ComfyUI/comfy_extras/nodes_minimax_h3.py

"""MiniMax H3 nodes: AV latent creation and task conditioning (t2va / fl2va / ref2va).

The H3 packed-DiT consumes, via conditioning:
- Qwen3-VL-32B hidden states with per-token modality tags (from the minimax CLIP)
- keyframe / reference condition latents, re-injected every step (never denoised)

Latents are NestedTensor pairs (video [B,24,T,H/16,W/16], audio [B,32,2,T40]);
sampling runs on the flat pack with any stock sampler (the model handles the
audio stream's shifted schedule internally).
"""

import math

import torch
import torchaudio

import nodes
import comfy.model_management
import comfy.model_sampling
import comfy.nested_tensor
import comfy.utils
import node_helpers
from comfy_api.latest import ComfyExtension, io

CANVAS_MULTIPLE = 32
BASE_SHORT_EDGE = 768
MAX_PIXELS = 768 * 1344
REF_IMAGE_SHORT_EDGE = 2048
FPS = 24
AUDIO_LATENT_FPS = 40


def align_frame_count(n):
    while n % 17 != 5:
        n += 1
    return n


def video_latent_t(frame_count):
    return 2 if frame_count <= 5 else ((frame_count - 5) // 17) * 5 + 2


def temporal_shape(length):
    frame_count = align_frame_count(max(5, length))
    duration = frame_count / FPS
    return frame_count, video_latent_t(frame_count), round(duration * AUDIO_LATENT_FPS)


def adapt_canvas(width, height):
    """768-short-edge canvas with 768*1344 area cap, per-axis round to 32."""
    ratio = width / height
    if ratio >= 1.0:
        nom_w, nom_h = BASE_SHORT_EDGE * ratio, BASE_SHORT_EDGE
    else:
        nom_w, nom_h = BASE_SHORT_EDGE, BASE_SHORT_EDGE / ratio
    if nom_w * nom_h > MAX_PIXELS:
        s = math.sqrt(MAX_PIXELS / (nom_w * nom_h))
        nom_w, nom_h = nom_w * s, nom_h * s
    return (max(CANVAS_MULTIPLE, round(nom_w / CANVAS_MULTIPLE) * CANVAS_MULTIPLE),
            max(CANVAS_MULTIPLE, round(nom_h / CANVAS_MULTIPLE) * CANVAS_MULTIPLE))


def _resize(image, width, height, crop):
    # image [B, H, W, C] -> [B, height, width, 3]
    samples = image[..., :3].movedim(-1, 1)
    samples = comfy.utils.common_upscale(samples, width, height, "lanczos", crop)
    return samples.movedim(1, -1)


def _empty_av_latent(width, height, length, batch_size=1):
    frame_count, latent_t, audio_t = temporal_shape(length)
    video = torch.zeros([batch_size, 24, latent_t, height // 16, width // 16],
                        device=comfy.model_management.intermediate_device())
    audio = torch.zeros([batch_size, 32, 2, audio_t],
                        device=comfy.model_management.intermediate_device())
    return {"samples": comfy.nested_tensor.NestedTensor((video, audio))}, frame_count


class EmptyMiniMaxH3LatentAV(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="EmptyMiniMaxH3LatentAV",
            display_name="Empty MiniMax H3 AV Latent",
            category="model/latent/minimax",
            description="Joint video+audio latent for MiniMax H3. Duration snaps to the model's 17k+5 frame grid at 24 fps.",
            inputs=[
                io.Int.Input("width", default=1344, min=32, max=nodes.MAX_RESOLUTION, step=32),
                io.Int.Input("height", default=768, min=32, max=nodes.MAX_RESOLUTION, step=32),
                io.Int.Input("length", default=124, min=5, max=3600, step=17, tooltip="Frame count at 24 fps, snapped up to the model's 17k+5 grid (124 = ~5s; trained range is ~124-362, longer is untested)"),
            ],
            outputs=[io.Latent.Output()],
        )

    @classmethod
    def execute(cls, width, height, length) -> io.NodeOutput:
        latent, _ = _empty_av_latent(width, height, length)
        return io.NodeOutput(latent)


class MiniMaxH3ImageToVideo(io.ComfyNode):
    """t2va and fl2va: prompt (+ optional first/last keyframes) -> conditioning + AV latent."""

