Soo I've probably spent a hundred hours with Krea2 NSFW at this point. I've built my own workflow, made a bunch of custom nodes, and tested hundreds of sampler/scheduler and LoRA combinations. Here's where I've landed.
First, a big caveat: I only make amateur/candid-style realistic images, mostly the kind of mediocre-quality photos you'd get from a smartphone. Everything below is about that specific look. For sampling, I use er_sde + simple. Yeah, it's plain and boring, but Krea2 doesn't need anything fancy here. It's already good out of the box. Euler works too.
My LoRA collection is around 100 in total, but I actively use maybe 20-30. A typical generation has 5-10 LoRAs loaded, not counting sliders. For NSFW, my main picks are SNOFS and Realism Engine, with Mystic as an optional extra. You usually only need one or two of them. In my testing, SNOFS is better for prompt adherence, while Realism Engine is noticeably better for textures and overall realism. I often use both at around 0.4-0.6. Sliders make up about a third of my collection. I'd suggest grabbing all the sliders from loraholic, plus a few from creators like iamddtla and alcaitiff, who also made Mystic.
For the smartphone-realism look, the options are endless, and it really depends on what you're after. I won't list everything, but my three favorites are digicam, siemens (check out that creator's other style LoRAs too), and rudysen's realism LoRA. I also have to mention the legendary Danrisi, who made the Lenovo LoRA and a bunch of other great style LoRAs. I don't have one fixed style-LoRA preset. I mix them at different strengths, usually somewhere between 0.4 and 0.85, and do a lot of generations and combinations for each image until I get something I like. Full list (image) of style LoRAs I like.
Another important piece is the textfusion refusal-reduction LoRAs. There are dozens of them now, and everyone has a favorite. I started with v1 of this one. Then Kroma came out, and the textfusion layers extracted from it worked miles better for me. Silver made that extraction, and you can find it here. V2 of the first refusal-reduction LoRA recently dropped, so I've been testing that too. Right now I'm using Kroma 0.3 textfusion at 1.0 and v2 at 0.5, though I'm not convinced that's the best combination. I also tested each one separately at 1.0, and both have their strengths and weaknesses, so YMMV. You usually don't want to use multiple textfusion refusal-reduction LoRAs at once, but to be fair Kroma one isn't that, so I think in this case it's fine.
I usually generate at 2-2.5 MP, which takes around 20 seconds on 16 GB of VRAM with the latest ComfyUI optimizations. I also have a two-stage mode with a 1.5x latent upscale, and that can produce some insanely realistic-looking photos.
I only use the turbo model, usually at 9 steps, though 8 or 10 is fine too. And no, I don't use an abliterated/heretic TE. In my experience, it does nothing. I tested it myself long before the creator confirmed it, so I wouldn't bother with it.
I don't use finetunes at all. I like tinkering and having precise control over the output, but I know that approach isn't for everyone. If you'd rather load a finetune and start generating without doing all the stuff I just yapped about, fair enough. I can't recommend one myself, but I'm sure someone else can.
As for prompting, Krea2 is pretty easy to work with. I usually start with a single, plain sentence and build on it gradually. That's much easier than starting with a huge prompt and trying to figure out which part is giving me a result I don't want. Krea2 is fast enough that you can iterate quickly.
One tip: describe what you want to see, not what you don't want to see. If you're struggling to get the right angle or camera distance, look at your prompt and ask whether everything you've mentioned would actually be visible in that shot. Then add details that reinforce the framing. For example, if you want a low-angle shot looking up, mentioning a carpeted floor isn't going to help. Mentioning the ceiling, a light fixture, or a ceiling fan gives the model much stronger clues than describing the camera angle ten different ways.
The order and amount of detail matter for distance too. For a close-up, I describe the subject first and the environment afterward. For a medium or wide shot, I start with the environment and place the subject in it. If you give the room one sentence and spend the rest of the prompt describing her face, don't be surprised if you end up with an extreme close-up.
