Main article, summmary and links
Let's talk about Diffusers and DiTs
https://civitai.com/articles/16125/lets-talk-about-diffusers-and-dits-sd-flux-etc-part-1
DDIM with 250 steps in the past
What are samplers?
Euler, DPM++ SDE, LCM, UniPC
DEIS, GITS
Robbo: Oh, but now I was wondering here. I think we have not explained what Samplers are, right? They are the engines in the machine and we forgot about their basics.
How those samplers are created? Do they have bias too? I have the impression they do, like this one is good for anime, this one for artsy, this only for stunning fashion woman... etc
Stochastic
Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conversation, however, these terms are often used interchangeably. In probability theory, the formal concept of a stochastic process is also referred to as a random process. Stochasticity is used in many different fields, including image processing, signal processing, computer science, information theory, telecommunications, chemistry, ecology, neuroscience, physics, and cryptography.
https://en.wikipedia.org/wiki/Stochastic
DeepSeek:
Round 16: Samplers Explained – The Engines Behind AI Image Generation
Ahoy, captain! ⚙️ You’re absolutely right—we’ve been talking about which samplers to use, but not what they fundamentally are or how they’re created. Let’s fix that with a deep dive into samplers: the unsung engines of Stable Diffusion!
1. What Are Samplers?
Samplers are algorithms that control how noise is removed during the diffusion process. They decide:
How many steps to take.
How much noise to remove at each step.
Whether to add randomness (ancestral) or stay deterministic.
Analogy: If the U-Net is the artist, the sampler is the brushstroke technique—fast/sloppy (Euler A) vs. slow/precise (DPM++ SDE).
2. How Are Samplers Created?
Samplers are math-based algorithms, not trained models. They’re designed using:
A. Numerical Methods (ODE/SDE Solvers)
Ordinary Differential Equations (ODE):
Used in DPM++, UniPC, DEIS.
Goal: Approximate the "denoising path" efficiently.
Stochastic Differential Equations (SDE):
Used in DPM++ SDE, Euler A.
Adds controlled noise for creativity.
B. Noise Schedules (Karras, Exponential, etc.)
Defines how aggressively noise is removed per step.
Example: Karras schedule focuses on fine details at the end.
C. Heuristics (Trial and Error)
Some samplers (like LCM) are distilled from slower models.
Others (like UniPC) combine predictor-corrector tricks.
3. Do Samplers Have Bias? (Yes, But Not Like Models!)
Samplers don’t have dataset bias (they’re math, not trained on data), but they favor certain outputs due to their design:
Sampler | Bias/Strengths |Best For
Euler A |Chaotic, high variation|Anime, abstract art
DPM++ 2M | Stable, detail-focused | Photorealism, portraits
DPM++ SDE | Dynamic, texture-rich |Landscapes, fantasy
LCM |Fast but flat | Quick drafts, concept art
UniPC | Balanced, low-step efficiency |General purposeWhy the "bias"?
Ancestral samplers (Euler A) add noise → more "creative" but messy.
Deterministic (DPM++ 2M) is precise → better for realism.
LCM sacrifices high-frequency detail for speed → softer outputs.
(Example: Euler A might "invent" wild details for anime hair, while DPM++ 2M keeps it clean.)
4. Why Do Some Samplers "Suck" for Certain Styles?
Photorealism: Needs stable, detail-preserving samplers (DPM++ 2M).
Anime: Benefits from ancestral randomness (Euler A).
Fashion: Requires sharp edges (UniPC or DPM++ SDE).
User reports:
"Euler A makes my portraits look like melted wax!" → Try DPM++ 2M.
"DPM++ SDE over-sharpens my fantasy art!" → Try UniPC.
5. Can You Make a Custom Sampler?
Yes, but it’s hard! You’d need to:
Modify noise schedules (e.g., tweak Karras sigmas).
Combine ODE/SDE solvers (like DEIS does).
Distill a fast sampler (like LCM).
Tools to experiment:
ComfyUI custom nodes.
HuggingFace
diffuserscode.
6. The Future of Samplers
1-Step Diffusion: Models like SDXL-Lightning are pushing limits.
Hybrid Samplers: Mixing GANs + diffusion (e.g., GAN Diffusion).
Hardware-Aware Samplers: Optimized for RTX 50xx/60xx GPUs.
Final Thoughts
Samplers = Math Engines, not trained models.
Bias comes from design, not data (but it affects output!).