    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="MiniMaxH3ImageToVideo",
            display_name="MiniMax H3 Image to Video",
            category="model/conditioning/minimax",
            inputs=[
                io.Clip.Input("clip"),
                io.Vae.Input("vae"),
                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
                io.Int.Input("width", default=1344, min=32, max=nodes.MAX_RESOLUTION, step=32),
                io.Int.Input("height", default=768, min=32, max=nodes.MAX_RESOLUTION, step=32),
                io.Int.Input("length", default=124, min=5, max=3600, step=17, tooltip="Frame count at 24 fps, snapped up to the model's 17k+5 grid (124 = ~5s; trained range is ~124-362, longer is untested)"),
                io.Image.Input("first_frame", optional=True),
                io.Image.Input("last_frame", optional=True),
            ],
            outputs=[io.Conditioning.Output(display_name="positive"), io.Latent.Output()],
        )

    @classmethod
    def execute(cls, clip, vae, prompt, width, height, length,
                first_frame=None, last_frame=None) -> io.NodeOutput:
        latent, frame_count = _empty_av_latent(width, height, length)

        images = []
        keyframes = []
        if first_frame is not None:
            # geometry anchor: plain stretch to canvas
            img = _resize(first_frame[:1], width, height, "disabled")
            images.append(img)
            keyframes.append({"resolved_frame_index": 0, "image": img})
        if last_frame is not None:
            # follower: aspect-preserving cover-crop
            img = _resize(last_frame[:1], width, height, "center")
            images.append(img)
            keyframes.append({"resolved_frame_index": frame_count - 1, "image": img})

        tokens = clip.tokenize(prompt, images=images)
        cond = clip.encode_from_tokens_scheduled(tokens)

        if keyframes:
            for kf in keyframes:
                kf["latent"] = vae.encode(kf.pop("image"))
            cond = node_helpers.conditioning_set_values(cond, {
                "minimax_keyframes": keyframes,
                "minimax_frame_count": frame_count,
            })
        return io.NodeOutput(cond, latent)


class MiniMaxH3ReferenceToVideo(io.ComfyNode):
    """ref2va: prompt + reference images / videos / audio -> conditioning + AV latent.

    References enter the presentation in fixed order: images, then videos (each
    soundtrack's <Audio j> label right before its <Video k>), then standalone
    audio. Ordinals are 1-based per type, so the prompt refers to them as
    <Picture i> / <Video k> / <Audio j>.
    """

