Stumbled on this video today…nice balanced intro video. One could use a slightly different 10 steps than the author, but the theme and the philosophy of the approach is a good place to start I think and the right mindset to using DT when first switching to it…
I’m ok with the colors of the DT UI. It’s the colors of my images that I’m worried about 
You will get there don’t worry…you can also use the color calibration module ie a second instance for simplicity to create a correction based on a color checker or spyder card if you have one…essentially it creates a custom correction by applying an iterated channel mixer solution…
This can help in certain cases and then you just have to come to terms with the fact that you will start from a very untouched image if and until you create some auto applied presets or styles…those will come with some experimentation and experience…
Liberal use and multiple instances of colorbalance rgb and color calibration along with the color equalizer and some other tricks will give you nice control… Once you get the hang of it you can watch some of @s7habo Boris’s videos to get a more indepth technical and creative demonstration of the possible tools and workflows.
If you wanted to see a more out of the box like C1 you could set the workflow for a few images to legacy and make sure the module order is legacy… This will add a base curve and add it before the input profile…This is a more standard display referred workflow…maybe a bit more contrast and color out of the gate but much less control over tonal adjustments and the scene lighting as you have moved away from the scene referred and linear workflow in the early pipeline that DT has evolved towards… but you could try it for comparison…
The landscape channel that I shared with you though is a good one and I think it will help you to get up and running…
I would be interested in a separate playraw post where you supply your edit in C1 and your edit in DT. Then attach the raw file with license and let users on the forum show you how they would edit the image.
I switched from Win10 and C1 to Linux (Mint) and dt about a year ago. I had the occasional exposure to Linux before and that transition was fairly smooth. When it came to a raw developer, it was more of a meandering path. I started with dt, but was soon puzzled, as things weren’t working as I was used to. I then tried Raw Therapee for a while, but its myriad options left me bewildered. I then returned to dt and tried to make a more serious effort looking at the manual and watching instructional videos, as mentioned above. Mostly Boris’ videos, which was very helpful, although there isn’t really a canonical order in which to tackle them. So you have to just dive in somewhere, hop around and return occasionally.
I’m really glad I made the switch, as Linux is so much less drama. I’m somewhat comfortable with dt now, although far from being an expert. But it’s a joy to use and raw files can be pushed a bit further than e.g. in C1.
What hasn’t been mentioned so far, I believe, is the HDR merging and panorama stitching. This is possible in dt with LUA scripts. E.g. one of these calls Hugin, stitches selected images into a panorama and round-trips the result back into dt. Word of caution, though, as this does not work with the flatpak version of dt. I prefer flatpak, since the package manager of my distro is several version behind the latest dt release. So, if you want to be on the latest dt version and use this functionality, you’d probably have to compile from source.
Here are some good resources for DT novices:
https://notebook.stereofictional.com/how-to-get-started-with-darktable-2026-edition
For a super concise DT workflow:
Have you tried the appimage?
Also, Darktable does HDR merging natively with the ‘create HDR’ button under the ‘actions on selection’ module in lighttable.
Haven’t tried that. But AppImages don’t auto-update, so - as far as I know - you have to download and configure a new one every time an update comes out.
No config changes necessary; /home/user/.config/darktable persists when you get a new appimage.
(I mentioned putting a link to appimages in the Mint menu in another thread; right click menu icon, ‘edit menu’ → create a new entry somewhere and you can browse to the appimage file after setting it as executable. Then you can add it to favourites or pin it to the taskbar.)
@CDR0224 , this suggestion by @Terry is an excellent one, and I bet you will find it invaluable. If you want results the same as you got in C1, then post that image to PlayRaw along with the original RAW file.
Expert users on this forum can then try to mimic the results and post their edits. You can then load the edits into your Darktable and see which modules and which settings were used.
