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DALL-E 3 Double Exposure Images Using ChatGPT's Custom Instructions. 🔖 Bookmark and Repost! If you find this useful, please share it with others! You can now easily create double exposure images by using the syntax Color::subject1::subject2 You can either input this directly into DALL-E 3 before generating images or... incorporate it into your custom instructions. This command will automatically use the fixed prompt to generate 4 different images, each with a unique seed value. Paste this under ChatGPT's Custom Instructions or DALL-3 before starting conversation. { DE": { "Instruction": "Using will only use generic prompt and update place holder varaibles and nothing else' Genric Prompt: "Construct a [Color] double exposure image where [Subject #1] is intricately superimposed within the confines of [Subject #2], all set against a stark white background.' Create 4 images with different seeds without modifying the generic prompt." You will first create two images. Within the same request, you will generate two more without asking for any input from the user. In general, you will always create 4 images. Your response should be something like this: 'Here are the first two images along with their seed details.' You must always provide the seed number details for that image after it's rendered. Then 'I'll generate the next two images.' And finally 'Here are the remaining two images along with their seed details.' You will always use wide aspect ratio and You must always provide the seed number. When command is activated display full instruction and also confirm that you will only use genric prompt and nothing else and update only varaiable", "CommandFormat": "color::subject1::subject2", "Response": { "Initial": "Creating images based on the provided subjects...", "AfterFirstSet": "Here are the first two images along with their prompt and seed details.", "AfterSecondSet": "Here are the remaining two images along with their prompt and seed details." }, "ActivationCommand": "/activate DE" } } Example: To activate type: /activate DE Notes Start giving prompt like this blue::mountain::wolf red::city skyline::dancer green::forest::lion If you found this post helpful, don't forget to hit the like and follow buttons, and share it with others who might find it useful.show more

AshutoshShrivastava

81,042 subscribers

34,305 просмотров • 2 лет назад •via X (Twitter)

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Комментарии: 9

Фото профиля Jürgen Kättnis

Jürgen Kättnis2 лет назад

Do you know that this works with @bing as well I created the images below in MS Edge Browser using @bing I just used your wolf example

Фото профиля opensourceCM

opensourceCM1 год назад

What’s the cost of mistakes in your contracts? If you work with contracts day-to-day, it’s time to automate. Track every detail, streamline workflows ... ✨ Make managing contracts as easy as a few clicks. Visit our new website & book your demo today!

Фото профиля AshutoshShrivastava

AshutoshShrivastava2 лет назад

@gggzhangyufei Beautiful

Фото профиля Jacobien

Jacobien2 лет назад

Thanks! This is really cool.

Фото профиля AshutoshShrivastava

AshutoshShrivastava2 лет назад

Glad you liked it..

Фото профиля madpencil_

madpencil_2 лет назад

We don't need so much compilation Ashu , Just add "double Exposure ( Your Prompt) white background" is simple.

Фото профиля AshutoshShrivastava

AshutoshShrivastava2 лет назад

This is good.. But my goal is to remove repetitive prompt if I man working on 100 double exposure I it will be easy but thanks buddy as always 😊

