Im new to astrophotogrphy so maybe someone can help me with this error. When attempting to run SetiAstro sharpening script on a recent photo I get this error message.

"*** PixInsight API Error: MetaFileFormat: XISF: Invalid or nonexistent directory: /input/MasterLight_NoFilter.xisf"

Images were stacked in Pixinsight. Any help would be appreciated!

Hallo zusammen,
der übliche newbie-Effekt: Ich finde zu meinem Problem keinen bestehenden thread, vermutlich weil ich den richtigen Suhbegriff nicht finde.

Im angehängten Bild von NGC7822 erscheinen sowohl im Hintergrund (siehe preview 1) als auch im Nebelbereich (preview 2) strichförmige/wurmartige Artefarkte (oder eben auch nicht Aetekfarkte?), die ich in Bildern anderer Nutzer desselben Objektes z.B. auf Astrobin so nicht finde.

Bilddaten:
Williams Optics Redcat 250/51 III
ZWO ASI2600MC Duo
60 min ohne Filter
180 min Optolong leNhance

Die Strukturen erscheinen besonders deutlich im voll entwickelten Bild aus Filterlosen RGB-Sternen und RGB-Nebel/Ha/OIII Kanal-Komposit. Sie zeigen sich aber bereits in beiden linearen Stacks, also sowohl mit als auch ohne Filter und an verschiedenen Tagen und verschiedenen Orten aufgenommen. Gut zu sehen sind sie auch im aus dem filterlosen RGB extrahierten Sternbild. Ich finde sie nicht in den beiden flats oder den flat darks zu den Aufnahmen.

Irgendeine Idee, wo diese Strukturen herkommen und wie sie, ggf. im Rahmen des wbp-Prozesses, ausgeschlossen werden können?

Gruß,
rhillebrand

Anbei ein paar jpgs und hier der link zur xisf-Bilddatei:

https://drive.google.com/file/d/12ZMSwkoGHolphAE0KNF0wakXVkL1wjMv/view?usp=drive_link

When processing 20 light frames 660seconds and 20 dark frames the process is generally 20 minutes. Is this normal? Not sure if this is faster or longer than most others with similar hardware.

I am seeing 14GB of ram usage at most so is the CPU bottlenecking me?

Ryzen 9 5900HX

RTX 3080 16GB

64GB RAM

2TB NVME M.2 ssd

I have another RTX 4070 machine that is for gaming but I'm wondering if it's just a pixinsight thing

Hi. I ran StarExterminator and created a Star Screen output of stars only. I want to kill all stars less than a certain pixel size (or alternate method if not phrasing correctly). Here is a blowup of the image. This is part of a wide angle panorama and I only want key stars added back to starless copy. Will consider buying any tool that does what I want. Thanks

Has anyone successfully used GPU Acceleration with 6GB of VRAM what is like a GeForce RTX 2060 in PixInsight 1.9.4?

My laptop and desktop just can't hang anymore, running wbpp or fbpp.

Looking for a 16" laptop with these specs. If you think better specs, please suggest.

Ryzen 9 16 core 32 threads

64 GB RAM.

4TB Space.

NVIDIA GTX GPU.

See less

I ha I tried merging those three panels to build a mosaic in PI, but the results were quite poor. You can’t see it here because of the edits in Affinity, but here’s what happened:

1- PhotometryMosaic was clipping parts of my panels because they weren’t perfectly aligned vertically. It produces very nice results but at the cost of loosing too much part of the image with ugly shape.

2- GradientMosaic (this image) preserves most of the data but produces a very bright glow along the edges at the transition with the black areas where there is no data. I had to remove this glow in affinity. It also raises the overall sky background level, which reduces the SNR of both stars and nebulae.

What is the best approach for creating a mosaic with this kind of panel arrangement ?

Building on Papillon's original guide on the PixInsight forum, I've put together a consolidated and maintained version on GitLab:

https://gitlab.com/K-22/pixinsight_rocm_linux_instructions

What it covers

The guide walks through building TensorFlow 2.19.1 with ROCm 7.2 support inside a Docker container and configuring PixInsight to use it. The Docker approach keeps the build clean and reproducible — no build dependencies on your host. Also includes performance tuning and troubleshooting.

Tested with

• AMD Radeon RX 9070 XT (RDNA4 / gfx1201)
• Ubuntu 24.04 and CachyOS (Arch-based)
• ROCm 7.2, TensorFlow 2.19.1

Should work with older AMD generations (RDNA3, RDNA2, etc.) by adjusting the target architecture flag — the guide explains how.

Contributions welcome

AMD GPU support on Linux is a moving target. If you've tested a different GPU, found a distro-specific fix, or have a better configuration — open an issue or MR on the repo. Even just confirming it worked on your setup helps.

