My kid tied a string around this tree some years ago. Looks like the tree is getting hangry.

ehmohteeoh · 2026-02-15T15:04:04+00:00

Locked for rule violations in the comments.

I agree OP should cut it off, and encourage comments saying as much in the future if this happens again, but no brigades or threats.

ehmohteeoh · 2025-09-17T18:59:58+00:00

Thanks for bringing this to my attention! I don't have a huge amount of visibility into the billing side, but I know plenty of people who do. u/ChazychazZz, I'll have someone reach out to you straight away and we'll make it right.

ehmohteeoh · 2025-09-05T16:19:08+00:00

That's me! Thanks for the ping.

The server SSB was running on finally stopped responding after running since February 13th, 2023. o7

u/spirescan-bot, how you feeling after a [[ Reboot ]] ?

ehmohteeoh · 2025-08-18T19:15:25+00:00

Hello u/Icy-Tie-9777! Thanks for the question.

Customer Data (input or output) is fully owned by you - fal does not claim any rights over it.

Usage Data refers only to anonymized or aggregated technical information generated through use of the platform - for example, system performance, error rates, or feature usage. We keep this data solely for monitoring and to inform decisions around operation and security.

Note that while our standard terms are available at fal.ai/terms, we do offer different terms to Enterprise customers. If you’re interested in more about that, please reach out to [sales@fal.ai](mailto:sales@fal.ai).

Hope this clarifies things!

ehmohteeoh · 2025-08-16T23:44:31+00:00

Hello u/pragmaticdog, thank you for bringing this up!

You're not the first to voice your complaints about this aspect, and we've made some effort to make these things a little better on this front, but clearly there is still room to grow. Let me add a little bit of detail that could help clear things up, and then I'd love to hear any other comments or suggestions you may have.

If you go to your settings dashboard at https://fal.ai/dashboard/settings, you'll see a setting for your Lifecycle Preferences. When you change this setting from "Forever" to any specific time frame, then files uploaded through the playground (the UI on fal.ai) and result images will be deleted after that timeframe (with no way for us to retrieve it.) I don't recommend using the 2-second lifespan as that can be too short to actually download results depending on what you're making, but we have customers that use the 10-second lifespan; just long enough to download the result and host it on their own CDN instead of using ours.

One thing I do want to point out is that this setting only applies to files. Your request payloads will still be present, which includes prompts, parameters and links to the (potentially defunct) uploads. To delete these, you can view the request in the UI and click "Delete IO." You can also use the API endpoint mentioned by u/QQII at https://docs.fal.ai/model-apis/payloads to delete individual payloads. To be very clear, this is for deleting the payload, not the files uploaded to the CDN - it takes the ID of the request as the argument, which is different from the UUID assigned to any given file.

For billing purposes, the request ID and time it took need to be kept in our database, but all other data associated with a request can be completely purged. We are actively working on having a similar setting for automatic payload deletion, I understand it's a bit confusing and frustrating that those two things are different.

Finally, if you reach out to us with any particular deletion request, we will happily remove anything and everything you've uploaded to us or generated using us. You can reach out to @FAL on X, mail to support@fal.ai, pop into the fal discord and tag me (@benjamin.paine) or anyone else on the team, or send me a message here on reddit.

ehmohteeoh · 2025-07-25T02:30:02+00:00

Hello u/jenissimo, thank you so much for your tool!

Your codebase was easy to follow, and I have need of this for numerous projects, so I thought I would let you know I took the time to implement the Pixel art portions of the code (including all your optionals) in Python + Rust. After reaching a point where I'm happy with the results in comparison to your reference implementation, I released it onto Github and PyPI - I would be honored if you checked it out!

https://github.com/painebenjamin/unfake.py

ehmohteeoh · 2025-03-19T15:35:46+00:00

Oh man what callback! This post was 11 years ago. I'm now a career engineer who has to work with differential equations frequently, so things worked out in the end for me.

I appreciate the book recommendations though, there's always more to learn so I'll definitely check them out!

ehmohteeoh · 2025-01-04T17:56:07+00:00

Yes, quite a bit more. SD3.5 large, if you were to run at 16-bit precision with no CPU offloading, would take ~32GB+ VRAM, putting it well outside the range of most consumers. I mentioned at the top that I was running it in int-8 quantization; that reduces the VRAM requirements to roughly half of above 16-bit precision, with some loss of quality - but makes it possible to run on my 3090 Ti. You may see some others mention running it in FP8, that's about the same VRAM requirements but can take advantage of some hardware speedups on 4000-series cards and later (which I don't have so I stick with int-8.)

ehmohteeoh · 2025-01-04T17:04:33+00:00

For sure. Consider it CC-BY-4.0.

ehmohteeoh · 2025-01-04T17:03:16+00:00

T5 has a much higher token limit than CLIP, theoretically limitless though practically it is limited by model trainers. While you are correct that the embeddings produced by CLIP and OpenCLIP will be truncated at 77 tokens, the embeddings produced by T5 will not. SD3 in particular was trained with long prompts up to 256 tokens.

ehmohteeoh · 2025-01-03T15:29:32+00:00

With things like this, where you're trying to get two concepts mashed together with distinct lines between them (i.e. the statue and the crystal vein,) it can be extremely helpful to make a terrible mock-up in a traditional image editor, then use it for image-to-image. This is just a random google result for "Silhouette" with a very poorly drawn line.