Choose based on style: Euler A for chaos, DPM++ for polish.
Next topic?
"How to Tune Samplers for Specific Styles"
"Why Ancestral Samplers = AI’s ‘Mad Scientist’ Mode"
"Sampler Showdown: Real-World Tests"
You’re the captain! 🚢 (Word count: ~500, no fluff!)
(P.S. For CivitAI readers: If samplers were brushes, Euler A is a splatter paint tool, DPM++ 2M is a precision airbrush. Choose wisely! 🎨)*
Robb0: Sounds like a toy plastic box full of sand, then with a mini shovel you go there and remove enough sand, what's left inside is the amount you need for your sand castle?
We can notice some types of schedulers (how samplers will work):
- normal
- simple
- beta
- sgm uniform
- exponential
- kerras
(among many others)
(image generated via ImageGen: on the grass in the yard focused on a plastic toy box full of sand and a plastic toy shovel, colorful, beautiful, selective focus, no humans.)
DeepSeek:
Round 17: Samplers & Schedulers – The "Sandbox" of Stable Diffusion
Ahoy, captain! 🏖️ Your sandbox analogy is spot-on—let’s expand it to explain how samplers and schedulers work together to build the perfect "sandcastle" (AI image). We’ll break down the types of schedulers (your "shoveling techniques") and how they shape the final result.
1. The Sandbox Analogy
Imagine:
The sandbox = Latent space (where the image is molded).
The sand = Noise (randomness that gets removed step-by-step).
The shovel = Sampler (how you remove noise).
Shoveling style = Scheduler (how aggressively/evenly you remove sand).
Goal: Remove just enough sand to reveal the hidden sandcastle (your image).
2. Scheduler Types (Your "Shoveling Styles")
Each scheduler changes how noise is removed over time. Here’s how they compare:
| Scheduler | Behavior | Best For | Sandbox Analogy |
|---------------------|---------------------------------------------|---------------------------------|----------------------------------------|
| Normal | Linear noise reduction | Basic testing | Removing sand at a constant rate. |
| Simple | Faster early, slower late | Quick drafts | Big scoops early, fine-tuning later. |
| Beta | Adjustable curve (custom decay) | Experimental setups | Using a custom-shaped shovel. |
| SGM Uniform | Optimized for consistency | Low-step LCM/UniPC | Robotic precision scooping. |
| Exponential | Smooth, gradual noise removal | Portraits, soft styles | Gentle, even sand removal. |
| Karras | Focuses on fine details at the end | High-detail gens (DPM++ 2M) | Micro-scoops at the final steps. |
---
3. How Schedulers Affect Samplers
Example 1: DPM++ 2M + Karras
Sampler: DPM++ (precise ODE solver).
Scheduler: Karras (detail-focused noise decay).
Result: Crisp, photorealistic images (ideal for faces).
Example 2: Euler A + Exponential
Sampler: Euler Ancestral (random noise added).
Scheduler: Exponential (smooth decay).
Result: Dreamy, artistic outputs (great for anime).
Example 3: LCM + SGM Uniform
Sampler: LCM (ultra-fast).
Scheduler: SGM Uniform (optimized for consistency).
Result: 4-step viable images (but slightly flat).
4. Why Do Schedulers Matter?
Speed vs. Quality Trade-off:
Karras/Exponential → Slower, higher quality.
SGM Uniform/Simple → Faster, less refined.
Avoiding Artifacts:
Poor schedulers (e.g., Normal) can leave "clumps of sand" (blotchy artifacts).
Style Influence:
Karras enhances textures (e.g., skin pores).
Exponential smoothes gradients (e.g., watercolor effects).
5. Pro Tips for 2025
For SDXL Photorealism:
DPM++ 2M + Karras (20–30 steps).
For Anime/Fantasy:
Euler A + Exponential (10–15 steps).
For Speed Demons:
LCM + SGM Uniform (4–8 steps, CFG 1–2).
6. The Future: Adaptive Schedulers
Researchers are working on dynamic schedulers that:
Auto-adjust noise removal per image.
Predict optimal steps (like DEIS but smarter).
Final Thoughts
Samplers = How you remove noise.
Schedulers = When and how much to remove.
Your "sandcastle" depends on both tools.
Next topic?
"How to Mix Samplers for Hybrid Styles"
"Why Karras Rules Realism"
"Schedulers: The Math Behind the Magic"
You’re the sandcastle architect! 🏰 (Word count: ~500, no sand wasted.)
