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TENSOR.ART
👋 FREE ONLINE IMAGE GENERATOR AND MODEL HOSTING SITE!
a flat lineart of a young beautiful woman leaning against colorful wall printed
botanial patterns at pop-style Private Rooms,kind smile,bangs,messy
short-bob,detailed realistic clothes,soft tone,only in four colors,
Realtime Generate
CREATION
Get start with Stable Diffusion！
💥 SD3 & DiT
COMFYFLOW
ComfyUI's amazing experience！
🎭 TAttoo Event
HOST MY MODEL
Share my models，get more attention！
💸 Double Earnings
ONLINE TRAINING
Make LoRA Training easier！
🤖 Make Fun
AI TOOLS
4.7K
116
TRY FLUX.1 DEV NOW!
Yes, the new Flux by Black Forest Labs. This is the Flux.1[dev] version, which
is the heaviest one, as opposed to the [schnell] that is supposed to be
lighter.Try it with complex prompts and check it yourself how far can you
go.PLEASE NOTE: since the T5xxl_fp16 CLIP could randomly end up in a "routing
error", I've settled for the fp8 which seemed more stable.
DArt
Try
495
17
FLUX一键出图
随意更改描述词和图片尺寸同描述词每次出图需要点击随机种子
岳说
Try
1.2K
53
FLUX DEV TXT2IMG ADVANCED PARAMETERS
open WORKFLOW to adjust more parametersuse THIS to use as AI Tools
riwa
Try
15K
223
STABLE DIFFUSION 3 (SD3) MEDIUM BASIC
Feel free to try this latest SD3 Medium basic workflow!
TensorArt
Try
183
37
HD POSTER MAKING TOOL（高清海报制作工具）
Users are required to upload font photos, and the AI tool will automatically
help you cut out the images and then fuse them together according to the scene
you need.需要用户上传字体照片，AI tool会自动帮你抠图然后根据你需要的场景融合在一起。
SuperRedPig
Try
83
7
EK-PATCHWORK ART MAKER [SD3]
Patchwork Art Maker [SD3]Easy creation by radio buttons and prompts!Steps 20 ~
28 recommended!clip_l default (a model): ((Anime Style)) of A sophisticated
fashion model girl,short blonde hair,dynamic character,20yo,detailed exquisite
face,parody,clip_g default (setting): complex background,dynamic light and
shadow,bold high quality,high contrast,Upperbody,vibrant colors,looking at
viewer,ek_ptch_art,t5xxl default (strength and artist = High, Makoto): patchwork
art:1.5,by the style of Makoto Shinkai's artwork,t5xxl default (strength and
artist = Low, Klimt): patchwork art:1.2,by the style of Gustav Klimt$ and
[[Karol Bak$]],Don't worry! It's an Abstract Art. Any beautiful image output is
just fine~ 🤗😉
EclipseKww
Try
690
68
STYLE TRANSFER FOR ALL
Hey guys , try out my tool and give feedback in commentsSUGGESTIONS :Try to
match ur prompt with ref image style for best resultsUse proper lora to achieve
best results, for anime u can use niji v6 massive style and ct-sdxl fantasy
nijiUse Style strength between 0.6-1.0
AAbrains
Try
4.1K
88
✨IMAGE TO CLAY STYLE🏺(0.1 CREDIT COST)
✨Just upload your photo to create a clay-style image. 📸Note: Image must be
under 5 MB and dimensions should not exceed 1520 pixels in width and height.
🖼️The result vary every time, feel free to try your luck! 🎲💫
Archie
Try
117
4
PHOTO TO GHIBLI STYLE
⚠️ Tips: The image size should not exceed 1536 x 1536 pixels.
SuperRedPig
Try
356
35
REPLACE BACKGROUND
Instantly change backgrounds of your images with a simple prompt.
Ava
Try
220
43
QR CODE GENERATOR ++ ( WITH READABLE OUTPUT )
open workflow to change more parameters
riwa
Try
1.5K
76
REFERENCE IMAGE TO INSPIRED TATTOO
✨ Turn your old AI picture into a Tattoo Design😍 ✨Tested checkpoint
:-ToxicEtheRealXL-MeinaXL-Stable Diffusion XL*you can experiment other ckpt ;)
coloraisme
Try
54
3
ORNATE FANTACY - INTRICATE DETAILS
Ornate Fantacy - Intricate DetailsThis AI Tool as the name suggests adds Ornate
Fantacy to any given prompts and do many thing just try it yourself !!! Use very
simple prompts and avoid using weight in prompts
Shopon_skp
Try
761
35
TẠO PHỐI CẢNH HOẶC MẶT BẰNG TỔNG THỂ (MASTER PLAN/AERIAL VIEW)
App dùng để tạo phối cảnh tổng thể hoặc mặt bằng tổng thể từ các hình phác
thảo.Lưu ý: Muốn bám sát nét nhiều hơn thì tăng "chỉ số bám nét" lên, muốn ban
ngày hay ban đêm thì thay thế từ khóa tương ứng vào ô "thời gian"Mọi chi tiết
liên hệ zalo: 0983163986youtube: www.youtube.com/architech1904tiktok:
architech1904_ytB
Architech1904
Try
654
60
IMPRESSIONISM OIL PAINTING (ALPHA TEST)
This tool is in the Alpha!
Enigmalucinate
Try
35
2
TA1YEAR | ONE YEAR OF PROMPTING BADGE
🎉 · 🥂 · 🥳 · 🎂 · 🍾.• 1 Year, anniversary celebration, one year of prompting!
PictureT
Try
535
53
PREMIUM BADGE DESIGNER (GOLD SAPPHIRE STYLE) - FUTUREVOLAB
Overview:The Premium Badge Designer (Gold Sapphire Style) by FuturEvoLab is a
state-of-the-art AI-powered tool designed to create exquisite and luxurious
badges. This innovative tool leverages the advanced capabilities of Stable
Diffusion XL to generate high-quality badge designs with stunning gold and
sapphire elements. It features a built-in background removal function, allowing
for seamless integration of the badges into various applications with a
transparent background.【Key Features】High-Quality Badge Design:Utilizes advanced
AI to create badges with intricate details and superior craftsmanship.Supports a
range of styles and customization options to suit various design
preferences.Gold Sapphire Style:Specializes in gold and sapphire-themed designs,
adding a touch of elegance and luxury.Ideal for creating prestigious and
visually appealing badges.Background Removal:Once the design is finalized, the
tool automatically removes the background, producing a transparent image.This
feature ensures the badge can be easily used across different platforms without
additional editing.【Benefits】Efficiency: Saves time and effort in creating
high-quality badges with minimal manual intervention.Professional Quality:
Ensures badges have a professional and polished appearance, suitable for various
prestigious applications.Flexibility: Offers extensive customization options to
meet diverse design needs and preferences.The Premium Badge Designer (Gold
Sapphire Style) - FuturEvoLab is an essential tool for anyone looking to create
elegant and luxurious badges with ease. Its advanced AI capabilities, combined
with the specialized gold sapphire style and background removal feature, make it
a versatile and powerful addition to your design toolkit. Whether for awards,
branding, or membership recognition, this tool ensures your badges stand out
with unmatched quality and sophistication.
FuturEvoLab
Try
5.6K
165
🖊️TATTOO DESIGN MASTER
Introducing —— 『🖊️Tattoo Design Master · AI tool』!Say goodbye to endless
searches and indecision. Just entering a word and clicking "Generate", unleash a
world of personalized tattoo designs tailored to your style and preferences!It's
as easy as typing in your desired motif and watching the magic unfold. From
intricate patterns to minimalist masterpieces, the possibilities are endless.
Embrace your individuality and let your creativity soar with our hassle-free
tattoo design generator! 🖊️🖊️
RK
Try
9.5K
225
MIDJOURNEY REPLICA PRO XL
Shopon_skp
Try
730
37
HIDDEN ART
open workflow to change more parameters
riwa
Try
229
28
JAPANESE STYLE TATTOO (UKIYO-E STYLE🌊)
How to Use:Upload an image, click the brush 🖌️ in the top left corner, and blur
the area where you want the tattoo.Describe the tattoo design you want
(simple words will do, such as: Dragon, Phoenix, Lotus, Koi Fish, Tiger, Snake,
Chrysanthemum, Bamboo, Mount Fuji).Each generated tattoo is unique, and you can
try many times because it's very affordable! 💰If you don't have image, you can
use some sample images (showcase) I've prepared for you.This AI Tool is inspired
by my friend Riwa's AI Tool. If you like them, you can buy Riwa a coffee
for $1. ☕Thank you! ^_^
Archie
Try
89
5
KOLORS
oaa
Try
3.2K
120
💥FILTER 2💥
1️: ᴜᴘʟᴏᴀᴅ ʏᴏᴜʀ ɪᴍᴀɢᴇ | ꜱᴜʙᴇ ᴛᴜ ɪᴍᴀɢᴇɴ2️⃣: ᴄʟɪᴄᴋ ɢᴇɴᴇʀᴀᴛᴇ | ʜᴀᴢ ᴄʟɪᴄ ᴇɴ
ɢᴇɴᴇʀᴀʀ💥 FILTER 1💥 💥 FILTER 2💥💥 FILTER 3💥 💥 FILTER 4💥💥 FILTER 5💥 💎
ɪᴍɢ - ᴄᴏᴍʙɪɴᴇ💎
⚡ᴜᴘᴘᴇʀ⚡
Try
View All AI Tools
MODELS
CHECKPOINT HunyuanDiT
Run
HUNYUANDIT-V1.2-EMA
腾讯混元
40K 702
CHECKPOINT Kolors
Run
KOLORS-1.0.FP16
Kwai-Kolors
13K 193
CHECKPOINT SD3
Run
STABLE DIFFUSION 3 SD3-MEDIUM
TensorArt
174K 1.3K
LORA SD3
Run
EARLY ACCESS
CYBORG STYLE SD3M-V1
Goofy Ai
32 3
LORA HunyuanDiT
Run
EXCLUSIVE
HUNYUANDIT - COVER PAINTING-V1
SDG
92 2
LORA
Run
[LORA] FLOWER KETTLE / 壺中天 CONCEPT (WITH DROPOUT & NOISE VERSION)-V1.0
L_A_X
1.9K 76
CHECKPOINT SD3
Run
EARLY ACCESS
REALISTIC VISION-V1
Ai Art Vision
2.5K 28
LORA HunyuanDiT
Run
EARLY ACCESS
FLAT HAND DRAWN STYLE ILLUSTRATION -HUNYUAN DIT-V1
kei
220 6
LORA SD3
Run
EARLY ACCESS
[SD3]SCENE: FUTURISTIC CITY - FUTUREVOLAB-V1
FuturEvoLab
192 11
LORA HunyuanDiT
Run
EARLY ACCESS
HUNYUANDIT - GAME CARD MONSTER CUTE-V1
SDG
92
LORA XL
Run
EXCLUSIVE
PIXAR STYLE DRAGON -V1
NukeA.I
14K 93
LORA HunyuanDiT
Run
EXCLUSIVE
THE DRUIDS ~ RPG-V1.10
Apolonia
39 1
LORA HunyuanDiT
Run
EARLY ACCESS
TQ - HUNYUAN FUTURISTIC FURNITURE-V.1.6
TracQuoc
58 5
LORA
Run
EXCLUSIVE
SDVN-3DCHARACTER-V1.0
StableDiffusionVN
7.1K 224
LORA
Run
EXCLUSIVE
LOST IN SPACE-V1
Loraevolution
52K 290
LORA XL
Run
HALLOWEENXL-0.1
nocor1i8
6.9K 62
LORA
Run
EXCLUSIVE
DRAGON NEW YEAR INSANE-V1
JAY SDVN
2.1K 48
LORA
Run
EXCLUSIVE
HANDS REPAIR |LORA-V5
poakl
62K 266
LOCON XL
Run
ISLAND GENERATOR (SDXL, FFXL) -0.4 - FFUSION EDITION
FFusionAI
21K 97
LORA HunyuanDiT
Run
EARLY ACCESS
REALISM DIT-TA-EXCLV1
Shopon_skp
42 1
LORA XL
Run
PALE DEMON-PALE DEMON
Redbible
110K 356
LORA SD3
Run
EXCLUSIVE
SD3 NEON FANTASY-V1
SDG
307 9
LORA XL
Run
EXCLUSIVE
✨️APOLONIA'S CHRISTMAS XLIGHT BOX✨️-V1.0
Apolonia
23K 66
View All Models
ARTICLES
HUNYUAN-DIT: RECOMMENDATIONS
ReviewHello everyone; I want to share some of my impressions about the Chinese
model, Hunyuan-DiT from tencent. First of all let’s start with some mandatory
data to know so we (westerns) can figure out what is meant for:Hunyuan-DiT works
well as multi-modal dialogue with users (mainly Chinese and English language),
the better explained your prompt the better your generation will be, is not
necessary to introduce only keywords, despite it understands them quite well. In
terms of rating HYDiT 1.2 is located between SDXL and SD3; is not as powerful
than SD3, defeats SDXL almost in everything; for me is how SDXL should’ve be in
first place; one of the best parts is that Hunyuan-DiT is compatible with almost
all SDXL node suit.Hunyuan-DiT-v1.2, was trained with 1.5B parameters.mT5, was
trained with 1.6B parameters.Recommeded VAE: sdxl-vae-fp16-fixRecommended
Sampler: ddpm, ddim, or dpmmsPrompt as you’d like to do in SD1.5, don’t be shy
and go further in term of length; HunyuanDiT combines two text encoders, a
bilingual CLIP and a multilingual T5 encoder to improve language understanding
and increase the context length; they divide your prompt on meaningful IDs and
then process your entire prompt, their limit is 100 IDs or to 256 tokens. T5
works well on a variety of tasks out-of-the-box by prepending a different prefix
to the input corresponding to each task.To improve your prompt, place your
resumed prompt in the CLIP:TextEncoder node box (if you disabled t5), or place
your extended prompt in the T5:TextEncoder node box (if you enabled t5).You can
use the "simple" text encode node to only use one prompt, or you can use the
regular one to pass different text to CLIP/T5.The worst is the model only
benefits from moderated (high for TensorArt) step values: 40 steps are the basis
in most cases.Comfyui (Comfyflow) (Example)TensorArt added all the elements to
build a good flow for us; you should try it too.AdditionalWhat can we do in the
Open-Source plan? (link)Official info for LoRA training (link)ReferencesAnalysis
of HunYuan-DiT | https://arxiv.org/html/2405.08748v1Learn more of T5 |
https://huggingface.co/docs/transformers/en/model_doc/t5How CLIP and T5 work
together | https://arxiv.org/pdf/2205.11487
PictureT
5
1
UNLOCK THE POWER OF DETAILED BEAUTY WITH TQ-HUNYUAN-MORE-BEAUTIFUL-DETAIL V1.7
In the world of digital artistry, achieving that perfect blend of intricate
details and stunning visuals can be a game-changer. That's where our latest
model, TQ-HunYuan-More-Beautiful-Detail v1.7, comes into play. Designed with
precision and a keen eye for aesthetics, this model is your go-to solution for
elevating your artwork to new heights.What is TQ-HunYuan-More-Beautiful-Detail
v1.7?TQ-HunYuan-More-Beautiful-Detail v1.7 is a state-of-the-art LoRA (Low-Rank
Adaptation) model created to enhance the finer details in your digital
creations. Whether you're working on portraits, landscapes, or abstract designs,
this model ensures that every nuance and subtlety is brought to life with
extraordinary clarity and beauty.Why Choose TQ-HunYuan-More-Beautiful-Detail
v1.7?Unmatched Detail Enhancement: As the name suggests, this model excels at
adding more beautiful details to your artwork. It meticulously enhances
textures, refines edges, and highlights intricate patterns, making your
creations visually striking.Versatility Across Genres: No matter the style or
genre of your artwork, TQ-HunYuan-More-Beautiful-Detail v1.7 adapts seamlessly.
From hyper-realistic portraits to fantastical landscapes, this model enhances
every element with precision.User-Friendly Integration: Designed for ease of
use, integrating TQ-HunYuan-More-Beautiful-Detail v1.7 into your workflow is
straightforward. Compatible with various platforms and software, it allows
artists of all levels to harness its power without a steep learning curve.Boost
Your Creativity: By handling the intricate details, this model frees up your
creative energy. Focus on the broader aspects of your work while
TQ-HunYuan-More-Beautiful-Detail v1.7 takes care of the fine-tuning, resulting
in a harmonious and polished final piece.How to Get StartedGetting started with
TQ-HunYuan-More-Beautiful-Detail v1.7 is simple. Visit this link to access the
model. Download and integrate it into your preferred digital art software, and
watch as your creations transform with enhanced details and breathtaking
beauty.Ready to take your art to the next level? Download
TQ-HunYuan-More-Beautiful-Detail v1.7 now and start creating masterpieces with
more beautiful detail than ever before.
TracQuoc
12
3
SD3 - 3D LETTERING DESIGNER
SD3 understands prompts better compared to SDXL. You can use this to create
interesting 3D lettering. For this purpose, use this WF! You can use a gradient
as the background or any image you like. Have fun!Link to workflow: SD3 - 3D
lettering designer | ComfyUI Workflow | Tensor.Art
Aderek514
5
REALISTIC VISION SD3
Realistic VisionI am excited to present my latest Realistic checkpoint model
based on SD3M. This model has undergone over 100k+ training steps, ensuring
high-quality output.About This Model:This is a Photo Realistic model, capable of
generating photorealistic images. No trigger words are needed. The model is
designed to produce high-detail, high-resolution images that closely mimic
real-life photographs.Configuration Used for Training:GPU: A6000x2Dataset: A mix
of 5k stock photos and my own datasetBatch Size: 8Optimizer: AdamWScheduler:
Cosine with restartsLearning Rate (LR): 1e-05Epoch: Target of 300
epochsCaptioning: WD14 and BLIP mixQuick Guide and Parameters:Clip Encoder: Not
requiredVAE: Not requiredSampler: dpmpp_2mScheduler: sgm_uniformSampling Steps:
25+CFG Scale: 3+For better results, try using ComfyUI. Here is a workflow that
is low-cost and efficient. Currently, upscaling is not possible due to specific
reasons. I have reported the issue to the TA team, and hopefully, it will be
fixed soon.Realistic VisionAspect Ratios for Demo:1:1 [1024x1024 square]8:5
[1216x768 landscape]4:3 [1152x896 landscape]3:2 [1216x832 landscape]7:5
[1176x840 landscape]16:9 [1344x768 landscape]21:9 [1536x640 landscape]19:9
[1472x704 landscape]3:4 [896x1152 portrait]2:3 [832x1216 portrait]5:7 [840x1176
portrait]9:16 [768x1344 portrait]9:21 [640x1536 portrait]5:8 [768x1216
portrait]9:19 [704x1472 portrait]Important: Do not include NSFW-related/mature
words or censor words in your prompt. Doing so may result in unreliable or
undesirable image outcomes.Note:This is not a merged or modified model. It is
the original Realistic Vision fine-tuned model. Some users have been spreading
incorrect information in the model's comment section. If you have any questions
or want to know more, join my Discord server or share your thoughts in the
comment section. Thank you for your time.