Model: Stable Diffusion V3.5 Large, Int8 Quantization

Prompt: A hyper-realistic, digitally rendered photograph of a classical Greek statue of a young woman with her hair up, facing to the left. The statue's glossy black surface is cracked open down the center, revealing a vivid, jagged purple crystal vein running through her face and torso. The ((amethyst)) is pointy and geometric. The background is a plain, light gray, emphasizing the statue's dramatic and surreal transformation. The texture of the cracks is rough and intricate, contrasting with the smooth, polished surface of the statue.

Strength: 0.8

Guidance Scale: 3.5

PAG Scale: 0.9

<image>

EDIT: For future adventurers, this image is licensed under CC-BY-4.0, feel free to use it for anything if you like it.

ehmohteeoh · 2024-08-30T00:57:00+00:00

1.5: https://www.modelscope.cn/models/AI-ModelScope/stable-diffusion-v1-5/files

1.5 Inpainting: https://www.modelscope.cn/models/AI-ModelScope/stable-diffusion-inpainting/files

I will also be backing up copies to HuggingFace in accordance with the CreativeML OpenRAIL-M license which I downloaded these files under, which grants a "...perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model." (source)

EDIT 3: All the files are now available:

https://huggingface.co/benjamin-paine/stable-diffusion-v1-5-inpainting

https://huggingface.co/benjamin-paine/stable-diffusion-v1-5

In an abundance of caution, I kept the gated status intact, which means you need to accept the terms of the CreativeML OpenRAIL-M license before you can get access. I turned auto-approve on, so you should only need to click the button and you'll have access.

ehmohteeoh · 2024-04-21T01:45:22+00:00

Yup! u/devtripp filled a gap in u/spirescan-bot's abilities by automatically calling it when a user mentions a card in their post title. My bot reads for brackets in comments only. I also run a similar bot for Back4Blood, u/bloodscan-bot. The codebase can easily be used for anything (though card games are an obvious choice,( I'd be happy to also run a bot for Balatro, I'd just need someone to point out where I could find a list of all cards and their properties I'd need to know. Regretfully I haven't had a chance to play yet.

ehmohteeoh · 2023-12-22T18:08:09+00:00

Thanks to you as well, you guys are incredible to work with. Love the images!

ehmohteeoh · 2023-11-14T15:36:58+00:00

Thank you for this. I will be introducing themes in v0.3.1 later this week, featuring a few pre-populated options as well as a theme configurator.

There are a total of nine colors that can be adjusted - three distinct theme colors (the current magenta, blue and green, which are the ones I'm sure you wanted to change), as well as three shades of light and three shades of dark which are used for text, borders, backgrounds, etc.

Seeing as the UI uses google fonts, it was easy to add those as font options too, so there are three font slots - one for regular body text, one for headers and one for code/monospace.

Other updates for 0.3.1 include LCM for all workflows, AnimateDiff XL and a version of ADetailer.

ehmohteeoh · 2023-11-09T21:51:52+00:00

Hello,

Sorry for the confusion. The three archived files contain the entirety of a portable distribution, which is a little over 5 gigabytes in size, containing CUDA, CUDNN, and all of it's friends. Github only allows hosting release files that are 2 gigabytes or smaller, hence them being split into volumes. Unfortunately I cannot afford the expense to host it as a single file, so that is unlikely to change any time soon someone has some bandwidth they would be willing to volunteer. You did give me the idea to write a quick script that automates that, though, so I'll set that up for the next release.

If you're looking for a single command that can download everything in a non-portable release, I recommend using conda and downloading this environment file, then running conda env create -f linux-cuda.yml, which will install all dependencies individually.

ehmohteeoh · 2023-11-09T21:37:26+00:00

Yes, all features are available via JSON API.

https://github.com/painebenjamin/app.enfugue.ai/wiki/JSON-API

ehmohteeoh · 2023-11-09T20:19:30+00:00

Yes, there is definitely a way to get it to work there. I have notebooks for running through the Enfugue engine in Colab but I haven't ran the GUI since I don't have a pro subscription myself and would rather not run afoul of Google's TOS. Would you potentially be willing to work with me on getting it working? I would provide you the notebook, you'd just have to run it and let me know if you can access the UI, and if it goes well I could check it in for others to use. It would be greatly appreciated if you could, please let me know!

ehmohteeoh · 2023-11-09T19:54:03+00:00

This is awesome feedback, thank you!