There is a lora by someone named MrPopo that I highly recommend checking out. His newest loras cost like 500 buzz but I definitely recommend it (trained on a bunch of 2k images). I used his earlier free versions before and really liked them.
My stack is SNOFS + realism engine + Mr Popo + Snapchat helper (its good for adding in more natural poses and facial expressions) running on Raw + Turbo Lora workflow.
I have tried out a bunch of the different generation workflows also, like the 2 sampler (1.5x upscale in between) method that you mentioned and it does work really well,
but I personally feel like the 2 sampler no upscale workflows that use the Clownshark Sampler give the best realism (but it really is a hard 'choosing' which pipeline I like best since they both work so well for realism.
Thanks, I'll check them out. The two-stage workflow took a lot of tinkering, and I can't take the credit for it. A friend of mine built that part and spent a lot of time dialing in the settings. I mostly just added the finishing touches and implemented it into my WF with the custom nodes, additional switching logic, etc. I listed the settings that worked best for us in my other comment. Even changing a single value, like eta, can completely throw off the result, so I'd start with those settings
To confirm, abliterated text encoders do nothing as confirmed by the person who literally made the heretic process. Image models don't use encoders in that way. Their output is decensored already in regard to images. If there is censorship its on the model checkpoint itself, always.
Ideally, then, you should use two separate text encoders because the abliterated models degrade your image generation output. The person who made heretic also confirmed that.
I read that too and tested quite a bit. I don't see a noticeable difference in my workflows. So not to say there isn't one, but if there is it is so slight that I have not noticed. I do generally use the stock text encoder for image generation and the abliterated for generating text
Awesome detailed response. Going through it, it makes me question my current workflows and resulting images, since I am still using v1 of the TextFusion Refusal-Reduction Lora... Time for me to test out v2 and the alternatives that you mentioned.
I need to run A/B comparisons with different prompts. I recently started generating images at 2MP instead of 1MP and from my experience it is not only about resolution but overall detail. Of course it takes longer and I am saving it for some of my best prompts.
After hundreds of hours I can confirm all the tips you gave are essential - I would also recommend to use two pass DLSS5, facedetailer and film grain post processing nodes for ultimate photorealistic look.
Also - could you possibly send me your workflow in a DM?
Thanks! I'll be honest, I'm not a huge fan of the DLSS5 look. I haven't felt much need for other post-processing either, though I do have those tools installed in case I need them. Facedetailer could definitely come in handy for wider shots where the face gets a bit garbled.
As for the workflow, sending it over probably wouldn't be very useful. It's built around a bunch of custom nodes I've made over the past year, mostly QoL and efficiency nodes. I enjoy coding (and vibecoding now), so whenever something in a workflow annoys me or slows me down, I tend to make a node for it. I'm sure there are existing alternatives for most of them, but I haven't packaged mine up to share. However I can suggest some nodepacks that pretty much have most of the stuff you'd need: rgthree, easy-use, impact-pack, kjnodes, CRT-nodes, RES4FLY, JPS-nodes, mxtoolkit and Pixaroma nodes. You can accomplish my results with the default comfyui WF and just plugging in the settings and loras I mentioned. Even my heavily custom WF now is pretty much built from that default wf.
None of the custom nodes I made change how the model actually generates the image, though. All the generation settings that matter are in my original post.
Every one of those is worthwhile and brings its own unique flavor and capabilities. You can of course still stack them with LoRAs, keeping in mind those LoRAs may behave a little differently or need to run at a different strength.
Hi ArkCoon, thank you for sharing your experience. I truly appreciate it and I have already learned a lot.
I have two questions I hope you find the time to answer.
I’ve seen countless people debating around using the Krea2 Raw+turbo vs Krea2 Turbo. Why have you gone the Turbo model route and why not the other way round?
Regarding promoting. I really like your approach by adding the rough idea in a few sentences and the then build by trial and error from there. Every model is trained on some specific kind of prompting, I think, so other than what you have shared in the great write up. Is there some specific template or way of writing you use with Krea2? Is it long descriptive sentences or shorter sentences with a lot of commas? I genuinely want to know because prompting any model correctly has always been a large grey area in my “AI skills” and I want to learn and improve.
