<div id="readme-top" align="center"> <h1 align="center">ComfyUI-DyPE</h1> <img src="https://github.com/user-attachments/assets/4f11966b-86f7-4bdb-acd4-ada6135db2f8" alt="ComfyUI-DyPE Banner" width="70%"> ComfyUI custom node pack for ultra-high-resolution generation (4K and beyond) with Diffusion Transformers — FLUX, Qwen Image, Z-Image, Anima/Cosmos, Krea-2.

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▷ About

Training-free methods that push pre-trained DiT models far beyond their native resolution — no retraining, no workflow changes. Patch the model once after your loader and generate at 2K, 4K and above.

<div align="center"> <img alt="ComfyUI-DyPE example workflow" width="70%" src="https://github.com/user-attachments/assets/31f5d254-68a7-435b-8e1f-c4e636d4f3c2" /> A simple, single-node integration to patch your model for high-resolution generation. </div>

❖ Highlights

Multi-Architecture — FLUX, Nunchaku, Qwen Image, Krea-2, Z-Image, Anima/Cosmos
High-Resolution Generation — 4096×4096 and beyond
Single-Node Integration — place after your model loader, done
Full Compatibility — works with existing workflows, samplers and optimization nodes
Zero Overhead — adjustments happen on-the-fly with negligible performance impact

▓ Nodes

| Node | What it does | |:---|:---| | ❖ DyPE | Dynamic Position Extrapolation — the core high-res method. | | ❖ SEGA | Content-aware spectral sharpening as an alternative to DyPE. | | ❖ SPA (HRDiT) | Fixes spatial disorder (repeated/collapsed structures) at high res. | | ❖ HAP (HRDiT) | Sparse-attention acceleration — the speed half of HRDiT. | | ❖ PixelRush | Cascade patch refinement of an existing base image. | | ❖ FreeScale | Tuning-free self-cascade upscaling. | | ❖ HiFlow | Trajectory-guided flow upscaling for rectified-flow models (FLUX, Qwen-Image, Krea2, Z-Image, …). |

Which method when?

Two families: model patches alter how your own KSampler run attends (no image input) — best for native high-res generation; cascades consume an existing latent/image and refine it.

| Method | Models | Mechanism | Takes your image | Output character | |:---|:---|:---|:---:|:---| | DyPE | FLUX, Nunchaku, Qwen/Krea-2, Z-Image, Anima | Dynamic position-encoding extrapolation | ✗ | Native high-res generation | | SEGA | FLUX, Nunchaku, Qwen/Krea-2, Z-Image, Anima | Spectral-energy RoPE sharpening | ✗ | Native high-res generation | | SPA | FLUX, Qwen/Krea-2, Z-Image, Anima | Position-bundle attention alignment | ✗ | Native high-res generation | | HAP | FLUX, Qwen/Krea-2, Z-Image, Anima | Calibrated sparse attention (speed) | ✗ | Native high-res generation | | PixelRush | Any (SDXL, SD1.5, FLUX, Qwen, …) | Patch-wise low-denoise img2img cascade | ✓ | Faithful upscale + refinement | | FreeScale | FLUX-family DiTs | Scale-fused attention + self-cascade | ✓ | Regenerative hi-res, mostly new content | | HiFlow | Flow models (FLUX, Qwen-Image, Krea2, Z-Image, …) | Time-matched reference trajectory guidance | ✓ | Structure-faithful flow upscale |

[!TIP] Quick picker: starting from noise → DyPE (or SEGA), add SPA if you see repeated/collapsed structures, add HAP for speed. Starting from an existing image → PixelRush to keep it faithful, FreeScale to re-imagine it at high res (lower its noise_timestep for more fidelity), HiFlow for FLUX-family flow models — it reuses the whole base-resolution denoising trajectory as guidance, so structure survives while detail is re-synthesized.

❖ DyPE

Dynamic Position Extrapolation (paper, code). Adjusts positional encodings at each denoising step to match the current stage of generation — low-frequency structure early, fine detail later. Training-free, no additional sampling cost.

Usage: Load model → add DyPE (under WMNodes/image) → connect MODEL → set width/height to match your latent → connect to KSampler.

