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add nai diffusion

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Signed-off-by: hashcoko <hashcoko@gmail.com>
This commit is contained in:
hashcoko
2023-11-27 07:13:22 +09:00
parent d9e16cc427
commit b69f31caae
12 changed files with 226 additions and 40 deletions
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17
src/lib/Setting/Pages/OtherBotSettings.svelte
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@@ -8,7 +8,7 @@
import TextInput from "src/lib/UI/GUI/TextInput.svelte";
import SelectInput from "src/lib/UI/GUI/SelectInput.svelte";
import OptionInput from "src/lib/UI/GUI/OptionInput.svelte";
import CheckInput from "src/lib/UI/GUI/CheckInput.svelte";
import SliderInput from "src/lib/UI/GUI/SliderInput.svelte";
</script>
<h2 class="mb-2 text-2xl font-bold mt-2">{language.otherBots}</h2>
@@ -67,6 +67,21 @@
<span class="text-textcolor">Model</span>
<TextInput size="sm" marginBottom placeholder="nai-diffusion-3" bind:value={$DataBase.NAIImgModel}/>
<span class="text-textcolor">Enable I2I</span>
<Check bind:check={$DataBase.NAII2I}/>
{#if $DataBase.NAII2I}
<span class="text-textcolor">strength</span>
<SliderInput min={0} max={0.99} step={0.01} bind:value={$DataBase.NAIImgConfig.strength}/>
<span class="text-textcolor2 mb-6 text-sm">{$DataBase.NAIImgConfig.strength}</span>
<span class="text-textcolor">noise</span>
<SliderInput min={0} max={0.99} step={0.01} bind:value={$DataBase.NAIImgConfig.noise}/>
<span class="text-textcolor2 mb-6 text-sm">{$DataBase.NAIImgConfig.noise}</span>
{/if}
<span class="text-textcolor">Width</span>
<NumberInput size="sm" marginBottom min={0} max={2048} bind:value={$DataBase.NAIImgConfig.width}/>
<span class="text-textcolor">Height</span>

9
src/ts/process/generateSeed.ts Normal file
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@@ -0,0 +1,9 @@
export function generateRandomSeed(length) {
let result = '';
const characters = '0123456789';
const charactersLength = characters.length;
for (let i = 0; i < length; i++) {
result += characters.charAt(Math.floor(Math.random() * charactersLength));
}
return result;
}

15
src/ts/process/processzip.ts Normal file
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@@ -0,0 +1,15 @@
import JSZip from "jszip";
export async function processZip(dataArray: Uint8Array): Promise<string> {
const blob = new Blob([dataArray], { type: "application/zip" });
const zip = new JSZip();
const zipData = await zip.loadAsync(blob);
const imageFile = Object.keys(zipData.files).find(fileName => /\.(jpg|jpeg|png)$/.test(fileName));
if (imageFile) {
const imageData = await zipData.files[imageFile].async("base64");
return `data:image/png;base64,${imageData}`;
} else {
throw new Error("No image found in ZIP file");
}
}

103
src/ts/process/stableDiff.ts
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@@ -2,11 +2,13 @@ import { get } from "svelte/store"
import { DataBase, type character } from "../storage/database"
import { requestChatData } from "./request"
import { alertError } from "../alert"
import { globalFetch } from "../storage/globalApi"
import { globalFetch, readImage } from "../storage/globalApi"
import { CharEmotion } from "../stores"
import type { OpenAIChat } from "."
import { processZip } from "./processzip"
import { convertToBase64 } from "./uinttobase64"
import type { List } from "lodash"
import { generateRandomSeed } from "./generateSeed"
export async function stableDiff(currentChar:character,prompt:string){
const mainPrompt = "assistant is a chat analyzer.\nuser will input a data of situation with key and values before chat, and a chat of a user and character.\nView the status of the chat and change the data.\nif data's key starts with $, it must change it every time.\nif data value is none, it must change it."
let db = get(DataBase)
@@ -175,39 +177,80 @@ export async function stableDiff(currentChar:character,prompt:string){
}
const uri = new URL(db.NAIImgUrl)
uri.pathname = '/ai/generate-image'
const reqlist = {
body: {
"input": prompts.join(','),
"model": db.NAIImgModel,
"parameters": {
"width": db.NAIImgConfig.width,
"height": db.NAIImgConfig.height,
"sampler": db.NAIImgConfig.sampler,
"steps": db.NAIImgConfig.steps,
"scale": db.NAIImgConfig.scale,
"negative_prompt": neg,
"sm": db.NAIImgConfig.sm,
"sm_dyn": db.NAIImgConfig.sm_dyn
const charimg = currentChar.image; // Uint8Array 형태의 이미지 데이터
console.log("charimg:" + charimg);
const img = await readImage(charimg)
console.log("img:" + img);
const base64 = await convertToBase64(img);
const base64img = base64.split('base64,')[1];
console.log("base64img:" + base64img);
let reqlist= {}
if(db.NAII2I){
let randomseed = generateRandomSeed(10);
let seed = parseInt(randomseed, 10);
reqlist = {
body: {
"action": "img2img",
"input": prompts.join(','),
"model": db.NAIImgModel,
"parameters": {
"seed": seed,
"extra_noise_seed": seed,
"add_original_image": false,
"cfg_rescale": 0,
"controlnet_strength": 1,
"dynamic_threshold": false,
"n_samples": 1,
"width": db.NAIImgConfig.width,
"height": db.NAIImgConfig.height,
"sampler": db.NAIImgConfig.sampler,
"steps": db.NAIImgConfig.steps,
"scale": db.NAIImgConfig.scale,
"negative_prompt": neg,
"sm": false,
"sm_dyn": false,
"noise": db.NAIImgConfig.noise,
"noise_schedule": "native",
"strength": db.NAIImgConfig.strength,
"image": base64img,
"ucPreset": 2,
"uncond_scale": 1
}
},
headers:{
"Authorization": "Bearer " + db.NAIApiKey
}
}
}else{
reqlist = {
body: {
"input": prompts.join(','),
"model": db.NAIImgModel,
"parameters": {
"width": db.NAIImgConfig.width,
"height": db.NAIImgConfig.height,
"sampler": db.NAIImgConfig.sampler,
"steps": db.NAIImgConfig.steps,
"scale": db.NAIImgConfig.scale,
"negative_prompt": neg,
"sm": db.NAIImgConfig.sm,
"sm_dyn": db.NAIImgConfig.sm_dyn
}
},
headers:{
"Authorization": "Bearer " + db.NAIApiKey
}
},
headers:{
'Authorization:': 'Bearer ' + db.NAIApiKey,
'Content-Type': 'application/json'
}
}
console.log(uri)
console.log(reqlist)
try {
const da = await globalFetch(uri.toString(), reqlist)
const da = await globalFetch(db.NAIImgUrl, reqlist)
console.log(da)
if(da.ok){
if(da){
let charemotions = get(CharEmotion)
const img = `data:image/png;base64,${da.data.images[0]}`
console.log(img)
const img = await processZip(da.data);
const emos:[string, string,number][] = [[img, img, Date.now()]]
charemotions[currentChar.chaId] = emos
CharEmotion.set(charemotions)

