artokun ComfyUI Model Compatibility Matrix
Stable Diffusion 1.5 (SD 1.5)
Overview
The original widely-adopted Stable Diffusion model. Huge ecosystem of fine-tunes, LoRAs, ControlNets, and embeddings. Still the most compatible and lightweight model family.
Configuration
| Parameter | Value |
|---|---|
| Loader | CheckpointLoaderSimple |
| Native Resolution | 512x512 |
| Supported Resolutions | 512x512, 512x768, 768x512, 768x768 (some fine-tunes) |
| VAE | Built-in or external (vae-ft-mse-840000-ema-pruned.safetensors) |
| CLIP | Single CLIP-L (output index 1 from checkpoint) |
| Text Encoder Node | CLIPTextEncode |
| CFG Range | 7-12 (typical: 7.5) |
| Negative Prompt | Yes — very important for quality |
| Steps | 20-30 (standard samplers) |
| Sampler | All standard samplers: euler, euler_ancestral, dpmpp_2m, dpmpp_sde, ddim |
| Scheduler | normal, karras |
| Denoise | 1.0 (txt2img), 0.5-0.8 (img2img) |
| VRAM (FP16) | ~2-3GB |
Workflow Pattern
CheckpointLoaderSimple → MODEL(0), CLIP(1), VAE(2)
CLIP(1) → CLIPTextEncode (positive) → CONDITIONING
CLIP(1) → CLIPTextEncode (negative) → CONDITIONING
EmptyLatentImage (width=512, height=512) → LATENT
KSampler (cfg=7.5, steps=20, sampler="euler", scheduler="normal") → LATENT
VAEDecode → IMAGE
SaveImage
VAE Notes
- Most SD 1.5 checkpoints have a built-in VAE, but it's often mediocre
- Recommended: Use external
vae-ft-mse-840000-ema-pruned.safetensorsfor better color accuracy - Load via
VAELoadernode and connect toVAEDecode - FP16 VAE can produce NaN on some images. FP32 VAE is more stable
ControlNet Compatibility
SD 1.5 has the largest ControlNet ecosystem:
| ControlNet | Model File Pattern | Notes |
|---|---|---|
| Canny | control_v11p_sd15_canny |
Edge detection |
| Depth | control_v11f1p_sd15_depth |
Depth map |
| OpenPose | control_v11p_sd15_openpose |
Skeleton/pose |
| Scribble | control_v11p_sd15_scribble |
Hand-drawn lines |
| Lineart | control_v11p_sd15_lineart |
Clean lines |
| Softedge | control_v11p_sd15_softedge |
Soft edges (HED) |
| Normal | control_v11p_sd15_normalbae |
Normal maps |
| Seg | control_v11p_sd15_seg |
Semantic segmentation |
| Tile | control_v11f1e_sd15_tile |
Tile/upscale guidance |
| Inpaint | control_v11p_sd15_inpaint |
Inpainting guidance |
| IP-Adapter | ip-adapter_sd15 |
Image prompt |
LoRA Compatibility
- SD 1.5 LoRAs ONLY work with SD 1.5 base models
- Format:
.safetensorsinmodels/loras/ - Loader:
LoraLoadernode, which connects between checkpoint and CLIPTextEncode - Strength range: 0.5-1.0 (higher can cause artifacts)
SDXL (Stable Diffusion XL)
Overview
Major upgrade from SD 1.5 with dual CLIP encoders, higher native resolution, and better prompt understanding. Includes Turbo and Lightning variants for fast generation.