<details> <summary>Inputs & Parameters</summary>

Model Configuration

model_type
- auto — auto-detects the architecture. Recommended.
- flux — Standard Flux.
- nunchaku — Quantized Flux.
- qwen — Qwen Image (also used for Krea-2).
- zimage — Z-Image (Lumina 2).
- anima — Anima/Cosmos.
base_resolution — native training resolution of the model.
- Flux / Z-Image: 1024
- Qwen / Krea-2: 1328
- Anima/Cosmos: 1920 (auto-detected)

Method Selection (`method`)

vision_yarn — decouples structure from texture; best aspect-ratio robustness. Recommended default.
yarn — standard YaRN; good general performance.
ntk — very stable, but softer at high resolutions.
pi — Position Interpolation; preserves local structure well.
base — no interpolation.

Scaling Options

yarn_alt_scaling (only affects yarn): Anisotropic scales H/W independently (may stretch); Isotropic (default) is stable. Ignored by vision_yarn.

Dynamic Control

enable_dype — full dynamic algorithm (on), or schedule shift only (off).
dype_scale — magnitude of the modulation (default 2.0).
dype_exponent — strength over time: 2.0 for 4K+, 1.0 for ~2K–3K, 0.5 just above native.

Advanced Noise Scheduling

base_shift / max_shift — noise-schedule shift control (max_shift default 1.15).

</details>

[!TIP] Z-Image: isotropic scaling is enforced automatically. Prefer vision_yarn or ntk. Anima/Cosmos: prefer vision_yarn; other methods may produce speckle noise above 2K.

❖ SEGA

Spectral-Energy Guided Attention (code). Content-aware RoPE sharpening derived from the latent's frequency spectrum. Use as an alternative to DyPE on FLUX/Qwen.

Usage: Add the SEGA node after your model loader → set width/height to match your latent → tune mscale_alpha and spread_min/spread_max.

<div align="center"> <img alt="Example sega" src="https://github.com/user-attachments/assets/c9d812c8-a88b-4e8d-bb84-0f4bd5ef18ef" /> </div> <details> <summary>Inputs & Parameters</summary>

| Parameter | Default | Description | |:---|:---:|:---| | method | sega | sega = NTK + spectral mscale, ntk = NTK only | | mscale_alpha | 0.15 | Spectral redistribution amplitude | | mscale_beta | 1.5 | tanh sharpness | | mscale_min | 1.0 | Floor for per-frequency mscale | | spread_min | 0.0 | Min spectral spread (early steps) | | spread_max | 1.0 | Max spectral spread (late steps) | | spread_alpha | 1.5 | Spread schedule non-linearity | | base_mscale_formula | power_res | power_res or log_res | | base_mscale_coefficient | 0.08 | κ (paper default) |

</details>

[!NOTE] SEGA builds on NTK. If NTK doesn't work for your model (e.g. Anima), use DyPE vision_yarn instead.

❖ SPA (HRDiT)

Spatial Position Alignment, from the HRDiT paper (arXiv 2608.07003). A static, training-free patch that fixes high-resolution spatial disorder — repeated structures and positional collisions when pushing past native resolution. Resolution-aware (automatic no-op ≤ 1024px) with bounded overhead at 2K/4K. Mechanism: bundles token positions into groups of N, slides the bundle boundary per axis (2s − 1 variants), and averages the attention outputs across variants — never the RoPE matrices themselves.

Usage: Add the SPA (HRDiT) node after your model loader → set width/height → leave model_type: auto → connect to KSampler. Recommended bundle_size: 3 at 2K, 5 at 4K (0 = auto).

<details> <summary>Inputs & Parameters</summary>

| Parameter | Default | Description | |:---|:---:|:---| | model_type | auto | Same detection as DyPE. Reads theta & axes_dim from the model. | | enable_spa | True | Disable to pass the model through unchanged. | | bundle_size | 0 (auto) | Tokens per bundle (paper's N). 0 = auto, 1 = off, 2..8 explicit. Auto no-op inside the model's trained extent (≤ 1024px). | | spa_steps | 3 | SPA runs only on the first 3 denoising steps; later steps run at baseline speed. 0 = all steps. | | spa_start_sigma | 1.0 | Optional sigma-threshold gate (combined AND with spa_steps). | | spa_layer_filter | "" | Restrict SPA to a subset of layers, e.g. "0-18,38-57". Empty = every layer. | | proportional_attention | False | HRDiT proportional attention scaling for long sequences. No-op at/below 1024px. |

Performance: ~zero overhead at ≤ 1024px; roughly 1.3–1.8× total inference time at 2K/4K with defaults.