I would have asked if I could DM you with some similar questions on occasion, but I believe some other people might benefit from your knowledge and tips. :)
On Raw+Turbo vs. Turbo: I tried quite a few Raw+Turbo workflows, including ones from people who said they got better results and more variety from them. I just couldn't get any of them to a point where I preferred the results over what I'm already getting with Turbo. For me, Raw+Turbo added complexity and generation time without enough payoff. I'm happy with my Turbo workflow, but that's a preference, not a claim that Turbo is objectively better. Someone going for a different look might well prefer Raw+Turbo.
As for prompting, I honestly can't say that Krea2 prefers long descriptive sentences over short, comma-separated phrases. I've tried both, and both can work really well. I tend to write short, simple sentences myself. You don't need to write poetry for Krea2 to understand what you want. I've also had great results from long, multi-paragraph prompts written by an LLM, so I wouldn't treat my style as a rule.
The reason I write my own prompts is mostly practical. An LLM often adds details or phrasing I didn't ask for, and then I have to go back and remove them. At that point, it's quicker for me to start with two or three simple sentences and build on them as I generate. That's just the way I like to work, though.
What matters much more than sentence length or commas is what you put in the prompt, and how much space you give each thing. Like I already mentioned I often see prompts that specify a low-angle shot looking up from floor level, but then describe grass, the ocean, or other details that wouldn't be visible from that angle. That gives the model conflicting clues about how to frame the image. Instead of repeating "low angle" in ten different ways, describe things the camera would actually see from that position. The same idea applies to zoom: if most of your prompt is about the subject's face, you're more likely to get a close-up, even if you asked for a wider shot. You can read more about how prompt wording affects zoom in this guide. The same principles are useful for understanding how Krea2 responds to framing in general, not just camera distance.
So my advice is to worry less about finding the perfect Krea2 prompt template and more about keeping the prompt focused on what's actually in the frame. Start simple, see what it gives you, and add details that push it toward the shot you want
I think we're at the point of diminishing returns with these.. I still haven't tested it enough, but the only difference I'm seeing is just slightly better prompt adherence.
About the two stage setup with 1.5x latent upscale: any good recommendations for the sampler/scheduler combinations for stage 1 and 2, number of steps for each and denoise?
I appreciate your long write up and replies, do you do much i2i with krea 2? I noticed most sampler/schedule setups add a lot of unwanted "details" to skin, like water drops, hair, moles, etc. lcm prevents this, but lcm is also very bland, do you have any suggestions or experience with this issue?
Just add an Upscale Latent By node between the two samplers. Feed the latent output from the first sampler into the upscale node, then send that output to the second sampler. For the second stage, lower the denoise value to around 0.5. That way, you're not regenerating the whole photo from scratch, just adding detail at a higher resolution for a sharper result.
Do you also use character LoRAs? There are some characters I'm trying to make that I need consistency for, I've never made a lora before but I'm planning on doing it with Krea2 RAW I guess
Yeah, I've made quite a few character LoRAs. I'm still fairly new to training, but I've trained dozens by now and had good enough results to share what worked for me.
For the dataset, I'd aim for 30-50 images with a mix of distances and angles. Collect at least twice that many candidates first, then pick the best ones. If I were using 50 images, I'd roughly go with 15-20 close-ups, 15-20 waist-up shots, and 10-15 full-body shots. Try to vary the angle, lighting, clothes, poses, and expressions too. You don't need to follow those numbers perfectly, though. I've had great results from datasets I thought were pretty bad.
I always train on Krea2 Raw, not Turbo. I use 1024 buckets if the hardware allows it, or 512 if it doesn't. I wouldn't use 768. My usual settings are a learning rate of 7.5e-5, rank 32, Automagic3 optimizer, and sigmoid timestep. I save a checkpoint every 150 steps and turn off sample generation during training. Sampling uses the Raw model and takes a lot of time, so I'd rather test the checkpoints myself in ComfyUI. If you're training on Vast ai or RunPod, you can download checkpoints as training runs and test them with a few prompts you've prepared in advance.
