add textual inversion hashes to infotext (2b1bae0d) · Commits · github_fork / Stable Diffusion Webui

modules/processing.py

+4 −3

Original line number	Diff line number	Diff line
		@@ -732,10 +732,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:

		p.setup_conds()

		if len(model_hijack.comments) > 0:
		for comment in model_hijack.comments:
		comments[comment] = 1

		p.extra_generation_params.update(model_hijack.extra_generation_params)

		if p.n_iter > 1:
		shared.state.job = f"Batch {n+1} out of {p.n_iter}"

+4 −1

Original line number	Diff line number	Diff line
		@@ -147,7 +147,6 @@ def undo_weighted_forward(sd_model):

		class StableDiffusionModelHijack:
		fixes = None
		comments = []
		layers = None
		circular_enabled = False
		clip = None
		@@ -156,6 +155,9 @@ class StableDiffusionModelHijack:
		embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()

		def __init__(self):
		self.extra_generation_params = {}
		self.comments = []

		self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)

		def apply_optimizations(self, option=None):
		@@ -236,6 +238,7 @@ class StableDiffusionModelHijack:

		def clear_comments(self):
		self.comments = []
		self.extra_generation_params = {}

		def get_prompt_lengths(self, text):
		if self.clip is None:

+12 −3

Original line number	Diff line number	Diff line
		@@ -229,9 +229,18 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
		z = self.process_tokens(tokens, multipliers)
		zs.append(z)

		if len(used_embeddings) > 0:
		embeddings_list = ", ".join([f'{name} [{embedding.checksum()}]' for name, embedding in used_embeddings.items()])
		self.hijack.comments.append(f"Used embeddings: {embeddings_list}")
		if opts.textual_inversion_add_hashes_to_infotext and used_embeddings:
		hashes = []
		for name, embedding in used_embeddings.items():
		shorthash = embedding.shorthash
		if not shorthash:
		continue

		name = name.replace(":", "").replace(",", "")
		hashes.append(f"{name}: {shorthash}")

		if hashes:
		self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)

		return torch.hstack(zs)

+1 −0

Original line number	Diff line number	Diff line
		@@ -472,6 +472,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
		"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
		"extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
		"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_restart(),
		"textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
		"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *hypernetworks]}, refresh=reload_hypernetworks),
		}))

+8 −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
		from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
		import modules.textual_inversion.dataset
		from modules.textual_inversion.learn_schedule import LearnRateScheduler

		@@ -49,6 +49,8 @@ class Embedding:
		self.sd_checkpoint_name = None
		self.optimizer_state_dict = None
		self.filename = None
		self.hash = None
		self.shorthash = None

		def save(self, filename):
		embedding_data = {
		@@ -82,6 +84,10 @@ class Embedding:
		self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
		return self.cached_checksum

		def set_hash(self, v):
		self.hash = v
		self.shorthash = self.hash[0:12]


		class DirWithTextualInversionEmbeddings:
		def __init__(self, path):
		@@ -199,6 +205,7 @@ class EmbeddingDatabase:
		embedding.vectors = vec.shape[0]
		embedding.shape = vec.shape[-1]
		embedding.filename = path
		embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')

		if self.expected_shape == -1 or self.expected_shape == embedding.shape:
		self.register_embedding(embedding, shared.sd_model)