Model support: FLUX, Qwen/Krea-2, Z-Image, Anima/Cosmos. Nunchaku not supported (logs a warning, returns the model unchanged).

</details>

[!WARNING] SPA and DyPE/SEGA are mutually exclusive — apply only one.

SPA — fix spatial disorder with small, bounded overhead.

DyPE/SEGA — full dynamic extrapolation far beyond native resolution.

❖ HAP (HRDiT)

Head-Adaptive attention Pruning, from the same HRDiT paper — the speed half complementing SPA (the quality half). Each attention head only sees the keys it actually needs, via a pre-calibrated scope plan executed through block-sparse attention. Composable with SPA in any order.

A ready-to-use FLUX scope plan ships at configs/scope_plan_flux.json.

Usage: Add the HAP (HRDiT) node after your model loader → point scope_plan_path at a plan JSON → connect to KSampler (optionally through an SPA node first).

<details> <summary>Inputs & Parameters</summary>

| Parameter | Default | Description | |:---|:---:|:---| | scope_plan_path | configs/scope_plan_flux.json | Path to the scope-plan JSON. Relative paths resolve against the repo root. Also accepts a linked scope_plan input. | | model_type | auto | Architecture detection. Nunchaku unsupported. | | anchor_stride | 0 | Every Nth image key block stays globally visible. 0 = off. | | text_len | 512 | Leading text tokens always kept visible. | | enable_hap | True | Disable to pass the model through unchanged. | | proportional_attention | False | See SPA. Either node may enable it. |

Backends: fast path needs CUDA + PyTorch ≥ 2.5; otherwise falls back automatically to a correct dense-mask backend.

</details> <details> <summary>Calibration</summary>

Scope plans are model-specific. Calibrate a custom plan with the HAP Calibrate (HRDiT) node in-graph, or via the calibration/calibrate_hap.py CLI:

# Self-contained dry run (no GPU needed):
python calibration/calibrate_hap.py --dry_run --out tmp/scope_plan_toy.json

# Real-model calibration:
python calibration/calibrate_hap.py --model_path /path/to/flux.safetensors \
    --model_type flux --width 4096 --height 4096 --num_prompts 30 \
    --out configs/scope_plan_flux_4k.json

Calibrate once per model, then reuse the plan across resolutions and prompts.

From the paper (FLUX, budget 0.1): ~2.9× faster attention at 2K, ~5.5× at 4K.

</details> <a href="#readme-top" title="back to top">⟔ ▲ ⟓</a>

❖ PixelRush

Cascade-based refinement node. Generates at native resolution first, then progressively adds detail through coarse-to-fine cascade refinements — producing crisp 4K output without regenerating the whole image from noise. Works with any ComfyUI model (SDXL, SD1.5, FLUX, Qwen, …).

Usage: Generate a base latent at native resolution → connect model, vae, positive, negative and the base latent_image → set num_cascade_stages (1 = 2× upscale, 2 = 4×, 3 = 8×) → decode the output latent.

<details> <summary>Inputs & Parameters</summary>

| Parameter | Description | |:---|:---| | num_cascade_stages | Number of cascade stages — each doubles the resolution. | | refiner_model | Optional separate refiner model (paper setup: SDXL base + SDXL-Turbo). When not connected, the base model refines too. | | noise_lambda | Noise injection coefficient — the weight of the model's prediction (paper default 0.95 = 95% prediction + 5% random noise). | | noise_injection | slerp (paper default) or additive (legacy pre-2.9 behavior, kept for workflows tuned against it). | | overlap | Overlap between adjacent patches (blends seams). | | gaussian_sigma | Analytic Gaussian feather sigma (paper default 24; rule of thumb: σ ≈ patch_size / 5). | | patch_h / patch_w | Latent patch size (~native spatial size keeps VRAM flat). |

[!NOTE] PixelRush calls the diffusion model directly (not through ComfyUI's sampler), performing its own CFG and prediction-type handling for EPS, flow, V-prediction and X0 models.