17
src/ts/process/uinttobase64.ts Normal file
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@@ -0,0 +1,17 @@
export async function convertToBase64(data: Uint8Array): Promise<string> {
return new Promise((resolve, reject) => {
const blob = new Blob([data]);
const reader = new FileReader();
reader.onloadend = function() {
const base64String = reader.result as string;
resolve(base64String);
};
reader.onerror = function(error) {
reject(error);
};
reader.readAsDataURL(blob);
});
}

14
src/ts/storage/database.ts
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@@ -175,7 +175,7 @@ export function setDatabase(data:Database){
data.sdCFG = 7
}
if(checkNullish(data.NAIImgUrl)){
data.NAIImgUrl = 'https://api.novelai.net'
data.NAIImgUrl = 'https://api.novelai.net/ai/generate-image'
}
if(checkNullish(data.NAIApiKey)){
data.NAIApiKey = ''
@@ -183,6 +183,9 @@ export function setDatabase(data:Database){
if(checkNullish(data.NAIImgModel)){
data.NAIImgModel = 'nai-diffusion-3'
}
if(checkNullish(data.NAII2I)){
data.NAII2I = true
}
if(checkNullish(data.textTheme)){
data.textTheme = "standard"
}
@@ -248,7 +251,9 @@ export function setDatabase(data:Database){
steps:28,
scale:5,
sm:true,
sm_dyn:true
sm_dyn:true,
noise:0.0,
strength:0.3
}
}
if(checkNullish(data.customTextTheme)){
@@ -417,6 +422,7 @@ export interface Database{
NAIImgUrl:string
NAIApiKey:string
NAIImgModel:string
NAII2I:boolean
NAIImgConfig:NAIImgConfig
runpodKey:string
promptPreprocess:boolean
@@ -748,7 +754,9 @@ interface NAIImgConfig{
steps:number,
scale:number,
sm:boolean,
sm_dyn:boolean
sm_dyn:boolean,
noise:number,
strength:number
}
export type FormatingOrderItem = 'main'|'jailbreak'|'chats'|'lorebook'|'globalNote'|'authorNote'|'lastChat'|'description'|'postEverything'|'personaPrompt'

12
src/ts/storage/globalApi.ts
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@@ -661,7 +661,7 @@ export async function globalFetch(url:string, arg:{
method: method,
signal: arg.abortSignal
})
addFetchLog("Uint8Array Response", da.ok && da.status >= 200 && da.status < 300)
return {
ok: da.ok && da.status >= 200 && da.status < 300,
@@ -685,6 +685,16 @@ export async function globalFetch(url:string, arg:{
headers: headers,
method: method
})
if(da.headers.get('content-type')?.includes('application/x-zip-compressed')){
const daText = await da.blob()
addFetchLog(daText, da.ok && da.status >= 200 && da.status < 300)
return {
ok: da.ok && da.status >= 200 && da.status < 300,
data: daText,
headers: Object.fromEntries(da.headers)
}
}
const daText = await da.text()
try {
const dat = JSON.parse(daText)