to show/hide warnings (924e2220) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/LDSR/ldsr_model_arch.py

+0 −3

Original line number	Diff line number	Diff line
		import os
		import gc
		import time
		import warnings

		import numpy as np
		import torch
		@@ -15,8 +14,6 @@ from ldm.models.diffusion.ddim import DDIMSampler
		from ldm.util import instantiate_from_config, ismap
		from modules import shared, sd_hijack

		warnings.filterwarnings("ignore", category=UserWarning)

		cached_ldsr_model: torch.nn.Module = None

+1 −1

Original line number	Diff line number	Diff line
		@@ -11,7 +11,7 @@ ignore_ids_for_localization={
		train_embedding: 'OPTION',
		train_hypernetwork: 'OPTION',
		txt2img_styles: 'OPTION',
		img2img_styles 'OPTION',
		img2img_styles: 'OPTION',
		setting_random_artist_categories: 'SPAN',
		setting_face_restoration_model: 'SPAN',
		setting_realesrgan_enabled_models: 'SPAN',

+6 −1

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		@@ -12,7 +12,7 @@ import torch
		import tqdm
		from einops import rearrange, repeat
		from ldm.util import default
		from modules import devices, processing, sd_models, shared, sd_samplers, hashes
		from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint
		from modules.textual_inversion import textual_inversion, logging
		from modules.textual_inversion.learn_schedule import LearnRateScheduler
		from torch import einsum
		@@ -575,6 +575,8 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi

		pbar = tqdm.tqdm(total=steps - initial_step)
		try:
		sd_hijack_checkpoint.add()

		for i in range((steps-initial_step) * gradient_step):
		if scheduler.finished:
		break
		@@ -724,6 +726,9 @@ Last saved image: {html.escape(last_saved_image)}<br/>
		pbar.close()
		hypernetwork.eval()
		#report_statistics(loss_dict)
		sd_hijack_checkpoint.remove()



		filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
		hypernetwork.optimizer_name = optimizer_name

+0 −8

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		@@ -69,12 +69,6 @@ def undo_optimizations():
		ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward


		def fix_checkpoint():
		ldm.modules.attention.BasicTransformerBlock.forward = sd_hijack_checkpoint.BasicTransformerBlock_forward
		ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = sd_hijack_checkpoint.ResBlock_forward
		ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = sd_hijack_checkpoint.AttentionBlock_forward


		class StableDiffusionModelHijack:
		fixes = None
		comments = []
		@@ -107,8 +101,6 @@ class StableDiffusionModelHijack:

		self.clip = m.cond_stage_model

		fix_checkpoint()

		def flatten(el):
		flattened = [flatten(children) for children in el.children()]
		res = [el]

+37 −1

Original line number	Diff line number	Diff line
		from torch.utils.checkpoint import checkpoint

		import ldm.modules.attention
		import ldm.modules.diffusionmodules.openaimodel


		def BasicTransformerBlock_forward(self, x, context=None):
		return checkpoint(self._forward, x, context)


		def AttentionBlock_forward(self, x):
		return checkpoint(self._forward, x)


		def ResBlock_forward(self, x, emb):
		return checkpoint(self._forward, x, emb)


		stored = []


		def add():
		if len(stored) != 0:
		return

		stored.extend([
		ldm.modules.attention.BasicTransformerBlock.forward,
		ldm.modules.diffusionmodules.openaimodel.ResBlock.forward,
		ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward
		])

		ldm.modules.attention.BasicTransformerBlock.forward = BasicTransformerBlock_forward
		ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = ResBlock_forward
		ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = AttentionBlock_forward


		def remove():
		if len(stored) == 0:
		return

		ldm.modules.attention.BasicTransformerBlock.forward = stored[0]
		ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = stored[1]
		ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = stored[2]

		stored.clear()