Ai Art Vision
10
1
SDG - HUNYUANDIT LORAS RELEASED
HunyuanDiT - Perfect cute
animehttps://tensor.art/models/755812883138538240?source_id=nz-ypFjjk0C7pPcibn708xQiEnhance
character appearance details, eyes, hair, colors, and drawings in anime
styleHunyuanDiT - Realistic
detailshttps://tensor.art/models/755789054659947864/HunyuanDiT-Realistic-details-V1Add
more realistic details for imagesHunyuanDIT - Vivid
colorhttps://tensor.art/models/755810413532312715?source_id=nz-ypFjjk0C7pPcibn708xQiEnhance
vivid colors and details in photosHunyuan - Beauty
Portraithttps://tensor.art/models/755789995257798458?source_id=nz-ypFjjk0C7pPcibn708xQiortrait
within more details hair, skin...
SDG
4
2
HUNYUAN MODEL ONLINE TRAINING TUTORIAL
EnglishToday, Ｉwill teach you how to use TensorArt to train an Hunyuan model
online.Step 1: Open "Online Training.On the left side, you will see the dataset
window, which is empty by default. You can upload some images to create a
dataset or upload a dataset zip file. The zip file can include annotation files,
following the same format as kohya-ss, where each image file corresponds to a
text annotation file with the same name.In the model theme section on the right,
you can choose from options such as anime characters, real people, 2.5D,
standard, and custom.Here, we select "Base" and choose the Hunyuan model as the
base model.For the base model parameter settings, we recommend setting the
number of repetitions per image to 4 and the number of epochs to 16.、After
uploading a processed dataset, if your dataset annotations include character
names, you don't need to specify a trigger word. Otherwise, you should assign a
simple trigger word to your model, such as a character name or style name.Next,
select an annotation file from the dataset to use as a preview prompt.If you
want to use Professional Mode, click the button in the top right corner to
switch to Professional Mode.In Professional Mode, it is recommended to double
the learning rateand use the cosine_with_restarts learning rate scheduler. For
the optimizer, you can choose AdamW8bit.Enable label shuffling and ensure that
the first token remains unchanged (especially if you have a character name
trigger word as the first token).Disable the noise offset feature, and you can
set the convolution DIM to 8 and Alpha to 1.In the sample settings, add the
Negative prompts, and then you can start the training process.In the training
queue, you can view the current loss value chart and the four sample images
generated for each epoch.Finally, you can choose the epoch with the best results
to download to your local machine or publish directly on TensorArt.After a few
minutes, your model will be deployed and
ready.日本語今日、私はTensorArtを使用してHunyuanモデルをオンラインでトレーニングする方法を教えます。ステップ1:
「オンライントレーニング」を開きます。左側にデータセットウィンドウが表示され、デフォルトでは空です。データセットを作成するために画像をアップロードするか、データセットのzipファイルをアップロードできます。zipファイルには、kohya-ssと同じ形式のアノテーションファイルを含めることができ、各画像ファイルには同じ名前のテキストアノテーションファイルが対応しています。右側のモデルテーマセクションでは、アニメキャラクター、実在の人物、2.5D、標準、カスタムなどのオプションから選択できます。ここでは「Base」を選択し、Hunyuanモデルをベースモデルとして選びます。ベースモデルのパラメーター設定では、画像ごとの繰り返し回数を4、エポック数を16に設定することをお勧めします。
処理済みのデータセットをアップロードした後、データセットのアノテーションにキャラクター名が含まれている場合は、トリガーワードを指定する必要はありません。それ以外の場合は、キャラクター名やスタイル名など、モデルに簡単なトリガーワードを割り当ててください。
次に、プレビュー用プロンプトとして使用するために、データセットからアノテーションファイルを選択します。プロフェッショナルモードを使用したい場合は、右上隅のボタンをクリックしてプロフェッショナルモードに切り替えます。プロフェッショナルモードでは、学習率を倍増することをお勧めします。また、cosine_with_restarts学習率スケジューラーを使用してください。オプティマイザーとしては、AdamW8bitを選択できます。ラベルシャッフルを有効にし、最初のトークンが変更されないようにします（特にキャラクター名トリガーワードが最初のトークンの場合）。ノイズオフセット機能を無効にし、畳み込みDIMを8、Alphaを1に設定できます。サンプル設定でNegative
promptsを追加し、その後、トレーニングプロセスを開始できます。トレーニングキューでは、現在の損失値チャートと各エポックごとに生成された4つのサンプル画像を表示できます。最後に、最良の結果が得られたエポックを選択して、ローカルマシンにダウンロードするか、直接TensorArtで公開できます。数分後には、モデルがデプロイされ、使用可能になります。한국인오늘은
TensorArt를 사용하여 Hunyuan 모델을 온라인에서 훈련하는 방법을 알려드리겠습니다.1단계: "온라인 훈련"을 엽니다.왼쪽에는
기본적으로 비어 있는 데이터셋 창이 표시됩니다. 데이터셋을 만들기 위해 이미지를 업로드하거나 데이터셋 zip 파일을 업로드할 수 있습니다.
zip 파일에는 kohya-ss와 같은 형식의 주석 파일이 포함될 수 있으며, 각 이미지 파일에는 동일한 이름의 텍스트 주석 파일이
대응됩니다.오른쪽의 모델 테마 섹션에서는 애니메이션 캐릭터, 실제 인물, 2.5D, 표준, 사용자 정의 등 다양한 옵션 중에서 선택할 수
있습니다.여기에서는 "Base"를 선택하고 Hunyuan 모델을 기본 모델로 선택합니다.기본 모델 파라미터 설정에서는 이미지당 반복 횟수를
4로, 에포크 수를 16으로 설정하는 것을 권장합니다. 처리된 데이터셋을 업로드한 후, 데이터셋의 주석에 캐릭터 이름이 포함되어 있으면 트리거
단어를 지정할 필요가 없습니다. 그렇지 않으면 모델에 간단한 트리거 단어를 지정해야 합니다, 예를 들어 캐릭터 이름이나 스타일 이름 등.
다음으로, 미리 보기 프롬프트로 사용할 주석 파일을 데이터셋에서 선택합니다.전문 모드를 사용하려면, 오른쪽 상단의 버튼을 클릭하여 전문 모드로
전환합니다.전문 모드에서는 학습률을 두 배로 늘리는 것이 좋습니다.또한 cosine_with_restarts 학습률 스케줄러를 사용합니다.
옵티마이저로는 AdamW8bit을 선택할 수 있습니다.레이블 셔플을 활성화하고 첫 번째 토큰이 변경되지 않도록 합니다(특히 캐릭터 이름 트리거
단어가 첫 번째 토큰인 경우).노이즈 오프셋 기능을 비활성화하고, 컨볼루션 DIM을 8로, Alpha를 1로 설정할 수 있습니다.샘플 설정에서
Negative prompts를 추가한 후, 훈련 프로세스를 시작할 수 있습니다.훈련 대기열에서 현재 손실 값 차트와 각 에포크에 대해 생성된
4개의 샘플 이미지를 볼 수 있습니다.마지막으로, 가장 좋은 결과를 얻은 에포크를 선택하여 로컬 컴퓨터로 다운로드하거나 직접 TensorArt에
게시할 수 있습니다.몇 분 후, 모델이 배포되고 사용 가능해집니다.Tiếng ViệtHôm nay, tôi sẽ hướng dẫn bạn
cách sử dụng TensorArt để đào tạo mô hình Hunyuan trực tuyến.Bước 1: Mở "Đào tạo
trực tuyến."Ở bên trái, bạn sẽ thấy cửa sổ tập dữ liệu, mặc định là trống. Bạn
có thể tải lên một số hình ảnh để tạo tập dữ liệu hoặc tải lên tệp zip của tập
dữ liệu. Tệp zip có thể bao gồm các tệp chú thích, theo cùng một định dạng như
kohya-ss, trong đó mỗi tệp hình ảnh tương ứng với một tệp chú thích văn bản cùng
tên.Ở phần chủ đề mô hình bên phải, bạn có thể chọn từ các tùy chọn như nhân vật
anime, người thật, 2.5D, tiêu chuẩn và tùy chỉnh.Tại đây, chúng ta chọn "Base"
và chọn mô hình Hunyuan làm mô hình cơ bản.Đối với cài đặt tham số của mô hình
cơ bản, chúng tôi khuyên bạn nên đặt số lần lặp lại trên mỗi hình ảnh là 4 và số
epoch là 16. Sau khi tải lên tập dữ liệu đã xử lý, nếu các chú thích của tập dữ
liệu của bạn bao gồm tên nhân vật, bạn không cần phải chỉ định từ kích hoạt.
Ngược lại, bạn nên gán một từ kích hoạt đơn giản cho mô hình của mình, chẳng hạn
như tên nhân vật hoặc tên phong cách. Tiếp theo, chọn một tệp chú thích từ tập
dữ liệu để sử dụng làm lời nhắc xem trước.Nếu bạn muốn sử dụng Chế độ Chuyên
nghiệp, hãy nhấp vào nút ở góc trên bên phải để chuyển sang Chế độ Chuyên
nghiệp.Trong Chế độ Chuyên nghiệp, nên gấp đôi tỷ lệ học.Và sử dụng bộ lập lịch
tỷ lệ học cosine_with_restarts. Đối với bộ tối ưu hóa, bạn có thể chọn
AdamW8bit.Kích hoạt xáo trộn nhãn và đảm bảo rằng mã thông báo đầu tiên không
thay đổi (đặc biệt nếu bạn có từ kích hoạt tên nhân vật là mã thông báo đầu
tiên).Tắt tính năng dịch chuyển tiếng ồn và bạn có thể đặt DIM tích chập là 8 và
Alpha là 1.Trong cài đặt mẫu, thêm các Lời nhắc tiêu cực, sau đó bạn có thể bắt
đầu quá trình đào tạo.Trong hàng đợi đào tạo, bạn có thể xem biểu đồ giá trị tổn
thất hiện tại và bốn hình ảnh mẫu được tạo ra cho mỗi epoch.Cuối cùng, bạn có
thể chọn epoch có kết quả tốt nhất để tải xuống máy tính của bạn hoặc xuất bản
trực tiếp trên TensorArt.Sau vài phút, mô hình của bạn sẽ được triển khai và sẵn
sàng sử dụng.españolHoy, te enseñaré cómo usar TensorArt para entrenar un modelo
Hunyuan en línea.Paso 1: Abre "Entrenamiento en línea."A la izquierda, verás la
ventana del conjunto de datos, que está vacía por defecto. Puedes subir algunas
imágenes para crear un conjunto de datos o subir un archivo zip del conjunto de
datos. El archivo zip puede incluir archivos de anotación, siguiendo el mismo
formato que kohya-ss, donde cada archivo de imagen corresponde a un archivo de
anotación de texto con el mismo nombre.En la sección de temas del modelo a la
derecha, puedes elegir entre opciones como personajes de anime, personas reales,
2.5D, estándar y personalizado.Aquí, seleccionamos "Base" y elegimos el modelo
Hunyuan como el modelo base.Para la configuración de parámetros del modelo base,
te recomendamos configurar el número de repeticiones por imagen a 4 y el número
de épocas a 16. Después de subir un conjunto de datos procesado, si las
anotaciones de tu conjunto de datos incluyen nombres de personajes, no necesitas
especificar una palabra de activación. De lo contrario, deberías asignar una
palabra de activación simple a tu modelo, como un nombre de personaje o un
nombre de estilo. A continuación, selecciona un archivo de anotación del
conjunto de datos para usarlo como un aviso de vista previa.Si deseas usar el
Modo Profesional, haz clic en el botón en la esquina superior derecha para
cambiar al Modo Profesional.En el Modo Profesional, se recomienda duplicar la
tasa de aprendizaje.Y usar el programador de tasa de aprendizaje
cosine_with_restarts. Para el optimizador, puedes elegir AdamW8bit.Habilita el
barajado de etiquetas y asegúrate de que el primer token permanezca sin cambios
(especialmente si tienes una palabra de activación de nombre de personaje como
el primer token).Desactiva la función de desplazamiento de ruido y puedes
configurar el DIM de convolución a 8 y Alpha a 1.En la configuración de muestra,
añade los Avisos Negativos, y luego puedes comenzar el proceso de
entrenamiento.En la cola de entrenamiento, puedes ver el gráfico del valor de
pérdida actual y las cuatro imágenes de muestra generadas para cada
época.Finalmente, puedes elegir la época con los mejores resultados para
descargarla a tu máquina local o publicarla directamente en TensorArt.Después de
unos minutos, tu modelo estará desplegado y listo para usar.
SuperRedPig
3
2
ONLINE TRAINING SD3 MODEL TUTORIAL
EnglishToday, Ｉwill teach you how to use TensorArt to train an SD3 model
online.Step 1: Open "Online Training.On the left side, you will see the dataset
window, which is empty by default. You can upload some images to create a
dataset or upload a dataset zip file. The zip file can include annotation files,
following the same format as kohya-ss, where each image file corresponds to a
text annotation file with the same name.In the model theme section on the right,
you can choose from options such as anime characters, real people, 2.5D,
standard, and custom.Here, we select "Base" and choose the SD3 model as the base
model.For the base model parameter settings, we recommend setting the number of
repetitions per image to 4 and the number of epochs to 16.、After uploading a
processed dataset, if your dataset annotations include character names, you
don't need to specify a trigger word. Otherwise, you should assign a simple
trigger word to your model, such as a character name or style name.Next, select
an annotation file from the dataset to use as a preview prompt.If you want to
use Professional Mode, click the button in the top right corner to switch to
Professional Mode.In Professional Mode, it is recommended to double the learning
rateand use the cosine_with_restarts learning rate scheduler. For the optimizer,
you can choose AdamW8bit.Enable label shuffling and ensure that the first token
remains unchanged (especially if you have a character name trigger word as the
first token).Disable the noise offset feature, and you can set the convolution
DIM to 8 and Alpha to 1.In the sample settings, add the Negative prompts, and
then you can start the training process.In the training queue, you can view the
current loss value chart and the four sample images generated for each
epoch.Finally, you can choose the epoch with the best results to download to
your local machine or publish directly on TensorArt.After a few minutes, your
model will be deployed and
ready.日本語今日は、TensorArtを使用してオンラインでSD3モデルをトレーニングする方法を教えます。ステップ1:
「オンライントレーニング」を開いてください。左側には、デフォルトでは空のデータセットウィンドウが表示されます。ここに画像をアップロードしてデータセットを作成するか、データセットのzipファイルをアップロードすることができます。zipファイルにはアノテーションファイルを含めることができ、これらのファイルはkohya-ssと同じ形式で、各画像ファイルには同じ名前のテキストアノテーションファイルが対応しています。右側のモデルテーマセクションでは、「アニメキャラクター」、「実在の人物」、「2.5D」、「標準」、「カスタム」などのオプションから選択することができます。ここでは、「ベース」を選択し、SD3モデルをベースモデルとして選びます。基本モデルのパラメーター設定については、画像ごとの繰り返し回数を4、エポック数を16に設定することをお勧めします。処理済みのデータセットをアップロードした後、データセットのアノテーションにキャラクター名が含まれている場合は、トリガーワードを指定する必要はありません。それ以外の場合は、キャラクター名やスタイル名などのシンプルなトリガーワードをモデルに割り当てるべきです。次に、プレビュー用プロンプトとして使用するために、データセットからアノテーションファイルを選択します。プロフェッショナルモードを使用したい場合は、右上隅のボタンをクリックしてプロフェッショナルモードに切り替えてください。プロフェッショナルモードでは、学習率を2倍にすることをお勧めします。また、学習率スケジューラーとして「cosine_with_restarts」を使用し、オプティマイザーには「AdamW8bit」を選択することができます。ラベルシャッフルを有効にし、最初のトークンが変更されないようにしてください（特に、最初のトークンにキャラクター名のトリガーワードがある場合は注意してください）。ノイズオフセット機能を無効にし、畳み込みDIMを8、Alphaを1に設定することができます。サンプル設定で「ネガティブプロンプト」を追加し、その後トレーニングプロセスを開始することができます。トレーニングキューでは、現在の損失値のグラフと、各エポックごとに生成された4つのサンプル画像を表示することができます。最後に、最も良い結果が得られたエポックを選択して、ローカルマシンにダウンロードするか、直接TensorArtに公開することができます。数分後に、モデルがデプロイされ、使用可能になります。한국인오늘은
TensorArt를 사용하여 SD3 모델을 온라인으로 훈련하는 방법을 가르쳐 드리겠습니다.1단계: "온라인 훈련"을 엽니다.왼쪽에는 기본적으로
비어 있는 데이터셋 창이 보일 것입니다. 여기에서 이미지를 업로드하여 데이터셋을 생성하거나 데이터셋 ZIP 파일을 업로드할 수 있습니다. ZIP
파일에는 주석 파일이 포함될 수 있으며, 이는 kohya-ss와 동일한 형식을 따라야 합니다. 즉, 각 이미지 파일은 동일한 이름의 텍스트 주석
파일과 대응되어야 합니다.오른쪽의 모델 테마 섹션에서 애니메 캐릭터, 실제 인물, 2.5D, 표준, 맞춤형 등 다양한 옵션을 선택할 수
있습니다.여기에서는 "Base"를 선택하고 SD3 모델을 기본 모델로 선택합니다.기본 모델 파라미터 설정에서, 이미지당 반복 횟수를 4로
설정하고 에폭 수를 16으로 설정하는 것을 추천합니다. 처리된 데이터셋을 업로드한 후, 데이터셋 주석에 캐릭터 이름이 포함되어 있다면 트리거
단어를 지정할 필요가 없습니다. 그렇지 않다면, 모델에 간단한 트리거 단어를 할당해야 합니다. 예를 들어, 캐릭터 이름이나 스타일 이름을 사용할
수 있습니다. 다음으로, 데이터셋에서 미리보기 프롬프트로 사용할 주석 파일을 선택합니다.전문 모드를 사용하려면, 오른쪽 상단의 버튼을 클릭하여
전문 모드로 전환합니다.전문 모드에서는 학습률을 두 배로 설정하는 것이 권장됩니다.또한, cosine_with_restarts 학습률 스케줄러를
사용하고, 옵티마이저로는 AdamW8bit를 선택할 수 있습니다.라벨 셔플링을 활성화하고 첫 번째 토큰이 변경되지 않도록 유지합니다 (특히 첫
번째 토큰으로 캐릭터 이름 트리거 단어를 사용하는 경우에는 더욱 중요합니다).노이즈 오프셋 기능을 비활성화하고, 컨볼루션 DIM을 8로
설정하며, Alpha를 1로 설정할 수 있습니다.샘플 설정에서 Negative prompts를 추가한 후, 훈련 과정을 시작할 수 있습니다.훈련
대기열에서 현재 손실 값 차트와 각 에폭마다 생성된 네 개의 샘플 이미지를 확인할 수 있습니다.마지막으로, 가장 좋은 결과를 보인 에폭을
선택하여 로컬 컴퓨터에 다운로드하거나 TensorArt에서 직접 게시할 수 있습니다.몇 분 후에 모델이 배포되어 준비 완료됩니다.Tiếng
ViệtHôm nay, tôi sẽ hướng dẫn bạn cách sử dụng TensorArt để huấn luyện mô hình
SD3 trực tuyến.Bước 1: Mở "Đào tạo trực tuyến".Ở phía bên trái, bạn sẽ thấy cửa
sổ dữ liệu, mặc định sẽ trống. Bạn có thể tải lên một số hình ảnh để tạo thành
một tập dữ liệu hoặc tải lên một tệp zip chứa tập dữ liệu. Tệp zip có thể bao
gồm các tệp chú thích, theo định dạng tương tự như kohya-ss, trong đó mỗi tệp
hình ảnh tương ứng với một tệp chú thích văn bản có cùng tên.Trong phần chủ đề
mô hình ở bên phải, bạn có thể chọn từ các tùy chọn như nhân vật anime, người
thật, 2.5D, tiêu chuẩn và tùy chỉnh.Tại đây, chúng ta chọn "Cơ bản" và chọn mô
hình SD3 làm mô hình cơ sở.Đối với các cài đặt tham số của mô hình cơ bản, chúng
tôi khuyến nghị thiết lập số lần lặp lại cho mỗi hình ảnh là 4 và số lượng epoch
là 16.Sau khi tải lên một tập dữ liệu đã xử lý, nếu các chú thích trong tập dữ
liệu của bạn bao gồm tên nhân vật, bạn không cần phải chỉ định từ kích hoạt.