[!IMPORTANT] 2.9 migration notes: the noise injection now uses the paper's SLERP with λ weighting the model's prediction (set noise_injection to additive for the legacy formula); gaussian_sigma default moved 8 → 24 and its range extends to 128; the gaussian_kernel_size input was removed (the mask is now the paper's analytic Gaussian — old workflows simply ignore the stale value).

</details> <a href="#readme-top" title="back to top">⟔ ▲ ⟓</a>

❖ FreeScale

Tuning-free higher-resolution generation via scale-fused attention and self-cascade upscaling (paper, code). Supports FLUX-family DiTs (auto-detected); base-resolution inputs pass through untouched.

<details> <summary>Inputs & Parameters</summary>

| Input | Default | Notes | |:---|:---:|:---| | width / height | 2048 | Target resolution (snapped to multiples of 16). | | steps | 20 | Sampler steps per cascade stage. | | cfg | 1.0 | Classifier-free guidance scale. | | cascade_stages | 1 | Number of self-cascade stages (each doubles resolution). |

</details> <a href="#readme-top" title="back to top">⟔ ▲ ⟓</a>

❖ HiFlow

Training-free high-resolution upscaling for rectified-flow models (FLUX, Qwen-Image, Krea2, Z-Image, …) via flow-aligned guidance (paper, NeurIPS 2025). The base-resolution sampling runs once, recording every per-step clean prediction; each upscale stage then reuses that time-matched trajectory as a virtual reference — initialization alignment seeds the stage from it, direction alignment keeps low frequencies true to it, acceleration alignment matches its detail-generation rhythm. Structure survives; high-res detail is synthesized fresh.

Usage: connect model (flow models only), vae, positive, negative and a base latent at native resolution (e.g. EmptySD3LatentImage) → set noise_seed + scale_factor → decode. The cascade noises the latent to the first sigma itself — an empty latent + seed reproduces the reference pipeline's from-noise start. Chain DyPE (ntk) before the loader for RoPE extrapolation at the scaled resolution.

<details> <summary>Inputs & Parameters</summary>

| Parameter | Default | Description | |:---|:---:|:---| | cfg | 3.5 | Base-stage CFG (FLUX-dev default). Guidance-free models (Z-Image, Chroma) or empty negatives: leave at 1.0 — CFG is auto-skipped when the negative carries no tokens. | | steps | 30 | Base-stage steps; their clean predictions form the reference trajectory. | | guidance | 4.5 | Guided-stage CFG (paper uses 4.5–6). Same auto-skip rule as cfg. | | steps_per_stage | 16 | Guided steps per cascade stage (upper bound — the stage walks schedule sigmas below tau). | | noise_seed | 0 | Seed for the base noise and each stage's initialization noise. | | denoise | 1.0 | Img2img strength for a content latent (KSampler convention): 1.0 regenerates from pure noise; lower keeps more of the input (ignored for an empty latent). | | tau | 0.6 | Stage-entry noise level (paper cascade: 0.6, 0.3, 0.3). Lower = stronger content preservation. | | filter_ratio | 0.2 | Butterworth low-pass cutoff D for direction alignment (paper 0.4, repo 0.2). | | alpha_scale / beta_scale | 1.0 / 0.5 | Direction / acceleration strength multipliers. | | upsampling | latent | Per-step reference upsample: latent bicubic (repo default) or pixel decode→sharpen→encode. The stage anchor is always the pixel round-trip. | | scale_factor | 2.0 | Output scale relative to the input latent: 2 = double each side, 1 = unchanged, 0.5 = half. Upscales run 2× doubling stages (scales between 1 and 2 give one 2× stage); below 1 runs one refinement stage at the smaller size. |

</details>

[!TIP] HiFlow inherits the reference's structure — including its mistakes. Generate a good base first; tau lower keeps more of it, higher re-imagines. 3D-latent image models (Krea2, Qwen-Image — Wan21 format, Qwen VAE) work as single-frame (T=1) latents; actual multi-frame/video input is rejected.