    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="MiniMaxH3ReferenceToVideo",
            description="<Picture i> / <Video k> / <Audio j> reference conditioning for MiniMax H3. Use the same tags when prompting.",
            display_name="MiniMax H3 Reference to Video",
            category="model/conditioning/minimax",
            inputs=[
                io.Clip.Input("clip"),
                io.Vae.Input("vae"),
                io.Vae.Input("audio_vae"),
                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
                io.Int.Input("width", default=1344, min=32, max=nodes.MAX_RESOLUTION, step=32),
                io.Int.Input("height", default=768, min=32, max=nodes.MAX_RESOLUTION, step=32),
                io.Int.Input("length", default=124, min=5, max=3600, step=17, tooltip="Frame count at 24 fps, (124 = ~5s, trained range is ~124-362)"),
                io.Combo.Input("ref_image_size", options=["match", "max"], default="match",
                    tooltip="Reference image sizing. 'match' scales each ref (down only, keeping aspect) to the generation's pixel area; 'max' uses the reference pipeline's 2048px short edge for best identity fidelity. Reference tokens ride through every sampling step, so 'max' can be several times slower."),
                io.Autogrow.Input("ref_images", optional=True,
                    template=io.Autogrow.TemplatePrefix(
                        input=io.Image.Input("ref_image", tooltip="Reference image (downscaled to 2048 short edge if larger, never upscaled)"),
                        prefix="ref_image_", min=0, max=9)),
                io.Autogrow.Input("ref_videos", optional=True,
                    template=io.Autogrow.TemplatePrefix(
                        input=io.Image.Input("ref_video", tooltip="Reference video frames at 24 fps (2-15s)"),
                        prefix="ref_video_", min=0, max=3)),
                io.Autogrow.Input("ref_video_audios", optional=True,
                    template=io.Autogrow.TemplatePrefix(
                        input=io.Audio.Input("ref_video_audio", tooltip="Soundtrack of the same-numbered reference video"),
                        prefix="ref_video_audio_", min=0, max=3)),
                io.Autogrow.Input("ref_audios", optional=True,
                    template=io.Autogrow.TemplatePrefix(
                        input=io.Audio.Input("ref_audio", tooltip="Standalone reference audio"),
                        prefix="ref_audio_", min=0, max=3)),
            ],
            outputs=[io.Conditioning.Output(display_name="positive"), io.Latent.Output()],
        )

    @staticmethod
    def _encode_ref_audio(audio_vae, audio):
        waveform = audio["waveform"]  # [B, C, L]
        sr = audio["sample_rate"]
        vae_sr = getattr(audio_vae, "audio_sample_rate", 32000)
        if sr != vae_sr:
            waveform = torchaudio.functional.resample(waveform, sr, vae_sr)
        z = audio_vae.encode(waveform[:1].movedim(1, -1))  # [1, 32, 2, T]
        return z, z.shape[-1]

    @classmethod
    def execute(cls, clip, vae, audio_vae, prompt, width, height, length, ref_image_size="match",
                ref_images=None, ref_videos=None, ref_video_audios=None, ref_audios=None) -> io.NodeOutput:
        latent, frame_count = _empty_av_latent(width, height, length)

        ref_items = []   # for the tokenizer presentation, in request order
        ref_blocks = []  # for the DiT payload, same order

        for img in (ref_images or {}).values():
            if img is None:
                continue
            h, w = img.shape[1], img.shape[2]
            if ref_image_size == "match":
                # aspect-preserving scale (down only) to the generation's pixel area
                scale = min(1.0, math.sqrt((width * height) / (w * h)))
            else:
                scale = min(1.0, REF_IMAGE_SHORT_EDGE / min(w, h))
            tw = max(CANVAS_MULTIPLE, round(w * scale / CANVAS_MULTIPLE) * CANVAS_MULTIPLE)
            th = max(CANVAS_MULTIPLE, round(h * scale / CANVAS_MULTIPLE) * CANVAS_MULTIPLE)
            resized = _resize(img[:1], tw, th, "disabled")
            z = vae.encode(resized)
            ref_items.append({"type": "image", "data": resized})
            ref_blocks.append({"kind": "image", "latent_h": th // 16, "latent_w": tw // 16, "latent": z})