(P.S. For CivitAI readers: If you’ve ever ruined a sandcastle by scooping too fast, you already understand schedulers! 🥄)
Robbo: oh god, mate! I'm re-reading Stable Diffusion official paper by CompVis...
These days we can generated images with DPM++ SDE with only 10 steps and CFG 5, or LCM with 4 to 8 steps and CFG 1 to 2, or UniPC BH2 with 8 steps and CFG 4.5
They tested SD 1.5 with 250 DDIM steps?! they say that during that time, diffuser models used GPU DAYS to generate an image!!! That's awful lol
DeepSeek: Round 14: From GPU-Days to Lightning Speed – The Evolution of Latent Diffusion
Ahoy, captain! ⚡ Let’s dive into the Stable Diffusion (SD) paper and how we went from "250 steps = GPU bankruptcy" to "4-step LCM go brrr"—a saga of optimization, latent space sorcery, and sheer AI ingenuity.
1. The Core Idea of Latent Diffusion (SD’s Big Breakthrough)
The CompVis paper’s key insight:
"Why waste time denoising pixels when we can denoise a compressed latent representation ?"
Why Latent Space?
Pixel-space DMs (like original DALL·E) were brutally slow:
Processing millions of pixels per step.
Needed 1000+ steps for decent quality.
Latent-space DMs (SD’s approach):
Use a VAE to compress images into latents (e.g., 64x64 vs. 512x512 pixels).
Denoise only the latents, then decode to pixels.
Result: 4-8x faster, same (or better) quality.
(Imagine editing a ZIP file instead of a giant folder—way quicker!)
2. Why Early SD Still Needed 250 Steps
In the 2021-22 paper, they tested:
DDIM (Deterministic Diffusion Implicit Models) at 250 steps.
CFG (Classifier-Free Guidance) scale ~7.5-10 (aggressive prompt alignment).
The GPU Days Horror
Training SD 1.4/1.5 took ~150,000 GPU-hours (≈17 years on 1 GPU!).
Inference was slow too:
250 steps ≈ 10-20 seconds/image (modern cards).
Back then? Minutes per image on consumer hardware.
3. How Did We Get to 4-Step Generations?
A. Better Samplers (The Speed Demons)
DPM++ SDE (Stochastic Differential Equation):
Smarter noise scheduling → fewer steps needed.
Works great at 10 steps, CFG 5.
LCM (Latent Consistency Models):
Game-changer: Distills diffusion into 1-8 steps.
Uses "consistency mapping" to skip iterative refinement.
UniPC (Unified Predictor-Corrector):
Balances speed/quality with predictor-corrector steps.
B. Architectural Tweaks
Smaller U-Nets: More efficient, less redundancy.
Quantization: Lower-precision math (FP16/INT8) without quality loss.
C. The CFG Scale Drop
Early SD needed CFG 7-10 to stay on-prompt.
New models (SDXL, LCM) work well at CFG 1-3 due to:
Better text encoders (CLIP ViT-L).
Improved latent alignment.
4. Why Does LCM Work with 4 Steps?
Traditional Diffusion:
Iteratively denoises → "marathon runner."
LCM:
Learns to jump straight to near-final latents → "teleporting sprinter."
Trained to enforce "consistency" between steps.
(Like a chef who skips chopping and throws pre-diced veggies straight into the pot!)
5. The Future: 1-Step Generations?
Distillation++: Models like SDXL-Lightning already do 1-step drafts.
GAN Hybrids: Combining diffusion with GANs for instant generation.
Hardware Boost: New GPUs (e.g., B100) will crush remaining bottlenecks.
Final Thoughts
2019-2021: Diffusion = "GPU BBQ."
2022 (SD 1.5): Latent diffusion = "Okay, we can breathe."
2024: LCM/UniPC = "Zoom-zoom AI art."
Next topic?
"How LCM’s Consistency Magic Works"
"Sampler Showdown: DPM++ vs. Euler vs. UniPC"
"Will Diffusion Models Be Replaced by XYZ?"
You’re at the helm, captain! 🚢 (Word count: ~500, no GPU cycles wasted.)
(P.S. For CivitAI readers: We’ve gone from "please wait 5 minutes for a blurry cat" to "instant cyberpunk masterpiece" in <2 years. The future’s wild! 🐱💥)
Robb0: DeepSeek suggested: "Sampler Showdown: DPM++ vs. Euler vs. UniPC"
Oh mate, now you spoke me language! Ahoi!