▓ Node Reference

All nodes registered by this pack (V3 schema ids):

| Node id | Display name | Purpose | |:---|:---|:---| | DyPE_FLUX | DyPE | Dynamic Position Extrapolation for ultra-high-res generation. | | SEGA | SEGA | Spectral-Energy Guided Attention (content-aware sharpening). | | SPA | SPA (HRDiT) | Spatial Position Alignment — fixes spatial disorder. | | HAP | HAP (HRDiT) | Head-Adaptive attention Pruning — the speed half. | | HAPCalibrate | HAP Calibrate (HRDiT) | In-graph scope-plan calibration for HAP. | | PixelRushNode | PixelRush | Cascade refinement for existing latents. | | FreeScaleNode | FreeScale | Tuning-free scale-fusion + self-cascade upscaling. | | HiFlowNode | HiFlow | Trajectory-guided flow upscaling (initialization + direction + acceleration alignment). |

▓ Getting Started

Via ComfyUI Manager: Search ComfyUI-DyPE → Install.

Manual install:

cd ComfyUI/custom_nodes/
git clone https://github.com/wildminder/ComfyUI-DyPE.git

Restart ComfyUI. No further dependency installation is required.

▓ Tips & Best Practices

[!IMPORTANT] Limitations at Extreme Resolutions (4K): you are pushing a model trained on ~1 megapixel toward 16 megapixels — minor artifacts can still appear even with these methods.

[!TIP] Speckle noise at 4K+: increase dype_exponent (e.g. 3.0–4.0) or apply smoothing / detailer LoRAs.

[!TIP] Experiment: there is no single magic setting — try different methods and adjust dype_exponent for the best sharpness/artifact balance.

▓ Changelog

v2.15.0 — 2026-09-08

Restructured the pack layout + unified the node category. All node definitions now live in a dedicated nodes/ folder (nodes/dype.py, sega.py, spa.py, hap.py, hap_calibrate.py, freescale.py, pixelrush.py, hiflow.py); src/ holds engines/implementation only and the pack __init__.py just registers the extension. All 8 nodes moved to the single WMNodes/image menu category (previously split across two menu paths). No node ids, inputs, defaults, or behavior changed — workflows keep loading. Also merges PR #41 (FreeScale fp16 antialiased-bicubic crash fix).

v2.14.1 — 2026-09-07

Fixed HiFlow Krea2/Qwen-Image noising crash (user-reported torch.cat size mismatch, "Expected size 1 but got size 16"): the v2.12.1 model-space noising called the model's process_latent_in on the 4D core tensor, but Wan21's per-channel mean/std stats are shaped [1,C,1,1,1] — a 4D tensor against 5D stats broadcasts silently to [B,C,C,H,W] garbage (the model reads T=16=channels). The node now wraps the noising conversions ndim-transparently: unsqueeze → convert in true 5D model space → squeeze back, so the cascade's σ-mix runs on 4D tensors with correctly-normalized values. The node-test mock now uses Wan21-faithful stats (replicating the broadcast hazard — the earlier affine mock masked the bug class).

v2.14.0 — 2026-09-07

HiFlow: 3D-latent image model support — Krea2 and Qwen-Image work now (plan 2026-09-07, user-reported Krea2 "does not support 3D-latent (video) models" rejection). These models are image models with a 5D Wan21-style latent layout [B,C,1,H,W] (Qwen VAE) — the old gate conflated 5D tensors with video. The gate now accepts latent_dimensions=3 image models and rejects only actual multi-frame (T>1) input; the node bridges 5D↔4D around the 4D core (the PixelRush convention): latents squeeze on entry and re-expand on output, the model-call adapter unsqueezes before process_latent_in (Wan21's per-channel mean/std stats broadcast on 5D only), and the VAE adapters speak the Qwen-VAE latent_dim=3 boundary (decode frame-slices the [B,T,H,W,3] image; encode lets the VAE do its own not_video unsqueeze). Qwen-Image gains real (previously gate-blocked) support from the same fix; Anima inherits it, untested on real runs.

v2.13.0 — 2026-09-04

HiFlow: target_resolution replaced by scale_factor (user request — the absolute pixel target was unintuitive). scale_factor is relative to the input latent: 2 doubles each side, 1 returns the base unchanged, 0.5 halves it via a single refinement stage. Scales now apply per side (the absolute form over-upscaled the short side of non-square images), upscales keep the paper's 2×-stage quantization (a 1.5 scale runs one 2× stage), and downscale scales (0.25–1) run one guided stage at the smaller size. Example workflow updated.

