DominikDoom/a1111-sd-webui-tagcomplete
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Merge branch 'main' into feature-sort-by-frequent-use

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DominikDoom
2023-10-06 18:36:24 +02:00
parent 7128efc4f4 5ebe22ddfc
commit 4fba7baa69
6 changed files with 54 additions and 44 deletions
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21
javascript/_utils.js
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@@ -1,13 +1,14 @@
// Utility functions for tag autocomplete
// Parse the CSV file into a 2D array. Doesn't use regex, so it is very lightweight.
// We are ignoring newlines in quote fields since we expect one-line entries and parsing would break for unclosed quotes otherwise
function parseCSV(str) {
var arr = [];
var quote = false; // 'true' means we're inside a quoted field
const arr = [];
let quote = false; // 'true' means we're inside a quoted field
// Iterate over each character, keep track of current row and column (of the returned array)
for (var row = 0, col = 0, c = 0; c < str.length; c++) {
var cc = str[c], nc = str[c + 1]; // Current character, next character
for (let row = 0, col = 0, c = 0; c < str.length; c++) {
let cc = str[c], nc = str[c+1]; // Current character, next character
arr[row] = arr[row] || []; // Create a new row if necessary
arr[row][col] = arr[row][col] || ''; // Create a new column (start with empty string) if necessary
@@ -22,14 +23,12 @@ function parseCSV(str) {
// If it's a comma and we're not in a quoted field, move on to the next column
if (cc == ',' && !quote) { ++col; continue; }
// If it's a newline (CRLF) and we're not in a quoted field, skip the next character
// and move on to the next row and move to column 0 of that new row
if (cc == '\r' && nc == '\n' && !quote) { ++row; col = 0; ++c; continue; }
// If it's a newline (CRLF), skip the next character and move on to the next row and move to column 0 of that new row
if (cc == '\r' && nc == '\n') { ++row; col = 0; ++c; quote = false; continue; }
// If it's a newline (LF or CR) and we're not in a quoted field,
// move on to the next row and move to column 0 of that new row
if (cc == '\n' && !quote) { ++row; col = 0; continue; }
if (cc == '\r' && !quote) { ++row; col = 0; continue; }
// If it's a newline (LF or CR) move on to the next row and move to column 0 of that new row
if (cc == '\n') { ++row; col = 0; quote = false; continue; }
if (cc == '\r') { ++row; col = 0; quote = false; continue; }
// Otherwise, append the current character to the current column
arr[row][col] += cc;

5
javascript/ext_embeddings.js
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@@ -11,12 +11,15 @@ class EmbeddingParser extends BaseTagParser {
if (searchTerm.startsWith("v1") || searchTerm.startsWith("v2")) {
versionString = searchTerm.slice(0, 2);
searchTerm = searchTerm.slice(2);
} else if (searchTerm.startsWith("vxl")) {
versionString = searchTerm.slice(0, 3);
searchTerm = searchTerm.slice(3);
}
let filterCondition = x => x[0].toLowerCase().includes(searchTerm) || x[0].toLowerCase().replaceAll(" ", "_").includes(searchTerm);
if (versionString)
tempResults = embeddings.filter(x => filterCondition(x) && x[2] && x[2] === versionString); // Filter by tagword
tempResults = embeddings.filter(x => filterCondition(x) && x[2] && x[2].toLowerCase() === versionString.toLowerCase()); // Filter by tagword
else
tempResults = embeddings.filter(x => filterCondition(x)); // Filter by tagword
} else {

2
javascript/ext_modelKeyword.js
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@@ -20,7 +20,7 @@ async function load() {
// Add to the dict
csv_lines.forEach(parts => {
const hash = parts[0];
const keywords = parts[1].replaceAll("| ", ", ").replaceAll("|", ", ").trim();
const keywords = parts[1]?.replaceAll("| ", ", ")?.replaceAll("|", ", ")?.trim();
const lastSepIndex = parts[2]?.lastIndexOf("/") + 1 || parts[2]?.lastIndexOf("\\") + 1 || 0;
const name = parts[2]?.substring(lastSepIndex).trim() || "none"

4
javascript/ext_umi.js
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@@ -129,7 +129,7 @@ class UmiParser extends BaseTagParser {
return;
}
let umiTagword = diff[0] || '';
let umiTagword = tagCountChange < 0 ? '' : diff[0] || '';
let tempResults = [];
if (umiTagword && umiTagword.length > 0) {
umiTagword = umiTagword.toLowerCase().replace(/[\n\r]/g, "");
@@ -188,7 +188,7 @@ class UmiParser extends BaseTagParser {
}
}
function updateUmiTags( tagType, sanitizedText, newPrompt, textArea) {
function updateUmiTags(tagType, sanitizedText, newPrompt, textArea) {
// If it was a umi wildcard, also update the umiPreviousTags
if (tagType === ResultType.umiWildcard && originalTagword.length > 0) {
let umiSubPrompts = [...newPrompt.matchAll(UMI_PROMPT_REGEX)];