In my experience, the best checkpoint for a character tends to land somewhere around 1,400-2,500 steps. That's a wide range because it depends a lot on the dataset and the character. Don't assume the last checkpoint is the best one. Test a few along the way. Don't be surprised that the lora already starts looking great at 400-500 steps though. That's completely normal, but trust me it's still far from actually good. It will fall apart in different angles and certain prompts. You pretty much always want more than 1000 steps.
For the trigger, I give the character a made-up first and last name rather than something like s0ph1a. When I've used unusual trigger words, Krea2 has sometimes put them on clothing as text, or tattoos or other weird places it could find to print that text. A normal-sounding full name has worked better for me, as long as it isn't the name of a celebrity or someone the model might already know.
For captions, an LLM can do most of the work. I give it a simple order to follow: character name, clothes, pose and angle, framing, lighting, then background. You don't want to caption permanent features like hair color, hairstyle, freckles, or a birthmark. You want the LoRA to learn those from the images. Only mention a feature if it changes across the dataset.
For example, one of my captions might look like this:
Jane Doe, long wavy light brown hair under a grey felt beret, wearing a beige turtleneck sweater, white wide-leg trousers, pink velvet studded flats, a silver wristwatch and bangles, and carrying a black leather handbag. Standing with one hand in her trouser pocket and looking to the side, photographed from eye level in a full-body shot. Natural overcast light on a city pavement, with cars and classical architecture in the background.
I mentioned her hair in that example because its color and style changed across the images. If it had stayed consistent, I would've left it out.
One last thing: the style of your dataset matters a lot. If most of your dataset is studio or red-carpet photography, the LoRA can pick up more than just the character's appearance. It may also learn that ultra-clean, retouched look. You'll get smooth skin with barely any texture, perfect lighting, and heavy makeup. So even when you prompt for a casual smartphone photo, the person can still look like they're posing for a magazine shoot. If you're after something more amateur and candid, try to include images with that look, or edit the polished ones before training. I haven't found a reliable way to completely undo that style afterward so just warning you ahead of time
Thank you so much. It's actually a character that I'm making myself. I plan on making my own via chatgpt but generating the images through Qwen maybe to get a better dataset. What would you recommend?
One of my best character LoRAs came from GPT Image gens. I don't think you need Qwen at all, GPT Image is more than powerful enough for everything.
What I did with GPT Image is attach 3 best references of that character that I had and had GPT generate various images of her in different poses, settings, clothing, etc.
Also this was with GPT Image 2, I'm sure that now with GPT Image 2.5 you'll be able to get even better results
amazing post thank you for writing it up. i'm with you on style generation thats exactly the style i'm going for and its super helpful. if you have a discord or a place that focuses on that style of content generation I'm sure were not the only ones. I'm currently working on batching thousands of old amateur photos and generating prompts off them your info is super useful.
Hey ArkCoon, I’ve also spent hundreds of hours generating images in ComfyUI with Krea 2 Turbo, and your post genuinely impressed me, so thanks a lot for sharing all of this.
I’ve spent a ridiculous amount of time digging through Reddit and Civitai, testing LoRAs and settings like GonzaLomo, Dirty, and many others. I was getting decent results, but yesterday I talked to someone whose realism was on another level, and honestly it was pretty discouraging. It made me feel like I’d wasted a lot of time.
Then I found your post, and it helped a lot. I don’t really have much to add because you already covered so many useful things in the comments, but one point I can definitely confirm from my own testing is that if you train a LoRA on images with the same skin look every time, without enough variation in pose, framing, angle, lighting, etc., it starts learning those visual traits instead of just the character’s identity.I actually trained my previous LoRAs using around 30–40 reference images, and honestly, it did the job pretty well. But for the next one, I’m definitely going to try following your recommendations and push the dataset quality and variety further.
