Frisky Dingo🐕 (, IL-XL Hybrid) - CivArchive (CivitAI Archive)

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⚠️⚠️⚠️ This is a DDPM/DDIM optimized model. Karras should be avoided 99% of the time. ⚠️⚠️⚠️

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Frisky Dingo🐕 is a hybrid model that blends IL composition with XL Photorealistic outputs.

(Highly optimized for DDPM / DDIM_Uniform - Karras should be avoided except in rare cases).

This is a hybrid model with a heavily IL biased CLIP, (primarily iLustMix v7), & an XL biased UNET. It can use IL, XL & PONY LORAs to varying degrees, though you may need to adjust the weights a bit, (situationally depending). Poses and concepts tend to work well while things like artist styles generally don't translate to realism, but can still be compositionally useful. (I'll have a section @ the bottom for additional notes on prompting, LORAs & settings which I'll try to expand on it over time).

I used upscaling & a detailer in most example images, (4x_RealWebPhoto-v4 and / or 4x_foolhardy_Remacri).

V1 Suggested settings:

VAE: sdxl_vae (baked in).
Clip skip: 2 was used during merge, (setting 1 or 2 should yield same results).
Samplers/Schedulers: DDPM / DDIM_Uniform (will yield the best results consistently). DPM++ 2M SDE & DPM++ 3M SDE / SGM Uniform or Euler Ancestral, (even karras in rare cases), can be useful in a multi-pass where you're alternating between samplers. (experimentation is recommended).
Resolution: all standard 1MP resolutions work well in portrait and landscape, (depending on scene complexity). I often use 1024x1360, 1120x1440, 1232x1584, and occasionally 1344x1728, in my initial generation. Some scenes will benefit from the higher resolution in both composition & detail, but you will loose some prompt adherence and see more errors in the highest resolutions.
CFG: 3.8 - 7 (I typically use 4.6, -5.0 in my initial generation). A more stylized look will tend to creep in @ higher CFGs.
Steps: 32 - 38 (I use 36 most often, but I find 38 is sometimes required to nail a pose in more complex scenes).
DMD2 / LCM: I like to set the LORA strength low (around 0.6) & the CFG high (about 1.6-1.7) with 12-16 steps. This allows neg prompts to work & I find it to be the right balance of speed and output quality. (I'll expand on multi-pass refiner / upscaling once I do a bit more testing).

Prompting:

Natural language prompting supported by Danbooru tags. Generally less is more, (this model takes things very literally & has a slight learning curve, but I promise it's worth the time). For best results try to write a clear concise prompts summery in natural language, followed by tags to fill out details. A photo centric approach is best when trying to push realism. Too many IL tags associated with anime will start to push you into semi-realism, they are however effective and can be useful in composition. With a little time getting used to the nuance, you'll find the balance point where you can get an IL biased composition with photorealistic output. Quality tags should generally be put last, (look to my sample images for examples and general formatting). I'll add more on this in the notes section over time.
positive prompts - responds well to camera related tags: photorealistic, raw photo, amiture photo, depth of field, Fujifilm XF 50mm f/2 R WR lens, 35mm film, bokeh, etc.

negative prompts - I generally recommend keeping sepia in your negatives to overcome a sepia bias in lower CFGs. (artificial, anime, illustration, unreal) up to a weight of :1.4 if needed. (always best to keep weights as low as you can to achieve the scene). (board expressionless:1.2), is useful for getting away from the default XL poker-face.

Check in from time to time for for additional info: Prompting Insights, LORA Settings, Gen Settings and anything else useful I can think of, I'll be expanding on the Notes section over time. (see bellow).

(This was a passion project that I obsessed on for 3 weeks. I hope you dig it).

🗈 Notes & Tips:

(to be expanded over time),

Notes: Frisky essentially started as a "full- Realism" branch of Fabled Infusion, they have very similar ingredients but in different ratios. While testing the latest Fabled version and discovered that the CLIP had picked up some issues over the iterative versions, so I started from scratch. I followed a very similar process to what I did with FI, but I avoided the mistake of using LORA's to stabilize sub-mixes, before a final mix. (I suspect that was the biggest issue with my process). I used a couples LORAs here, but only subtly in the final step. Additionally, I rebuilt the clip in the final step. I think this is my most cohesive merge to date. It should be quite similar to what you're used to with FI, but way more photorealism biased. It also takes most LORAs better, (at least in my testing so far).

LORAs: It's best to split CLIP & Model strength if your UI allows for this. If I'm using an IL or pony pose LORA, I tend to set the CLIP at about 0.92-1.0 & the model much lower, (I test to find the point where I can get the pose, without influencing the visual output of the checkpoint), This tends to be in the 60-80 range. If you don't have this option you can just set LORA strength a little lower until you find the best balance point, (but it's very nice to have fine control over this).

Prompting: Your prompt should be structured in order of importance / what you want to see in the foreground, followed by additional details, and finally medium / quality tags.

FD handles specificity well, I.E "Gibson Les Paul guitar" will yield much better results than guitar.

Be aware that the model can be hypersensitive some tags & phrases. This is true across most SDXL models, but I notice it hear. For example using terms like "hyperdetailed warm skin texture with viable pores" or "freckles" can overcook your output, especially at higher CFGs. I generally use lower weights like (freackles:0.25) to compensate.

I was using "peach fuzz" in my list of skin detailed but peaches started appearing everywhere. I've switched to "baby hairs on body" or "baby hairs illuminated by sunlight". You'll likely find examples where you need to find a creative way to reword something.

(I'll add to this section and organized it shortly).

⚠️⚠️⚠️ This is a DDPM/DDIM optimized model. Karras should be avoided. ⚠️⚠️⚠️