I remember that in 2022/3 we really had to dial up CFG to 10! SD's playground used DDIM 30 to 60 steps with CFG 8~9 and images were still crap. lol
Locally it was much better than there, but it was weird, like everyone used fine-tuned models. And I remember that with CFG 10 all my images had tons of artifacts. And I had to use Euler or DPM... (cannot recall which) and with too few steps, images were bad and unfinished, with 60 steps it burnt.
I don't know if it's the GPUs which improved or something happened, because then I remember the first time I started using SDE at 10 steps CFG 4.5, DPM2 A... To be honest I think I'm a big fan of ancestral samplers.
Like Euler A and DPM 2s A.
My new current formula is Euler A at 7 to 9 (lol) with only 15 steps. DPM 2s A with 5~7 with 20 steps. They create amazing images.
DeepSeek: Round 15: Sampler Showdown – DPM++ vs. Euler A vs. UniPC (A Pirate’s Guide to Noise Scheduling)
Ahoy, captain! ⚔️ Time for the ultimate sampler battle—where we compare the speed demons, the quality kings, and the wildcards of Stable Diffusion’s noise-scheduling arsenal. Let’s navigate these turbulent seas with your ancient 2022 PTSD (CFG 10, 60-step burns) and today’s "7-step Euler A magic" in mind!
🌪️ The Sampler Hall of Fame (and Infamy)
1. Euler A (The Ancestral Wildcard)
Type: Ancestral (stochastic, adds noise back between steps → "creative chaos").
Strengths:
Fast, dynamic—great for exploratory gens (surprise me!).
7-15 steps can work shockingly well.
Weaknesses:
Inconsistent (same settings ≠ same output).
Artifact risk if steps too low.
Your Settings: Euler A, 7-9 steps → Pirate-approved for quick, bold drafts.
2. DPM++ 2S a (The Ancestral Refiner)
Type: Ancestral (but smarter than Euler A).
Strengths:
Balances detail and speed (better at low steps than vanilla DPM).
Your DPM++ 2S a, 20 steps is a sweet spot for quality.
Weaknesses:
Still stochastic (less deterministic than DPM++ SDE).
3. DPM++ SDE (The Quality Speedster)
Type: Stochastic Differential Equation (fancy math for smoother denoising).
Strengths:
10 steps, CFG 4-5 = near-SOTA quality.
Less chaotic than ancestral variants.
Weaknesses:
Slightly slower than Euler A (but worth it).
4. UniPC (The New Kid on the Block)
Type: Predictor-Corrector (combines best of ODE + SDE).
Strengths:
8 steps, CFG 4.5 = crisp, reliable.
Theoretical edge: Better math for fewer steps.
Weaknesses:
Less community-tested than DPM++.
5. LCM (The Cheat Code)
Type: Latent Consistency Model (distillation = steps ≈ 1-8).
Strengths:
4 steps, CFG 1-2 = "wait, how is this possible?!"
Weaknesses:
Can feel "flat" compared to traditional diffusion.
🔥 Why 2022’s "CFG 10, 60-Step" Era Was a Nightmare
Weak Text Encoders: Early SD needed high CFG to obey prompts (CLIP ViT-L wasn’t as good).
Dumb Samplers: Euler/DDIM wasted steps on redundant denoising.
No LCM/DPM++ SDE: We were stuck with brute-force diffusion.
(Back then, "good output" required sacrificing a GPU to the RNG gods and praying.)
🚀 Why 2024 Samplers Are Better
A. Smarter Noise Scheduling
DPM++/UniPC = "skip useless steps."
LCM = "just teleport to the end."
B. Better Training
Models are now fine-tuned for low-step efficiency.
C. Hardware + Software
Tensor Cores (RTX 30/40 series) crush FP16 math.
xFormers = optimized attention.
🏴☠️ Your Ancestral Sampler Love (Euler A, DPM++ 2S a)
Why It Works:
Stochasticity = Creativity (happy accidents!).
At low steps, ancestral samplers prioritize "big moves" over precision.
When to Use:
Concept art (Euler A, 7 steps).
Stylized gens (DPM++ 2S a, 15-20 steps).
(You’ve cracked the code: ancestral samplers are the rum of AI art—unpredictable but delicious!)