However, there’s no replacement for proper learning, and starting with the basics is still the best way. Don’t try to immediately do advanced edits. Concentrate on mastering the Exposure module, then add the Color Balance RGB module. This will get you about 90% of the way there (assuming WB is auto-applied). Then gradually add modules to put the finishing touches on your image. Think of it like a stack of layers (modules), or a pyramid. It’s much more manageable if you break it up into bite-sized chunks and tackle it one layer at a time. Only add the next layer when you’re comfortable with the layer(s) below.
This is a good suggestion. I would add that mastering includes reading the user manual section for each module and experimenting with the controls while reading the manual. For example exposure in most programs is a single slider, but DT is a little more powerful than a single slider. The area exposure mapping for instance is a brilliant feature that many users would overlook.
Hi! I did the switch to darktable from LR couple of months ago, and to Linux recently, welcome to the club haha.
I remember being overwhelmed at first too. What I did was find a key basic workflow: exposure, tone mapper, white balance, etc. Then experiment and grow from there. ChatGPT gave me good advice on things like shortcuts, how to make defaults presets etc because although I had read the doc it was still confusing to me! I followed this one from Nick Long that’s very simply explained and doesn’t seem to have been mentioned: https://youtube.com/playlist?list=PLAoorcd3Ha4LyldiDf1_S2WB3DLywP1K0&si=rth02ALdDk17DNw3
What I discovered is that there’s not just one “correct” workflow. And for your pictures it’s the same, you can get inspiration from what people do but in the end it’s YOUR choice and that’s what really rocks 
And same for the drivers, chatGPT helped me debug my own weird issues partly and gave me enough hints that I found a solution (my laptop wasn’t exposing enough vram, I had to find the “secretBIOS mode” in the uefi to fix.)
But for Darktable the commands are :
darktable-cltest
for details, or also
darktable -d opencl
That will show you the status. You can then ask here but since it’s more a Linux stuff another forum or just ChatGPT will give you maybe more details. Like I just ran it and passed my current laptop result where Darktable support is off, and it showed me immediately how to download the right drivers for me.
Another thing that I did was tagging with AI: there’s a small software called stag Stag that works very well to tag objects in images with a local model, for things like trees, cars, etc.
But I also ran my own-made script with a local model (LlAVA or equivalent) to tag according to my own criteria, very cool and powerful. For example sort environment between urban/indoor/nature or assign a topic between a fix selection. With 32gb ram it’s very doable, I have only 24.
Again, welcome and good luck on your Linux journey 
I’d love to learn more, if you’d like to share details.
Sure.
I’m using llama.cpp on a 24GB ram base Mac mini to run qwen or LlAVA 8b. I made a prompt wrapped in a python script that also then writes tags inside darktable:
def analyze_with_llava(image):
prompt = “”"You are classifying a photograph.
Rules:
Subject (choose ONE): Animals | Constructions | Nature | Objects | People | Space | Text
Environment (choose ONE): Indoor | Nature | Urban
Caption:
Return ONLY in this exact format:
Subject:
Environment:
Caption: <5-15 word description>
“”"
and then run it in the terminal on the background, it takes 2-3 sec by photo, I run it by batches of 1 to 2 thousands.
Right now LlAVA is good, but it has a tendency to hallucinate categories while my previous attempt with qwen2-VL was better. In both cases classification is very exact.
I find it useful because STAG can reliably tag items that are simple and similar but that’s not high value in my experience, what I care more about is my own classification.
For this, starring and in which series image belong are key, I still do this manually, but there’s help to get the classification for subject and environment, hence my setup. Especially because I have tons of photos left untagged after I moved to dt from Lightroom, and I didn’t see myself running through 70k images haha.
Plus it’s just interesting to play with local models and explore what they can do.
Actually I didn’t know that there are AI features in DT so maybe I’ve been doing something not useful as I haven’t checked AI features yet. Reading the forum I feel that I stand some kind of middle ground here. I’m not using AI blindly but if it can reliably tag my 70k photos once, run on a local setup without crazy power, and I can use this to practice understanding/writing python, then why not