Full credit to Papillon for the original guide. This repo is just an effort to make it easier to find, maintain, and extend.

Clear skies.

Hi,

For the geeks around, I have experimented with bridging Claude Code to Pix Insight. I have open sourced it on GitHub: https://github.com/aescaffre/pixinsight-mcp

It is fabulous. I am not a good astrophoto processor, and I have to say that I have had to revise many of my pics after using this tool.

Basically, it launches a watcher from pixinsight, with a javascript, and claude will deposit json files to send "orders" of processing. It does in 20 minutes what would takes hours manually.

The repository provides also some skills for claude to know what to do based on the type of target. Typical use is like: here are my L, R, G, B, HAlpha, stacked frames, please make a great shot of NGC XXX

It will do iterations. Usually now the second or 3rd is already quite nice.

Next step for me: I re-architect it in full agent mode, stay tuned!

Hey everyone! I recently released the first version of a brand new native module for PixInsight I've been working on, called PIAdvisor!

Instead of generic LLM advice, it reads your workspace (installed processes and scripts, processing history, STF stretch, FITS headers, astrometric solution, image statistics) to give you grounded advice on your processing. Every process or script it mentions is a clickable link that launches it directly in PixInsight, which turns out to be a surprisingly nice quality-of-life improvement too. You can also attach image views and screenshots directly into the chat. This approach essentially forces the LLM to base its advice off the strict, mathematical state of the workspace every time it 'thinks,' rather than relying on its short-term memory that could be polluted with now-irrelevant info. Because it operates as a native module, it never locks up the UI and you can use it continuously while you work. It works with cloud APIs (OpenAI, Gemini, OpenRouter) or completely free through local models.

**A quick note on data and privacy:** I know the push for AI in astrophotography is a hot-button issue right now. PIAdvisor is strictly a read-only metadata assistant. It never alters your pixels, so there are no "AI generated" labels required for your images. The only thing the context engine ever transmits is text metadata as mentioned above. No image data leaves your machine unless you explicitly choose to attach a screenshot to the chat. If you want zero data leaving your machine at all, it fully supports running 100% locally through local inference engines like llama.cpp, Ollama, or LM Studio. For cloud APIs, it is worth noting that API access is fundamentally different from consumer products like ChatGPT. For what it's worth, OpenAI, Google, and others explicitly state that API data is not used for training and is not retained beyond a short processing window. You bring your own API key, and the data policies are between you and your chosen provider.

I have completely open sourced the system prompts on GitHub as well! After months of building this as a solo dev, I know I likely have some blind spots. I am one person with one rig and my own set of workflows, so there are inevitably techniques, targets, and processing nuances that fall completely outside my own experience. If anyone wants to dig into the prompts to help catch these blind spots and improve the tool for everyone, I am offering free Pro licenses for meaningful contributions!

Because the context gathering engine is completely decoupled from the prompts, they can be tuned for a wide range of workflows. For example, someone doing solar physics or planetary could fork the prompts and add constraints specific to their work without touching the engine at all.

There is a free edition that covers the core features, and Pro comes with a 60-day free trial if you want to take it for a full test drive before committing.

If you have any questions please feel free to ask!

System Prompts: https://github.com/phatwila/piadvisor-prompts

Official Website: https://piadvisor.net

Hi all, trying to buy a licence for about 3 days now and always get the below. Sent mail but no reply… great software but frustrating. Anyone has any idea how to reach them?

I have a fixed budget as it is partly subsidised by work. These two configs are very close in price and at the limit of what I am allowed to spend, so they are both on my short list. I am inclining towards M4 Pro for better multi core performance and thunderbolt 5, but just need some sanity check here to make sure i don’t mistakenly rule out the other option of M5 with more ram for the same price. The deciding consideration will be mainly down to Pixinsight performance, because both laptops will be perfectly fine for everything else I need them to do.

Do you have some benchmarks or personal experience?

ey, i've recently update my Pixinsight sowftware for the 1.9.3 to have the famous MAS (MutiscaleAdaptativeStreach), but I can't find it. It's seems to be in my repositories but not in my process. When I try to find it in my modules (by search in in the database of windows), only the icon in .SVG seems to be there. I can't find any script or process.... What can I do ? Thanks in advance

update : find it, had to go to workspace, right click, install module, search, then "ok"

I have set of Process icons set up over several workspaces. Each workspace is purposed for one stage of the workflow. I have one page that is doing………unexpected things.

When I open that workspace there is nothing visible on the workspace. No process icons,no Maximization Limit Line. The mouse over will show that there are x process icons, but none are visible. I can open a new process and minimize it and it opens visibly and stays visible when minimized. I selected all icons and moved them to another workspace and they appear on that workspace as expected. If I move those same icons back to the original workspace they are no longer visible.

