Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

You can basically generate your own custom visual stories using Gemini with just a single prompt 'This is a fictional movie set in a fantasy world similar to The Lord of the Rings. Use this as a starting point and imagine the next sequence of scenes. Create a series... of separate images, each depicting a distinct moment in the story, presented in a sequential order like a storyboard. Include quotes for each image to narrate the events happening within it. Ensure the flow between images is consistent and logical, with each one styled like a cinematic movie shot that advances the narrative. Maintain the same format and visual style across all images to keep them cohesive'show more

Cristian Peñas ░░░░░░░░

10,739 subscribers

38,500 views • 1 year ago •via X (Twitter)

Science & Technology Arts

Anya Rossi• Live Now

Private livecam show

11 Comments

Akisama - e/acc1 year ago

which gemini model did you use ?

Cristian Peñas ░░░░░░░░1 year ago

Gemini 2.0 Flash Experimental

bebooq1 year ago

BebooQ 2.0: simply magic! The only AI-powered writing suite that helps you craft compelling novels with structured plotting and natural flow. Create fantasy, sci-fi, romance, and more with intelligent story development tools. Start free today.

Pseudonym 🦅1 year ago

Future of cinema in this. Also end of a lot of apps.

DiffLander 🗺️🤖1 year ago

OH MY GOD this is so much fun

Ai_irca1 year ago

Hi Cristian, thank you for sharing this! Awesome use case and inspiration. 🪻

kvick1 year ago

Wow, that's amazing

Metaverse | Eric1 year ago

Change the pictures to videos and add some voice interaction and it's an RPG game

Phil1 year ago

Fantastic, thanks.

Jaitan Martini1 year ago

Nice prompt

Risichad 🦾1 year ago

Waw !!! Truely amazing !

Related Videos

When creating ingredient images, try to create stylistically consistent images. If you use input images that are too disparate, the output may lead to a cut scene. For example, when generating a reference image, try using a prompt like: “Create the next scene in this storyboard, now show a roadside diner with the same look and feel as the original image.”

When creating ingredient images, try to create stylistically consistent images. If you use input images that are too disparate, the output may lead to a cut scene. For example, when generating a reference image, try using a prompt like: “Create the next scene in this storyboard, now show a roadside diner with the same look and feel as the original image.”

Google AI

23,526 views • 7 months ago

Here's a movie trailer from ChatGPT Images 2.0. Create a storyboard style image with Images 2.0, and use it in Seedream 2.0 as a reference. Enter a prompt to create a 15 second video from the image. The model will use the character and environments. Quick tutorial:

Here's a movie trailer from ChatGPT Images 2.0. Create a storyboard style image with Images 2.0, and use it in Seedream 2.0 as a reference. Enter a prompt to create a 15 second video from the image. The model will use the character and environments. Quick tutorial:

Jerrod Lew

15,723 views • 1 month ago

One prompt. Multiple gaming scenes 🎮 The Nano Banana agent can generate several images at once, using a reference image to keep the same person consistent across scenes. This example spans different gaming eras, but the same approach works for any theme or style. Images and videos, all in one place. Agent + prompt 👇

One prompt. Multiple gaming scenes 🎮 The Nano Banana agent can generate several images at once, using a reference image to keep the same person consistent across scenes. This example spans different gaming eras, but the same approach works for any theme or style. Images and videos, all in one place. Agent + prompt 👇

GLIF

10,882 views • 4 months ago

Collaborative Score Distillation for Consistent Visual Synthesis paper page: Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as multiple images (e.g., video), achieving consistency across a set of images is challenging. In this paper, we address this challenge with a novel method, Collaborative Score Distillation (CSD). CSD is based on the Stein Variational Gradient Descent (SVGD). Specifically, we propose to consider multiple samples as "particles" in the SVGD update and combine their score functions to distill generative priors over a set of images synchronously. Thus, CSD facilitates seamless integration of information across 2D images, leading to a consistent visual synthesis across multiple samples. We show the effectiveness of CSD in a variety of tasks, encompassing the visual editing of panorama images, videos, and 3D scenes. Our results underline the competency of CSD as a versatile method for enhancing inter-sample consistency, thereby broadening the applicability of text-to-image diffusion models.

Collaborative Score Distillation for Consistent Visual Synthesis paper page: Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as multiple images (e.g., video), achieving consistency across a set of images is challenging. In this paper, we address this challenge with a novel method, Collaborative Score Distillation (CSD). CSD is based on the Stein Variational Gradient Descent (SVGD). Specifically, we propose to consider multiple samples as "particles" in the SVGD update and combine their score functions to distill generative priors over a set of images synchronously. Thus, CSD facilitates seamless integration of information across 2D images, leading to a consistent visual synthesis across multiple samples. We show the effectiveness of CSD in a variety of tasks, encompassing the visual editing of panorama images, videos, and 3D scenes. Our results underline the competency of CSD as a versatile method for enhancing inter-sample consistency, thereby broadening the applicability of text-to-image diffusion models.