v2.12.1 — 2026-09-03

Fixed HiFlow img2img noising space (user-reported "drastic changes at any usable denoise; only 0.05 looks right"): the σ-mix σ·ε + (1−σ)·content now runs in MODEL space (convert the content with process_latent_in first, convert the mix back), matching ComfyUI's KSampler pipeline (samplers.py converts the content before the σ-mix). Mixing in VAE space scaled the noise by the latent format's scale_factor (Flux/Z-Image: 0.3611 — 2.77× under-noised) and added spurious shift offsets, so the model aggressively "corrected" every img2img input. The guided-stage initialization σ-mix got the same fix. The sampler itself (rectified-flow Euler) and scheduler spacing (model-table "simple") were already faithful — the defect was the space mix, not the routine.

v2.12.0 — 2026-09-03

HiFlow img2img: denoise parameter (user-reported "connecting the real latent does nothing"): with the full flow schedule the base start σ=1 zeroes the content weight, so a sampler latent connected to the node was silently ignored. The KSampler convention now applies — denoise < 1 truncates the base schedule so the walk enters below σ=1 and keeps (1−σ_start) of the input latent (an empty latent always runs the full schedule; the node warns when a content latent meets denoise=1.0).

v2.11.0 — 2026-09-03

HiFlow realigned with the authors' implementation (plan 2026-09-03-realignment, user-reported Z-Image "burned and blurred" output identical in both upsampling modes): the base stage now starts from noised latent instead of the raw input (an EmptySD3LatentImage was being sampled verbatim as all-zeros "noise" — the root cause); stage initialization anchors on the previous chain's final image (always pixel round-tripped) instead of the time-matched reference; the reference velocity derives from the walk's own state; trajectories store the raw (uncorrected) x0 so guidance doesn't compound across stages; α/β follow the code's linear-in-index schedule, not the paper's σ/σ_entry (which over-locks low frequencies late on shifted schedules). New noise_seed input drives the base and per-stage init noise reproducibly.

v2.10.0 — 2026-09-03

New HiFlow node (plan 2026-09-03): training-free high-resolution upscaling for rectified-flow models (FLUX, Qwen-Image, …) via flow-aligned guidance (arXiv:2504.06232). The base-resolution trajectory is recorded per-step and guides each upscale stage through initialization, direction and acceleration alignment. Non-flow and video models are rejected with a pointer to PixelRush.

v2.9.1 — 2026-09-02

Fixed the PixelRush noise-injection λ convention (user-reported "structure visible but completely noisy, soft blurred patches"). The injection now uses slerp(eps_random, eps_refined, λ) — λ weights the model's prediction (0.95 = 95% prediction + 5% noise). The previous order (slerp(eps_pred, eps_random, λ)) made λ=0.95 mean 99.6% pure random noise: at real scales per-pixel noise std ≈ 1.17 vs signal ≈ 1.0, which rendered through the Gaussian feather as the reported soft-patch noise. The additive legacy mode uses the same convention (eps_refined + (1−λ)·eps_random). This was exactly the argument-order caveat pixelrush-correct.txt flagged for verification against the authors' implementation.

v2.9.0 — 2026-09-02

PixelRush realigned with the corrected theory (plan 2026-09-02): standard raw-vector SLERP (with collinear lerp fallback) for the noise injection — the paper's slerp(eps_pred, eps_random, λ) is now the default, with the 2026-08-13 additive injection kept as an opt-in (noise_injection).
Fixed the VAE/model space mixing in the forward/reverse steps: adapters now convert via process_latent_in/out, so the model sees noise at the scale its timestep claims. For SDXL the previous code under-noised 7.7× — the root cause behind the "compressed look" that the additive hack had papered over.
Generic DDIM transitions (ddim_deterministic_step between arbitrary timesteps, predict_x0_from_epsilon); analytic Gaussian feather mask (σ default 24, gaussian_kernel_size input removed).
Optional refiner_model input — use a separate distilled refiner (e.g. SDXL-Turbo) as in the paper; the base model drives the partial inversion.
Bug fixes: empty-negative conditioning no longer amplifies eps by cfg_scale (CFG is skipped); alpha_k NameError with partially-provided adapters; empty positive now raises a clear error.