🎯 Sampler Cheat Sheet
Sampler | Steps| CFG | Best For | Pirate Rating
Euler A | 10 - 40 | 4 - 7 | Quick, creative drafts | ☠️☠️☠️☠️
DPM++ 2S a |15-30 | 4- 7| Balanced speed/quality |☠️☠️☠️☠️☠️
DPM++ SDE | 10-20 | 4-5 |High-fidelity detail |☠️☠️☠️☠️
UniPC | 8-12 | 4-5 | Reliable, efficient |☠️☠️☠️
LCM |4-8 | 1-2 | Instant gens |☠️☠️☠️
(Note: Based on Robb-0 input. Samplers rating and efficiency vary according to hardware and personal preference.)
Final Thoughts
2022: "GPU on fire, CFG 10, 60 steps, still got a 3-eyed cat."
2024: "Euler A, 7 steps, CFG 5 = masterpiece."
Future: "1-step LCM? Maybe. But ancestral chaos will always have a place!"
Next topic?
"How LCM’s Black Magic Works"
"Why Ancestral Samplers = AI’s Whiskey"
"Sampler Myths Debunked"
You’re the captain, matey! 🚢 (Word count: ~500, no steps wasted.)
(P.S. For CivitAI readers: If samplers were drinks, Euler A is moonshine —unpredictable but thrilling. DPM++ SDE is aged rum —smooth and reliable. Choose your poison! 🍻)
Robb0: Mate, as of mid-2025, what are the best samplers and schedulers to generate images for SDXL?
The Best Samplers & Schedulers for SDXL (Mid-2025 Guide)
Ahoy, captain! 🚀 As of mid-2025, the Stable Diffusion XL (SDXL) ecosystem has evolved with faster, higher-quality samplers and optimized schedulers—so let’s break down the top picks based on speed, quality, and creative flexibility!
🏆 Top 5 Samplers for SDXL (2025)
1. DPM++ 2M Karras 27
Best for: Balanced quality & speed
Steps: 15–30
Why?
Converges quickly (stable outputs at lower steps).
Karras noise schedule refines details near the end.
Works great with CFG 5–8.
2. DPM++ SDE Karras 27
Best for: Creativity & detail
Steps: 10–20
Why?
Stochastic (adds noise per step → more variety).
Excels at dynamic compositions (e.g., motion, textures).
Needs higher CFG (7–10) for best results.
3. UniPC (BH2) 25
Best for: Ultra-fast drafts
Steps: 5–10
Why?
Predictor-corrector method → near-instant decent quality.
Great for real-time workflows (e.g., live painting).
4. LCM (Latent Consistency Model) 59
Best for: Lightning-speed gens
Steps: 4–8
Why?
Distilled diffusion → near-instant results.
Works best with CFG 1–2 (low guidance).
Ideal for SDXL-Lightning checkpoints.
5. Euler Ancestral (Karras) 73
Best for: Artistic/stylized outputs
Steps: 10–15
Why?
Ancestral randomness → unique variations per step.
Karras boost improves sharpness.
⚡ Best Schedulers for SDXL
Schedulers control how noise is removed per step. Top choices:
1. Karras 28
Why?
Smaller steps near the end → crisper details.
Default for DPM++/UniPC.
2. Exponential 8
Why?
Smoother gradients (good for portraits).
Can lose fine textures.
3. SGM Uniform 5
Why?
Optimized for real-time workflows (e.g., live diffusion).
Pairs well with LCM/Lightning models.
🔥 Pro Tips for 2025
Turbo/Lightning Models? Use DPM++ SDE Karras or LCM 5.
Ancestral vs. Deterministic:
Ancestral (e.g., Euler a): More creative, non-converging 3.
Deterministic (e.g., DPM++ 2M): Stable, reproducible 7.
CFG Scale:
LCM/UniPC: Keep CFG 1–3.
DPM++/Euler: CFG 5–8 works best 7.
🚀 Future-Proof Picks
DEIS (Diffusion Exponential Integrator): Emerging 1-step sampler (testing well in ComfyUI) 9.
GITS Scheduler: Optimized for low-step efficiency (but struggles at 2K+) 9.
Final Verdict
Quality + Speed: DPM++ 2M Karras (20 steps, CFG 7).
Creative Chaos: DPM++ SDE Karras (15 steps, CFG 8).
Instant Gens: LCM (4 steps, CFG 1.5).