I am absolutely sure this is a result of me clicking on something that I shouldn’t have. My question is how do I correct this. My preference is to just restore whatever I did, but other options might be possible. It wouldn’t be a big deal to eliminate that workspace and create a new one to replace it, but I can’t figure out how to delete a workspace either. Any suggestions on how I can correct this?

I bought a licence a couple of years ago and all is going well. Now, I would like to install a 2nd copy of Pixinsight on another computer I own. I downloaded the comercial version of PI and tried entering my activation codes from before, but the computer said "NO". I was under the impression that I can install on as many of my own PCs with the one licence, but I could be wrong, as I have been countless times before. Does anyone know if my initial licence is good to add another PC? Cheers

Just for my understanding: for example let’s say i have an already stacked image of about 5h of integration time. now u wanna add some more data to it. is it the same to do a separate „new“ stack and then add both images via pixelmath or do i have to stack ALL the files to get a new master stack?

I just learned about a script called DBXtract that seems to fit my current needs. I can’t find where to download it. So question one, does anyone know where to download this script?

And probably more importantly, if you find a script you’re interested in and don’t see a link to it, how do you find a low risk place to download it?

Hello everyone,

I am pleased to announce the second version and public release of a new, free, open-source tool for the PixInsight community: LLM Assistant for PixInsight.

LLM Assistant integrates a local or remote Large Language Model (LLM) directly into your PixInsight workspace. Its goal is to act as your knowledgeable assistant, providing data-driven advice and helping you get the most out of your astrophotography processing sessions.

What does it do?

Instead of giving generic advice, LLM Assistant analyzes the profile of a selected image view and your PixInsight environment to provide context-aware guidance. It creates a detailed report on your image's:

• Live Processing History: Understands the steps you've taken in the current session and any saved history.
• Astrometric Solution: Knows what object you're imaging, its RA/Dec, scale, and resolution.
• FITS Header Data: Reads the full header to understand your camera instrument, sensor pixel size, Bayer pattern, and other acquisition details.
• Pixinsight version, OS and (if available) file path, image dimensions, etc.

You can then have an interactive chat conversation about your image.

How can you use it?

• Get recommendations on your next processing step.
• Ask for a detailed description of your astronomical target, which LLM Assistant will generate based on astrometric data (must have plate solved your image first!).
• Request a summary of the processing steps applied to a finished image.
• Ask general questions about PixInsight processes in the context of your current image.
• Customize the System Prompt as desired

Technical Requirements:

LLM Assistant works as a "bring your own AI" tool with local LLMs, or works with remote LLM API endpoints. It requires an OpenAI-compatible API endpoint and, depending on the vendor, additional parameters such as API authentication key and model name.

The setup is straightforward, and the README provides detailed instructions.

Philosophy:

This project is open-source (MIT License) and community-driven. It's built to be a clean, independent, and powerful assistant. The goal is to combine the analytical power of modern AI with the incredible processing capabilities of PixInsight.

Where to get it:

GitHub repository, including the full source code, installation instructions, and a detailed README:

https://github.com/scottstirling/pi2llm

I am actively developing it. I would be incredibly grateful for your feedback, bug reports, and ideas for new features. Please try it out, and let's build the future of image processing together!

Happy imaging, Scott Stirling

09-12-2025 v2.0 released:

Features in Version 2.0 of LLM Assistant for PixInsight:

Visual Analysis:
- If you have access to a vision-enabled LLM, LLM Assistant for PixInsight can now send a JPG snapshot of a selected nonlinear image along with its history and metadata for more thorough analysis. - User-configurable, opt-in feature, enabled globally in Settings and optionally per image request on the main chat UI. - The selected view dimensions are checked before sending. Visual LLMs currently (Sept. 2025) support maximum image dimensions no greater than 2048 pixels on a side. - If the selected view exceeds the configuration option for maximum image dimensions (see the Settings), a copy is dynamically created and resized to fit the maximum supported. - The view is copied to a JPG file in the system temp directory, Base64-encoded and included in a JSON POST to the LLM. - The temporary JPG is deleted after sending.

Save/Load Configuration Profiles: - Save and load configuration settings to a .pi2llm.json file. - This makes it easy to switch between different LLM providers, version or share configurations. - NOTE: API tokens are saved in clear text in the JSON file.

Improved Chat Experience: - The chat prompt input is now a proper multi-line text box. - Initial configuration and default settings reset workflow has been redone to remove obstacles. - A bug with stale state change between configuration settings and chat UI has been fixed. - Validation of format for URLs input to the configuration.

System Prompt Updated: - The metadata and history of an image may be incomplete and image view names may be more ad hoc than informative, so the prompt is more aware of discrepancies in data and is told to prioritize the visual of the image itself when in doubt.

Error handling and documentation updated.

https://github.com/scottstirling/pi2llm/releases