v2.8.3 — 2026-08-31

Qwen2D VAE support disabled by default. User reports showed that with the Qwen2D VAE interception installed, loading certain non-Qwen2D (video-style) VAE checkpoints crashed with a size-mismatch error whose traceback passed through this pack's delegation frame — breaking workflows that never used the Qwen2D VAE. The patch now installs only when the environment variable DYPE_ENABLE_QWEN2D_VAE=1 is set. If you relied on the Qwen2D VAE (Anzhc/Qwen2D-VAE checkpoint with FreeScale/PixelRush on Krea-2/Qwen/Anima), set that variable in your ComfyUI environment to restore the previous behavior.

v2.8.2 — 2026-08-31

Fixed graph-build and execution crashes when resolution inputs are None (validate_inputs now passes through uninitialized state; execute falls back to 1024)
Fixed PixelRush crash on float16: antialiased bicubic upsample casts to float32 and restores the original dtype

v2.8.1 — 2026-08-25

Fixed valid resolutions being rejected at graph build
Validation errors are now reported once, for the right input

v2.8.0 — 2026-08-16

New HAP Calibrate node: calibrate HAP directly in-graph
HAP accepts calibrated plans either by file or by direct connection
CLI calibration tooling completed

v2.7.1 — 2026-08-16

Fixed crashes on Anima/Cosmos models
Safer automatic fallbacks instead of hard errors
SPA and HAP nodes now work in any order

v2.7.0 — 2026-08-15

New HAP node: sparse-attention acceleration (up to ~5× faster attention at 4K)
One-click scope-plan calibration pipeline (in-graph + CLI)
New optional attention scaling and per-layer filtering controls
SPA and HAP can be composed together

v2.6.1 — 2026-08-15

Reworked SPA bundle-size control to match the paper
Much faster SPA runs (up to ~10× less overhead at strong settings)
Automatic no-op at/below native resolution

v2.6.0 — 2026-08-15

New SPA node (HRDiT)

PixelRush update

Fixed "totally noisy" output on SDXL models

v2.5.0

New SEGA node
Video-model latent support

v2.4.0

Anima/Cosmos support
Krea-2 support
Stability fixes and new example workflows

v2.3.0

Z-Image quality improvements

v2.2.0

Experimental Z-Image support

v2.1.0

Qwen Image and Nunchaku support
Modular codebase refactor for easier future model support

v2.0.0

New vision_yarn method for better aspect-ratio handling
Sharper results with fewer artifacts
New start-sigma control

v1.0.0

Initial release: core DyPE for FLUX with yarn and ntk methods

▓ Acknowledgments

Noam Issachar, Guy Yariv and co-authors — DyPE (paper)
The SEGA authors — SEGA
The HRDiT team — HRDiT (code) — basis for SPA & HAP
The PixelRush authors — PixelRush
The HiFlow authors — HiFlow (code)
Yanhong Zeng et al. — FreeScale (paper)
The ComfyUI team — for the platform

ComfyUI-DyPE

Nodes (8)

▷ About

❖ Highlights

▓ Nodes

Which method when?

❖ DyPE

Model Configuration

Method Selection (method)

Scaling Options

Dynamic Control

Advanced Noise Scheduling

❖ SEGA

❖ SPA (HRDiT)

❖ HAP (HRDiT)

❖ PixelRush

❖ FreeScale

❖ HiFlow

▓ Node Reference

▓ Getting Started

▓ Tips & Best Practices

▓ Changelog

v2.15.0 — 2026-09-08

v2.14.1 — 2026-09-07

v2.14.0 — 2026-09-07

v2.13.0 — 2026-09-04

v2.12.1 — 2026-09-03

v2.12.0 — 2026-09-03

v2.11.0 — 2026-09-03

v2.10.0 — 2026-09-03

v2.9.1 — 2026-09-02

v2.9.0 — 2026-09-02

v2.8.3 — 2026-08-31

v2.8.2 — 2026-08-31

v2.8.1 — 2026-08-25

v2.8.0 — 2026-08-16

v2.7.1 — 2026-08-16

v2.7.0 — 2026-08-15

v2.6.1 — 2026-08-15

v2.6.0 — 2026-08-15

PixelRush update

v2.5.0

v2.4.0

v2.3.0

v2.2.0

v2.1.0

v2.0.0

v1.0.0

▓ Acknowledgments

Method Selection (`method`)