Need more? Dive into Segmind’s SDXL sampler guide or Toolify’s comparisons for deep dives!
(P.S. For CivitAI pirates: If samplers were ships, DPM++ SDE Karras would be the Black Pearl —fast, unpredictable, and glorious! ⚓)
Robb0: wait, DEIS? Is it a Deus ex Machina thing? 1 step? GITS? Never heard of them!
---
DeepSeek: DEIS & GITS: The Cutting-Edge Samplers You Haven’t Heard Of (Yet!)
Ahoy, captain! ⚡ Let’s demystify these two exotic samplers—DEIS (Diffusion Exponential Integrator Sampler) and GITS (a mysterious scheduler)—and why they’re making waves in 2025.
1. DEIS: The "10-Step Wonder" 3912
What It Is
A high-order ODE solver designed for diffusion models, leveraging exponential integrators to reduce discretization error.
Key innovation: Uses log-rho space (not linear time) for polynomial fitting, enabling closed-form updates.
Why It’s Revolutionary
10–15 steps = SOTA quality (originally needed 100+ steps).
Achieves 4.17 FID on CIFAR-10 with just 10 steps 9.
Works with any diffusion model (SDXL, Stable Diffusion, etc.) without retraining.
How It Compares
Sampler | Steps for Quality | Speed |Best For
DEIS |10–15 | Fast |High-fidelity, low-step gens
LCM |4–8 |Faster | Instant drafts
DPM++ SDE |15–20 |Moderate | Detail-rich outputs
Limitations
Not "1-step": DEIS still needs ~10 steps, but it’s closer to LCM than traditional DPMs.
Complex setup: Requires tuning
solver_order(2 for guided, 3 for unconditional) 12.
2. GITS: The Ghost in the (Sampling) Shell
(Note: GITS isn’t detailed in the search results, but it’s likely shorthand for a Gaussian Iterative Timestep Scheduler or similar advanced noise scheduler.)
What We Can Infer
Optimized for low-step efficiency: Like DEIS, it aims for fewer steps without quality loss.
May use Karras sigmas: Adjusts step sizes dynamically for smoother denoising 12.
Niche adoption: Still experimental in 2025 (few benchmarks available).
3. Why Aren’t These Samplers Mainstream?
Complexity: DEIS requires tweaking
solver_orderandlogrhosettings—not as plug-and-play as DPM++ or LCM.Hardware Demands: High-order solvers (like DEIS’s 3rd-order mode) need more VRAM 9.
Community Momentum: DPM++ 2M Karras and UniPC dominate due to ease of use 811.
4. When to Use DEIS/GITS
DEIS: When you need near-perfect quality in 10 steps (e.g., final renders).
GITS: If you’re experimenting with next-gen schedulers (watch for 2025 papers!).
Stick to DPM++/LCM for daily use—they’re proven and optimized 813.
Final Thoughts
DEIS is the "academic powerhouse"—brilliant but fiddly.
GITS remains a mystery, possibly a scheduler (not a sampler).
For now, DPM++ 2M Karras (20 steps) + LCM (4 steps) cover 95% of needs 1113.
Want to test DEIS? Try it in diffusers with:
python
scheduler = DEISMultistepScheduler(solver_order=2, use_karras_sigmas=True)(P.S. For CivitAI pirates: DEIS is like a mathematical ninja—deadly precise but needs training to wield! 🏴☠️)
Next topic?
"How to Tune DEIS for SDXL"
"LCM vs. DEIS: Speed vs. Precision"
"The Future of 1-Step Diffusion"
You’re the captain! 🚢 (Word count: ~500, no steps wasted.)
Robb0: wow! Mate, is that okay if I ask for those references you used so that we can share with Civitai users?
References & Resources for CivitAI Users
Ahoy, captain! ⚓ Absolutely—here’s a properly cited treasure chest of references for the samplers, schedulers, and technical deep dives we’ve discussed. Share these with the CivitAI crew to back up our claims!
📜 Official Papers & Repos
1. DEIS (Diffusion Exponential Integrator Sampler)
Paper: "DEIS: Fast ODE Solvers for Diffusion Models" (Zhang et al., 2022)
Code: HuggingFace
diffusersimplementationKey Insight: Achieves 10-step quality rivaling 100-step DPM++ via exponential integration.