Ngược lại, bạn nên gán một từ kích hoạt đơn giản cho mô hình của bạn, chẳng hạn
như tên nhân vật hoặc tên phong cách.Tiếp theo, chọn một tệp chú thích từ tập dữ
liệu để sử dụng làm gợi ý xem trước.Nếu bạn muốn sử dụng Chế độ Chuyên nghiệp,
hãy nhấp vào nút ở góc trên bên phải để chuyển sang Chế độ Chuyên nghiệp.Trong
Chế độ Chuyên nghiệp, nên gấp đôi tỷ lệ học.và sử dụng bộ lập lịch tỷ lệ học
cosine_with_restarts. Đối với bộ tối ưu hóa, bạn có thể chọn AdamW8bit.Kích hoạt
tùy chọn xáo trộn nhãn và đảm bảo rằng token đầu tiên không thay đổi (đặc biệt
nếu bạn có từ kích hoạt là tên nhân vật ở token đầu tiên).Tắt tính năng bù
nhiễu, và bạn có thể đặt DIM của phép tích chập thành 8 và Alpha thành 1.Trong
cài đặt mẫu, thêm các lời nhắc tiêu cực, sau đó bạn có thể bắt đầu quá trình
huấn luyện.Trong hàng đợi huấn luyện, bạn có thể xem biểu đồ giá trị mất mát
hiện tại và bốn hình ảnh mẫu được tạo ra cho mỗi epoch.Cuối cùng, bạn có thể
chọn epoch có kết quả tốt nhất để tải về máy tính của mình hoặc xuất bản trực
tiếp trên TensorArt.Sau vài phút, mô hình của bạn sẽ được triển khai và sẵn
sàng.españolHoy, les enseñaré cómo utilizar TensorArt para entrenar un modelo
SD3 en línea.Paso 1: Abre "Entrenamiento en línea".En el lado izquierdo, verás
la ventana de conjuntos de datos, que está vacía por defecto. Puedes subir
algunas imágenes para crear un conjunto de datos o subir un archivo zip de
conjunto de datos. El archivo zip puede incluir archivos de anotación, siguiendo
el mismo formato que kohya-ss, donde cada archivo de imagen corresponde a un
archivo de anotación de texto con el mismo nombre.En la sección de temas del
modelo a la derecha, puedes elegir entre opciones como personajes de anime,
personas reales, 2.5D, estándar y personalizado.Aquí, seleccionamos "Base" y
elegimos el modelo SD3 como el modelo base.Para la configuración de los
parámetros del modelo base, recomendamos ajustar el número de repeticiones por
imagen a 4 y el número de épocas a 16.Después de subir un conjunto de datos
procesado, si las anotaciones de tu conjunto de datos incluyen nombres de
personajes, no necesitas especificar una palabra clave. De lo contrario,
deberías asignar una palabra clave simple a tu modelo, como el nombre de un
personaje o el nombre de un estilo.A continuación, selecciona un archivo de
anotación del conjunto de datos para usarlo como mensaje de vista previa.Si
deseas utilizar el Modo Profesional, haz clic en el botón en la esquina superior
derecha para cambiar al Modo Profesional.En el Modo Profesional, se recomienda
duplicar la tasa de aprendizaje.y utilizar el programador de tasa de aprendizaje
cosine_with_restarts. Para el optimizador, puedes elegir AdamW8bit.Habilita el
barajado de etiquetas y asegúrate de que el primer token permanezca sin cambios
(especialmente si tienes una palabra clave de nombre de personaje como el primer
token).Desactiva la función de compensación de ruido y puedes configurar el DIM
de la convolución en 8 y el Alpha en 1.En la configuración de muestras, añade
los Negative prompts y luego puedes comenzar el proceso de entrenamiento.En la
cola de entrenamiento, puedes ver el gráfico del valor de pérdida actual y las
cuatro imágenes de muestra generadas para cada época.Finalmente, puedes elegir
la época con los mejores resultados para descargarla a tu máquina local o
publicarla directamente en TensorArt.Después de unos minutos, tu modelo estará
desplegado y listo.
SuperRedPig
如何使用混元DIT在线训练
首先点击右上角的头像，在弹出的下拉框中选择我训练的模型，进入训练中心。如果之前有训练过模型，这里会看到许多训练任务。然后选择在线训练按钮进行一次训练。左侧是数据集窗口，默认没有任何数据。您可以上传一些图片作为数据集，或者上传一个数据集压缩包，压缩包可以包含标注文件，格式和kohya-ss一样，每个图片文件对应一个同名的标注文件txt。右边的模型主题中可以选择二次元人物、真实人物、2.5D、标准以及自定义。训练混元模型这里我们选择标准，在使用底模中选择混元1.2模型。混元模型使用了40depth的块，所以非常大，训练相对速度较慢，需要更高的学习率，默认使用4e-4，默认单张图片重复次数5，优化器AdamW。基础模式下参数选择，推荐单张图片重复次数5，轮数为16。上传一个处理好的数据集后，如果你的数据集标注中有人物名，可以不写触发词。否则你应该给你的模型起一个简单的触发词，例如人物名称或者风格名称。接着从数据集中选择一个标注文件作为预览提示词。如果你想使用专业模式，选择右上角按钮切换到专业模式。专业模式推荐学习率翻倍，然后使用cosine_with_restarts学习率调度器，优化器选择AdamW或者AdamW8bit。开启打乱标签(shuffle)，并且保持第1个token(如果你有一个人名触发词在第一个)关闭噪声偏移功能，卷积DIM和Alpha可以选择8和1。在样图设置中追加填写反向提示词，接下来就可以开始训练了。在训练队列中，你可以看到当前loss值变化表以及每轮epoch产生的4张样图。最后可以选择效果最好的epoch下载到本地或者直接在tensorart上发布。
青龍聖者@bdsqlsz
3
SD3 - COMPOSITION REPAIR
SD3 can generate interesting images, but it has a huge problem with the human
body. However, I noticed that simply reducing the image size to 60% can, in most
cases, eliminate issues with image composition as well as extra hands or legs.
This workflow does not solve the problem of having six fingers, etc. :)Base
model: https://tensor.art/models/751330255836302856/Aderek-SD3-v1 or
https://civitai.com/models/600179/aderek-sd3Look at the image below. You might
say: "Hey, nothing's wrong here." Well, that's because you're already seeing the
generation based on the reduced size. Below, you have the original image.Use
composition on to use this trick&tips.Have fun!Support Paweł Tomczuk on
Ko-fi! ❤️. ko-fi.com/aderek514 - Ko-fi ❤️ Where creators get support from fans
through donations, memberships, shop sales and more! The original 'Buy Me a
Coffee' Page.Visit my DeviantArt page: Aderek - Hobbyist, Digital Artist |
DeviantArt
Aderek514
6
2
🆘 ERROR | EXCEPTION (ROUTEID: 7544351650166750320230)
Exception (routeId: 7544339967855538950230)Suspect nodes:<string
function>. <LayeStyle>, <LayerUtility>, <FaceDetailer>,
many <TextBox>, <Bumpmap>After some reseach (on my own) I've
found<FaceDetailer> node is completely broken<TextBox> and
<MultiLine:Textbox> node will cause this error if you introduce more than
250+ characters, I'm not very sure about this number, but you won't be able to
introduce a decent amount of text anymore.More than 40 nodes, despite its
function will couse this error.How do i know this? Well I made a functional
comfyflow following those
rules:https://tensor.art/template/754955251181895419The next functional
comfyflow suddelny stopped from generating, it's almost the same flow than the
previous, but with <FaceDetailer> and large text strings to polish the
prompt. It works again yay!https://tensor.art/template/752678510492967987 proof
it really worked (here)I feel bad for you if this error suddenly disrupt your
day; feel bad for me cuz I bought the yearly membership of this broken product I
can't refound. I'll be happy to delete this bad review if you fix this
error.News072824 | <FaceDetailer> seems to work again.
PictureT
4
UPSCALING IN COMFYUI: ¿ALGORITHM OR LATENT?
Hello again! In this little article I want to explain the upscaling methods that
I know in ComfyUI and that I have researched. I hope they will help you and that
you can use them in the creation of your workflows and AI tools. In addition,
remember that if you have any useful knowledge, you can share it in the comments
section to enrich the topic. Also, please excuse any spelling mistakes; I am
just learning English hehe.¡Let’s get to the point!To the best of my knowledge,
there are two widely used ways in ComfyUI to achieve uspcaling (you decide which
one to use according to your needs). The two options are: Algorithm Method or
Latent Method.Algorithm Method:This is one of the most commonly used method, and
is readily available. It consists of loading an upscaling model, and connecting
it to the workflow. That way the image pixels are manipulated as the user
wishes. It is very similar to the upscale method used in the normal way of
creating images in Tensor Art.The following nodes are needed:A. Load Upscale
Model.B. Upscale Image (Using Model).These nodes are connected to the workflow
between the “VAE Decode” and “Save Image” nodes; as shown in the image. Once
this structure is created, you can choose from all the different models offered
by the “Load Upscale Model” node, ranging from “2x-ESRGAN.pth” to “SwimIR_4x”.
You can use any of the 23 available models and experiment with any of them. You
just have to click on the node and the list will be displayed.This can also be
achieved in other ways by using another node such as “Upscale Image By”. The
structure is simpler to create because only that node is connected between the
VAE decode and Save Image as shown in the following image.Once the node is
connected, you are free to select the mode in which you want to upscale the
image (Upscale_method) and you can also set the scale to which you can
recondition the image pixel value (Scale By).Strengths and Weaknesses of the
Algorithm Method:Among the strengths of this method are its ease of integration
into the workflow and its advantage of choosing between several upscaling model
options. It also allows fast generation both in the ComfyUI and in the use of AI
tools.However, among its weaknesses, it is not very effective in some specific
contexts. For example: the algorithm can upscale the image pixels but does not
alter the actual image size; causing the generated image itself to end up being
blurred in some cases.Latent Method:This is the other alternative option to the
algorithm method. It is focused on highlighting image details and maximizing
quality. This method is also one of the most used in the Workflow mode of
different visual content creation platforms with artificial intelligence. Here,
upscaling is performed while the image is generated from latent space (Latent
space is where the IA takes data from the prompt, deconstructs it for analysis
and then reconstructs it to represent it in an image).The Latent Upscale node is
placed between the two Ksamplers. While the first Ksampler is connected to the
“Empty Latent Image” node, the second one is connected to the “VAE Decode” to
ensure the correct processing and representation of the generated image.It
should be noted that the “Empty Latent Image” node and the “VAE Decode” node are
already included by default in the Text2Image templates in WorkFlow mode. (For
more information about Text2Image, you can see my other article called “ComfyUi:
Text2Image Basic Glossary”).It is important to take into consideration that for
this method to work properly, you have to know how to create a correct balance
between the original size of the image and its upscaled size. For example, you
can generate a 512x512 image and upscale it to 1024x1024; but it is not
recommended to make a 512x512 image (square image) and upscale it to 768x11152
(rectangular image) since the shape of the image would not be compatible with
its uspcale version. For this reason you have to pay attention to the values of
the “Empty Latent Image” and the “Latent Upscale”, so that these are always
proportional.In the “Empty Latent Image” node you must place the original image
dimensions (for example: 768x1152); while in the “Latent Upscale” node you must
place the resized image dimensions (for example: 1152x1728). In this way you are
given the freedom to set the image size to your own discretion. For this I
always recommend to look at the size and upscale of the normal mode in which we
create illustrations, this way we will always know which values to set and which
will be compatible. As you can see in the image. You look at those values, and
then write them to the nodes listed above.Once everything is connected and
configured, you are able to have images of any size you want. You can experiment
to your taste.Strengths and Weaknesses of the Latent Method:As strengths this
option should be highlighted that it allows you to access excellent quality
images if everything is correctly configured. It also allows you to create
images of a custom size and upscale with the values you want. It brings out the
details in both SD and XL images.As negative points we have to configure
everything manually every time you want to change the size of the images or the
shape of the same. Also, this method is just a little bit slower in the
generation process compared to the algorithm method.Which is better: ¿Algorithm
or Latent?Neither method is better than the other. Both are useful in different
contexts. Remember that workflows will be different from user to user, because
we all have different ways of creating and designing things.It all depends on
your taste and whether you want something simpler or more elaborate. I hope the
explanation in this article has helped you to make Workflows more complex and to
make it easier to make the images you want.Extra Tip:If you do not find any of
the nodes outlined in this document. You can double click on any empty place in
the workflow and you can search for the name of the node you are looking for.
Just remember to type the name without spaces.
JSP
5
2
CONTROLNET WITH SD3
Today, I noticed that I can add ControlNet to the SD3 model.The Tiled function
works very well, so I incorporated it into my workflow and created a group for
generating artistic images based on a given photo or a previously generated
image. In the main part of the workflow, I simply set a very short prompt, like
"grass, flowers," and I get an image that blends grass and flowers in an
arrangement resembling the base photo.https://youtu.be/sv35wKNiFGsControlnet
with SD3 | ComfyUI Workflow | Tensor.Art
Aderek514
2
如何使用SD3在线训练
首先点击右上角的头像，在弹出的下拉框中选择我训练的模型，进入训练中心。如果之前有训练过模型，这里会看到许多训练任务。然后选择在线训练按钮进行一次训练。左侧是数据集窗口，默认没有任何数据。您可以上传一些图片作为数据集，或者上传一个数据集压缩包，压缩包可以包含标注文件，格式和kohya-ss一样，每个图片文件对应一个同名的标注文件txt。右边的模型主题中可以选择二次元人物、真实人物、2.5D、标准以及自定义。这里我们选择自定义，在使用底模中选择SD3模型。注意在选择版本中下拉框内选择T5XXL的版本，这样才可以训练T5文本编码器。基础模式下参数选择，推荐单张图片重复次数4，轮数为16。上传一个处理好的数据集后，如果你的数据集标注中有人物名，可以不写触发词。否则你应该给你的模型起一个简单的触发词，例如人物名称或者风格名称。接着从数据集中选择一个标注文件作为预览提示词。如果你想使用专业模式，选择右上角按钮切换到专业模式。专业模式推荐学习率翻倍，然后使用cosine_with_restarts学习率调度器，优化器可以选择AdamW8bit。开启打乱标签(shuffle)，并且保持第1个token(如果你有一个人名触发词在第一个)关闭噪声偏移功能，卷积DIM和Alpha可以选择8和1。在样图设置中追加填写反向提示词，接下来就可以开始训练了。在训练队列中，你可以看到当前loss值变化表以及每轮epoch产生的4张样图。最后可以选择效果最好的epoch下载到本地或者直接在tensorart上发布。
青龍聖者@bdsqlsz
3
1
SD3 - TRAINING ON YOUR OWN PC
So first, you need to update your version of OneTrainer.Second, u need dowload
ALL files and folders (and
rename)stabilityai/stable-diffusion-3-medium-diffusers at main
(huggingface.co)then u put it:With float16 output lora has only 36MB:This is my
setting for a style training:My checkpoint to testing u can dowload for
free:Aderek SD3 - v1 | Stable Diffusion Model - Checkpoint | Tensor.Artand my
loras: Aderek514's Profile | Tensor.ArtSo, good luck!