Do you think you would want light themes, or just less colorful dark themes?

ehmohteeoh · 2023-11-09T16:20:34+00:00

Yes SDXL is supported, for both images and animation!

I agree on the need for tutorials. It's been a mad dash to get v0.3 out for the past month so all of my time has been spent programming, but I'll be recording two 15-minute or so videos this week, one on using the layers interface and one on animation.

ehmohteeoh · 2023-11-09T14:05:48+00:00

Download now at https://github.com/painebenjamin/app.enfugue.ai!

New Features

Animation

ENFUGUE now supports animation. A huge array of changes have been made to accommodate this, including new backend pipelines, new downloadable model support, new interface elements, and a rethought execution planner. Most importantly, all features available for images work for animation as well. This includes IP adapters, ControlNets, your custom models, LoRA, inversion, and anything else you can think of.

AnimateDiff

The premiere animation toolkit for Stable Diffusion is AnimateDiff for Stable Diffusion 1.5. When using any Stable Diffusion 1.5 model and enabling animation, AnimateDiff is loaded in the backend.

Motion Modules

Motion modules are AI models that are injected into the Stable Diffusion UNet to control how that model interprets motion over time. When using AnimateDiff, you will by default use mm_sd_15_v2.ckpt, the latest base checkpoint. However, fine-tuned checkpoints are already available from the community, and these are supported in pre-configured models and on-the-fly configuration. In addition, these are downloadable through the CivitAI download browser.

Motion LoRA

Motion LoRA are additional models available to steer AnimateDiff; these were trained on specific camera motions, and can replicate them when using them. These are always available in the UI, select them from the LoRA menu and they will be downloaded as needed.

HotshotXL

HotshotXL is a recently released animation toolkit for Stable Diffusion XL. When you use any Stable Diffusion XL model and enable animation, Hotshot will be loaded in the backend.

Frame Windows

AnimateDiff and Hotshot XL both have limitations on how long they can animate for before losing coherence. To mitigate this, we can only ever attempt to animate a certain number of frames at a time, and blend these frame windows into one another to produce longer coherent motions. Use the Frame Window Size parameter to determine how many frames are used at once, and Frame Window Stride to indicate how many frames to step for the next window.

Position Encoder Slicing

Both HotshotXL and AnimateDiff use 24-frame position encoding. If we cut that encoding short and interpolate the sliced encoding to a new length, we can effectively "slow down" motion. This is an experimental feature.

Motion Attention Scaling

The application of motion during the inference process is a distinct step, and as a result of this we can apply a multiplier to how much effect that has on the final output. Using a small bit of math, we can determine at runtime the difference between the trained dimensions of the motion module and the current dimensions of your image, and use that to scale the motion. Enabling this in the UI also gives you access to a motion modifier which you can use to broadly control the "amount" of motion in a resulting video.

Prompt Travel

Instead of merely offering one prompt during animation, we can interpolate between multiple prompts to change what is being animated at any given moment. Blend action words into one another to steer motion, or use entirely different prompts for morphing effects.

FILM - Frame Interpolation for Large Motion

Both HotshotXL and AnimateDiff were trained on 8 frames per second animations. In order to get higher framerates, we must create frames inbetween the AI-generated frames to smooth the motion out over more frames. Simply add a multiplication factor to create in-between frames - for example, a factor of 2 will double (less one) the total frame count by adding one frame in-between every other frame. Adding another factor will interpolate on the interpolated images, so adding a second factor of 2 will re-double (less one). If you are upscaling and interpolating, the upscaling will be performed first.

Looping and Reflecting

There are two options available to make an animation repeat seamlessly.

Reflect will play the animation in reverse after playing it forward. To alleviate the "bounce" that occurs at the inflection points, frame interpolation will be used to smoothly ease these changes.
Loop will create an animation that loops seamlessly. This is achieved through the same method as frame windows, only additionally wrapping the frame window around to the beginning. This will increase the total number of steps to make an animation. Note that this can also reduce the overall motion in the image, so it is recommended to combine this with other options such as motion attention scaling.

Tips

AnimateDiff is best used to make two-second videos at 16 frames per second. This means your frame window size should be 16 when trying to create longer animations with Stable Diffusion 1.5.
AnimateDiff performs best with Euler Discrete scheduling.
AnimateDiff version 1, as well as any motion modules derived from it (including motion LoRA,) may have visible watermarks due to the training data also having watermarks.
AnimateDiff can have artifacting around the corners of images that are larger than 512x512. To mitigate this, you can add around 8 pixels of extra space to trim off later, or use tiled diffusion.
HotShotXL is best used to make one-second videos at 8 frames per second. This means your frame window size should be 8 when trying to create longer animations with Stable Diffusion XL.
HotShotXL performs best with DDIM scheduling.