Configuration — SDXL 1.0 (Base)
| Parameter | Value |
|---|---|
| Loader | CheckpointLoaderSimple |
| Native Resolution | 1024x1024 |
| Supported Resolutions | 1024x1024, 832x1216, 1216x832, 896x1152, 1152x896, 768x1344, 1344x768 |
| VAE | Built-in (SDXL has good integrated VAE) |
| CLIP | Dual CLIP: CLIP-L + CLIP-G |
| Text Encoder Node | CLIPTextEncode (unified) or CLIPTextEncodeSDXL (separate G/L) |
| CFG Range | 5-10 (typical: 7.0) |
| Negative Prompt | Yes — moderately important |
| Steps | 20-40 |
| Sampler | euler, euler_ancestral, dpmpp_2m, dpmpp_sde |
| Scheduler | normal, karras |
| Denoise | 1.0 (txt2img), 0.5-0.8 (img2img) |
| VRAM (FP16) | ~6-7GB |
Configuration — SDXL Turbo
| Parameter | Value |
|---|---|
| Loader | CheckpointLoaderSimple |
| Resolution | 512x512 (optimized for lower res) |
| CFG | 1.0-2.0 |
| Steps | 1-4 |
| Sampler | euler_ancestral |
| Scheduler | normal |
| Negative Prompt | Minimal or empty |
| Denoise | 1.0 |
Configuration — SDXL Lightning
| Parameter | Value |
|---|---|
| Loader | CheckpointLoaderSimple + LoraLoader (Lightning LoRA) |
| Resolution | 1024x1024 |
| CFG | 1.0-2.0 |
| Steps | 4-8 (match the Lightning variant: 2-step, 4-step, 8-step) |
| Sampler | euler |
| Scheduler | sgm_uniform |
| Negative Prompt | Empty or minimal |
| Special | Requires matching Lightning LoRA for the step count |
SDXL Refiner
The optional SDXL refiner model does a second pass to improve fine details:
CheckpointLoaderSimple (base) → KSampler (steps=25, start=0, end=20)
CheckpointLoaderSimple (refiner) → KSampler (steps=25, start=20, end=25)
- The refiner uses
KSamplerAdvancedwithstart_at_stepandend_at_step - Typically run the base for 80% of steps, refiner for the last 20%
- Refiner checkpoint:
sd_xl_refiner_1.0.safetensors
Workflow Pattern
CheckpointLoaderSimple → MODEL(0), CLIP(1), VAE(2)
CLIP(1) → CLIPTextEncode (positive) → CONDITIONING
CLIP(1) → CLIPTextEncode (negative) → CONDITIONING
EmptyLatentImage (width=1024, height=1024) → LATENT
KSampler (cfg=7.0, steps=25, sampler="dpmpp_2m", scheduler="karras") → LATENT
VAEDecode → IMAGE
SaveImage
ControlNet Compatibility
SDXL ControlNets are separate from SD 1.5 ControlNets:
| ControlNet | Model File Pattern | Notes |
|---|---|---|
| Canny | control-lora-canny-rank256 or diffusers_xl_canny |
Often LoRA-based |
| Depth | control-lora-depth-rank256 or diffusers_xl_depth |
|
| T2I-Adapter | t2i-adapter-*-sdxl |
Lighter alternative to ControlNet |
| IP-Adapter | ip-adapter_sdxl |
Image prompt adapter |
| InstantID | instantid-* |
Face-specific |
LoRA Compatibility
- SDXL LoRAs ONLY work with SDXL base models, NOT with SD 1.5
- Same
LoraLoadernode as SD 1.5 - Lightning LoRAs are SDXL LoRAs that enable few-step generation
Flux (Flux.1)
Overview
Black Forest Labs' model with a T5-XXL text encoder. Produces high-quality images without negative prompts. Available in schnell (fast) and dev (quality) variants.
Configuration — Flux Schnell
| Parameter | Value |
|---|---|
| Loader | CheckpointLoaderSimple (single-file) or DualCLIPLoader + UNETLoader + VAELoader (split) |
| Native Resolution | 1024x1024 (flexible aspect ratios) |
| Supported Resolutions | Flexible: 512x512 to 2048x2048, any aspect ratio |
| VAE | Separate Flux VAE (ae.safetensors) — NOT shared with SD models |
| CLIP | T5-XXL + CLIP-L via DualCLIPLoader |
| Text Encoder Node | CLIPTextEncode (single combined) |
| CFG | 1.0 (MUST be 1.0 — higher values cause severe artifacts) |
| Negative Prompt | NONE — do not connect negative conditioning |
| Steps | 4 |
| Sampler | euler |
| Scheduler | simple or sgm_uniform |
| Denoise | 1.0 |
| VRAM (FP16) | ~24GB (FP8: ~12GB) |
Configuration — Flux Dev
| Parameter | Value |
|---|---|
| Same as Schnell except: | |
| Steps | 20-50 (typical: 30) |
| Scheduler | sgm_uniform |
| VRAM (FP16) | ~24GB (FP8: ~12GB) |
Loading Methods
Method 1: Single Checkpoint (simplest)
CheckpointLoaderSimple (ckpt_name="flux1-schnell.safetensors")
→ MODEL(0), CLIP(1), VAE(2)
Method 2: Split Components (recommended for FP8)
UNETLoader (unet_name="flux1-schnell-fp8.safetensors") → MODEL
DualCLIPLoader (clip_name1="t5xxl_fp16.safetensors", clip_name2="clip_l.safetensors", type="flux") → CLIP
VAELoader (vae_name="ae.safetensors") → VAE
CRITICAL Rules
- CFG MUST be 1.0. Flux uses guidance embedded in the model, not classifier-free guidance
- No negative prompt. Empty string or don't connect the negative input at all
- Separate VAE required. Flux uses its own VAE (
ae.safetensors), not SD VAEs - FP8 strongly recommended for 24GB cards. FP16 Flux barely fits in 24GB VRAM
- T5-XXL encoder can be loaded in FP8 to save additional VRAM
- Kitchen quant column (this GPU):
kitchenaction:"status" reportsgpu.fp8(SM ≥ 8.9, Ada),gpu.nvfp4andgpu.mxfp8(SM ≥ 10.0, Blackwell). A UNETLoader onweight_dtype: defaultwith an unquantized file and kitchen present ispanel_kitchenaction:"assess" recfp8_unet_fast(setfp8_e4m3fn_fast). An NVFP4 sibling on disk is recnvfp4_swap. MXFP8 is reported in status but not recommended until a loader path exposes it.