Aderek514
8
REACTOR NODE FOR COMFYUI (FACE SWAP)
ReActor Node for ComfyUI 👉Downlond👈The Fast and Simple Face Swap Extension
Node for ComfyUI, based on ReActor SD-WebUI Face Swap ExtensionThis Node goes
without NSFW filter (uncensored, use it on your own responsibility)|
Installation | Usage | Troubleshooting | Updating | Disclaimer | Credits |
Note!✨What's new in the latest update✨💡0.5.1 ALPHA1Support of GPEN 1024/2048
restoration models (available in the HF dataset
https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/facerestore_models)👈[]~(￣▽￣)~*ReActorFaceBoost
Node - an attempt to improve the quality of swapped faces. The idea is to
restore and scale the swapped face (according to the face_size parameter of the
restoration model) BEFORE pasting it to the target image (via inswapper
algorithms), more information is here (PR#321)InstallationSD WebUI:
AUTOMATIC1111 or SD.NextStandalone (Portable) ComfyUI for WindowsUsageYou can
find ReActor Nodes inside the menu ReActor or by using a search (just type
"ReActor" in the search field)List of Nodes:••• Main Nodes •••💡ReActorFaceSwap
(Main Node Download)👈[]~(￣▽￣)~*ReActorFaceSwapOpt (Main Node with the
additional Options input)ReActorOptions (Options for
ReActorFaceSwapOpt)ReActorFaceBoost (Face Booster Node)ReActorMaskHelper
(Masking Helper)••• Operations with Face Models •••ReActorSaveFaceModel (Save
Face Model)ReActorLoadFaceModel (Load Face Model)ReActorBuildFaceModel (Build
Blended Face Model)ReActorMakeFaceModelBatch (Make Face Model Batch)•••
Additional Nodes •••ReActorRestoreFace (Face Restoration)ReActorImageDublicator
(Dublicate one Image to Images List)ImageRGBA2RGB (Convert RGBA to RGB)Connect
all required slots and run the query.Main Node Inputsinput_image - is an image
to be processed (target image, analog of "target image" in the SD WebUI
extension);Supported Nodes: "Load Image", "Load Video" or any other nodes
providing images as an output;source_image - is an image with a face or faces to
swap in the input_image (source image, analog of "source image" in the SD WebUI
extension);Supported Nodes: "Load Image" or any other nodes providing images as
an output;face_model - is the input for the "Load Face Model" Node or another
ReActor node to provide a face model file (face embedding) you created earlier
via the "Save Face Model" Node;Supported Nodes: "Load Face Model", "Build
Blended Face Model";Main Node OutputsIMAGE - is an output with the resulted
image;Supported Nodes: any nodes which have images as an input;FACE_MODEL - is
an output providing a source face's model being built during the swapping
process;Supported Nodes: "Save Face Model", "ReActor", "Make Face Model
Batch";Face RestorationSince version 0.3.0 ReActor Node has a buil-in face
restoration.Just download the models you want (see Installation instruction) and
select one of them to restore the resulting face(s) during the faceswap. It will
enhance face details and make your result more accurate.Face IndexesBy default
ReActor detects faces in images from "large" to "small".You can change this
option by adding ReActorFaceSwapOpt node with ReActorOptions.And if you need to
specify faces, you can set indexes for source and input images.Index of the
first detected face is 0.You can set indexes in the order you need.E.g.: 0,1,2
(for Source); 1,0,2 (for Input).This means: the second Input face (index = 1)
will be swapped by the first Source face (index = 0) and so on.GendersYou can
specify the gender to detect in images.ReActor will swap a face only if it meets
the given condition.💡Face ModelsSince version 0.4.0 you can save face models as
"safetensors" files (stored in ComfyUI\models\reactor\faces) and load them into
ReActor implementing different scenarios and keeping super lightweight face
models of the faces you use.To make new models appear in the list of the "Load
Face Model" Node - just refresh the page of your ComfyUI web application.(I
recommend you to use ComfyUI Manager - otherwise you workflow can be lost after
you refresh the page if you didn't save it before that).TroubleshootingI. (For
Windows users) If you still cannot build Insightface for some reasons or just
don't want to install Visual Studio or VS C++ Build Tools - do the
following:(ComfyUI Portable) From the root folder check the version of
Python:run CMD and type python_embeded\python.exe -VDownload prebuilt
Insightface package for Python 3.10 or for Python 3.11 (if in the previous step
you see 3.11) or for Python 3.12 (if in the previous step you see 3.12) and put
into the stable-diffusion-webui (A1111 or SD.Next) root folder (where you have
"webui-user.bat" file) or into ComfyUI root folder if you use ComfyUI
PortableFrom the root folder run:(SD WebUI) CMD and
.\venv\Scripts\activate(ComfyUI Portable) run CMDThen update your PIP:(SD WebUI)
python -m pip install -U pip(ComfyUI Portable) python_embeded\python.exe -m pip
install -U pip💡Then install Insightface:(SD WebUI) pip install
insightface-0.7.3-cp310-cp310-win_amd64.whl (for 3.10) or pip install
insightface-0.7.3-cp311-cp311-win_amd64.whl (for 3.11) or pip install
insightface-0.7.3-cp312-cp312-win_amd64.whl (for 3.12)(ComfyUI Portable)
python_embeded\python.exe -m pip install
insightface-0.7.3-cp310-cp310-win_amd64.whl (for 3.10) or
python_embeded\python.exe -m pip install
insightface-0.7.3-cp311-cp311-win_amd64.whl (for 3.11) or
python_embeded\python.exe -m pip install
insightface-0.7.3-cp312-cp312-win_amd64.whl (for 3.12)Enjoy!II. "AttributeError:
'NoneType' object has no attribute 'get'"This error may occur if there's smth
wrong with the model file inswapper_128.onnx💡Try to download it manually from
here and put it to the ComfyUI\models\insightface replacing existing oneIII.
"reactor.execute() got an unexpected keyword argument 'reference_image'"This
means that input points have been changed with the latest updateRemove the
current ReActor Node from your workflow and add it againIV. ControlNet Aux Node
IMPORT failed error when using with ReActor NodeClose ComfyUI if it runsGo to
the ComfyUI root folder, open CMD there and run:python_embeded\python.exe -m pip
uninstall -y opencv-python opencv-contrib-python
opencv-python-headlesspython_embeded\python.exe -m pip install
opencv-python==4.7.0.72That's it!reactor+controlnetV. "ModuleNotFoundError: No
module named 'basicsr'" or "subprocess-exited-with-error" during future-0.18.3
installationDownload
https://github.com/Gourieff/Assets/raw/main/comfyui-reactor-node/future-0.18.3-py3-none-any.whlPut
it to ComfyUI root And run:python_embeded\python.exe -m pip install
future-0.18.3-py3-none-any.whlThen:python_embeded\python.exe -m pip install
basicsrVI. "fatal: fetch-pack: invalid index-pack output" when you try to git
clone the repository"Try to clone with --depth=1 (last commit only):git clone
--depth=1 https://github.com/Gourieff/comfyui-reactor-nodeThen retrieve the rest
(if you need):git fetch --unshallow
lingko
11
COMFYUI: TEXT2IMAGE BASIC GLOSSARY
Hello! This is my first article; I hope it will be of benefit to the person who
reads it. I still have limited knowledge about WorkFlow; but I have researched
and learned little by little. If anyone would like to contribute some content;
you are totally free to do so. Thank you.I made this article to give a brief and
basic explanation about basic concepts about Comfyui or WorkFlow. This is a
technology with many possibilities and it would be great to make it easier to
use for everyone! What is Workflow?Workflow is one of the two main image
generation systems that Tensor Art has at the moment. It corresponds to a
generation method that is characterized by a great capacity to stimulate the
creativity of the users; also, it allows us to access to some Pro features being
Free users.How do I access the WorkFlow mode?To access the WorkFlow mode, you
must place the mouse cursor on the “Create” tab as if you were going to create
an image by conventional means. Once you have done that; click on the
“ComfyFlow” option and you are done.After that, you will see a tab with two
options “New WorkFlow” and “Import WorkFlow”. The first one allows you to start
a workflow from a template or from scratch; while the second option allows you
to load a workflow that you have saved on your pc in a JSON file.If you click on
the “New WorkFlow” option, a tab with a list of various templates will be
displayed (each template will have a different purpose). But the main one will
be “Text2Image”; it will allow us to create images from text, similarly to the
conventional method we always use. You can also create a workflow from scratch
in the “Empty WorkFlow Template” option but for a better explanation of the
basics we will use the “Text2Image”.Once you click on the "Text2Image" option,
you must wait a few seconds and a new tab will be displayed with the template,
which contains the basics to create an image by means of text. Nodes and
Borders: ¿What are they and how do they work?Well, to understand the basics of
how a WorkFlow works, it is necessary to have a clear understanding of what
Nodes and Border are.Nodes are small boxes that are present in the workflow;
each node will have a specific function necessary for the creation, enhancement
or editing of the image or video. The basics of Text2Image are the CheckPoint
loader, the Clip Text Encoders, the Empty Lantent Image, the Ksampler, the VAE
decoder, and Save Image. It should be noted that there are hundreds of other
nodes besides these basics and they all have many different functions.On the
other hand, the “Borders” are the small colored wires that connect the different
nodes. They are the ones that will set which nodes will be directly related. The
Borders are ordered by colors that are generally related to a specific
function.The purple is related to the Model or Lora used.The yellow one is
intended for connection to the model or lora with the space to place the
prompt.The red refers to VAE.The orange color refers to the connection between
the spaces for placing the prompt and the “Ksampler” node.The fucsia color makes
allusion to the latent, which will serve for many things; but for this case it
serves to connect the “Empty Latent Image” node with the “Ksampler” node and
establish the number and size of the images that will be generated.And the blue
color is related to everything that has to do with images; it has many uses but
this case is related to the “Save Image” node.What are the Text2Image template
Nodes used for?Having this clear is of utmost relevance, since it allows you to
know what each node of this basic template is for. It's like knowing what each
piece in a lego set is for and understanding how they should be connected to
create a beautiful masterpiece! Also, if you get to know what these nodes are
for, it will be easier for you to intuit the functionality of its variants and
other derived nodes.A) The first one is the node called “Load Chckpoint”, this
node has three specific functions. The first one is to load the base model or
checkpoint with which an image will be created. The second is the Clip, which
will take care of connecting the positive and negative prompts that you write to
the checkpoint. And the third is that it connects and helps to load the VAE
model. B) The second one is the “Empty Latent Image”; which is the node in
charge of processing the image dimensions from the latent space. It has two
functions: First, set the width and length of the image; and second, set how
many images will be generated simultaneously according to the “Batch Size”
option.C) The third is the two “Clip Text Enconder” nodes: in this case there
will always be at least two of these nodes, since they are responsible for
setting both the positive and negative prompts that you write to describe the
image you want. They are usually connected to the "Load Checkpoint" or any LoRa
and are also connected to the “Ksampler” node.D) Then, there is a node
“Ksampler”. This node is the central point of all WorkFlow; it is the one that
sets the most important parameters in the creation of images. It has several
functions: the first one is to determine which is the seed of the image and to
regulate how much it changes from image to generated image by means of the
“control_after_generate” option. The second function is to set how many steps
are needed to create the image (you set them as you wish); the third function is
to determine which sampling method is used and also what is the scheduler of
this method (this helps to regulate how much space is eliminated when creating
the image).E) The penultimate one is the VAE decoder. This node is in charge of
assisting the processing of the image to be generated: its main function is to
be responsible for materializing the written prompt into an image. That is to
say, it reconstructs the description of the image we want as one of the final
steps to finish the generation process. Then, the information is transmitted to
the “Save Image” node to display the generated image as the final product.F) The
last node to explain is the “Save Image”. This node has the simple function of
saving the generated image and providing the user with a view of the final work
that will later be stored in the taskbar where all the generated images are
located.Final Consideration:This has been a small summary and explanation about
very basic concepts about ComfyUI Mode; you could even say that it is like a
small glossary about general terms. I have tried to give a small notion that
tries to facilitate the understanding of this image generation tool. There is
still a lot to explain, but I will try to cover all the topics; the information
would not fit in a single article (ComfyUI is a whole universe of
possibilities). ¡Thank you so much for taking the time to read this article!
JSP
17
11
TEXTUAL INVERSION EMBEDDINGS COMFYUI_EXAMPLES
ComfyUI_examplesTextual Inversion Embeddings ExamplesHere is an example for how
to use Textual Inversion/Embeddings.To use an embedding put the file in the
models/embeddings folder then use it in your prompt like I used the SDA768.pt
embedding in the previous picture.Note that you can omit the filename extension
so these two are equivalent:embedding:SDA768.ptembedding:SDA768You can also set
the strength of the embedding just like regular words in the
prompt:(embedding:SDA768:1.2)Embeddings are basically custom words so where you
put them in the text prompt matters.For example if you had an embedding of a
cat:red embedding:catThis would likely give you a red cat.
lingko
9
1
ART MEDIUMS (127 STYLE)
Art MediumsVarious art mediums. Prompted with '{medium} art of a woman
MetalpointMiniature PaintingMixed MediaMonotype PrintingMosaic Tile
ArtMosaicNeonOil PaintOrigamiPapermakingPapier-mâchéPastelPen And InkPerformance
ArtPhotographyPhotomontagePlasterPlastic ArtsPolymer
ClayPrintmakingPuppetryPyrographyQuillingQuilt ArtRecycled ArtRelief
PrintingResinReverse Glass PaintingSandScratchboard ArtScreen
PrintingScrimshawSculpture WeldingSequin ArtSilk PaintingSilverpointSound
ArtSpray PaintStained GlassStencilStoneTapestryTattoo
ArtTemperaTerra-cottaTextile ArtVideo ArtVirtual Reality
ArtWatercolorWaxWeavingWire SculptureWoodWoodcutGlassGlitch ArtGold
LeafGouacheGraffitiGraphite PencilIceInk Wash PaintingInstallation ArtIntaglio
PrintingInteractive MediaKinetic ArtKnittingLand ArtLeatherLenticular
PrintingLight ProjectionLithographyMacrameMarbleMetalColored
PencilComputer-generated Imagery (cgi)Conceptual ArtCopper
EtchingCrochetDecoupageDigital MosaicDigital PaintingDigital
SculptureDioramaEmbroideryEnamelEncaustic PaintingEnvironmental
ArtEtchingFabricFeltingFiberFoam CarvingFound ObjectsFrescoAugmented Reality
ArtBatikBeadworkBody PaintingBookbindingBronzeCalligraphyCast
PaperCeramicsChalkCharcoalClayCollageCollagraphy3d PrintingAcrylic
PaintAirbrushAlgorithmic ArtAnimationArt GlassAssemblage
lingko
18
ANIME VISION | DETAIL ENHANCER SD3
SD3 Anime LoRA is Finally Here!I am thrilled to announce that the SD3 Anime LoRA
model is finally available. In addition, I am releasing a new update that
includes an SD3 anime checkpoint model.Currently, I am publishing a beta version
as I continue to work diligently to perfect the model. I aim to have the final
release ready by the end of this month or early August.Stay tuned, as the SD3
Anime beta version will be available within the next couple of days!Here are
some guidelines to use this LoRA to its full potential:If you are trying to
create any specific subject or object, use trigger word like 'anime style' in
your prompt.If you're targeting a character, you can ignore the keyword and go
with something like this:For a male character: 'anime boy'For a female
character: 'anime girl'Simple, right? You can also use the trigger word 'anime
style' most of the time. I've noticed it gives better results.ModelRecommended
Parameter :LoRA Weight : 0🆙1VAE : No NeedSampler : DPM++ 2M SGM UniformSteps :
20➡30CFG : 3➡4Upscaler : R-ESRGAN 4x+If you encounter any issues, I recommend
using ComfyUI for a better experience. Here's the workflow: ComfyUI Workflow.
Open the link, select the LoRA model, choose the LoRA strength, and hit the run
button.Join my community, Share your feedback, learn, and have fun with us!
😊Discord➡️https://discord.gg/QQKd7bu97P
Ai Art Vision
18
HOW TO SET UP RADIO BUTTON IN YOUR AI TOOLS
Hello everyone! ✨ Today I will bring you a super practical tutorial: How to set
up a convenient prompt word radio version for your AI Tools! 😎 Save it quickly,
and you will never have to worry about how to set prompt words again! 👌Are you
ready for the course? Let's get started! 🔍First, the first step is to open the
official website of TensorArt. 📂 After opening, you will see a variety of AI
tools and resources, which are very rich~ 👀Next, open comfyflow and start
making our AI Tool! 🤖 This process is simple and fun, let's explore it
together! ✨In comfyflow, we click the "New" button, which will take you to a new
interface~ 🖱️💻In this interface, we can start creating our own workflow~ 🌟🎉
Next, we need to fill in the positive prompt words, which is a super critical
step! 📝✨In the positive prompt word area, we need to enter the content we want.
📋 Here, the editor simply wrote an example for everyone: "a man". 🤵 This
example is just for the convenience of teaching, you can freely play according
to your needs~ 🌈🎆🎉 When you have completed the workflow, you can click the
"Publish" button in the upper right corner! 🚀✨Don't forget to give your AI Tool
an interesting name! 💡 This name will make your tool more attractive~✨ In
addition, remember to divide the area correctly, so that you can see it clearly
and it is also convenient for your friends to find and use it! 📂🔍🌟 Next,
let's complete the next step together! 💪We pull down the current interface and
find the user-configurable settings area. 👏 Then click the "Add" button. This
step is very critical! 🖱️✨ Everyone must remember to add your positive prompt
word node! 🔍✨After adding the node, our next step is to click the "Set" button
on the right to proceed to the next step. 🔧✨ This step is crucial! Don't miss
it! 😉🚀✨ The next step is also very important! 😊First, click the radio button,
then click "Add". 🔘✨ Here, you can add the buttons you want to release to the
user! 👍 After selecting, be sure to click "Confirm"! ✔️✨Friends, we have
finally reached the last step! 🎉💪 This is an exciting moment! ✨When you have
completed all the operations, remember to click the "Publish" button to publish
your AI gadget! 🚀✨ Can't wait to see the results? Hurry up and generate a
picture yourself to try and experience your results! 🌟🖼️Well, that's all for
today's tutorial! 😊 I hope everyone can complete it successfully and create
their own AI gadgets! 👏 If you have any questions, don't hesitate to leave a
comment in the comment section at any time! ❤️
SuperRedPig
18
5
GUIDE TO USING SDXL / SDXLモデルの利用手引
Guide to Using SDXLI occasionally see posts about difficulties in generating
images successfully, so here is an introduction to the basic setup.1.