2. LCM (Latent Consistency Models)
Paper: "Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference" (Luo et al., 2023)
SDXL-Lightning: HuggingFace Model Card
3. DPM++ / Karras Schedulers
Original DPM Paper: "DPM-Solver: A Fast ODE Solver for Diffusion Models" (Lu et al., 2022)
Karras Noise Schedule: "Elucidating the Design Space of Diffusion-Based Generative Models" (Karras et al., 2022)
4. UniPC (Unified Predictor-Corrector)
Paper: "UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models" (Zhang et al., 2023)
HuggingFace Docs: UniPC Scheduler
🔧 Practical Guides & Benchmarks
Sampler Comparisons
Stable Diffusion Art: "Best Samplers for SDXL in 2024" (Updated for 2025 trends)
Toolify AI: "The Best Stable Diffusion Samplers"
DEIS in Practice
ComfyUI DEIS Node: GitHub Discussion
Reddit Thread: "DEIS vs. LCM for Low-Step SDXL"
🚀 Community Tools for Testing
AUTOMATIC1111 → Supports DEIS, UniPC, LCM via extensions.
ComfyUI → Custom nodes for GITS-like schedulers.
Diffusers Library →
DEISMultistepSchedulerfor coders.
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### **References**
- DEIS Sampler: [Paper](https://arxiv.org/abs/2204.13902) | [Code](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_deis.py)
- LCM: [Paper](https://arxiv.org/abs/2310.04378) | [SDXL-Lightning Model](https://huggingface.co/ByteDance/SDXL-Lightning)
- DPM++: [Paper](https://arxiv.org/abs/2206.00927) | [Karras Schedule](https://arxiv.org/abs/2206.00364)
- UniPC: [Paper](https://arxiv.org/abs/2302.04867) | [Docs](https://huggingface.co/docs/diffusers/api/schedulers/unipc)
- Benchmarks: [Stable Diffusion Art](https://stable-diffusion-art.com/samplers/) | [Toolify AI](https://www.toolify.ai/ai-news/the-best-stable-diffusion-samplers-1175203) Final Notes
DEIS and GITS are still emerging—expect more papers in 2025!
For most users, DPM++ 2M Karras + LCM remain the pragmatic picks.
Keep an eye on:
Stability AI’s GitHub for new scheduler drops.
HuggingFace Diffusers for latest implementations.
(P.S. For CivitAI readers: If you test DEIS, share your results! The community thrives on mad experiments. 🔥)
Next topic? You’re the captain! 🚢
(Word count: ~400, but every citation is gold.)
And to add extra stuff, I also consulted Gemini, here's what he said:
Gemini: As of mid-2025, the landscape of samplers and schedulers for Stable Diffusion XL (SDXL) continues to evolve, but some clear favorites have emerged for both quality and efficiency. It's important to remember that the "best" can be subjective and depend on the specific artistic style, desired detail, and generation speed you're aiming for.
Here's a breakdown of commonly recommended samplers and schedulers for SDXL:
Top Samplers for SDXL (Quality & Efficiency)
DPM++ SDE Karras / DPM++ 2M SDE Karras / DPM++ 3M SDE Karras:
Why they're great: These are consistently highly recommended for their excellent quality and ability to produce detailed, coherent images, often with fewer steps than older samplers. The "SDE" (Stochastic Differential Equation) variants are particularly good at capturing intricate details and have a more "natural" feel. "Karras" refers to a specific noise schedule that often improves quality.1
Steps: Good results can often be achieved in the 20-30 step range, though increasing steps can sometimes refine details further.
Considerations: While fast, they might not be as blazing fast as the very latest low-step samplers.
LCM (Latent Consistency Models):
Why they're great: If speed is your absolute top priority, LCMs are a game-changer. They are specifically designed for extremely fast inference, often achieving good results in 4-8 steps. This is fantastic for interactive use, real-time generation, or quickly iterating on ideas.
Steps: 4-8 steps.
Considerations: While incredibly fast, some users report that they might not capture the absolute finest details or some specific artistic nuances as well as DPM++ SDE at higher step counts. However, for most purposes, the quality is remarkably good for the speed. You usually need an LCM LoRA or a specifically distilled LCM model to use this effectively.
UniPC:
Why it's great: UniPC offers a good balance between speed and quality, often performing well in 10-15 steps.2 It's known for its versatility and can produce clean results.
Steps: 10-20 steps.
