Alternatives
Products that do what DynamiCrafter: Animating Open-Domain Images with Video Diffusion Priors does
Hello HN! We have released a major update of our image-to-video diffusion model, DynamiCrafter, with better dynamic, higher resolution, and stronger coherence. DynamiCrafter can animate open-domain still images based on text prompt by leveraging the pre-trained video diffusion priors. Please check our project page and paper for more information. We will continue to improve the model's performance. Comparisons with Stable Video Diffusion and PikaLabs can be found at https://www.youtube.com/watch?v=0NfmIsNAg-g Online demo:…
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It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/blog/ideogram-4.0/ We will be happy to answer any questions :)
Jun 2026 · github.com
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Hey HN, this is Lina, Andrew, and Sidney from Lemon Slice. We’ve trained a custom diffusion transformer (DiT) model that achieves video streaming at 25fps and wrapped it into a demo that allows anyone to turn a photo into a real-time, talking avatar. Here’s an example conversation from co-founder Andrew: https://www.youtube.com/watch?v=CeYp5xQMFZY. Try it for yourself at: https://lemonslice.com/live. (Btw, we used to be called Infinity AI and did a Show HN under that name last year: https://news.ycombinator.com/item?id=41467704.) Unlike existing…
2025
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This is a character-level language diffusion model for text generation. The model is a modified version of Nanochat's GPT implementation and is trained on Tiny Shakespeare! It is only 10.7 million parameters, so you can try it out locally.
Nov 2025 · github.com
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Hey HN! Really excited to share this new image de-identifier. Kind of like a game of image telephone, you can upload an image and our app will convert the image to a caption, then will generate an image using stable diffusion using the parameters you set for the model yourself. We had so much fun making this and seeing the varying levels of scary generated images.
2023 · imafake.tonic.ai
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ChatGPT + Stable Diffusion to convert links into OG images
2023
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Hi HN! We're proud to share Hotshot, a large-scale diffusion transformer model for text-to-video generation that we built with just a 4-person team. You can try it today in beta at https://hotshot.co, with 2 free generations per day. The model generates 5 seconds of 720p video from text prompts. It excels at prompt alignment, and consistency. It also excels at generating people, animals, and nature. In blind tests with 100 users, Hotshot generations were preferred to Runway ML 60% of the time. Hotshot generations were preferred to Luma 80% of the time. Overall, users preferred…
2024 · hotshot.co
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I tried to train text to video and image to video models to generate 2D game animations. I used public game animation data and 3D mixamo model rendered animations to train the animation generation models. I'm open sourcing the model, training data, training code and data generation code. More technical details can be found in my blog (https://www.gavinliblog.com/posts/godmodeanimation).
2024 · github.com
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I know I'm very late to the game but tried to realize Van Gogh's work with AI. Workflow is quite straightforward, generated all the video samples through Automatic1000's Web-UI by leveraging SD1.5 + Motionv3 in AnimateDiff. Rendered everything on my RTX 3080TIM laptop. Took me decent 40 mins for different experiments and generations.
2024 · youtube.com
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2023 · app.visoid.com
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2022 · getimg.ai
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This is a quickstart example using LeRobot and Flower that demonstrates how to train a diffusion model collaboratively across 10 individual nodes (each with its own dataset). This example uses the push-t dataset, where the task is to move a letter T object on top of another that is to remain static. The example it's pretty easy to run, and can do so efficiently if you have access to a recent gaming GPU. Although the diffusion model only take 2GB of VRAM (of course you can decide to scale it up), the compute needed to train them isn't negligible. For context, running the example until…
2025 · github.com
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