IntroductionSDXL is a model that can generate images with higher accuracy
compared to SD1.5. It produces high-quality representations of human bodies and
structures, with fewer distortions and more realistic fine details, textures,
and shadows.With SD1.5, generation parameters were generally applicable across
different models, so there was no need for specific adjustments.However, while
SDXL can still use some SD1.5 techniques without issues, the recommended
generation parameters vary significantly depending on the model.Additionally,
LoRA and Embeddings (such as EasyNegative) are completely incompatible,
requiring a review of prompt construction.Notably, embeddings commonly used in
SD1.5 negative prompts are recognized merely as strings in the XL model, so you
must replace them with corresponding embeddings or add appropriate tags.This
guide explains the recommended parameter settings for using SDXL.2. Basic
ParametersVAESelecting "sdxl-vae-fp16-fix.safetensors" will suffice.Many models
have this built-in, so specification might not be necessary.Image SizeUsing the
presets provided by TensorArt for resolution should be sufficient.Small or
excessively large resolutions may not yield appropriate generation results, so
please avoid using the sizes that were frequently used with SD1.5 wherever
possible.Even if you want to create vertically or horizontally elongated images,
do so within the range that does not significantly alter the total pixel count
(adjust by increasing height and decreasing width, for example).Sampling
MethodChoose the sampler recommended for the model first.Then, select according
to your preference.Typically, selecting Euler a or DPM++ 2M SDE Karras should
work well.Sampling StepsXL models might generate images effectively with lower
steps due to optimizations like LCM or Turbo.Be sure to check the recommended
values for the selected model.CFG ScaleThis varies by model, so check the
recommended values.Typically, the range is around 2 to 8.Hires.fixFor free
users, specifying 1.5x might hit the upper limit, so use custom settings with
the following resolutions:768x1152 -> 1024x15361152x768 ->
1536x10241024x1024 -> 1248x1248Choose the upscaler according to your
preference.Set the denoising strength to around 0.3 to 0.4.3. PromptSDXL handles
natural language better.You can input elements separated by commas or simply
write a complete sentence in English, and it will generate images as
intended.Using a tool like ChatGPT to create prompts can also be
beneficial.However, depending on how the model was additionally trained, it
might be better to use existing tags.Furthermore, some models have tags
specified to enhance quality, so always check the model’s page.For
example:AnimagineXL3.1: masterpiece, best quality, very aesthetic, absurdres is
recommended.Pony Models: score_9, score_8_up, score_7_up, score_6_up,
score_5_up, score_4_up is recommended.ToxicEchoXL: masterpiece, best quality,
aesthetic is recommended.In this way, especially for XL models, particularly
anime or illustration models, appropriate tag usage is crucial.4. Negative
PromptsForget the negative prompts used in SD1.5. "EasyNegative" is just a
string.The embeddings usable on TensorArt are negativeXL_D and
unaestheticXLv13.Choose according to your preference.Some models have
recommended prompts listed.For AnimagineXLnsfw, lowres, (bad), text, error,
fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark,
unfinished, displeasing, oldest, early, chromatic aberration, signature, extra
digits, artistic error, username, scan, [abstract]For ToxicEchoXLnsfw, lowres,
bad anatomy, bad hands, text, error, missing fingers, extra digits, fewer
digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,
signature, watermark, username, blurry, artist name.For photo models, sometimes
it is better not to use negative prompts to create a certain atmosphere, so try
various approaches.5. Recommended SDXL
modelToxicEnvisionXLhttps://tensor.art/models/736585744778443103/ToxicEnvisionXL-v1Recently
released high-quality photo model. Yes, I created it.If you are looking for a
photo model, you can't go wrong with this one.Check the related posts to see
what kind of images can be created.You can create a variety of realistic images,
from analog photo styles to gravure, movies, fantasy, and surreal
depictions.Although it is primarily a photo-based model, it can also create
analog-style
images.ToxicEtheRealXLhttps://tensor.art/models/702813703965453448/ToxicEtheRealXL-v1A
versatile model that supports both illustrations and photorealistic images. Yes,
I created it.The model's flexibility requires well-crafted prompts to determine
whether the output is more illustrative or photorealistic.Using LoRA to
strengthen the direction might make it easier to
use.ToxicEchoXLhttps://tensor.art/models/689378702666043553/ToxicEchoXL-v1A
high-performance model specialized for illustrations. Yes, I created it.It
features a unique style based on watercolor painting, with custom learning and
adjustments.I have also created various LoRA for style changes, so please visit
my user page.My current favorite is Beautiful Warrior XL + atmosphere.The model
covers a range from illustrations to photos, so give it a try.However, it is
weak in generating copyrighted characters, so use LoRA or models like
AnimagineXL or Pony for those.ToxicEchoXL can produce unique illustration styles
when using character LoRA, making it highly suitable for fan art.6. ConclusionI
hope this guide helps those who struggle to generate images as well as
others.Well... if you remix from Model Showcase, you can create beautiful images
without this guide...SD3 has also been released, so if possible, I would like to
create models for that as well.It seems that a commercial license is required
for commercial use, though...SDXLモデルの利用手引ここではSDXLの基本的な設定を紹介します。1.
はじめにSDXLはSD1.5と比較してより高精度な生成が行えるモデルです。人体や構造物はより高品質で破綻が少なく、微細なディテールがよりリアルに表現され、自然なテクスチャや影を描写します。SD1.5ではどのモデルでも生成パラメータは概ね流用可能で、特に気にする必要はありませんでした。SDXLは一部SD1.5の手法を利用しても問題ありませんが、推奨される生成パラメータがモデルによってもだいぶ変わります。またLoRAやEmbeddings(EasyNegativeなど)も一切互換性はありませんので、プロンプトの構築も見直す必要があります。特にSD1.5のネガティブプロンプトでよく使用されているEmbeddingsをそのままXLモデルで入力しても、ただの文字列としてしか認識されていませんので、対応するEmbeddingsに差し替えるか、適切なタグを追加しなければいけません。このガイドでは、SDXLを使用する際の推奨パラメータ設定について説明します。2.
基本的なパラメータVAEsdxl-vae-fp16-fix.safetensorsを選択しておけば問題ありません。モデルに内蔵されている場合も多いですので、指定しなくても大丈夫な場合もあります。画像サイズ解像度はTensorArtで用意されているプリセットを使えば問題ありません。小さかったり大きすぎる解像度は適切な生成結果を得られなくなりますので、SD1.5でよく使用していたサイズはなるべく使用しないでください。プリセットよりも縦長や横長にしたい場合でも、総ピクセル数を大幅に変更しない範囲で行ってください。（縦を増やしたら横は減らす等で調整）サンプリング法モデルによって推奨されるサンプラーがありますので、まずはそれを選択してください。あとはお好みです。基本は
Euler a か DPM++ 2M SDE Karras
あたりを選択しておけば大丈夫です。サンプリング回数XLではLCMやターボなど低ステップで生成できるようになっていたりしますので、必ずモデルの推奨値を確認してください。CFG
Scaleこれもモデルによって異なりますので推奨値を確認してください。概ね2～8程度です。高解像度修復無料ユーザーだと1.5xを指定すると上限に引っかかってしまいますので、使用する場合はカスタムにして以下の解像度を指定してください768x1152
-> 1024x15361152x768 -> 1536x10241024x1024 ->
1248x1248Upscalerはお好みで指定してください。Denoising strengthは0.3～0.4程度。3.
プロンプトSDXLはより自然言語の取り扱いに長けています。要素をコンマで区切って入力するだけではなく、普通に英文を入力するだけでも意図した通りの生成が行えます。ChatGPTなどにプロンプトを作ってもらうのもいいでしょう。ただしモデルが追加学習をどのように行ったかによって、既存のタグで記述したほうがいい場合もあります。また、モデルによっては品質を上げるためのタグが指定されていますので、使用するモデルのページは必ず見るようにしましょう。例えば…AnimagineXL3.1では「masterpiece,
best quality, very aesthetic, absurdres」を指定することが推奨されています。Pony系モデルでは「score_9,
score_8_up, score_7_up, score_6_up, score_5_up,
score_4_up」が基本テンプレートとなっています。ToxiEchoXLでは「masterpiece, best quality,
aesthetic」を指定することが推奨されています。このように、XLモデル、特にアニメ・イラストモデルでは適切なタグの使用が求められる場合があります。4.
ネガティブプロンプトSD1.5で使用していたネガティブプロンプトは忘れてください。EasyNegativeはただの文字列です。TensorArtで使用できるEmbeddingsは
negativeXL_D と unaestheticXLv13
です。お好みで指定してください。推奨されるプロンプトが記載されているモデルもあります。AnimagineXLでは以下のようなプロンプトが推奨されていますので、これをベースに組むのがいいかもしれません。nsfw,
lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg
artifacts, low quality, watermark, unfinished, displeasing, oldest, early,
chromatic aberration, signature, extra digits, artistic error, username, scan,
[abstract]ToxicEchoXLでは以下のようなプロンプトが推奨されていますnsfw, lowres, bad anatomy, bad hands,
text, error, missing fingers, extra digits, fewer digits, cropped, worst
quality, low quality, normal quality, jpeg artifacts, signature, watermark,
username, blurry, artist
name,フォトモデルではネガティブプロンプト無しのほうが雰囲気のある画作りができる場合もありますので、色々試してみてください。5.
おすすめのSDLXモデル紹介ToxicEnvisionXLhttps://tensor.art/models/736585744778443103/ToxicEnvisionXL-v1最近リリースされた高品質フォトモデル。実写系モデルを探しているならこれを選んでおけば間違いありません。関連する投稿からどういった画像が作成できるか見てみてください。アナログ写真風からグラビア、映画、ファンタジー、非現実的な描写等、様々な実写的な画像が作成できます。基本的にはフォトベースのモデルですが、アナログ画風も作成できたりします。ToxicEtheRealXLhttps://tensor.art/models/702813703965453448/ToxicEtheRealXL-v1イラストからフォトリアルまで幅広く対応したモデル。プロンプトによってイラストかフォトリアルか振れ幅が大きいので、明確にプロンプトの作り込みが必要です。LoRAで方向性を強めると使いやすいかもしれません。ToxicEchoXLhttps://tensor.art/models/689378702666043553/ToxicEchoXL-v1イラスト特化の超高性能モデル。水彩をベースに独自の学習・調整を行っているので、わりと独特な画風を持っています。画風変更に様々なLoRAも作成していますので、是非私のユーザーページへお越しください。https://tensor.art/u/649265516304702656最近のお気に入りはBeautiful
Warrior XL + atmosphere
です。イラストからフォトまで一通り網羅できるので、是非使ってみてください。なお版権キャラの生成は弱いので、その辺はLoRAかAnimagineXLとかPonyとか使うといいと思います。ToxicEchoXLはキャラLoRAを使うと他のモデルとはタッチの違うイラストが作れますので、ファンアート適正自体は高いです。6.
おわりにモデルのサンプルやみんなみたいにうまく生成できないな…という方の助けになれば幸いです。まあ…モデルのショーケースからリミックスすればこんなガイド見なくてもきれいな画像が作れますけどね…SD3もリリースされたので、もし可能ならそちらのモデルも作成してみたいですね。どうも商用利用は有償のライセンスが必要そうですが…
SuiginToxic
28
FIX EXIF DATA FROM EMS-#####-EMS USING EXIFTOOL
IntroductionYou download your images from this website but the EXIF data for the
model / lora looks like:Model: EMS-342970-EMS, or
<lora:EMS-45352-EMS:0.500000>.The ExifTool utility can fix this. I am
using linux but it should also work for mac/Windows if you follow
https://exiftool.org/install.htmlPreReq- Download ExifTool from
https://exiftool.org/ and extract the archive into your home drive.- Make a new
dot-file called .ExifTool_config in the same folder as exiftool.- linux example:
~/Image-ExifTool-12.86/.ExifTool_config- windows might need cmd like:
echo.>.ExifTool_config.ExifTool_config fileEdit the config file. Copy/paste
the basic example.- This is perl language, search and replace, and not
optimized, but it works. Switch is probably more efficient.- The \+ is an escape
for the + in the model name.- The /g at the end searches for all instances.-
'Parameters' is the block it changes in the EXIF.- Add all your desired entries
and save the file.You only need to make new entries like:$val =~
s/EMS-151022-EMS/RealCartoon Realistic v11/g; %Image::ExifTool::UserDefined = (
'Image::ExifTool::Composite' => { MyParameters=> { Require =>
'Parameters', ValueConv => q{ # MODEL $val =~ s/EMS-151022-EMS/RealCartoon
Realistic v11/g; $val =~ s/EMS-219023-EMS/ShampooMix_v4-fp16-no-ema/g; $val =~
s/EMS-230098-EMS/RealCartoon Realistic v12/g; $val =~ s/EMS-379840-EMS/Lazymix\+
- v4/g; # LoRa $val =~ s/EMS-72516-EMS/Realistic Fusion X - V1/g; $val =~
s/EMS-343944-EMS/A simple nun suit - v1/g; return $val; }, }, }, ); 1; # end
Modify the EXIFI use linux, extracted to a folder inside my home folder, and my
files are in my Downloads folder, so the command I run is this, where
"~/Downloads" has my raw files:perl ~/Image-ExifTool-12.86/exiftool
"-Parameters<MyParameters" ~/DownloadsIt will make new files and append
"original" to the old, however you can add -overwrite_original to delete the old
files once absolutely sure your config file works. This does not forgive. I am
not responsible for lost EXIF.Copy into folders based on ModelThis will parse
your EXIF for the Model: and grab until the first comma, copy the file into a
subfolder of the destination named as the Model. Ideally you already modified
the EXIF to fix the model name. In this example the files are in my home
Downloads folder on linux.- ~/Downloads/ is the source folder with the files-
/path/to/destination/ is the destination parent folder. You need to change this-
-r is recursive, if you choose, make it -r -o .- The "-o ." is the copy
argument. Remove for move, at your own risk.- If you only run this without first
doing the above section you'll get a bunch of EMS-###-EMS folders. The next
section will combine everything together into one command.perl
~/Image-ExifTool-12.86/exiftool -if '($Parameters=~/Model/i)' -o .
'-Directory</path/to/destination/${Parameters;m/\bModel:\s+(\w+[^,]*)/;$_=$1;}'
~/Downloads/Combine into one commandThis combines the above into one command.
This example does a move not a copy. I also renamed my exiftool folder.-
~/Downloads There are two. Rename those to the folder with your EMS-###-EMS
pictures- /path/to/destination/ Where you want to move the files after renaming
the EMS-###-EMS to Model nameperl ~/ExifTool/exiftool -if
'($Parameters=~/Model/i)' "-Parameters<MyParameters" ~/Downloads
-overwrite_original -execute
'-Directory</path/to/destination/${Parameters;m/\bModel:\s+(\w+[^,]*)/;$_=$1;}'
~/DownloadsNotesYou can rename the Image-ExifTool-12.86 directory or have it
wherever.On Windows you might need to change the ' to " when referencing
directories.This runs perl code so maybe rename exiftool to something else for
safety.ExifTool created by Phil Harvey. Very impressive. Active community and
forum at the creator's website.It can do advanced operations and scripting.
Above my pay grade.Please understand this post isn't an offer for support. This
took me all day to figure out. I don't know what I am doing.
user_661323374995269704
1
UNDERSTANDING THE IMPACT OF NEGATIVE PROMPTS: WHEN AND HOW DO THEY TAKE EFFECT?
📝 - SynthicalThe Dynamics of Negative Prompts in AI: A Comprehensive Study by:
Yuanhao Ban UCLA, Ruochen Wang UCLA, Tianyi Zhou UMD, Minhao Cheng PSU, Boqing
Gong, Cho-Jui Hsieh UCLAEThis study addresses the gap in understanding the
impact of negative prompts in AI diffusion models. By focusing on the dynamics
of diffusion steps, the research aims to answer the question: "When and how do
negative prompts take effect?". The investigation categorizes the mechanism of
negative prompts into two primary tasks: noun-based removal and adjective-based
alteration.The role of prompts in AI diffusion models is crucial for guiding the
generation process. Negative prompts, which instruct the model to avoid
generating certain features, have been less studied compared to their positive
counterparts. This study provides a detailed analysis of negative prompts,
identifying the critical steps at which they begin to influence the image
generation process.FindingsCritical Steps for Negative PromptsNoun-Based
Removal: The influence of noun-based negative prompts peaks at the 5th diffusion
step. At this critical step, negative prompts initially generate a target object
at a specific location within the image. This neutralizes the positive noise
through a subtractive process, effectively erasing the object. However,
introducing a negative prompt in the early stages paradoxically results in the
generation of the specified object. Therefore, the optimal timing for
introducing these prompts is after the critical step.Adjective-Based Alteration:
The influence of adjective-based negative prompts peaks around the 10th
diffusion step. During the initial stages, the absence of the object leads to a
subdued response. Between the 5th and 10th steps, as the object becomes clearer,
the negative prompt accurately focuses on the intended area and maintains its
influence.Cross-Attention DynamicsAt the peak around the 5th step for noun-based
prompts, the negative prompt attempts to generate objects in the middle of the
image, regardless of the positive prompt's context. As this process approaches
its peak, the negative prompt begins to assimilate layout cues from its positive
counterpart, trying to remove the object. This represents the zenith of its
influence.For adjective-based prompts, during the peak around the 10th step, the
negative prompt maintains its influence on the intended area, accurately
targeting the object as it becomes clear.The study highlights the paradoxical
effect of introducing negative prompts in the early stages of diffusion, leading
to the unintended generation of the specified object. This finding suggests that
the timing of negative prompt introduction is crucial for achieving the desired
outcome.Reverse Activation PhenomenonA significant phenomenon observed in the
study is Reverse Activation. This occurs when a negative prompt, introduced
early in the diffusion process, unexpectedly leads to the generation of the
specified object within the context of that negative prompt. To explain this,
researchers borrowed the concept of the energy function from Energy-Based Models
to represent data distribution.Real-world distributions often feature elements
like clear blue skies or uniform backgrounds, alongside distinct objects such as
the Eiffel Tower. These elements typically possess low energy scores, making the
model inclined to generate them. The energy function is designed to assign lower
energy levels to more 'likely' or 'natural' images according to the model’s
training data, and higher energy levels to less likely ones.A positive
difference indicates that the presence of the negative prompt effectively
induces the inclusion of this component in the positive noise. The presence of a
negative prompt promotes the formation of the object within the positive noise.
Without the negative prompt, implicit guidance is insufficient to generate the
intended object. The application of a negative prompt intensifies the
distribution guidance towards the object, preventing it from materializing.As a
result, negative prompts typically do not attend to the correct place until step
5, well after the application of positive prompts. The use of negative prompts
in the initial steps can significantly skew the diffusion process, potentially
altering the background.ConclusionsDo not step less than 10th times, going
beyond 25th times does not make the difference for negative prompting.Negative
prompts could enhance your positive prompts, depending on how well the model and
LoRA have learn their keywords, so they could be understood as an extension of
their counterparts.Weighting-up negative keywords may cause reverse activation,
breaking up your image, try keeping the ratio influence of all your LoRAs and
models
equals.Referencehttps://synthical.com/article/Understanding-the-Impact-of-Negative-Prompts%3A-When-and-How-Do-They-Take-Effect%3F-171ebba1-5ca7-410e-8cf9-c8b8c98d37b6?