10
javascript/tagAutocomplete.js
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@@ -546,6 +546,14 @@ async function insertTextAtCursor(textArea, result, tagword, tabCompletedWithout
let nameDict = modelKeywordDict.get(result.hash);
let names = [result.text + ".safetensors", result.text + ".pt", result.text + ".ckpt"];
// No match, try to find a sha256 match from the cache file
if (!nameDict) {
const sha256 = await fetchAPI(`/tacapi/v1/lora-cached-hash/${result.text}`)
if (sha256) {
nameDict = modelKeywordDict.get(sha256);
}
}
if (nameDict) {
let found = false;
names.forEach(name => {
@@ -715,6 +723,8 @@ function addResultsToList(textArea, results, tagword, resetList) {
linkPart = linkPart.split("[")[0]
}
linkPart = encodeURIComponent(linkPart);
// Set link based on selected file
let tagFileNameLower = tagFileName.toLowerCase();
if (tagFileNameLower.startsWith("danbooru")) {

56
scripts/tag_autocomplete_helper.py
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@@ -12,7 +12,7 @@ import gradio as gr
import yaml
from fastapi import FastAPI
from fastapi.responses import FileResponse, JSONResponse
from modules import script_callbacks, sd_hijack, shared
from modules import script_callbacks, sd_hijack, shared, hashes
from pydantic import BaseModel
from scripts.model_keyword_support import (get_lora_simple_hash,
@@ -171,44 +171,30 @@ def get_embeddings(sd_model):
# Version constants
V1_SHAPE = 768
V2_SHAPE = 1024
VXL_SHAPE = 2048
emb_v1 = []
emb_v2 = []
emb_vXL = []
results = []
try:
# Get embedding dict from sd_hijack to separate v1/v2 embeddings
emb_type_a = sd_hijack.model_hijack.embedding_db.word_embeddings
emb_type_b = sd_hijack.model_hijack.embedding_db.skipped_embeddings
# Get the shape of the first item in the dict
emb_a_shape = -1
emb_b_shape = -1
if (len(emb_type_a) > 0):
emb_a_shape = next(iter(emb_type_a.items()))[1].shape
if (len(emb_type_b) > 0):
emb_b_shape = next(iter(emb_type_b.items()))[1].shape
loaded = sd_hijack.model_hijack.embedding_db.word_embeddings
skipped = sd_hijack.model_hijack.embedding_db.skipped_embeddings
# Add embeddings to the correct list
if (emb_a_shape == V1_SHAPE):
emb_v1 = [(Path(v.filename), k, "v1") for (k,v) in emb_type_a.items()]
elif (emb_a_shape == V2_SHAPE):
emb_v2 = [(Path(v.filename), k, "v2") for (k,v) in emb_type_a.items()]
for key, emb in (loaded | skipped).items():
if emb.filename is None or emb.shape is None:
continue
if (emb_b_shape == V1_SHAPE):
emb_v1 = [(Path(v.filename), k, "v1") for (k,v) in emb_type_b.items()]
elif (emb_b_shape == V2_SHAPE):
emb_v2 = [(Path(v.filename), k, "v2") for (k,v) in emb_type_b.items()]
if emb.shape == V1_SHAPE:
emb_v1.append((Path(emb.filename), key, "v1"))
elif emb.shape == V2_SHAPE:
emb_v2.append((Path(emb.filename), key, "v2"))
elif emb.shape == VXL_SHAPE:
emb_vXL.append((Path(emb.filename), key, "vXL"))
# Get shape of current model
#vec = sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
#model_shape = vec.shape[1]
# Show relevant entries at the top
#if (model_shape == V1_SHAPE):
# results = [e + ",v1" for e in emb_v1] + [e + ",v2" for e in emb_v2]
#elif (model_shape == V2_SHAPE):
# results = [e + ",v2" for e in emb_v2] + [e + ",v1" for e in emb_v1]
#else:
# raise AttributeError # Fallback to old method
results = sort_models(emb_v1) + sort_models(emb_v2)
results = sort_models(emb_v1) + sort_models(emb_v2) + sort_models(emb_vXL)
except AttributeError:
print("tag_autocomplete_helper: Old webui version or unrecognized model shape, using fallback for embedding completion.")
# Get a list of all embeddings in the folder
@@ -553,6 +539,18 @@ def api_tac(_: gr.Blocks, app: FastAPI):
async def get_lyco_info(lyco_name):
return await get_json_info(LYCO_PATH, lyco_name)
@app.get("/tacapi/v1/lora-cached-hash/{lora_name}")
async def get_lora_cached_hash(lora_name: str):
path_glob = glob.glob(LORA_PATH.as_posix() + f"/**/{lora_name}.*", recursive=True)
paths = [lora for lora in path_glob if Path(lora).suffix in [".safetensors", ".ckpt", ".pt"]]
if paths is not None and len(paths) > 0:
path = paths[0]
hash = hashes.sha256_from_cache(path, f"lora/{lora_name}", path.endswith(".safetensors"))
if hash is not None:
return hash
return None
def get_path_for_type(type):
if type == "lora":
return LORA_PATH