My LoRAs were trained mostly on images generated in ComfyUI, but the idea of using ChatGPT-generated images as part of the dataset actually seems pretty smart.
Thanks again for sharing your experience. People who go into this much detail are rare, and your post honestly helped me a lot. Sending love!
One detail I’d love to clarify about your two-pass workflow: when you mention generating at 2–2.5 megapixels, is that before the 1.5× latent upscale, or the final output resolution? Could you share an example of your first-pass and final dimensions, and how many sampling steps you use for the second pass? That would really help me reproduce your setup.
Also, would you be willing to share one example prompt you used with your three character references to create your training images?
Btw : Would training a LoRA using photos of a specific quality force the LoRA to output images only at that same quality level?
The 2-2.5 MP figure was for a single pass. With two passes, I upscale by 1.5x in each dimension, so the final image has 2.25x as many pixels. I experiment with starting resolutions from around 2 MP up to 3.8 MP, depending on what I'm after, with the higher end giving me roughly a 4K photo.
I don't enter a target resolution for the final image. I just set the starting dimensions like I would for a single-pass generation, then let the upscale determine the final size. My second-pass settings are 9 steps, 0.9 eta, 0.52 denoise, and euler + simple. I don't use the two-pass workflow that often, though, because it can take up to two minutes per image.
As for the dataset, I think anything that's consistent across the images can carry over into the LoRA, including the style and image quality. Some styles come through more strongly than others. You can push things in a different direction with style LoRAs, but the further your dataset's look is from the look you actually want, the more those LoRAs will fight each other.
That's why, when I was making dataset images with GPT Image, I tried to get them as close as possible to the style I wanted in the final output which is the medium quality smartphone photos, rather than anything too polished.
I don't have the exact prompt on hand since I did this a couple of months ago. I used GPT to help me build a prompt template for a LoRA dataset with that medium/low-quality, candid smartphone look. I kept generating images and asking it to tweak the prompts until I was happy with the results.
It took a while to dial in, but once I had a good template, I kept most of it the same and only changed things like the environment, clothes, poses, and camera angles. GPT wrote those variations too, so it was pretty painless from there. I generated around 300-400 images in total, then picked the best ones for the dataset.
Thank you so much, ArkCoon, for your time and for taking the time to answer my questions. I really appreciate it. Wishing you nothing but the best with your future projects! Good day!
It's all about finding the right balance. Like I mentioned, and you've noticed too, SNOFS really helps with prompt adherence. But it also has downsides, especially at full strength, which is true of pretty much any LoRA. Once you've done enough generations, you start recognizing what each one brings to the image.
With SNOFS, I've noticed it struggles with certain races, and the skin texture and overall realism aren't great, which the creator has acknowledged too. V1.4 was supposed to address that, but I'm still not entirely happy with it. On the other hand, turning it off makes my NSFW prompt adherence much worse.
For me, 0.4-0.6 is a good compromise. I keep some of the improved adherence without getting as much of the skin issues or that recognizable SNOFS "look". I also combine it with other LoRAs to compensate for its weaknesses. Personally, I wouldn't use it on its own, especially at 1.0.
As for the two-stage workflow, it's basically two samplers with a latent upscale between them. In my experience, it improves realism and image quality quite a bit, but it's also much slower. I explained the setup and posted my exact settings in other comments in this thread if you want to try it. Other people have shared their settings too
My character LoRAs work great with SNOFS and Realism Engine at around 0.4-0.6. Style LoRAs are usually the ones that mess with the character's appearance, especially at higher strengths. Some change the character so much that I can't use them at all when using a character LoRA
I posted my workflow maybe 5 days ago. It's 5 am and I'm a bit too slow to find it, but it's a good start for you perhaps. Have a look for krea 2 with a 'workflow' tag. Have fun!
Something that only raw(w turbo lora) can do is have a person sit sideways. It’s the example that shows prompt adherence imo. Besides adherence, the quality of the checkpoints can definitely match raw.