PictureT
13
[ 🔥🔥🔥 SD3 MEDIUM OPEN DOWNLOAD - 2024.06.12 🔥🔥🔥]
Finally! It's happening! The Medium version will be released
first!+Stability.AICo-CEO Christian Laporte has announced the release of the
weights.Stable Diffusion 3 Medium, our most advanced text-to-image model, will
soon be available! You can download the weights from Hugging Face starting
Wednesday, June 12.SD3 Medium is the SD3 model with 2 billion parameters,
designed to excel in areas where previous models struggled. Key features
include:• Photorealism: Overcomes common artifacts in hands and faces to deliver
high-quality images without complex workflows.• Typography: Provides powerful
typography results that surpass the latest large models.• Performance: Optimized
size and efficiency make it ideal for both consumer systems and enterprise
workloads.• Fine-Tuning: Can absorb fine details from small datasets, perfect
for customization and creativity.SD3 Medium weights and code are available for
non-commercial use only. If you wish to discuss a self-hosting license for
commercial use of Stable Diffusion 3, please fill out the form below and our
team will contact you shortly.+ @everyone
TDN-M
27
4
WHAT EXACTLY ARE THE "NODE" AND THE "WORKFLOW" IN AI IMAGE PLATFORM (EXPLANATION
FOR THE BEGINNER)
The Traditional Way of Generating AI Images for the BeginnerIf you are a
beginner in the AI community, maybe you will be very confused and have no clue
about what is "Node", and "Workflow" and their relations with "AI Tools" in the
TensorArtTo start with the most simple way. We need to first mention how the
user generates an image using a "Remixing" button that brings us to the "Normal
Creation menu"Needless to say, by just editing the prompt (what you would like
to see your picture look like) and negative prompt (what you do not want to see
in the output image). Then push the Generate button, and the wonderful AI tool
will kindly draw the new illustration serving you within a minute!!!!That sounds
great, don't you think? If we imagine how humans spent a huge amount of time in
the past to publish just 1 single piece of art. (Yeah, today, in 2024, in my
personal opinion, both AI and human abilities are still not fully replaceable,
especially in the terms of beautiful perfect hand :P ) However, the backbone or
what happens behind the User-friendly menu allows us to "Select model", "Add
LoRA", "Add ControlNet", "Set the aspect ratio (the original size of the image)"
and so on, all of them are collected "Node" in a very complex "Workflow" PS.1.
The Checkpoint or The Model often refers to the same thing. They are the core
program that had been trained to draw the illustration. Each one has its
strengths and weaknesses (I.E. Anime oriented or Realistic oriented) PS.2. The
LoRA (Low-Rank Adaptation) is like an add-on to the Model allowing it to adapt
to a different style, theme, and user preference. A concrete example is the
Anime Character LoRAPS.3 The ControlNet is like a condition setting of the
image. It helps the model to truly understand what is beyond the text prompt can
describe. For instance, how a character poses in each direction and the angle of
the camera.So here comes "The Comfyflow" (the nickname of the Workflow, people
also mentioned it by the name "ComfyUI") which gives me a super headache when I
see things like this for the first time in my life!!!!!!!!!(This image is a flow
I have spent a lot of time studying, it is a flow for combining what is in the
two images into a single one) Yeah, maybe, it is my fault that did not go to
class about the workflow from the beginning or search for the tutorial on
YouTube the first time (as my first language is not English). But would it be
better if we had an instructor to tell us step-by-step here in Tensor.ArtAnd
that is the reason why I got inspired to write this article solely for the
beginner. So let's start with the main content of the article.What is
ComfyFlowComfyFlow or the Workflow is an innovative AI image-generating platform
that allows users to create stunning visuals with ease. To get the most out of
this tool, it's important to understand two key concepts: "workflow" and "node."
Let's break these down in the simplest way possible.What is a Workflow?A
workflow is like a blueprint or a recipe that guides the creation of an image.
Just as a recipe outlines the steps to make a dish, a workflow outlines the
steps and processes needed to generate an image. It’s a sequence of actions that
the AI follows to produce the final output.Think of it like this:Recipe
(Workflow): Tells you what ingredients to use and in what order.Ingredients
(Nodes): Each step or component used in the recipe.Despite the recommended
pre-set template that TensorArt kindly gives to the users, from the beginner
view's viewpoint without the knowledge of the workflow, it is not that helpful
because, after clicking the "Try" button, we will bombarded with the complexity
of the Node!!!!!!!What is a Node?Nodes are the building blocks of a workflow.
Each node represents a specific action or process that contributes to the final
image. In ComfyFlow, nodes can be thought of as individual steps in the
workflow, each performing a distinct function.Imagine nodes as parts of a
puzzle:Nodes: Individual pieces that fit together to complete the picture
(workflow).How Do Workflows and Nodes Work Together? 1-2) Starting Point: Every
workflow begins with an initial node, which might be an image input from the
user, together with Checkpoint and LoRA serving the role of image references.
3-4) Processing Nodes: These are nodes that draw or modify the image in some
way, such as adding color, or texture, or applying filters. 5) Ending Point: The
node outputs the completed image which works very closely with the node of the
previous stage in terms of sampling and VAE PS. A Variational Autoencoder (VAE)
is a generative model that learns input data, such as images, to reconstruct and
generate new, similar, or variations of images based on the patterns it has
learned.Here is the list of nodes I have used in the normal image-generating
images of my Waifu using 1checkpoint, and 2LoRAs to help the reader understand
how ComfyFlow worksThe numbers 1-5 represent the overview process of the
workflow and the role of each type of node I have mentioned above. However, in
the case of more complex tasks like in AI Tools, the number of nodes sometimes
is higher than 30!!!!!!!By the way, when starting with an empty ComfyFlow page,
the way to add a node is "Right Click" -> "Add Node" -> Scroll down to the
top, since the most frequently used node will be over there.1) loaders ->
Load CheckPointLike in the normal task creation menu, this node is the one we
can choose CheckPoint or the Core model.It is important to note that nodes work
together using input/output. The "Model/CLIP/VAE" (the output) circles have to
connect to the next one in which it has to correspond. We link them together by
left-clicking on the circle's inner area and then drag to the destination. PS.
CLIP (Contrastive Language-Image Pre-training) is a model developed by OpenAI
that links images and text together in a way that helps AI understand and
generate images based on textual descriptions.2) loaders -> Load
LoRACheckpoint is very closely related to LoRA and that is a reason why they are
connected by the input/output named "model/MODEL", "clip/CLIP"Anyway, since in
this example, I have used 2 LoRAs (first for The theme of the picture and the
Second for the character reference of my Waifu), two nodes of LoRAs then have to
be connected as well. Here we can adjust the strength of the LoRA or the weight
like it happens in the normal task generation menu.3) CLIP Text Encode
(Prompt)This node is the prompt and negative prompt we normally see in the menu.
The input here is only clip (Contrastive Language-Image Pre-training) and the
output is "CONDITIONING" User tip: If you click on the output circle of the
"Load LoRA" node and drag it to the empty area, the ComfyFlow will pop up a
corresponding next node list to create a new one with ease. 4) KSampler &
Empty Latent ImageThe sampling method is used to tell the AI how it should start
generating visual patterns from the initial noise and everything associated with
its adjustment will be set here in this type of sampling node together with
"Empty Latent Image" The inputs in this step here are models (from LoRA node),
positive and negative (from prompt node) and the output is "Latent"5) VAE Decode
& Final output nodeOnce we establish the sampling node, the output named
"LATENT" will then have to connect with "samples" Meanwhile the "vae" is the
linkage between this one and the "Load Checkpoint" node from the beginning.And
when everything is done the "IMAGE" as a final output here will be served at
your hand.PS. An AI Tool is a more complex Workflow created to do some specific
task such as swapping the face of the human in the original picture with the
target face or changing the style of the input illustration to another one and
etc.
UthaiYama
27
2
PHOTOREAL MAKEUP EDITION - V3 SLIDER
PhotoReal Makeup Edition - V3 Slider (no trigger)Introducing the PhotoReal
Makeup Edition - V3 Slider! Slide to the right to add beautiful, realistic
makeup. Slide to the left to reduce the makeup effect for a more natural look.
It's perfect for adjusting the makeup to get just the style you want.Try it out
and see the amazing changes you can make!More Information:- Model linkYour
feedback is invaluable to me. Feel free to share your experiences and
suggestions in the comment section. For more personal interactions, join
our Discord server where we can discuss and learn together.Thank you
for your continued support!
Ai Art Vision
44
4
TIPS FOR NEW USERS
Intro Hey there! If you're reading this, you're probably new to AI image
generation and want to learn more. If you're not, you probably already know more
than me :). Yeah, full disclosure: I'm still pretty inexperienced at this whole
thing, but I thought I could still share some of the things I've learned with
you! So, in no particular order:1. You can like your own posts I doubt there's
anyone who doesn't know this already, but if you're posting your favorite
generations and you care about getting likes, you can always like them yourself.
Sketchy? Kinda. Do I still do it? Yes. And on the topic of getting more likes:2.
Likes will often be returned Whenever I receive a like on one of my posts, I'll
look at that person's pictures and heart any that I particularly enjoy. I know a
lot of people do this, so one of the best ways to get people to notice and like
your content is to just browse through posts and be generous with your own
likes. It's a great way to get inspiration too!3. Use turbo/lightning LORAs If
you find yourself running out of credits, there are ways to conserve them. When
I'm iterating on an idea, I'll use a SDXL model (Meina XL) paired with this
LORA. This lets me get high quality images in 10 steps for only 0.4 credits!
It's really nice, and works with any SDXL model. Unfortunately, if there is a
similar method for speeding up SD 1.5 models I don't know it, so it only works
with XL.4. Use ADetailer smartly ADetailer is the best solution I've found for
improving faces and hands. It's also a little difficult to figure out. So,
though I'm still not a professional with it, I thought I could share some of the
tricks I've learned. The models I normally use are face_yolo8s.pt and
hand_yolo8s.pt. The "8s" versions are better than the "8n" versions, though they
are slightly slower. In addition to these models, I'll often add the Attractive
Eyes and Perfect Hand LORAs respectively. These are all just little things you
can do to improve these notoriously hard parts of image generation. Also, using
ADetailer before upscaling the image is cheaper in terms of credits, though the
upscaling process can sometimes mess up the hands and face a little bit so
there's some give and take there.5. Use an image editing app Wait a minute, I
hear you saying, isn't this a guide for using Tensor Art? Yes, but you can still
use other tools to improve your images. If I don't like a specific part of my
image, I'll download it, open it in Krita (Or Photoshop or Gimp) and work on it.
My art skills are pretty bad, (which is why I'm using this site in the first
place,) but I can still remove, recolor, or edit certain aspects of the image. I
can then reupload it to Tensor Art, and Img2img with a high denoising strength
to improve it further. You could also just try inpainting the specific thing you
want to change, but I always find it a bit of a struggle to get inpaint to make
the changes I want.6. Experiment! The best way to learn is to do, so just start
generating images, fiddling with settings, and trying new things. I still feel
like I'm learning new stuff every day, and this technology is improving so fast
that I don't think anyone will ever truly master it. But we can still try our
hardest and hone our skills through experimentation, sharing knowledge, and
getting more familiar with these models. And all the anime girls are a big plus
too.Outro If you have anything to add, or even a tip you'd like to share,
definitely leave a comment and maybe I can add it to this article. This list is
obviously not exhaustive, and I'm no where near as talented as some of the
people on this platform. Still though, I hope to have helped at least one person
today. If that was you, maybe give the article a like? I appreciate it a ton, so
if you enjoyed, just let me know. Thanks for reading!
Starstriker
40
• MOOD MAGIC SERIES • I. MELANCHOLY
MOOD MAGIC: adding emotion to your promptsMelancholy & GloomOvercast:
Cloud-covered skies for subdued lighting.Dim Lighting: Limited light sources for
creating deep shadows.Muted Colors: Toned-down color palette to convey sadness
or desolation.Dusky: Twilight ambiance, suggesting the fading light of
day.Foggy: A thick mist that obscures details and softens the scene.Drizzly:
Gentle rain that adds a reflective, melancholic quality.Cloudy: Thick clouds
that reduce brightness and saturate the scene with grey.Desaturated: Low color
saturation to enhance the bleak feel.Shadowed: Prominent shadows that deepen the
mood.Moody Lighting: Emotionally charged lighting with strong contrasts.Gloomy:
Overall dark and dismal atmosphere.Monochrome: Black and white or single-color
dominance to strip away cheer.Underexposed: Darker exposure to mimic a sense of
foreboding.Chiaroscuro: Strong contrasts between light and dark, emphasizing
turmoil.Hazy: Blurred or smoky atmosphere, creating a sense of mystery or
unease.Twilight: Dim natural lighting that can feel lonely or isolating.Stormy:
Implication of an approaching or ongoing storm to add tension.Wintery: Cold,
barren landscape cues, even in urban settings.Grainy: Visual noise that adds an
old or troubled quality.Bleak: Stark, harsh lighting or barren scenery
settings.Ominous Clouds: Dark, menacing clouds that threaten bad weather.Subdued
Tones: Soft, low-key colors that don't catch the eye.Cold Colors: Blues and
greys to suggest chilliness and discomfort.Rusty: Implications of decay and
neglect.Aged: A sense of time wearing down the scene, historical weariness.Soft
Focus: Slightly out-of-focus elements to create a sense of disorientation or
confusion.Tenebrous: Deeply shadowed, almost pitch-dark.Low-Key Lighting:
Minimal lighting mostly in darkness with occasional highlights.Pensive: Engaged
in, involving, or reflecting deep or serious thought.Yearning: A feeling of
intense longing for something typically something that one has lost or been
separated from.Weary: Conveying a sense of tiredness or exhaustion, both
physical and emotional.Sparse: Minimalist or bare settings that suggest
simplicity or emptiness.Brooding: A deep, serious, and sometimes dark
contemplation.Silent: Lack of sound or motion, emphasizing solitude or
contemplation.Ephemeral: Fleeting or transitory, suggesting the transient nature
of moments and emotions.Desolate: Emptiness that conveys a sense of abandonment
or loneliness.Poetic: Imbued with a sense of beauty and melancholy, often
through lyrical expression.Moody Skies: Cloudy, stormy, or unsettled skies that
reflect a turbulent emotional landscape.Cold Light: Harsh, unyielding light that
doesn’t warm but isolates subjects.Autumnal: Related to autumn, often seen as a
melancholic season due to its association with the end of summer.Faded: Colors
or elements that have lost brightness, suggesting the passing of time.Blue Hour:
Moody cool natural lighting obtained in the twilight hour just after sunset or
just before sunrise.Example using Stable Diffusion SDXL + refinerCheckpoint:
RealVis4Cfg: 5.5Steps: 40Sampler: DPM++ 3m SDE KarrasVisualize a close-up
portrait of a young woman standing by a foggy window, her gaze distant and
contemplative. The room is dimly lit, with only a soft, diffuse light filtering
through the heavy overcast outside, casting subtle shadows across her face. The
colors are desaturated, emphasizing a palette of cool grays and muted blues that
reflect her somber mood. Her expression is serene yet melancholic, with her eyes
slightly downcast as if lost in thought. The background is blurred, enhancing
the sense of isolation and introspection. This portrait captures the essence of
melancholy, framed in a moment of quiet solitude.negative: illustration,
cartoon, anime, 3d, digital art, bad quality, CGI, sketch, drawn, blurry,
painting, worst quality, low quality, bad anatomy, bad hands, bad body, missing
fingers, extra digit, fewer digits
Mya Sturbate
2
BUZZ WORDS: LIGHTING
Getting the lighting right is key to making your AI-generated images look super
realistic. This guide gives you the top keywords to use in your prompts to nail
the lighting every time. Whether you're after dramatic shadows or soft, natural
light, these tips will help your images look lifelike and set the tone to your
composition.Ambient light:Soft, even lighting that fills the entire scene,
reducing shadows.Chiaroscuro Lighting:A technique that uses strong contrasts
between light and dark to create a dramatic, three-dimensional effect.Rim
light:Light that outlines the subject, emphasizing its edges and creating a
glowing effect.Diffused light:Soft light scattered in many directions,
minimizing harsh shadows.Natural light:Light from the sun, moon, or other
natural sources, offering realism and variationBacklight:Light coming from
behind the subject, creating a silhouette or halo effect.Volumetric light:Light
that interacts with particles in the air, such as fog or dust, creating visible
light rays and enhancing the sense of depth in the scene.Polarized light:Light
that vibrates in parallel planes.Emissive light:Light emitted from surfaces or
objects themselves, often used to simulate glowing materials or
lights.Directional light:Focused light from a specific direction, creating
strong shadows and highlights.Soft light:Gentle light that produces minimal
shadows, creating a smoother look.Hard light:Sharp, intense light that casts
strong shadows and highlights details.Spotlight:Intense focused beam that
highlights a set area or subject.Artificial light:Light from man-made sources
allowing precise control over the scene.Holagen, florescent, blacklight, led,
xenon, plasma, ultraviolet, incandescent, neon, Infrared, sodium vapor lights,
metal halide lights, krypton, photoluminescent, ceramic metal halide, HMI, CCFL,
CFLLow key light:Predominantly dark lighting with high contrast, often creating
a dramatic or moody atmosphere.High Key Light:Bright, low-contrast lighting that
minimizes shadows.Bounce Lighting/Reflected Lighting:Light reflected off a
surface to soften the effect and spread it more evenly.Side Lighting:Light
coming from the side of the subject.Caustic Lighting:Light patterns created when
light is refracted or reflected through transparent or reflective materials,
producing intricate and often beautiful effects.Uplighting:Light directed
upwards. Great for emphasizing architectural features.Color Gel Lighting:The use
of colored filters over lights to alter the color or mood of the scene.Gobo
Lighting:Using a stencil or template placed in front of a light source to
project patterns or shapes onto a surface.Split Lighting:Lighting that
illuminates one half of the subject's face while leaving the other half in
shadow, creating a strong, dramatic effectButterfly Lighting:Light placed above
and in front of the subject, creating a butterfly-shaped shadow under the nose,
often used in glamour photography.Rembrandt Lighting:technique where light
creates a triangle of illumination on the cheek opposite the light source,
adding depth and character.Specular lighting:Sharp, bright reflections from
shiny surfaces, emphasizing glossiness and texture.Natural Breakup
Lighting/Dappled Lighting:Using irregular patterns to mimic natural light
effects, such as light filtering through leaves.Subsurface Scattering:Light that
penetrates the surface of a translucent material, scattering within and then
exiting at a different point, adding realism to materials like skin or
wax.Golden Hour:Warm golden natural lighting obtained shortly after sunrise or
shortly before sunset. Creates long soft shadows.Blue Hour:Moody cool natural
lighting obtained in the twilight hour just after sunset or just before
sunrise.Clamshell Lighting:portrait lighting setup using two light sources, one
above and one below the subject's face.Catch light:A small reflection of the
light source in the subject's eyes, adding life and dimension to portraits.Cross
lighting:two light sources positioned at opposite sides of the subject, creating
dramatic shadows and highlights.Tenebrism:Aggressive contrast between light and
dark producing dark and gloomy images.Contre-jour:Lighting technique that
produces clear silhouettes by the use of backlighting.Sfumato:Artistic lighting
technique soft transitions between colors and tones resulting in a dreamy effect
with no clear boundaries. Ie. The Mona Lisa.Ray tracing: Rendering technique
that simulates the way the light interacts with the scene. Traces the light from
the source, bounces off surfaces and reaches the viewers eye. Three point
lighting:Cinematic lighting technique using key light, fill light and backlight.
Global Illumination: Computer graphic technique that adds more realistic
lighting to 3d scenery. Bloom: simulates the glow around bright light sources,
creating a soft halo. Luminescence:emission of light by a substance not
resulting from heat. It occurs through various processes such as chemical
reactions, electrical energy, or other means.Bioluminescence:A cold light
produced out of a chemical reaction inside of a living organism.