Фото профиля erna Krumbach

erna Krumbach2 лет назад

How beneficial . Thank you for sharing . Pro tips ! 🙏

Фото профиля AshutoshShrivastava

AshutoshShrivastava2 лет назад

@ernkrum Thanks Erna 😊 enjoy

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The Peel

51,632 просмотров • 6 месяцев назад

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As I promised yesterday, I'll briefly explain LoRA training and share a workflow I made so you can do it quickly. First, let me answer a very common question: 'Why train LoRAs when we have such advanced models?' Even though we have incredibly advanced models now (like NBP), we still can't always get them to do specific things we want. Simplest example: the spritesheet LoRA I made the other day. I generated 1000 images with Nano Banana and only 100 were what I wanted. The LoRA I trained using those 100 images gives me nearly 100% consistent results. Second point is cost and speed. With LoRA, we can cut costs by 4-5x. And while doing that, we're generating 4-5x faster. How many images do you need for a good LoRA? This depends on your LoRA's complexity. For example, when I training the spritesheet LoRA, even though I used 100 images, I didn't include buildings in the training data, so this LoRA doesn't work for buildings. So think about your LoRA's use cases and add examples for as many use cases as possible to improve quality. What are paired images and how to train LoRAs for image-editing? When training LoRAs for image editing on fal, we call each edit example paired images - one with _start suffix, one with _end suffix. For example, if you're training a background remove LoRA, the unedited original photo will be your '_start' image. The image with background removed will be the '_end' image. Simply put: images we want to edit or use as reference get _start, target images we want to achieve get '_end'. Important: save both images with the same name. Like image332_start.jpg and image332_end.jpg. This way the system knows which images pair together. What about training LoRAs for models with multiple image inputs? Same logic. We still use _start and _end suffixes, but with one difference. Since there are multiple input images, we can number them: _start, _start1, _start2. Example: start images, 1st image = Woman portrait (image35_start.jpg) 2nd image = Glasses photo (image35_start1.jpg) 3rd image = Hat photo (image35_start2.jpg) Output image = portrait of woman wearing glasses and hat (image35_end.jpg) Can we do more detailed captioning? Yes. Similarly, you can improve training quality by creating a txt file for each set with the caption inside. Example: create image35.txt and write: 'Recreate the image by putting the glasses from the second image and the hat from the third image on the woman in the first image.' What are Steps? How many should I use? What's Learning Rate? Steps determines how many times the model sees and processes your training data (your images). Each step, the model learns a bit more. But as steps increase, so does the risk of overfitting. So there's no real default. But for a simpler LoRA with 20 paired images, 1000 steps is ideal. Here's a metaphor for the Steps and Learning Rate relationship: Imagine you have a balloon. Our goal is to inflate it to the optimal size. Steps = How many times we blow into the balloon Learning rate = How hard we blow each time If we blow too softly, we need to blow many more times. If we blow too hard, we risk popping it quickly and can't reach optimal size. Of course training won't explode, but it won't work as intended because it wasn't trained optimally. Training's done, now what? Once training's complete, you'll have a safetensors file. Every model you train on fal has a LoRA inference endpoint. In that inference, add your safetensors file link to the LoRA url input, and you can use your LoRA. Thanks for the read! The workflow in the video: If I forgot anything, let me know in the replies.

ilker

15,192 просмотров • 7 месяцев назад

I work within the British media. Let me tell all attendees of this event something… There are scores of my fellow media folk desperate for you to show violence and/or disorder. They are desperate to fill their front pages & their headlines with ‘racist’, ‘far-right’ etc. Even the smallest incidents will be amplified & will become the defining images. Agitators will be among you. On top of this, thousands of counter protesters will be close by, marching for their own cause (as is their right) and also to show rejection of yours. And on top of all this too, police will likely be heavy handed with you. If you’re attending today. You will be subject to provocation. Do NOT rise to it. If you do, you will shame yourselves & your cause. You will also give politicians even more excuses to clamp down on your free speech. Stay out of trouble and if you see any trouble near you, help calm it down. Don’t give outlets the headlines & images they are desperately craving...

I work within the British media. Let me tell all attendees of this event something… There are scores of my fellow media folk desperate for you to show violence and/or disorder. They are desperate to fill their front pages & their headlines with ‘racist’, ‘far-right’ etc. Even the smallest incidents will be amplified & will become the defining images. Agitators will be among you. On top of this, thousands of counter protesters will be close by, marching for their own cause (as is their right) and also to show rejection of yours. And on top of all this too, police will likely be heavy handed with you. If you’re attending today. You will be subject to provocation. Do NOT rise to it. If you do, you will shame yourselves & your cause. You will also give politicians even more excuses to clamp down on your free speech. Stay out of trouble and if you see any trouble near you, help calm it down. Don’t give outlets the headlines & images they are desperately craving...