AK

33,500 views • 2 years ago

It’s Moodboard Monday, and this week we’re exploring a style we’re calling Jelly Pop, inspired by the textures of candy. A quick and easy way to maintain a consistent visual style across outputs is to repeat key descriptors in the prompt. In this case we used: translucent materials, bright candy-colored palettes, and strong direct lighting. As a result, the style remained visually consistent across very different subjects, like a handbag, a jelly burger, and a gummy bear jacket. You can use these techniques to create with the Jelly Pop style 👇(1/2)

It’s Moodboard Monday, and this week we’re exploring a style we’re calling Jelly Pop, inspired by the textures of candy. A quick and easy way to maintain a consistent visual style across outputs is to repeat key descriptors in the prompt. In this case we used: translucent materials, bright candy-colored palettes, and strong direct lighting. As a result, the style remained visually consistent across very different subjects, like a handbag, a jelly burger, and a gummy bear jacket. You can use these techniques to create with the Jelly Pop style 👇(1/2)

Stability AI

13,786 views • 11 months ago

Using a pasta machine to send a photo through creates pixelated, simplified versions of the image. This is a great way to illustrate how Convolutional Neural Networks (CNNs) work. Each pass through the machine represents a layer in a CNN, where images are processed step by step. In CNNs, each layer identifies specific features like edges, textures, or colors. Just as the pasta machine alters the photo with each pass, CNNs refine and simplify the image for better understanding. This step-by-step process allows CNNs to recognize complex images, making them useful in areas like image classification and object detection. It ain’t perfect but it’s a simple presentation.

Using a pasta machine to send a photo through creates pixelated, simplified versions of the image. This is a great way to illustrate how Convolutional Neural Networks (CNNs) work. Each pass through the machine represents a layer in a CNN, where images are processed step by step. In CNNs, each layer identifies specific features like edges, textures, or colors. Just as the pasta machine alters the photo with each pass, CNNs refine and simplify the image for better understanding. This step-by-step process allows CNNs to recognize complex images, making them useful in areas like image classification and object detection. It ain’t perfect but it’s a simple presentation.

Brian Roemmele

97,202 views • 1 year ago

Here's the secret behind Cinematic Video Overviews: We put Gemini in the director's chair. This means that Gemini decides the best format (tutorial vs. documentary etc), visual style, and visual capabilities to tell the story of your sources. It then critiques its own footage, refining the visuals and narrative to ensure a seamless, consistent final cut. The result? A bespoke video that converts even the most mundane sources into an engaging, immersive story.

Here's the secret behind Cinematic Video Overviews: We put Gemini in the director's chair. This means that Gemini decides the best format (tutorial vs. documentary etc), visual style, and visual capabilities to tell the story of your sources. It then critiques its own footage, refining the visuals and narrative to ensure a seamless, consistent final cut. The result? A bespoke video that converts even the most mundane sources into an engaging, immersive story.

NotebookLM

24,429,289 views • 3 months ago

Filmmaker Öner S. Biberkökü shares his "screenshot strategy" for building a full narrative from a single starting point during this In the Flow interview. Thanks Oner for the partnership! "At the end of the shot, you can take a screenshot, give it again [to Flow], and you now have a new image of that character in a different situation... So I think, with one image, you can stay in the flow, and make a story." Watch his breakdown:

Filmmaker Öner S. Biberkökü shares his "screenshot strategy" for building a full narrative from a single starting point during this In the Flow interview. Thanks Oner for the partnership! "At the end of the shot, you can take a screenshot, give it again [to Flow], and you now have a new image of that character in a different situation... So I think, with one image, you can stay in the flow, and make a story." Watch his breakdown:

Google Flow

34,772 views • 3 months ago

Within two years, you can create a full game like this from scratch in less than a week. In the meantime, here's the prompt to create the images for a game like this with Midjourney 👇🏼

Within two years, you can create a full game like this from scratch in less than a week. In the meantime, here's the prompt to create the images for a game like this with Midjourney 👇🏼

PJ Ace

354,001 views • 1 year ago

Sequels A photograph tells us the objective truth of what the photographer and the camera saw in that first moment of recognition. But besides delighting in the purity of the singular image photographs can also act like an alphabet of meanings when seen in relationships with other photographs. For me, this asset has been the secret thrill of the medium. I have always loved the way images can sit next to each other and give off sparks of fresh associations through sequencing. These pairings and runs of images produce a rhythm between them that brings to my mind what I call the 'third voice.' And what is The Third Voice? It is recognizing the fresh sensations that occur when a sequence of unrelated images produces a quickening sense of new meaning which isn't in any of the photographs on their own but is the product of their being seen together. Sequels is the outcome of many years of playing this 'game of seeing.' My hope is that it brings to all of you who see these images a new and playful sense of the range of photography's ability to make unexpected connections that carry new meaning.