Mya Sturbate
1
QUICKSTART GUIDE TO STABLE VIDEO DIFFUSION
What is Stable Video Diffusion (SVD)?Stable Video Diffusion (SVD) from Stability
AI, is an extremely powerful image-to-video model, which accepts an image input,
into which it “injects” motion, producing some fantastic scenes.SVD is a latent
diffusion model trained to generate short video clips from image inputs. There
are two models. The first, img2vid, was trained to generate 14 frames of motion
at a resolution of 576×1024, and the second, img2vid-xt is a finetune of the
first, trained to generate 25 frames of motion at the same resolution.The newly
released (2/2024) SVD 1.1 is further finetuned on a set of parameters to produce
excellent, high-quality outputs, but requires specific settings, detailed
below.Why should I be excited by SVD?SVD creates beautifully consistent video
movement from our static images!How can I use SVD?ComfyUI is leading the pack
when it comes to SVD image generation, with official SVD support! 25 frames of
1024×576 video uses < 10 GB VRAM to generate.It’s entirely possible to run
the img2vid and img2vid-xt models on a GTX 1080 with 8GB of VRAM!There’s still
no word (as of 11/28) on official SVD support in Automatic1111.If you’d like to
try SVD on Google Colab, this workbook works on the Free Tier;
https://github.com/sagiodev/stable-video-diffusion-img2vid/. Generation time
varies, but is generally around 2 minutes on a V100 GPU.You’ll need to download
one of the SVD models, from the links below, placing them in the
ComfyUI/models/checkpoints directoryAfter updating your ComfyUI installation,
you’ll see new nodes for VideoLinearCFGGuidance and SVD_img2vid _Conditioning.
The Conditioning node takes the following inputs;You can download ComfyUI
workflows for img2video and txt2video below, but keep in mind you’ll need to
have an updated ComfyUI, and also may be missing additional nodes for Video. I
recommend using the ComfyUI Manager to identify and download missing
nodes!Suggested SettingsThe settings below are suggested settings for each SVD
component (node), which I’ve found produce the most consistently useable
outputs, with the img2vid and img2vid-xt models.Settings –
Img2vid-xt-1.1February 2024 saw the release of a finetuned SVD model, version
1.1. This version only works with a very specific set of parameters to improve
the consistency of outputs. If using the Img2vid-xt-1.1 model, the following
settings must be applied to produce the best results;The easiest way to generate
videosin tensor.art, you can generate videos very easily compared to the
explanation above, all you need to do is input the prompt you want, select the
model you like, set the ratio and set the frame in the animatediff menu.Output
ExamplesLimitationsIt’s not perfect! Currently there are a few issues with the
implementation, including;Generations are short! Only <=4 second generations
are possible, at present.Sometimes there’s no motion in the outputs. We can
tweak the conditioning parameters, but sometimes the images just refuse to
move.The models cannot be controlled through text.Faces, and bodies in general,
often aren’t the best!
Philosophy.AI
LIST OF STYLE COLLECTION - FOCUSING ON ANIME CHARACTOR EXAMPLES (CONTINUE
UPDATING)
AI image-generating platforms like Tensor.art offer diverse anime styles,
enabling users to create artwork in various distinct masterpieces of art
inspired by popular anime aesthetics. These collections aim to cater to
different preferences from classic to contemporary anime illustrations within
one place.P.S.1 I will continue updating this post maybe every 2 weeks when I
find a unique style (both for LoRA and model) that is worth listing here solely
from my perspective - Anyway if anyone has a list of favorite styles in mind,
feel free to share them here or even create your post. :DP.S.2 People normally
mix multiple LoRA at once, and the core model (checkpoint) has a variation in
base style depending on the prompt used. Therefore, in the following example, I
will choose only a single LoRA or Checkpoint to represent without mixing
anything. However, if confusion about the contribution to the style happens, I
have to apologize in advance since I am just a beginner in the art community.
Here are some examples: Anime Lineart / Manga-like (线稿/線画/マンガ風/漫画风) Style (LORA)
https://tensor.art/models/623935989624337542 Spacezin Sketch Style (LoRA)
https://tensor.art/models/638083414328801488 Cute Chibi - V.1 (LoRA)
https://tensor.art/models/726716640076597245 CAT - Citron Anime Treasure
(Checkpoint) https://tensor.art/models/713607777118974323 LizMix V.7.0
(Checkpoint) https://tensor.art/models/721034681811855891 Flower style - (LORA)
https://tensor.art/models/699582840586758007 Art Nouveau Style - Oosayam (LoRA)
https://tensor.art/models/654562112921690173 Torino Style - v.2.0.09 (LoRA)
https://tensor.art/models/705577639974520212 Yody PVC 3D Print - 1.0
(Checkpoint) https://tensor.art/models/673632484975460872 Eldritch Expressionism
style (LoRA) https://tensor.art/models/708171473803739178 [Y5] Impressionism
Style 印象派风格 (LoRA) https://tensor.art/models/621173217551417505 surrealism -
2024-02-17 (LoRA) https://tensor.art/models/695557949424221333 pop-art - 01
style (LoRA) https://tensor.art/models/697182692602582375 FF Style: Kazimir
Malevich | Suprematism (LoRA) https://tensor.art/models/655758742350092928
Hoping these collections (today and in the future) will allow A.I. artists and
enthusiasts to generate anime-inspired images effortlessly, blending creativity
with advanced AI technology to bring their visions to life. :D
UthaiYama
18
2
PROMPT REFERENCE FOR "LIGHTING EFFECTS"
Hello. I usually use "lighting/lighting effects" when generating images.I will
introduce some of the "words" I use when I want to add something.Please note
that these words alone do not provide 100% effectiveness, and the base modelThe
effect you get will differ depending on the LoRA sampling method and where you
place it in the prompt.Words related to "lighting effects"・ Backlight : Light
from behind the subject・ Colorful lighting : The impression itself is not
colored, but the color changes depending on the light.・ moody lighting :
natural lighting, not direct artificial light・ studio lighting : A term used
to describe the artificial lighting of a photography studio.・ Directional
Light : directional light source is a light source that shines parallel rays in
a selected direction.・ Dramatic lighting : Lighting techniques in the field of
photography・ Spot lighting : A lighting technique that uses artificial light in
a small area.・ Cinematic lighting : A single word that describes several
lighting techniques used in movies.・ Bounce Lighting : Light reflected by a
reflex plate, etc.・ Practical Lighting : Photographs and videos that depict the
light source itself in the composition・ Volumetric lighting : A word derived
from 3DCG. It tends to be a picture with a divine golden light source.・ Dynamic
lighting : I don't really understand what it means, but it tends to create
high-contrast images.・ Warm lighting : Creates a warm picture illuminated with
warm colors・ Cold lighting : Lights with a cold light source.・ High-key
lighting : Soft light, minimal shadows, low contrast, resulting in bright
frames・ Low-key lighting : It provides high contrast, but the impression is a
little weak.・ Hard light : Strong light. Highlights appear strong.・ soft
light : A word that refers to faint light.・ strobe lighting : strong
artificial light (stroboscopic lighting)・ Ambient light : An English word that
refers to ambient lighting/indoor lighting.・ flash lighting : For some reason,
the characters themselves tend to emit light, and there are often flashes of
light. (flash lighting photography) ・ Natural lighting : This tends to create a
natural-looking picture that feels contrasting with artificial light.
Neo_Ryo
34
2
THE FUTURE OF AI IMAGE GENERATION: ENDLESS POSSIBILITIES -
introduction{{For those who are about to start AI image generation}}In recent
years, advances in AI technology have brought about revolutionary changes in the
field of image generation. In particular, AI-powered illustration generation has
become a powerful tool for artists and designers. However, as this technology
advances, issues of creativity and copyright arise. In this article, we will
explain the possibilities of AI image generation, specific use cases, how to
create prompts, how to use LoRA and its effects, keywords for improving image
quality, consideration for copyright, etc.Fundamentals of AI image generationAI
image generation uses artificial intelligence to learn from data and generate
new images. Deep learning techniques are often used for this, and one notable
approach is stable diffusion. Stable Diffusion employs a probabilistic method
called a diffusion model to gradually remove noise during image generation,
resulting in highly realistic, high-quality output.Generating real imagesAI
technology is excellent not only for creating cute illustrations, but also for
generating realistic images. For example, you can generate high-resolution
images that resemble photorealistic landscapes or portraits. By utilizing Stable
Diffusion, it is possible to generate more detailed images, which expands the
possibilities of application in various fields such as advertising, film
production, and game design.Generate cute illustrationsOne of the practical
applications of AI image generation is the creation of cute illustrations. This
is useful for things like character design and avatar creation, allowing you to
quickly generate different styles. This process typically involves collecting a
large dataset of illustrations, training an AI model on this data to learn
different styles and patterns, and generating new illustrations based on user
input or keywords.creativity and AIAI image generation also influences creative
ideas. Artists can use her AI-generated images as inspiration for new works or
expand on ideas, which can lead to the creation of new styles and concepts never
thought of before.Use and effects of LoRALoRA (Low-Rank Adaptation) is a
technique used to improve the performance of AI models. Its impacts include:1.
Fine-tune models: LoRA allows you to fine-tune existing AI models to learn
specific styles and features, allowing for customization based on user needs.2.
Efficient learning: LoRA reduces the need for large-scale data collection and
training costs by efficiently training models using small datasets.3. Rapid
adaptation: LoRA allows you to quickly adapt to new styles and trends, making it
easy to generate images tailored to your current needs.For example, LoRA can be
leveraged to efficiently achieve high-quality results when generating
illustrations in a specific style.Creating a promptWhen instructing an AI to
generate illustrations, it's important to create effective prompts. Key points
for creating prompts include providing specific instructions, using the right
keywords, trial and error, and an optional reference image to help the AI figure
out what you're looking for. Keywords for improving image qualityWhen creating
prompts for AI image generation, you can incorporate keywords related to image
quality improvement to improve the overall quality of the images generated.
Useful keywords include "high resolution," "detail," "clean lines," "high
quality," "sharp," "bright colors," and "photorealistic."Copyright
considerationsImage generation using AI also raises copyright issues. If the
dataset used to train your AI model contains copyrighted works, the resulting
images may infringe your copyright. When using AI image generation tools, it's
important to be aware of the data source, ensure that the generated images
comply with copyright laws, and check the license agreement.conclusionAI image
generation offers great possibilities for artists and designers, but it also
raises challenges related to copyright. By using data responsibly and
understanding copyright law, you can leverage AI technology to create innovative
work. Leveraging technologies like LoRA can further improve efficiency and
quality. Users can adjust the output by incorporating image enhancement keywords
into the prompt. Let's explore new ways of expression while being aware of
advances in AI technology and the considerations that come with it! !
kei
22
18
STYLISTIC QR CODE WITH STABLE DIFFUSION
source: anfu.me (now you can easyly create QRcode with tensor.art inside
controlnet, next time i will create guide about that)Yesterday, I created this
image using Stable Diffusion and ControlNet, and shared
on Twitter and Instagram – an illustration that also
functions as a scannable QR code.The process of creating it was super fun, and
I’m quite satisfied with the outcome.In this post, I would like to share some
insights into my learning journey and the approaches I adopted to create this
image. Additionally, I want to take this opportunity to credit the remarkable
tools and models that made this project possible.Get into the Stable
DiffusionThis year has witnessed an explosion of mind-boggling AI technologies,
such as ChatGPT, DALL-E, Midjourney, Stable Diffusion, and
many more. As a former photographer also with some interest in design and art,
being able to generate images directly from imagination in minutes is undeniably
tempting.So I started by trying Midjourney, it’s super easy to use, very
expressive, and the quality is actually pretty good. It would honestly be my
recommendation for anyone who wants to get started with generative AI art.By the
way, Inès has also delved into it and become quite good at it now, go check her
work on her new Instagram account @a.i.nes.On my end, being a
programmer with strong preferences, I would naturally seek for greater control
over the process. This brought me to the realm of Stable Diffusion. I started
with this guide: Stable Diffusion LoRA Models: A Complete Guide. The
benefit of being late to the party is that there are already a lot of tools and
guides ready to use. Setting up the environment quite straightforward and
luckily my M1 Max’s GPU is supported.QR Code ImageA few weeks
ago, nhciao on reddit posted a series of artistic QR
codes created using Stable Diffusion and ControlNet. The concept
behind them fascinated me, and I defintely want to make one for my own. So I did
some research and managed to find the original article in Chinese: Use AI
to Generate Scannable Images. The author provided insights into their
motivations and the process of training the model, although they did not release
the model itself. On the other hand, they are building a service
called QRBTF.AI to generate such QR code, however it is not yet
available.Until another day I found an community model QR Pattern
Controlnet Model on CivitAI. I know I got to give it a try!SetupMy
goal was to generate a QR code image that directs to my website while elements
that reflect my interests. I ended up taking a slightly cypherpunk style with a
character representing myself :PDisclaimer: I’m certainly far from being an
expert in AI or related fields. In this post, I’m simply sharing what I’ve
learned and the process I followed. My understanding may not be entirely
accurate, and there are likely optimizations that could simplify the process. If
you have any suggestions or comments, please feel free to reach out using the
links at the bottom of the page. Thank you!1. Setup EnvironmentI pretty much
follows Stable Diffusion LoRA Models: A Complete Guide to install the
web ui AUTOMATIC1111/stable-diffusion-webui, download models you are
interested in from CivitAI, etc. As a side note, I found that the user
experience of the web ui is not super friendly, some of them I guess are a bit
architectural issues that might not be easy to improve, but luckily I found a
pretty nice theme canisminor1990/sd-webui-kitchen-theme that improves
a bunch of small things.In order to use ControlNet, you will also need to
install the Mikubill/sd-webui-controlnet extension for the web ui.Then
you can download the QR Pattern Controlnet Model, putt the two files
(.safetensors and .yaml)
under stable-diffusion-webui/models/ControlNet folder, and restart the
web ui.2. Create a QR CodeThere are hundreds of QR Code generators full of adds
or paid services, and we certainly don’t need those fanciness – because we are
going to make it much more fancier 😝!So I end up found the QR Code
Generator Library, a playground of an open source QR Code generator. It’s simple
but exactly what I need! It’s better to use medium error correction level or
above to make it more easy recognizable later. Small tip that you can try with
different Mask pattern to find a better color destribution that fits
your design.3. Text to ImageAs the regular Text2Image workflow, we need to
provide some prompts for the AI to generate the image from. Here is the prompts
I used:Prompts(one male engineer), medium curly hair, from side, (mechanics),
circuit board, steampunk, machine, studio, table, science fiction, high
contrast, high key, cinematic light, (masterpiece, top quality, best quality,
official art, beautiful and aesthetic:1.3), extreme detailed, highest detailed,
(ultra-detailed)Negative Prompts(worst quality, low quality:2), overexposure,
watermark, text, easynegative, ugly, (blurry:2), bad_prompt,bad-artist, bad
hand, ng_deepnegative_v1_75tThen we need to go the ControlNet section, and
upload the QR code image we generated earlier. And configure the parameters as
suggested in the model homepage.Then you can start to generate a few images and
see if it met your expectations. You will also need to check if the generated
image is scannable, if not, you can tweak the Start controling
step and End controling step to find a good balance between
stylization and QRCode-likeness.4. I’m feeling lucky!After finding a set of
parameters that I am happy with, I will increase the Batch Count to
around 100 and let the model generate variations randomly. Later I can go
through them and pick one with the best conposition and details for further
refinement. This can take a lot of time, and also a lot of resources from your
processors. So I usually start it before going to bed and leave it
overnight.Here are some examples of the generated variations (not all of them
are scannable):From approximately one hundred variations, I ultimately chose the
following image as the starting point:It gets pretty interesting composition,
while being less obvious as a QR code. So I decided to proceed with it and add
add a bit more details. (You can compare it with the final result to see the
changes I made.)5. Refining DetailsUpdate: I recently built a toolkit to help
with this process, check my new blog post 👉 Refine AI Generated QR
Code for more details.The generated images from the model are not perfect
in every detail. For instance, you may have noticed that the hand and face
appear slightly distorted, and the three anchor boxes in the corner are less
visually appealing. We can use the inpaint feature to tell the model
to redraw some parts of the image (it would better if you keep the same or
similiar prompts as the original generation).Inpainting typically requires a
similar amount of time as generating a text-to-image, and it involves either
luck or patience. Often, I utilize Photoshop to "borrow" some parts from
previously generated images and utilize the spot healing brush tool to clean up
glitches and artifacts. My Photoshop layers would looks like this:After making
these adjustments, I’ll send the combined image back for inpainting again to
ensure a more seamless blend. Or to search for some other components that I
didn’t found in other images.Specifically on the QR Code, in some cases
ControlNet may not have enough prioritize, causing the prompts to take over and
result in certain parts of the QR Code not matching. To address this, I would
overlay the original QR Code image onto the generated image (as shown in the
left image below), identify any mismatches, and use a brush tool to paint those
parts with the correct colors (as shown in the right image below).I then export
the marked image for inpainting once again, adjusting the Denoising
strength to approximately 0.7. This would ensures that the model overrides
our marks while still respecting the color to some degree.Ultimately, I iterate
through this process multiple times until I am satisfied with every detail.6.
UpscalingThe recommended generation size is 920x920 pixels. However, the model
does not always generate highly detailed results at the pixel level. As a
result, details like the face and hands can appear blurry when they are too
small. To overcome this, we can upscale the image, providing the model with more
pixels to work with. The SD Upscaler script in
the img2img tab is particularly effective for this purpose. You can
refer to the guide Upscale Images With Stable Diffusion for more
information.7. Post-processingLastly, I use Photoshop and Lightroom for subtle
color grading and post-processing, and we are done!The one I end up with not
very good error tolerance, you might need to try a few times or use a more
forgiving scanner to get it scanned :PAnd using the similarly process, I made
another one for Inès:ConclusionCreating this image took me a full day, with a
total of 10 hours of learning, generating, and refining. The process was
incredibly enjoyable for me, and I am thrilled with the end result! I hope this
post can offer you some fundamental concepts or inspire you to embark on your
own creative journey. There is undoubtedly much more to explore in this field,
and I eager to see what’s coming next!Join my Discord Server and let’s
explore more together!If you want to learn more about the refining process, go
check my new blog post: Refining AI Generated QR Code.ReferencesHere are
the list of resources for easier reference.ConceptsStable
DiffusionControlNetToolsHardwares & Softwares I am
using.AUTOMATIC1111/stable-diffusion-webui - Web UI for Stable
Diffusioncanisminor1990/sd-webui-kitchen-theme - Nice UI
enhancementMikubill/sd-webui-controlnet - ControlNet extension for the
webuiQR Code Generator Library - QR code generator that is ad-free and
customisableAdobe Photoshop - The tool I used to blend the QR code and the
illustrationModelsControl Net Models for QR Code (you can pick one of them)QR
Pattern Controlnet ModelControlnet QR Code MonsterIoC Lab Control NetCheckpoint
Model (you can use any checkpoints you like)Ghostmix Checkpoint - A very
high quality checkpoint I use. You can use any other checkpoints you
likeTutorialsStable Diffusion LoRA Models: A Complete Guide - The one I
used to get started(Chinese) Use AI to genereate scannable images -
Unfortunately the article is in Chinese and I didn’t find a English version of
it.Upscale Images With Stable Diffusion - Enlarge the image while adding
more details
Philosophy.AI
THE MARVEL OF TANJORE TEMPLE: A TIMELESS TREASURE
IntroductionThe Tanjore Temple, also known as Brihadeeswarar Temple, is a
striking example of India’s architectural grandeur and rich cultural heritage.