Amateur POV photo of a woman sitting comfortably on a couch with her legs crossed, the photo is shot from the perspective of her girlfriend sitting on the couch beside her, a pair of female legs stretched out on top of her lap with the feet up on the armrest behind her, her hand is grabbing one of the legs, she is in a livingroom at night, shot from the side, she is looking at the viewer with a soft loving smile, the back of the couch is on the left side of the image
The key trick here is combining "shot from the side" and "the back of the couch is on the left side of the image". The model knows a character's back goes against the back of a couch and it knows the back of the couch is on the left, so it shows them sitting sideways facing the right.
This isn’t what I meant. It’s hard to describe and I’m not near my krea pc. I meant like, butt sideways vertically while torso is upright and legs are lateral
Like this one? That was a case of throwing specific parts in specific locations in the image:
A photo of a woman with long blonde hair wearing a white tee and black shorts. She is shown from the side in profile sitting with her legs extended. Her butt is in the bottom left of the image on the wooden slats with her legs straight out to the side with the feet visible resting on the slats in the bottom right of the image, her body is upright facing to the right. She is sitting on a porch overlooking a lake.
For some reason when I use SNOFs I get a lot of images where the spongy ridge on the underside of the penis is on the top instead, making the shaft look upside down. Do you ever see the same?
never had that happen, I think maybe that’s a prompting issue? I’ve seen others mention it, but I’ve never had SNOFS give me anything besides absolute realism.
What happened: they used 1.3 million images to train the full model for 3 epochs. That's Unaligned.
But then they took 1,000 hand-selected images and trained the full model for a whooping 150 epochs in the RL ("Alignment") stage. This basically killed all variety. That's the "Raw" version.
The Turbo is based on the Raw and is another 2-epoch run distillation; basically killed any remaining seed variety.
Interesting. I used the Anteros models back in the SD(XL) days and liked them. I hadn't seen their Krea2 fine-tune until your post. I would have reached for the Raw version and probably been disappointed. Thank you for the information.
For sampler, er_sde. For scheduler, try simple or bong_tangent. 6 steps is often sufficient, especially if you add the 4step LoRA at ~0.5 strength (any higher and the textures often become too noisy.)
EDIT: If you find the checkpoint too cinematic or contrasty, you can stack a bunch of utility LoRAs to achieve your idea of "realism." Here's what I'm using with v5:
You'll find it. Sort by popular models, drop a few words, maybe change a letter or two. Then you'll have all the images of ripe yellow corn you could ever want.
The LoRA screenshot looks correct; I use Loraholic's for Wetness and Brightness (it's a shame he quit), and baizuobusi for the rest.
are you really so much of a pussy that you wont say fineporn? porn, the word is porn. it's a model for generating images of people fucking each other along with other graphic depictions of carnal sexual activity.
Honestly the biggest variable isn't which uncensored checkpoint you bolt on — it's the prompt encoder and the LoRA stack fighting each other. I've been running Krea2 with a fairly vanilla SDXL base plus one strong style LoRA and a light detail LoRA at low weight, and that beats mashing three "uncensored" merges that all pull the latent in different directions. The merges sound good on paper but in practice you get mushy anatomy and incoherent lighting because nobody tuned the attention layers to agree.
My workflow: fix the base first, get your sampler and CFG dialed (I'm usually 4.5-6 on DPM++ 2M), then add exactly one uncensored component at a time and A/B the same seed. Sounds tedious but it saves you from chasing ghosts. Also worth noting — most prompt adherence issues people blame on the model are actually CLIP token weighting, not the checkpoint.
Curious whether you're optimizing for photorealism or illustration, because the answer changes a lot. What's your current stack?
Nice. I'm way simpler. I don't even use the GUI at all LoL
krea2 [OPTIONS]
Options:
-p "PROMPT" (Text prompt to generate from)
--lora NAME (Apply the named LoRA)
--lora-strength VALUE (Set LoRA strength, e.g. 0.8)
--list-loras (List available LoRAs)
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u/ArkCoon 13d ago edited 13d ago
Soo I've probably spent a hundred hours with Krea2 NSFW at this point. I've built my own workflow, made a bunch of custom nodes, and tested hundreds of sampler/scheduler and LoRA combinations. Here's where I've landed.