Sequels A photograph tells us the objective truth of what the photographer and the camera saw in that first moment of recognition. But besides delighting in the purity of the singular image photographs can also act like an alphabet of meanings when seen in relationships with other photographs. For me, this asset has been the secret thrill of the medium. I have always loved the way images can sit next to each other and give off sparks of fresh associations through sequencing. These pairings and runs of images produce a rhythm between them that brings to my mind what I call the 'third voice.' And what is The Third Voice? It is recognizing the fresh sensations that occur when a sequence of unrelated images produces a quickening sense of new meaning which isn't in any of the photographs on their own but is the product of their being seen together. Sequels is the outcome of many years of playing this 'game of seeing.' My hope is that it brings to all of you who see these images a new and playful sense of the range of photography's ability to make unexpected connections that carry new meaning.

Joel Meyerowitz

128,933 views • 2 years ago

Create animated fight scenes with ChatGPT Images 2.0. Ask Images 2.0 to choreograph a scene and place it into a storyboard. Then use that image as a reference in Seedance 2.0 to bring it to life. It follows the storyboard sheet really well. Some examples:

Create animated fight scenes with ChatGPT Images 2.0. Ask Images 2.0 to choreograph a scene and place it into a storyboard. Then use that image as a reference in Seedance 2.0 to bring it to life. It follows the storyboard sheet really well. Some examples:

Jerrod Lew

12,501 views • 1 month ago

I'm playing around with generative AI tools and stitching them together into visual stories. Here I took the first few sentences of Pride and Prejudice and made it into a video. The gen stack used for this one: - Anthropic Claude took the first chapter, generated the scenes and the individual prompts to to the image generator. - Ideogram took the prompts and generate the images - Luma took the images and animated them - for narration - VEED | AI Video Creation to stitch it together (Many of these choices are just what I happened to use for this one while exploring a bunch of things). Anyway honestly it was pretty messy and there is a ton of copy pasting between all of the tools, and even this little video with 3 scenes took me about an hour. There is a huge storytelling opportunity here for whoever can make this convenient. Who is building the first 100% AI-native movie maker?

I'm playing around with generative AI tools and stitching them together into visual stories. Here I took the first few sentences of Pride and Prejudice and made it into a video. The gen stack used for this one: - Anthropic Claude took the first chapter, generated the scenes and the individual prompts to to the image generator. - Ideogram took the prompts and generate the images - Luma took the images and animated them - for narration - VEED | AI Video Creation to stitch it together (Many of these choices are just what I happened to use for this one while exploring a bunch of things). Anyway honestly it was pretty messy and there is a ton of copy pasting between all of the tools, and even this little video with 3 scenes took me about an hour. There is a huge storytelling opportunity here for whoever can make this convenient. Who is building the first 100% AI-native movie maker?

Andrej Karpathy

608,387 views • 1 year ago

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.

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.

AshutoshShrivastava

34,298 views • 2 years ago

If editing is the operation that determines the position of images in consciousness — that is, in the time of the viewer — artificial imagination, by contrast, does not juxtapose images. It continuously transforms one visual state into another within a latent vector space. The early signs of this new language can already be seen in Jean-Luc Godard’s remarkable Histoire(s) du cinéma. The film thus becomes a trajectory, a continuous deformation that strikingly corresponds to the way images transform within the flow of thought. The Flow (work in progress)

If editing is the operation that determines the position of images in consciousness — that is, in the time of the viewer — artificial imagination, by contrast, does not juxtapose images. It continuously transforms one visual state into another within a latent vector space. The early signs of this new language can already be seen in Jean-Luc Godard’s remarkable Histoire(s) du cinéma. The film thus becomes a trajectory, a continuous deformation that strikingly corresponds to the way images transform within the flow of thought. The Flow (work in progress)

Benjamin Bardou

13,425 views • 4 months ago

This Grok Project will take any image and transform it into a prompt with details, such as lighting, style, and subject matter. You can then use the prompt in Grok Imagine for images and videos. Click the video, and it will take you to the Grok Project.