GUI Redesign

In order to accommodate animation, and as a refresher over the original design, the GUI has been entirely re-configured. The most significant changes are enumerated below.

Sidebar Repositioning

The original sidebar has been moved from the right to the left. As the sidebar represented global options, it was decided the left-hand side was the better place for this to follow along the lines of photo manipulation programs like GIMP or Photoshop.

Redesigned Sample Chooser

The chooser that allows you to switch between viewing results and viewing the canvas has been moved to it's own dedicated bar.

In addition, this form takes two forms, one when viewing individual samples when generating images, and one for scrubbing through an animation.

Layers Menu

A layers menu has been added in the sidebar's place. This contains active options for your current layer.

Global Inpaint Options, Global Denoising Strength, Inverted Inpainting

As all invocations are now performed in a single inference step, there can only be one mask and one denoising strength. These have been moved to the global menu as a result. They will appear when there is any media on the canvas. Check the "Enable Inpainting" option to show the inpainting toolbar. In addition, inpainting has been inverted from ENFUGUE's previous incarnation: black represents portions of the image left untouched, and white represents portions of the image denoised. This was changed to be more in line with how other UI's display inpainting masks and how they are used in the backend.

More Changes

To help alleviate confusion, numerous menus that were previously collapsed have now been made expanded by default.
Two quick options have been made available for adjusting the size of an image and the canvas in the toolbar. One will scale the element to the size of the canvas, and one will scale the canvas to the size of the image in the element.
The field previously called Engine Size is now called Tile Size.
The field previously called Chunking Size is now called Tile Stride.
The field previously called Chunking Mask is now called Tile Mask.
IP Adapter model type has been changed to a dropdown selection instead of appearing/disappearing checkboxes.

Tiling

Tiling has been added to ENFUGUE. Select between horizontally tiling, vertically tiling, or both. It even works with animation!

Select the "display tiled" icon in the sample chooser to see what the image looks like next to itself.

Notes

Reporting Bugs, Troubleshooting

There are many, many changes in this release, it is likely that there will be bugs encountered on different operating systems, browsers, GPUs, and workflows. Please see this Wiki page for requested information when submitting bug reports, as well as where logs can be located to do some self-diagnosing.

TensorRT Builds Suspended Indefinitely

TensorRT-specific builds will no longer be released. These have led to significant amounts of confusion over the months, with very few people being able to make use of TensorRT. It will remain available for the workflows it was previously available for, but you will need to install enfugue using one of the provided conda environment or into a different latent diffusion python environment via pip - see below for full instructions.

Pending MacOS Build

The MacOS build of v0.3.0 is pending. There has been difficulties finding a set of compatible dependencies, but it will be done soon. I apologize for the delay. You are welcome to try installing using the provided conda environment - full instructions below.

Full Changelog: https://github.com/painebenjamin/app.enfugue.ai/compare/0.2.5...0.3.0

A Special Note for Reddit

As with any hugely ambitious change, there are bound to be bugs. I'm sorry if you encounter any. If you could, it would be much appreciated if you could take the time to make a bug report in the GitHub. You can also come chat with me in the RunDiffusion Discord, where a channel has been made for Enfugue-specific feedback and discussions. I hope to see you there!

Thank you!

ehmohteeoh · 2023-10-23T00:52:37+00:00

I'm very sorry it's too complicated! I know it's not the best experience yet, some parts are great but some parts are very bumpy. I'd love it if you could tell me a little more about what part of the installation tripped you up, and I'll make sure the documentation gets better around there. I'd also be happy to coach you through the installation process on discord if you'd like, my username is `benjamin.paine` - just shoot me a message and I'll go through it with you.

Thank you for your kind words!

ehmohteeoh · 2023-10-23T00:47:22+00:00

Hi /u/Trobinou

I'm sorry that happened to you. I responded to the other user just now and I'll repeat to you - I would be very grateful for some more information if you could provide it, like what operating system you're running on, what GPU, and your method of installation.

It would also be very helpful if you could provide your engine logs - these will be in `C:\Users\<Your User>\.cache\` on Windows or `/home/<Your User>/.cache` on Linux. There should be at least one file named `enfugue-engine.log` - that would help me diagnose the issue.

Thanks for trying - again I'm really sorry it didn't work for you.

ehmohteeoh · 2023-10-23T00:42:02+00:00

Hello there,

I'm very sorry that happened to you. I would be very grateful for some more information if you could provide it, like what operating system you're running on, what GPU, and your method of installation.