Michelle Dewberry

413,321 просмотров • 3 месяцев назад

Open your ChatGPT and click on your profile photo in the bottom-left corner. Select "Customize ChatGPT." You will see two boxes for custom instructions. In the first box, tell ChatGPT a bit about yourself. For example: I am an academic researcher with a PhD in [your field]. For the second box, use the following instruction: You will respond like my research assistant. You will never mention that you are an AI model. I already know that. Repeating it is a waste of time and resources. If I ask you a question, you will never use any meta remarks in your answers. For example, if I ask you proofread a paragraph, you will not say anything like "Here is the proofread version of your paragraph." You will simply give me the proofread version. You will keep your answers precise and will not use more words than necessary. You will decline all requests asking for citations and references to academic articles or any other sources. You will follow these instructions strictly and will never deviate from them.

Mushtaq Bilal, PhD

50,689 просмотров • 2 лет назад

📖THE SEEDANCE 2.0 PROMPT FORMAT THAT NOBODY IS USING BUT EVERYONE SHOULD Most people use Seedance 2.0 wrong. They type a normal prompt and wonder why the result looks generic. The difference between these two visuals isn't a better idea — it's a better prompt format. Normal prompt gives you a good result. JSON prompt gives you a cinematic one. Here's why it works: A normal prompt describes what you want. A JSON prompt tells Seedance exactly how to build the scene — camera movement, lighting angle, atmosphere, texture, depth, timing. You're not asking for a video. You're engineering one. The crystal dolphin jumping above an ancient courtyard with floating jellyfish — same concept, two completely different outputs. The JSON version has golden hour, dramatic clouds, volumetric light, and a sense of scale the normal prompt never reached. The glowing lotus fountain — same story. Normal prompt gives you a clean render. JSON gives you a full cinematic frame with layered depth, warm lantern light, and a composition that looks like it belongs in a feature film. The workflow that produces this: • Step 1 — Write your scene idea in plain language first •Step 2 — Ask Claude to convert it into a structured JSON prompt with fields for: subject, environment, camera, lighting, mood, motion, color grade • Step 3 — Paste the JSON into Seedance 2.0 • Step 4 — Take the best clip into Premiere Pro for final color correction and sound design The tools are free or cheap. The knowledge of how to prompt is what separates the results. This is the gap most creators never close. 📥 BACK TOMORROW. There will be new life hacks 🔖 The full guide is pinned below. Save it before you need it.

📖THE SEEDANCE 2.0 PROMPT FORMAT THAT NOBODY IS USING BUT EVERYONE SHOULD Most people use Seedance 2.0 wrong. They type a normal prompt and wonder why the result looks generic. The difference between these two visuals isn't a better idea — it's a better prompt format. Normal prompt gives you a good result. JSON prompt gives you a cinematic one. Here's why it works: A normal prompt describes what you want. A JSON prompt tells Seedance exactly how to build the scene — camera movement, lighting angle, atmosphere, texture, depth, timing. You're not asking for a video. You're engineering one. The crystal dolphin jumping above an ancient courtyard with floating jellyfish — same concept, two completely different outputs. The JSON version has golden hour, dramatic clouds, volumetric light, and a sense of scale the normal prompt never reached. The glowing lotus fountain — same story. Normal prompt gives you a clean render. JSON gives you a full cinematic frame with layered depth, warm lantern light, and a composition that looks like it belongs in a feature film. The workflow that produces this: • Step 1 — Write your scene idea in plain language first •Step 2 — Ask Claude to convert it into a structured JSON prompt with fields for: subject, environment, camera, lighting, mood, motion, color grade • Step 3 — Paste the JSON into Seedance 2.0 • Step 4 — Take the best clip into Premiere Pro for final color correction and sound design The tools are free or cheap. The knowledge of how to prompt is what separates the results. This is the gap most creators never close. 📥 BACK TOMORROW. There will be new life hacks 🔖 The full guide is pinned below. Save it before you need it.

Zentrix⌚️

136,558 просмотров • 2 месяцев назад