resolve some of circular import issues for kohaku (f0c1063a) · Commits · github_fork / Stable Diffusion Webui

modules/hypernetworks/hypernetwork.py

+2 −3

Original line number	Diff line number	Diff line
		@@ -10,7 +10,7 @@ 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, sd_hijack_checkpoint, errors
		from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
		from modules.textual_inversion import textual_inversion, logging
		from modules.textual_inversion.learn_schedule import LearnRateScheduler
		from torch import einsum
		@@ -469,8 +469,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,


		def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
		# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
		from modules import images
		from modules import images, processing

		save_hypernetwork_every = save_hypernetwork_every or 0
		create_image_every = create_image_every or 0

+1 −6

Original line number	Diff line number	Diff line
		@@ -30,6 +30,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
		from einops import repeat, rearrange
		from blendmodes.blend import blendLayers, BlendType

		decode_first_stage = sd_samplers_common.decode_first_stage

		# some of those options should not be changed at all because they would break the model, so I removed them from options.
		opt_C = 4
		@@ -572,12 +573,6 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
		return samples


		def decode_first_stage(model, x):
		x = model.decode_first_stage(x.to(devices.dtype_vae))

		return x


		def get_fixed_seed(seed):
		if seed is None or seed == '' or seed == -1:
		return int(random.randrange(4294967294))

+3 −3

Original line number	Diff line number	Diff line
		@@ -2,7 +2,6 @@ import torch
		from torch.nn.functional import silu
		from types import MethodType

		import modules.textual_inversion.textual_inversion
		from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
		from modules.hypernetworks import hypernetwork
		from modules.shared import cmd_opts
		@@ -164,12 +163,13 @@ class StableDiffusionModelHijack:
		clip = None
		optimization_method = None

		embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()

		def __init__(self):
		import modules.textual_inversion.textual_inversion

		self.extra_generation_params = {}
		self.comments = []

		self.embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
		self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)

		def apply_optimizations(self, option=None):

+8 −2

Original line number	Diff line number	Diff line
		@@ -2,7 +2,7 @@ from collections import namedtuple
		import numpy as np
		import torch
		from PIL import Image
		from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared
		from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared
		from modules.shared import opts, state

		SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
		@@ -35,7 +35,7 @@ def single_sample_to_image(sample, approximation=None):
		x_sample = sample * 1.5
		x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
		else:
		x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
		x_sample = decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5

		x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
		x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
		@@ -44,6 +44,12 @@ def single_sample_to_image(sample, approximation=None):
		return Image.fromarray(x_sample)


		def decode_first_stage(model, x):
		x = model.decode_first_stage(x.to(devices.dtype_vae))

		return x


		def sample_to_image(samples, index=0, approximation=None):
		return single_sample_to_image(samples[index], approximation)

+3 −1

Original line number	Diff line number	Diff line
		@@ -13,7 +13,7 @@ import numpy as np
		from PIL import Image, PngImagePlugin
		from torch.utils.tensorboard import SummaryWriter

		from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
		from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
		import modules.textual_inversion.dataset
		from modules.textual_inversion.learn_schedule import LearnRateScheduler

		@@ -387,6 +387,8 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat


		def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
		from modules import processing

		save_embedding_every = save_embedding_every or 0
		create_image_every = create_image_every or 0
		template_file = textual_inversion_templates.get(template_filename, None)