ComfyUI-Hyperflow

ComfyUI-Hyperflow

中文文档 (README_ZH)

<img width="1475" height="663" alt="image" src="https://github.com/user-attachments/assets/1a476e02-d108-4184-b850-994c8094a32b" />

ComfyUI node pack for HyperFlow — Video Rebirth's 8-step LoRA for MiniMax-H3 (video + audio), ported onto ComfyUI's native MiniMax-H3 model. No ComfyUI core files are modified; no custom sampler is needed.

Note — don't want this custom node? Use the standalone LoRA builds instead — extracted and converted to plain ComfyUI format, they load with the stock Load LoRA node, nothing to install: drbaph/MiniMax-H3-Turbo-Lora-ComfyUI — minimax_h3_hyperflow_8step_v1.0_comfyui_bf16.safetensors (3.93 GB) / ..._pruned_bf16.safetensors (3.91 GB), plus rank-20 resized variants (~318/316 MB). Those apply the backbone LoRA only — the two-time (t, r) conditioning is exclusive to this node, so without it the output deviates from the released model.

The adapter is two things on top of the official base: a LoRA (rank 256, unmerged bf16 branches) and two-time (t, r) conditioning — every step is conditioned on the interval it integrates, r = 1 - sigma_next. This pack replaces exactly one call in the native model (time_embedder(t_vals)) with the blended embedding and applies the LoRA through ComfyUI's own weight-adapter machinery (bypass default / merge optional), so quantized bases, model offloading, and the memory visualizer all behave exactly like any other MODEL patch.

https://github.com/user-attachments/assets/86f10b24-8ac1-4290-8e5e-ebcb3c274f93

https://github.com/user-attachments/assets/3e2e2d5c-6a39-46e9-8946-aa42f08c3d9d

https://github.com/user-attachments/assets/c83164e8-fd71-4cbd-bbfa-25d65cef7769

Install

1. Clone into ComfyUI/custom_nodes/ComfyUI-Hyperflow.
2. Download one converted weights file from the drbaph/Hyperflow-Comfyui Hugging Face repo (or just enable the node's download_if_missing toggle and it fetches itself on first run):

📂 ComfyUI/
└── 📂 models/
    └── 📂 hyperflow/
        ├── custom_node_hyperflow_8step_v1.0_comfyui.safetensors         (3.67 GiB, full base — the released 8-step model)
        └── custom_node_hyperflow_8step_v1.0_comfyui_pruned.safetensors  (3.64 GiB, pruned/curve bases — backbone only, single-time)

The hyperflow.json manifests ship with this pack in assets/ — users only ever download the .safetensors. Converted files carry the full self-describing HyperFlow header (gate, sigma grid, rank); the original diffusers-layout file is rejected with a clear message, never translated at load time.

3. Restart ComfyUI.

Usage

Load Diffusion Model (MiniMax-H3)
  -> ApplyHyperFlow            (MODEL -> MODEL + SIGMAS)
  -> [optional] Model Attention Backend        (core node; dense fallback incl. comfy-kitchen int8)
  -> [optional] Model Sparse Attention         (core node; sol-attn / sla / vsa)
  -> SamplerCustomAdvanced + guider + Euler    (feed the SIGMAS output, not a scheduler)

• ApplyHyperFlow outputs the trained 9-point sigma grid as SIGMAS — wire it into SamplerCustomAdvanced in place of BasicScheduler. Any core sampler/guider works; the two-time endpoints are derived from sample_sigmas (the same mechanism the native H3 final layer uses).
• lora_mode: bypass (default) applies the LoRA at run time — matches the reference's unmerged bf16 branches. merge folds it into the weights — lowest VRAM, softer on quantized bases.
• download_if_missing fetches the chosen variant (auto matches the detected base) from drbaph/Hyperflow-Comfyui into models/hyperflow/ — exactly the published .safetensors, nothing else. Off by default.
• ApplyHyperFlowAdvanced adds gate and sigma-grid overrides (ablations; defaults reproduce the released model).

Sol-Attn (optional sparse attention) — core node settings

HyperFlow's validated Sol-Attn recipe maps onto the core Model Sparse Attention node:

| HyperFlow (reference) | Model Sparse Attention | | --- | --- | | dense_steps = 2 (of 8) | start_percent = 0.25 | | dense_layers = (0, 1) | dense_blocks = "0,1" | | tau = 1.0 | tau = 1.0 (method sol-attn) | | sink tokens: none | keep default exact_kv_and_rows (strictly more exact on cond rows) | | — | extra_tokens = 0 for the closest recipe match (256 = more quality headroom) |

SLA is a different sparse method — use it only with SLA-trained weights.

Notes

• Base detection is automatic: the node inspects the loaded model — full base (has time_embedder) or pruned/curve base (no time_embedder) — and enforces the matching weights build with a clear error that names the right file. The pruned-base build applies the backbone LoRA only and runs single-time (off-recipe).
• Quantized bases (int8/fused ops): LoRA targets that the base folds into a fused kernel (no hookable module) are detected and applied through the merge path automatically — the console report lists them as N fused/int8 targets via merge.
• Model sampling shifts: the H3 model already defaults to video shift 12 / audio shift 3. The core ModelSampling node goes after ApplyHyperFlow in the chain, and is only needed if you want non-default shifts.
• aimdo malloc-graph: on Comfy builds whose model compiler crashes on patched MiniMax-H3 forwards, the node disables the compiler for its own model calls.
• Weights are a Model Derivative of MiniMax-H3 under the MiniMax H3 Community License; this pack's code is Apache-2.0 (the schedule/embedder ports derive from the HyperFlow and diffusers code, see the upstream THIRD_PARTY_NOTICES.md).

Acknowledgements

• MiniMax-H3 (GitHub): the base model, VAEs, conditioner and official workflows.
• AnyFlow (GitHub; Gu et al., 2026): the flow-map formulation behind the two-time (t, r) conditioning.
• Sol-Attn (GitHub; Li et al., 2026): optional attention kernels.

Thanks to their authors.