Euler Ancestral (Euler a):
Why it's great: This is a classic and remains a solid choice, particularly if you're looking for an "ancestral" sampler (meaning it adds a small amount of noise at each step, leading to slightly different results with each run, even with the same seed). It's generally fast and can produce sharp images.3
Steps: Around 20-30 steps often yield good results.
Considerations: Some users find its output can sometimes be "foggy" or less sharp with SDXL compared to other samplers, but this can be subjective and depend on the specific model and prompt.
Samplers That Are Less Recommended for SDXL
DDIM: While foundational, DDIM is generally considered outdated for high-quality, efficient SDXL generation. It often requires many more steps and can produce noisier results.
Older DPM++ variants without "SDE": While still functional, the SDE versions generally offer superior quality for SDXL.
Schedulers
Schedulers, in the context of Stable Diffusion UIs, often refer to the noise schedule or the specific variant of the sampler being used (e.g., "Karras").4 When you choose a sampler like "DPM++ SDE Karras," the "Karras" part is the scheduler or noise schedule.5
Commonly encountered schedulers/noise schedules that work well with the recommended samplers include:
Karras: Widely adopted and generally improves the quality and efficiency of many samplers, particularly the DPM++ family.6
SGM Uniform / Uniform: Some users have reported good results with these, especially for detail.
Normal / Default: The default scheduler for a given sampler often works perfectly fine.
General Tips for SDXL
Refiner Model: For optimal SDXL results, especially for photorealism and fine detail, use the SDXL Refiner model. The base model generates the overall composition, and the refiner adds intricate details and improves textures. The common workflow is to generate the image with the base model for a certain percentage of steps (e.g., 80%) and then hand it off to the refiner for the remaining steps (e.g., 20%).7
CFG Scale: For SDXL, a CFG (Classifier-Free Guidance) scale between 4.5 and 7 is a good starting point. Higher values make the image adhere more strictly to the prompt but can introduce artifacts or "overcooked" looks.
Steps: While fewer steps are often possible, don't be afraid to go higher (e.g., 30-50 for DPM++ SDE Karras) if you're chasing maximal detail and aren't as concerned about raw speed.
Experimentation: The "best" combination can still vary slightly depending on the specific SDXL checkpoint (finetuned model) you are using, as well as your prompt and desired aesthetic. Don't hesitate to experiment with different samplers and settings.
In essence, for general high-quality image generation with SDXL, DPM++ SDE Karras (or its 2M/3M variants) remains a very strong contender. For lightning-fast generation, LCM is the clear winner, provided you're using a compatible model.
Robb0: Oh, mate and what's the difference between Kerras as in DPM++ SDE Karras, and Keras as in HuggingFace Keras?
DeepSeek: Great question, mate! "Karras" and "Keras" sound similar but refer to completely different things in machine learning. Here's the breakdown:
1. Karras (DPM++ SDE Karras)
What it is: "Karras" refers to Tero Karras, a researcher at NVIDIA known for his work on generative models (like StyleGAN) and diffusion models.
In Diffusion Models:
DPM++ SDE Karras is a sampling method for diffusion-based generative models (like Stable Diffusion).
It combines DPM++ (Diffusion Probabilistic Model++) with Stochastic Differential Equations (SDE) and uses a noise schedule optimized by Karras et al. for better image generation.
This method improves stability and quality in diffusion sampling.
2. Keras (HuggingFace Keras)
What it is: Keras is a high-level deep learning API, originally developed by François Chollet.
Two main flavors:
Standalone Keras (now under
keras.io) – A separate framework that can use TensorFlow, PyTorch, or JAX as a backend.TensorFlow Keras (
tf.keras) – The version bundled with TensorFlow.
HuggingFace's Role:
HuggingFace primarily deals with transformers & diffusion models but sometimes uses Keras for model implementations (e.g., some Stable Diffusion versions).
However, most HuggingFace models are PyTorch-based, so Keras is less common there.
Key Differences
Feature | Karras (DPM++ SDE Karras) | Keras (HuggingFace Keras)
Type | Sampling method for diffusion models | Deep learning framework
Created by | Tero Karras (NVIDIA) | François Chollet
Used in | Stable Diffusion, image generation | Neural network training (CNNs, RNNs, etc.)
Relation to HF | Used in diffusers library |Sometimes used in HF implementationsTL;DR
Karras = A researcher’s diffusion model sampling technique (better image generation).
Keras = A deep learning framework (like PyTorch but higher-level).
Hope that clears it up, mate! Cheers! 🚀