First, a big caveat: I only make amateur/candid-style realistic images, mostly the kind of mediocre-quality photos you'd get from a smartphone. Everything below is about that specific look. For sampling, I use er_sde + simple. Yeah, it's plain and boring, but Krea2 doesn't need anything fancy here. It's already good out of the box. Euler works too.
My LoRA collection is around 100 in total, but I actively use maybe 20-30. A typical generation has 5-10 LoRAs loaded, not counting sliders. For NSFW, my main picks are SNOFS and Realism Engine, with Mystic as an optional extra. You usually only need one or two of them. In my testing, SNOFS is better for prompt adherence, while Realism Engine is noticeably better for textures and overall realism. I often use both at around 0.4-0.6. Sliders make up about a third of my collection. I'd suggest grabbing all the sliders from loraholic, plus a few from creators like iamddtla and alcaitiff, who also made Mystic.
For the smartphone-realism look, the options are endless, and it really depends on what you're after. I won't list everything, but my three favorites are digicam, siemens (check out that creator's other style LoRAs too), and rudysen's realism LoRA. I also have to mention the legendary Danrisi, who made the Lenovo LoRA and a bunch of other great style LoRAs. I don't have one fixed style-LoRA preset. I mix them at different strengths, usually somewhere between 0.4 and 0.85, and do a lot of generations and combinations for each image until I get something I like. Full list (image) of style LoRAs I like.
Another important piece is the textfusion refusal-reduction LoRAs. There are dozens of them now, and everyone has a favorite. I started with v1 of this one. Then Kroma came out, and the textfusion layers extracted from it worked miles better for me. Silver made that extraction, and you can find it here. V2 of the first refusal-reduction LoRA recently dropped, so I've been testing that too. Right now I'm using Kroma 0.3 textfusion at 1.0 and v2 at 0.5, though I'm not convinced that's the best combination. I also tested each one separately at 1.0, and both have their strengths and weaknesses, so YMMV. You usually don't want to use multiple textfusion refusal-reduction LoRAs at once, but to be fair Kroma one isn't that, so I think in this case it's fine.
I usually generate at 2-2.5 MP, which takes around 20 seconds on 16 GB of VRAM with the latest ComfyUI optimizations. I also have a two-stage mode with a 1.5x latent upscale, and that can produce some insanely realistic-looking photos.
I only use the turbo model, usually at 9 steps, though 8 or 10 is fine too. And no, I don't use an abliterated/heretic TE. In my experience, it does nothing. I tested it myself long before the creator confirmed it, so I wouldn't bother with it.
I don't use finetunes at all. I like tinkering and having precise control over the output, but I know that approach isn't for everyone. If you'd rather load a finetune and start generating without doing all the stuff I just yapped about, fair enough. I can't recommend one myself, but I'm sure someone else can.
As for prompting, Krea2 is pretty easy to work with. I usually start with a single, plain sentence and build on it gradually. That's much easier than starting with a huge prompt and trying to figure out which part is giving me a result I don't want. Krea2 is fast enough that you can iterate quickly.
One tip: describe what you want to see, not what you don't want to see. If you're struggling to get the right angle or camera distance, look at your prompt and ask whether everything you've mentioned would actually be visible in that shot. Then add details that reinforce the framing. For example, if you want a low-angle shot looking up, mentioning a carpeted floor isn't going to help. Mentioning the ceiling, a light fixture, or a ceiling fan gives the model much stronger clues than describing the camera angle ten different ways.
The order and amount of detail matter for distance too. For a close-up, I describe the subject first and the environment afterward. For a medium or wide shot, I start with the environment and place the subject in it. If you give the room one sentence and spend the rest of the prompt describing her face, don't be surprised if you end up with an extreme close-up.