Workflow Pattern
UNETLoader (flux fp8) → MODEL
DualCLIPLoader (t5xxl + clip_l, type="flux") → CLIP
VAELoader (ae.safetensors) → VAE
CLIPTextEncode (positive prompt) → CONDITIONING
(no negative CLIPTextEncode needed)
EmptyLatentImage (width=1024, height=1024) → LATENT
KSampler (cfg=1.0, steps=4, sampler="euler", scheduler="simple") → LATENT
VAEDecode (vae from VAELoader) → IMAGE
SaveImage
ControlNet Compatibility
Flux ControlNets are model-specific:
| ControlNet | Notes |
|---|---|
| Flux ControlNet (Canny) | Specific Flux-compatible ControlNet |
| Flux ControlNet (Depth) | Specific Flux-compatible ControlNet |
| InstantX ControlNets | Community Flux ControlNets |
| Flux IP-Adapter | Image prompt for Flux |
SD 1.5 and SDXL ControlNets do NOT work with Flux.
LoRA Compatibility
- Flux LoRAs ONLY work with Flux models
- Typically loaded via
LoraLoadersame as SD models - Flux LoRA ecosystem is smaller than SD 1.5/SDXL but growing
- Some Flux LoRAs require specific trigger words
Stable Diffusion 3 / 3.5 (SD3)
Overview
Stability AI's next-generation model with triple CLIP architecture. Better prompt adherence and longer prompt support via T5-XXL.
Configuration
| Parameter | Value |
|---|---|
| Loader | CheckpointLoaderSimple or triple-clip loader |
| Native Resolution | 1024x1024 |
| VAE | Built-in (integrated) |
| CLIP | Triple: CLIP-L + CLIP-G + T5-XXL |
| Text Encoder Node | CLIPTextEncode or CLIPTextEncodeSD3 |
| CFG Range | 4-7 (typical: 5.0) |
| Negative Prompt | Minimal — SD3 needs very little negative guidance |
| Steps | 20-30 |
| Sampler | euler, dpmpp_2m |
| Scheduler | sgm_uniform, normal |
| Denoise | 1.0 (txt2img) |
| Shift | Some samplers support a shift parameter for SD3 |
| VRAM (FP16) | ~12GB (without T5-XXL: ~6GB) |
Triple CLIP Loading
CheckpointLoaderSimple → MODEL(0), CLIP(1), VAE(2)
Or for separate CLIP control:
DualCLIPLoader (clip_l + clip_g) → CLIP
CLIPLoader (t5xxl) → CLIP
Key Differences from SD 1.5/SDXL
- Much better text rendering capabilities
- Handles spatial relationships better ("cat on the left, dog on the right")
- T5-XXL enables long, detailed prompts (no 77-token limit concern)
- Lower CFG values (4-7 vs 7-12)
- Minimal negative prompting needed
shiftparameter in sampling affects noise schedule
ControlNet Compatibility
- SD3-specific ControlNets are limited
- Check for SD3-compatible community ControlNets
- SD 1.5 and SDXL ControlNets do NOT work with SD3
LTXV (Video Models)
Overview
Latent video diffusion models for text-to-video and image-to-video generation. VRAM-intensive.