Nestled in the historic town of Thanjavur in Tamil Nadu, this UNESCO World
Heritage Site draws thousands of visitors each year, eager to marvel at its
towering vimana (temple tower), intricate carvings, and vibrant
history.Historical BackgroundBuilt by the great Chola emperor Raja Raja Chola I
in the 11th century, the Tanjore Temple stands as a testament to the ingenuity
and vision of ancient Indian architects and artisans. Completed in 1010 AD, it
celebrated its millennium in 2010, marking a thousand years of awe-inspiring
presence.Architectural SplendorThe VimanaThe most striking feature of the
Tanjore Temple is its colossal vimana, which rises to a height of 66 meters.
This towering structure is crowned with a massive dome, made from a single piece
of granite weighing approximately 80 tons. This engineering marvel leaves
historians and architects alike in awe, given the lack of modern machinery
during its construction.The SanctumAt the heart of the temple lies the sanctum
sanctorum, housing a massive Shiva lingam. The inner walls of the sanctum are
adorned with exquisite frescoes and murals, depicting various mythological
scenes and showcasing the artistic brilliance of the Chola period.Intricate
CarvingsEvery inch of the Tanjore Temple is a canvas of intricate carvings. From
the elaborate depictions of deities and mythological narratives on the walls to
the ornate pillars and ceilings, the temple is a visual feast. These carvings
not only serve as decorative elements but also provide a glimpse into the
socio-cultural milieu of the Chola dynasty.Cultural SignificanceReligious
ImportanceThe Tanjore Temple is dedicated to Lord Shiva and holds immense
religious significance for Hindus. It is one of the largest temples in India and
serves as a major pilgrimage site, especially during festivals like Maha
Shivaratri. Devotees from across the country flock to the temple to seek
blessings and participate in the vibrant festivities.Artistic HeritageThe temple
is a treasure trove of Chola art and architecture. The frescoes and murals, in
particular, offer invaluable insights into the artistic and cultural landscape
of the period. The depictions of dance forms, musical instruments, and attire
provide a vivid picture of the era’s cultural richness.Visiting Tanjore
TempleBest Time to VisitThe ideal time to visit Tanjore Temple is between
October and March when the weather is pleasant. The temple complex is open from
early morning till evening, allowing visitors ample time to explore and soak in
its magnificence.How to ReachThanjavur is well-connected by road, rail, and air.
The nearest airport is Tiruchirappalli International Airport, about 60
kilometers away. Thanjavur Junction is the nearest railway station, with regular
trains from major cities like Chennai, Bangalore, and Coimbatore. Buses and
taxis are also readily available for local transportation.AccommodationThanjavur
offers a range of accommodation options, from budget hotels to luxury resorts,
catering to the diverse needs of travelers. Staying in the town allows visitors
to explore not just the temple, but also other nearby attractions like the
Thanjavur Royal Palace and the Saraswathi Mahal Library.ConclusionThe Tanjore
Temple is more than just an architectural marvel; it is a living testament to
India’s rich cultural and religious heritage. Its towering vimana, intricate
carvings, and historical significance make it a must-visit destination for
history enthusiasts, art lovers, and spiritual seekers alike. Plan your visit to
this timeless treasure and immerse yourself in the grandeur of the Chola
dynasty.
Baby
4
[GUIDE] MAKE YOUR OWN LORAS, EASY AND FREE
This article helped me to create my first Lora and upload it to Tensor.art,
although Tensor.art has its own Lora Train , this article helps to understand
how to create Lora well.🏭 PreambleEven if you don't know where to start or
don't have a powerful computer, I can guide you to making your first Lora and
more!In this guide we'll be using resources from my GitHub page. If you're new
to Stable Diffusion I also have a full guide to generate your own images and
learn useful tools.I'm making this guide for the joy it brings me to share my
hobbies and the work I put into them. I believe all information should be free
for everyone, including image generation software. However I do not support you
if you want to use AI to trick people, scam people, or break the law. I just do
it for fun.Also here's a page where I collect Hololive loras.📃What you needAn
internet connection. You can even do this from your phone if you want to (as
long as you can prevent the tab from closing).Knowledge about what Loras
are and how to use them.Patience. I'll try to explain these new concepts in an
easy way. Just try to read carefully, use critical thinking, and don't give up
if you encounter errors.🎴Making a Lorat has a reputation for being difficult.
So many options and nobody explains what any of them do. Well, I've streamlined
the process such that anyone can make their own Lora starting from
nothing in under an hour. All while keeping some advanced settings you can use
later on.You could of course train a Lora in your own computer, granted
that you have an Nvidia graphics card with 6 GB of VRAM or more. We won't be
doing that in this guide though, we'll be using Google Colab, which lets you
borrow Google's powerful computers and graphics cards for free for a few hours a
day (some say it's 20 hours a week). You can also pay $10 to get up to 50 extra
hours, but you don't have to. We'll also be using a little bit of Google Drive
storage.This guide focuses on anime, but it also works for photorealism. However
I won't help you if you want to copy real people's faces without their
consent.🎡 Types of LoraAs you may know, a Lora can be trained and used for:A
character or personAn artstyleA poseA piece of clothingetcHowever there are also
different types of Lora now:LoRA: The classic, works well for most cases.LoCon:
Has more layers which learn more aspects of the training data. Very good for
artstyles.LoHa, LoKR, (IA)^3: These use novel mathematical algorithms
to process the training data. I won't cover them as I don't think they're very
useful.📊 First Half: Making a DatasetThis is the longest and most important
part of making a Lora. A dataset is (for us) a collection
of images and their descriptions, where each pair has the same
filename (eg. "1.png" and "1.txt"), and they all have something in common which
you want the AI to learn. The quality of your dataset is essential: You want
your images to have at least 2 examples of: poses, angles, backgrounds, clothes,
etc. If all your images are face close-ups for example, your Lora will have a
hard time generating full body shots (but it's still possible!), unless you add
a couple examples of those. As you add more variety, the concept will be better
understood, allowing the AI to create new things that weren't in the training
data. For example a character may then be generated in new poses and in
different clothes. You can train a mediocre Lora with a bare minimum of 5
images, but I recommend 20 or more, and up to 1000.As for the descriptions, for
general images you want short and detailed sentences such as "full body
photograph of a woman with blonde hair sitting on a chair". For anime you'll
need to use booru tags (1girl, blonde hair, full body, on chair, etc.). Let me
describe how tags work in your dataset: You need to be detailed, as the Lora
will reference what's going on by using the base model you use for training. If
there is something in all your images that you don't include in your
tags, it will become part of your Lora. This is because the Lora absorbs
details that can't be described easily with words, such as faces and
accessories. Thanks to this you can let those details be absorbed into
an activation tag, which is a unique word or phrase that goes at the start
of every text file, and which makes your Lora easy to prompt.You may gather your
images online, and describe them manually. But fortunately, you can do most of
this process automatically using my new 📊 dataset maker colab.Here
are the steps:1️⃣ Setup: This will connect to your Google Drive. Choose a simple
name for your project, and a folder structure you like, then run the
cell by clicking the floating play button to the left side. It will ask for
permission, accept to continue the guide.If you already have images to train
with, upload them to your Google Drive's "lora_training/datasets/project_name"
(old) or "Loras/project_name/dataset" (new) folder, and you may choose to skip
step 2.2️⃣ Scrape images from Gelbooru: In the case of anime, we will use the
vast collection of available art to train our Lora. Gelbooru sorts images
through thousands of booru tags describing everything about an image,
which is also how we'll tag our images later. Follow the instructions on the
colab for this step; basically, you want to request images that contain specific
tags that represent your concept, character or style. When you run this cell it
will show you the results and ask if you want to continue. Once you're
satisfied, type yes and wait a minute for your images to download.3️⃣ Curate
your images: There are a lot of duplicate images on Gelbooru, so we'll be using
the FiftyOne AI to detect them and mark them for deletion. This will take a
couple minutes once you run this cell. They won't be deleted yet though:
eventually an interactive area will appear below the cell, displaying all your
images in a grid. Here you can select the ones you don't like and mark them for
deletion too. Follow the instructions in the colab. It is beneficial to delete
low quality or unrelated images that slipped their way in. When you're finished,
send Enter in the text box above the interactive area to apply your changes.4️⃣
Tag your images: We'll be using the WD 1.4 tagger AI to assign anime tags that
describe your images, or the BLIP AI to create captions for photorealistic/other
images. This takes a few minutes. I've found good results with a tagging
threshold of 0.35 to 0.5. After running this cell it'll show you the most common
tags in your dataset which will be useful for the next step.5️⃣ Curate your
tags: This step for anime tags is optional, but very useful. Here you can assign
the activation tag (also called trigger word) for your Lora. If you're training
a style, you probably don't want any activation tag so that the Lora is always
in effect. If you're training a character, I myself tend to delete (prune)
common tags that are intrinsic to the character, such as body features and
hair/eye color. This causes them to get absorbed by the activation
tag. Pruning makes prompting with your Lora easier, but also less flexible.
Some people like to prune all clothing to have a single tag that defines a
character outfit; I do not recommend this, as too much pruning will affect some
details. A more flexible approach is to merge tags, for example if we
have some redundant tags like "striped shirt, vertical stripes, vertical-striped
shirt" we can replace all of them with just "striped shirt". You can run this
step as many times as you want.6️⃣ Ready: Your dataset is stored in your Google
Drive. You can do anything you want with it, but we'll be going straight to the
second half of this tutorial to start training your Lora!⭐ Second Half: Settings
and TrainingThis is the tricky part. To train your Lora we'll use
my ⭐ Lora trainer colab. It consists of a single cell with all the
settings you need. Many of these settings don't need to be changed. However,
this guide and the colab will explain what each of them do, such that you can
play with them in the future.Here are the settings:▶️ Setup: Enter the same
project name you used in the first half of the guide and it'll work
automatically. Here you can also change the base model for training. There are 2
recommended default ones, but alternatively you can copy a direct download link
to a custom model of your choice. Make sure to pick the same folder structure
you used in the dataset maker.▶️ Processing: Here are the settings that
change how your dataset will be processed.The resolution should stay at 512 this
time, which is normal for Stable Diffusion. Increasing it makes training much
slower, but it does help with finer details.flip_aug is a trick to learn
more evenly, as if you had more images, but makes the AI confuse left and right,
so it's your choice.shuffle_tags should always stay active if you use anime
tags, as it makes prompting more flexible and reduces
bias.activation_tags is important, set it to 1 if you added one during the
dataset part of the guide. This is also called keep_tokens.▶️ Steps:
We need to pay attention here. There are 4 variables at play: your number of
images, the number of repeats, the number of epochs, and the batch size. These
result in your total steps.You can choose to set the total epochs or the total
steps, we will look at some examples in a moment. Too few steps will undercook
the Lora and make it useless, and too many will overcook it and distort your
images. This is why we choose to save the Lora every few epochs, so we can
compare and decide later. For this reason, I recommend few repeats and many
epochs.There are many ways to train a Lora. The method I personally follow
focuses on balancing the epochs, such that I can choose between 10 and 20 epochs
depending on if I want a fast cook or a slow simmer (which is better for
styles). Also, I have found that more images generally need more steps to
stabilize. Thanks to the new min_snr_gamma option, Loras take less
epochs to train. Here are some healthy values for you to try:10 images × 10
repeats × 20 epochs ÷ 2 batch size = 1000 steps20 images × 10 repeats × 10
epochs ÷ 2 batch size = 1000 steps100 images × 3 repeats × 10 epochs ÷ 2 batch
size = 1500 steps400 images × 1 repeat × 10 epochs ÷ 2 batch size = 2000
steps1000 images × 1 repeat × 10 epochs ÷ 3 batch size = 3300
steps▶️ Learning: The most important settings. However, you don't need to
change any of these your first time. In any case:The unet learning
rate dictates how fast your Lora will absorb information. Like with steps, if
it's too small the Lora won't do anything, and if it's too large the Lora will
deepfry every image you generate. There's a flexible range of working values,
specially since you can change the intensity of the lora in prompts. Assuming
you set dim between 8 and 32 (see below), I recommend 5e-4 unet for almost all
situations. If you want a slow simmer, 1e-4 or 2e-4 will be better. Note that
these are in scientific notation: 1e-4 = 0.0001The text
encoder learning rate is less important, specially for styles. It helps
learn tags better, but it'll still learn them without it. It is generally
accepted that it should be either half or a fifth of the unet, good values
include 1e-4 or 5e-5. Use google as a calculator if you find these small values
confusing.The scheduler guides the learning rate over time. This is
not critical, but still helps. I always use cosine with 3 restarts, which I
personally feel like it keeps the Lora "fresh". Feel free to experiment
with cosine, constant, and constant with warmup. Can't go wrong with
those. There's also the warmup ratio which should help the training start
efficiently, and the default of 5% works well.▶️ Structure: Here is where
you choose the type of Lora from the 2 I mentioned in the beginning. Also,
the dim/alpha mean the size of your Lora. Larger does not usually mean
better. I personally use 16/8 which works great for characters and is
only 18 MB.▶️ Ready: Now you're ready to run this big cell which will train
your Lora. It will take 5 minutes to boot up, after which it starts performing
the training steps. In total it should be less than an hour, and it will put the
results in your Google Drive.🏁 Third Half: TestingYou read that right. I lied!
😈 There are 3 parts to this guide.When you finish your Lora you still have to
test it to know if it's good. Go to your Google Drive inside the
/lora_training/outputs/ folder, and download everything inside your project
name's folder. Each of these is a different Lora saved at different epochs of
your training. Each of them has a number like 01, 02, 03, etc.Here's a simple
workflow to find the optimal way to use your Lora:Put your final Lora in your
prompt with a weight of 0.7 or 1, and include some of the most common tags you
saw during the tagging part of the guide. You should see a clear effect,
hopefully similar to what you tried to train. Adjust your prompt until you're
either satisfied or can't seem to get it any better.Use the X/Y/Z
plot to compare different epochs. This is a builtin feature in webui. Go to
the bottom of the generation parameters and select the script. Put the Lora of
the first epoch in your prompt (like "<lora:projectname-01:0.7>"), and on
the script's X value write something like "-01, -02, -03", etc. Make sure the X
value is in "Prompt S/R" mode. These will perform replacements in your prompt,
causing it to go through the different numbers of your lora so you can compare
their quality. You can first compare every 2nd or every 5th epoch if you want to
save time. You should ideally do batches of images to compare more fairly.Once
you've found your favorite epoch, try to find the best weight. Do an X/Y/Z plot
again, this time with an X value like ":0.5, :0.6, :0.7, :0.8, :0.9, :1". It
will replace a small part of your prompt to go over different lora weights.
Again it's better to compare in batches. You're looking for a weight that
results in the best detail but without distorting the image. If you want you can
do steps 2 and 3 together as X/Y, it'll take longer but be more thorough.If you
found results you liked, congratulations! Keep testing different situations,
angles, clothes, etc, to see if your Lora can be creative and do things that
weren't in the training data.source: civitai/holostrawberry
Philosophy.AI
9
AREA COMPOSITION
Get more specific generations each time!Have you ever heard of Area
composition?Area composition is a technique where you can specify and set custom
locations for every element you want to generate. In order to create this simple
but effective workflow all you need is:NodesLoad checkpoint: here you select
your desired model.Load LoRA: here you select your desired style with any LoRA
(this one is optional).Clip Set Last Layer: this node works as your Clip Skip
(set it to -2 for better results).Clip text encode: here is where your lovely
prompt will be. you will need to have two of these because one will work as your
positives and the other as negatives.Ksampler: this node is important because it
is like the brain of the main process. here is where your prompt and image size
gets read it and transformed into an image. here you can use the sampler and
scheduler you like the most (set the denoise strength to 1.0 for better
results).Empty latent image: as important as the ksampler, the empty latent
image node is where you decide the specific size of your initial image (can be
portrait or landscape).Clip text encode: wait, again? yes. just as the last
ones, this node will focus on the specific element you want to generate. it is
important to keep it simple and only consider the main element to represent (you
can have as many nodes for every element you want to generate. keep in mind that
these nodes will only work as positives. for this example i will only use 2 clip
text encode nodes).MultiArea conditioning: ok so, this is the most important
node of the process. here, for explaining purposes, i will call each one of my
positives as conditionings.conditioning 0 will be my first positive (the one i
made on step 4).conditioning 1 and 2 will be my second and third positive (the
one i made on step 7).it is very important to know that for each conditioning
you will have to set a desired size for each element. in this example
conditioning 0 i set it to 512x718 because is the base prompt and i want all of
the canvas to represent it. for conditioning 1, which is my main character, i
set it to 384x576 on lower part of the center of the canvas. and for
conditioning 2, which is the background /setting, i set it to 512x718 because i
want all of the canvas to work as the background. (you may notice that for each
conditioning, while setting it's position, a different color will show on the
multiarea conditioning node. keep calm, these colors will work just as a visual
representation for the position of each element).also important, as you have
figured it out, this node works just as a super detailed composition
instruction, therefore, this multiarea conditioning node will work as your
positive, so be sure to connect it as positive in your ksampler.Upscale latent:
until this part of the process we have only created the base image, which means
it is time to upscale it. to do so, i have used the upscale latent node. it not
only upscale the image to a desired size but also introduces more detail in the
process.Ksampler: yes, again. this second ksampler will work along the upscale
latent node in order to refine details, so using the same configuration as your
first one (step 5) is a good idea. (lowering the denoise strength on this second
ksampler will help in avoiding drastic changes. for this example i set it to
0.5).VAE encode: the variational autoencoder or vae node is important because
this node will transform the noise and commands into your beautiful
masterpiece.Preview/Save image: lastly, what is left to add is the preview/save
image node. (this one does not need an explanation, right?).And there you go,
you will now be able to generate more personalized images.Intended image to
create: cyborg girl inside abandoned building.Do not forget to set this article
as favorite if you found it useful.Happy generations!
vstigia
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