This Grok Project will take any image and transform it into a prompt with details, such as lighting, style, and subject matter. You can then use the prompt in Grok Imagine for images and videos. Click the video, and it will take you to the Grok Project.

tetsuo

51,879 views • 7 months ago

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)

Lovis Odin

56,518 views • 8 months ago

Ok, so the new Midjourney Style Tuner is here and it's a MASSIVE update. There is so much to unpack, no way I can do it all in one post, but here is an overview. You start by typing in /tune followed by your prompt. MJ will generate a range of sample images (between 16-128) showing different visual styles based on your prompt. Then you choose your favorite images from the sample to generate a unique code you can use to customize the look of future jobs. (link below) So for example, I just tried it with the prompt: 💬 1990s, a photo of a woman dressed in fuzzy velour, shimmering midnight hues reflecting sadness by the lake, a contemporary nightmare on cross-processed film --ar 4:5 --c 4 I selected 10 images, which generated the code zlPhgLCHDXol64kPlwSUzu97. I can then add it to my prompt using --style {code} But it makes more sense if you try it yourself, so try playing around with my Style Tuner for that prompt and generate your own code from your selections: Some quick notes: -You can control the level of style applied by adding the --s parameter to your prompt. -You can combine multiple styles together by separating with a hyphen, i.e --style code1-code2 I will be obsessing over this all day tomorrow and sharing everything I learn along the way. This is an incredible update.

Ok, so the new Midjourney Style Tuner is here and it's a MASSIVE update. There is so much to unpack, no way I can do it all in one post, but here is an overview. You start by typing in /tune followed by your prompt. MJ will generate a range of sample images (between 16-128) showing different visual styles based on your prompt. Then you choose your favorite images from the sample to generate a unique code you can use to customize the look of future jobs. (link below) So for example, I just tried it with the prompt: 💬 1990s, a photo of a woman dressed in fuzzy velour, shimmering midnight hues reflecting sadness by the lake, a contemporary nightmare on cross-processed film --ar 4:5 --c 4 I selected 10 images, which generated the code zlPhgLCHDXol64kPlwSUzu97. I can then add it to my prompt using --style {code} But it makes more sense if you try it yourself, so try playing around with my Style Tuner for that prompt and generate your own code from your selections: Some quick notes: -You can control the level of style applied by adding the --s parameter to your prompt. -You can combine multiple styles together by separating with a hyphen, i.e --style code1-code2 I will be obsessing over this all day tomorrow and sharing everything I learn along the way. This is an incredible update.

Nick St. Pierre

377,928 views • 2 years ago

Here is a killer way to use embeddings: I built a notebook to show you how to do anomaly detection on images. Anomaly detection is one of the most common problems in the real world. Learn how to implement anomaly detection, and you'll be able to solve a ton of use cases that happen every day: • Predicting equipment failures • Identifying defective products • Identifying tumors in medical images • Spotting counterfeit products • Detecting fraud • Flagging fake reviews • Detecting bot traffic My notebook focuses on images. It uses: • Langchain • OpenClip embeddings • Oracle vector store This is the way my example works: • You start with a collection of similar images • You can then check whether a new image "belongs" to that collection I generate embeddings and use Oracle 26ai to store them and retrieve similar images on demand.

Here is a killer way to use embeddings: I built a notebook to show you how to do anomaly detection on images. Anomaly detection is one of the most common problems in the real world. Learn how to implement anomaly detection, and you'll be able to solve a ton of use cases that happen every day: • Predicting equipment failures • Identifying defective products • Identifying tumors in medical images • Spotting counterfeit products • Detecting fraud • Flagging fake reviews • Detecting bot traffic My notebook focuses on images. It uses: • Langchain • OpenClip embeddings • Oracle vector store This is the way my example works: • You start with a collection of similar images • You can then check whether a new image "belongs" to that collection I generate embeddings and use Oracle 26ai to store them and retrieve similar images on demand.

Santiago

24,452 views • 2 months ago

Part 1/2 This one took me a while. I made the images in #Midjourney using a bunch of images for ref. I ran them through RUNWAY then used @lumaAI to create the transitions between each video. Music made with #Sunoai. #fyp #ai #aiart #aiimages #aivideo #aimusic #airt

Part 1/2 This one took me a while. I made the images in #Midjourney using a bunch of images for ref. I ran them through RUNWAY then used @lumaAI to create the transitions between each video. Music made with #Sunoai. #fyp #ai #aiart #aiimages #aivideo #aimusic #airt

Kelly Boesch🏳️‍🌈

17,728 views • 1 year ago