Configuration
| Parameter | Value |
|---|---|
| Loader | Special video checkpoint loader (varies by node pack) |
| Resolution | 512x512 or 768x768 per frame (depends on model) |
| Frames | 16-64 (depends on VRAM) |
| FPS | 8-24 |
| VRAM | 20GB+ FP16, ~6-10GB FP8 |
| Key Warning | Can OOM on 24GB VRAM — always use FP8 quantized models |
VRAM Management
- Always use FP8 quantized models on 24GB cards
- Reduce frame count if OOM persists
- Lower resolution helps a lot
- Close other GPU-using applications
- Consider
--lowvramflag for ComfyUI
Cross-Family Compatibility Rules
LoRA Compatibility
LoRAs are model-family specific and are NOT interchangeable:
| LoRA Trained For | Works With | Does NOT Work With |
|---|---|---|
| SD 1.5 | SD 1.5 and its fine-tunes | SDXL, Flux, SD3 |
| SDXL | SDXL and its fine-tunes | SD 1.5, Flux, SD3 |
| Flux | Flux models only | SD 1.5, SDXL, SD3 |
| SD3 | SD3/3.5 models only | SD 1.5, SDXL, Flux |
Using a LoRA with the wrong base model will produce garbage images or errors.
ControlNet Compatibility
ControlNets are also model-family specific:
| ControlNet Trained For | Works With | Does NOT Work With |
|---|---|---|
| SD 1.5 (v1.1 series) | SD 1.5 base + fine-tunes | SDXL, Flux, SD3 |
| SDXL | SDXL base + fine-tunes | SD 1.5, Flux, SD3 |
| Flux | Flux models only | SD 1.5, SDXL, SD3 |
VAE Compatibility
| VAE | Compatible Models | Notes |
|---|---|---|
vae-ft-mse-840000-ema-pruned |
SD 1.5 family | Best external VAE for SD 1.5 |
| SDXL built-in VAE | SDXL family | Good quality, no external needed |
sdxl_vae.safetensors |
SDXL family | External SDXL VAE option |
ae.safetensors (Flux VAE) |
Flux only | Required for Flux, incompatible with SD |
| SD3 built-in VAE | SD3 family | Integrated, no external needed |
Rule: Never mix VAEs across model families. An SD 1.5 VAE decoding Flux latents will produce garbage.
Embedding/Textual Inversion Compatibility
| Embedding Type | Compatible Models |
|---|---|
| SD 1.5 embeddings | SD 1.5 family only |
| SDXL embeddings | SDXL family only |
| Flux/SD3 | Generally don't use traditional embeddings |
Sampler/Scheduler Compatibility
Most samplers work across all models, but some combinations are optimal:
| Model | Best Sampler | Best Scheduler | Notes |
|---|---|---|---|
| SD 1.5 | euler_ancestral, dpmpp_2m |
karras, normal |
All standard samplers work |
| SDXL | dpmpp_2m, euler |
karras, normal |
Same as SD 1.5 |
| SDXL Turbo | euler_ancestral |
normal |
Must use 1-4 steps |
| SDXL Lightning | euler |
sgm_uniform |
Must match step count to LoRA |
| Flux Schnell | euler |
simple |
4 steps only |
| Flux Dev | euler |
sgm_uniform |
20-50 steps |
| SD3 | euler, dpmpp_2m |
sgm_uniform, normal |
Lower CFG needed |
Quick Decision Guide
Choosing a Model
| Use Case | Recommended Model | Why |
|---|---|---|
| Maximum ecosystem/community support | SD 1.5 | Most LoRAs, ControlNets, embeddings |
| High quality, good prompt following | SDXL | Best balance of quality and ecosystem |
| Fastest generation | SDXL Turbo/Lightning | 1-4 steps |
| Best prompt understanding | Flux Dev | T5-XXL encoder, natural language |
| Fast + good quality | Flux Schnell | 4 steps, no negative needed |
| Text in images | SD3.5 | Best text rendering |
| Low VRAM (<6GB) | SD 1.5 | Smallest memory footprint |
| Video generation | LTXV / AnimateDiff | Only options for video |
Choosing Resolution
| Model | Minimum | Recommended | Maximum (before OOM on 24GB) |
|---|---|---|---|
| SD 1.5 | 256x256 | 512x512 | 768x768 |
| SDXL | 512x512 | 1024x1024 | 1536x1536 |
| Flux (FP8) | 512x512 | 1024x1024 | 2048x2048 |
| Flux (FP16) | 512x512 | 1024x1024 | 1024x1024 (tight) |
| SD3 | 512x512 | 1024x1024 | 1536x1536 |
Going below the recommended resolution produces blurry/low-quality results. Going above the maximum risks OOM errors or quality degradation (tiling artifacts).
Sources
- Official: none found as a vendor pairing matrix. Kitchen hardware gates: ComfyUI
comfy/model_management.py(supports_fp8_compute,supports_nvfp4_compute,supports_mxfp8_compute); UNETLoaderweight_dtypeinnodes.py. - Empirical: base-model / VAE / CLIP pairing rules from observed load failures.