Snakelite is an offshoot of Snakebite 2.4 that strives for realistic, pleasantly-imperfect photography. It is more experimental than Snakebite and can change drastically from version to version.
Note: All demo images generated using DMD2 LoRA at full strength.
❤️ If you enjoy Snakelite, you can help offset the cost of training:
⚠️ IMPORTANT:
This model uses Flow Matching, so you must connect it to the ModelSamplingSD3 node in ComfyUI to get correct results. Even better is ModelSamplingSD3Advanced.
🤓 Technical details
Over the course of Snakebite 2, I developed a complex merging chain that consisted of around 15 LoRAs - each contributing to model consistency, fine detail, and vivid colors. I'm very happy with the results and I believe v2.4 represents the peak of SDXL realism in some ways.
However, these efforts came at a cost. Snakebite has a tendency to make things look "too perfect" in a Hollywood sort of way. This limits facial diversity and affects compatibility with your LoRAs. It can also suffer from "stylistic collapse": if your prompt is very complex or unusual, Snakebite may struggle to maintain the intended style in its pursuit of aesthetic perfection.
Enter Snakelite:
I have radically simplified my merging chain down to 5 LoRAs. I'm still trying to improve the baseline quality of bigASP, but only in ways that avoid the side effects described above.
I mixed in some Lustify to impart an "amateur photography" feeling. As a result, Snakelite may have potential as a successor to the beloved-but-aging BigLust 1.6/1.7.
Snakelite is still finetuned on the same 1400 images as Snakebite 2.3 for improved aesthetics.
Which version is best is a matter of taste, but I'm guessing there are many Civitai users who may prefer the look of this new model. Let me know what you think!
👍 Advantages over Snakebite v2.4
Colors are more natural, and photographs look more like photographs.
Small faces have more detail.
Better at retaining photographic style even if pushed hard with crazy prompts.
Highly compatible with LoRAs trained on BigLust. (edit - this specifically applies to v1.0, not v1.1)
A bit more tolerant of "quality-boosting" embeddings and LoRAs. You can use DMD2 at full strength or experiment with your own acceleration stacks.
👎 Disadvantages
Not as vivid, sharp, or as contrasty as Snakebite 2.4.
Lower success rate with limbs, extremities, and complex interactions.
Concepts that are unique to bigASP 2.5 are not represented quite as well.
🛠️ Recommended Settings
>=v1.1 (special because they use bigASP 2.6):
8-9 steps
LCM sampler
Beta scheduler
CFG 1
DMD2 LoRA at full strength
ModelSamplingSD3Advanced; decay from 3 to 1
👉 Optimized Workflow (HIGHLY RECOMMENDED) 👈
https://pastebin.com/pZ022SD4
v1.0 with DMD2:
6-9 steps
LCM sampler
Beta, normal, or simple scheduler
CFG 1
Model shift of 3
v1.0 without DMD2:
25-40 steps
Euler ancestral sampler for speed, dpmpp_2s_ancestral for quality
Simple scheduler
CFG 4-6
Model shift of 3
Negative prompt strongly recommended (e.g.
worst quality)
Note: increasing the model shift may improve prompt adherence at the cost of quality. This is particularly useful with character LoRAs. Try a value between 6-8.
Thank you. As always, I look forward to your feedback. Please share the model and upload some images to help it gain traction. It would be amazing if we could make Snakebite eligible for Civitai's onsite generator someday!
Description
Small update:
Swapped out both CLIPs with BA 2.5; improves overall color and aesthetics, while the UNET remains based on BA 2.6
Slight aesthetic boost using a couple LoRAs
As a result, Snakelite v1.2 is like an early "preview" of what Snakebite 2.5 might be. I will probably wait for the final build of bigASP 2.6 before attempting to use its CLIP again.
BTW, Snakebite 2.4 = most cinematic, best prompt adherence. Snakelite 1.2 = most photo-like
Have fun and be well!
FAQ
Comments (14)
I think 1.2 is another improvement overall. If you can somehow manage to make the style more coherent (sometimes it feels like single words completely change the style, even if they arent intended to) it would be great!
Greatly looking forward to what you cook up with Snakebite next
Thank you 🙂 I'm always looking for ways to improve the consistency - would you mind providing a couple examples of terms that cause style drift?
(Also, make sure you start every prompt with photograph of... if you're not doing that already!)
@liftweights one word that I can think of immediately is "fantasy", even if you refer with it to the clothing for example.
"photograph of 1 woman wearing casual (-fantasy-) clothing"
seed:1264519153
Sure, fantasy is a bit brighter and weirder, but the shift is strong imo.
@XpomulCivi Ah, good example - thanks. This is the result of SDXL's aging CLIP architecture. Compared to modern LLMs, CLIP is not very good at understanding intent by contextual clues, and it's kind of a miracle that it works for image prompting at all. So its knowledge of "fantasy" vastly overpowers any secondary meaning like "fantasy clothes." The model has to undergo a lot of captioned training to perform otherwise. (I still think it's amazing that phrases like "hands behind back" don't end up generating hands - which is thanks to a ton of community effort)
Anyway, prompt scheduling is probably the best workaround right now. You can use comfyui-prompt-control with a prompt like [casual clothes:fantasy clothes:0.2] and it will switch to "fantasy" 20% into the generation. Example results:
@liftweights thanks for the tip again, yea, SDXL in general has a tendency to not really understand context, Ive also tried Comfy Cutoff for a bit but to not much success.
That being said, I do think youve done a great job so far getting so much out of it. I cant really use even ZIT due to aging hardware (dreading the update, RAM prices say hello), but Snakebite is Id say a good alternative together with a bit of refinement with e.g. Cyberrealistic
Another question, does Snakebite/-lite work with Negative Attention Guidance (NAG)? Negpip works great with it, but I cant get it work with Krita Regions.
From my testing so far NAG had no effect, but that might just be because of the setup.
Hmm, the issue is indeed probably related your setup; I just tried NAG via the sd-perturbed-attention node for ComfyUI and can confirm that it does change the output of Snakelite 1.2. It also slows down inference by ~50%.
https://huggingface.co/fancyfeast/bigaspv26-training/tree/main
Fancyfeast put out a complete model for bigaspv26 on huggingface.
Whoa, thanks for the heads up! I'll try merging this into Snakelite soon.
@liftweights looking forward to that!
Done 🙂
Unexpected and greatly appreciated surprise to see this @liftweights , thanks so much for another truly unique creation / model / contribution <3
My pleasure! I still really enjoy working with SDXL even though Klein and ZIT have a lot to offer.
@liftweights I would argue your sdxl snake models are a beast entirely of there own - everyone else just seems to merge the same old tired sdxl models and they put new labels on it - where you on the other hand have something that actually produces uniqueness in bright contrast to the competition that has been doing the samethings for the last 3+ years lol >.< ** on a side note though Klein 9B specifically I think with more lora infusions which are nearing what sdxl was when it started to get good it will soon surpass sdxl due to the LLM text encoder prompt adherence is just flat out superior . especially when coupled with like : https://huggingface.co/huihui-ai/Huihui-Qwen3-8B-abliterated-v2 instead of the standard recommended - few people know though you can use ^ that though and many others of the same variety. worth noting too that the 4B version of that ^ linked model also works with Zit - (gguf version): https://huggingface.co/mradermacher/Huihui-Qwen3-8B-abliterated-v2-GGUF