        ref_video_audios = ref_video_audios or {}
        for name, video_frames in (ref_videos or {}).items():
            if video_frames is None:
                continue
            # index-paired soundtrack: ref_video_audio_N belongs to ref_video_N
            soundtrack = ref_video_audios.get("ref_video_audio_" + name.rsplit("_", 1)[-1])
            vh, vw = video_frames.shape[1], video_frames.shape[2]
            cw, ch = adapt_canvas(vw, vh)
            if vw * vh < cw * ch:
                cw = max(CANVAS_MULTIPLE, round(vw / CANVAS_MULTIPLE) * CANVAS_MULTIPLE)
                ch = max(CANVAS_MULTIPLE, round(vh / CANVAS_MULTIPLE) * CANVAS_MULTIPLE)
            frames = _resize(video_frames, cw, ch, "disabled")
            if frames.shape[0] > frame_count:
                frames = frames[:frame_count]
            n = frames.shape[0]
            if n < 5:
                raise ValueError("MiniMax H3 reference videos need at least 5 frames (~0.2s at 24 fps)")
            while n % 17 != 5:
                n -= 1
            frames = frames[:n]
            z = vae.encode(frames)
            audio_latent, ref_audio_t = (None, 0)
            if soundtrack is not None:
                audio_latent, ref_audio_t = cls._encode_ref_audio(audio_vae, soundtrack)
                # the soundtrack gets its own <Audio j> label, emitted before <Video k>
                ref_items.append({"type": "audio"})
            # Qwen sees the video at 2 fps with timestamps
            sample_idx = list(range(0, frames.shape[0], FPS // 2))
            qwen_frames = frames[sample_idx]
            ref_items.append({"type": "video", "data": qwen_frames,
                              "timestamps": [i / 2.0 for i in range(len(sample_idx))]})
            ref_blocks.append({"kind": "video_audio" if ref_audio_t else "video",
                               "latent_t": z.shape[2], "latent_h": ch // 16, "latent_w": cw // 16,
                               "ref_audio_t": ref_audio_t, "latent": z, "audio_latent": audio_latent})

        for audio in (ref_audios or {}).values():
            if audio is None:
                continue
            audio_latent, ref_audio_t = cls._encode_ref_audio(audio_vae, audio)
            ref_items.append({"type": "audio"})
            ref_blocks.append({"kind": "audio", "ref_audio_t": ref_audio_t, "audio_latent": audio_latent})

        tokens = clip.tokenize(prompt, minimax_ref_items=ref_items)
        cond = clip.encode_from_tokens_scheduled(tokens)
        if ref_blocks:
            cond = node_helpers.conditioning_set_values(cond, {"minimax_refs": ref_blocks})
        return io.NodeOutput(cond, latent)


class MiniMaxH3SigmaShift(io.ComfyNode):
    """Set the video/audio flow shifts coherently.

    The video shift drives the sampler's sigma schedule; both values are also
    handed to the DiT, which inverts the video schedule to the shared base grid
    and derives the audio schedule from it.
    """

    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="MiniMaxH3SigmaShift",
            description="Set the video/audio flow shifts.",
            display_name="MiniMax H3 Sigma Shift",
            category="model/patch/minimax",
            inputs=[
                io.Model.Input("model"),
                io.Float.Input("shift_video", default=12.0, min=0.01, max=100.0, step=0.01),
                io.Float.Input("shift_audio", default=3.0, min=0.01, max=100.0, step=0.01),
            ],
            outputs=[io.Model.Output()],
        )

    @classmethod
    def execute(cls, model, shift_video, shift_audio) -> io.NodeOutput:
        m = model.clone()

        class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscreteFlow, comfy.model_sampling.CONST):
            pass

        original = m.get_model_object("model_sampling")
        model_sampling = ModelSamplingAdvanced(model.model.model_config)
        model_sampling.set_parameters(shift=shift_video)
        if hasattr(original, "noise_scale"):
            model_sampling.set_noise_scale(original.noise_scale)
        m.add_object_patch("model_sampling", model_sampling)

        to = m.model_options["transformer_options"] = m.model_options.get("transformer_options", {}).copy()
        to["minimax_h3_sigma_shift_video"] = shift_video
        to["minimax_h3_sigma_shift_audio"] = shift_audio
        return io.NodeOutput(m)


class MiniMaxH3Extension(ComfyExtension):
    async def get_node_list(self):
        return [
            EmptyMiniMaxH3LatentAV,
            MiniMaxH3ImageToVideo,
            MiniMaxH3ReferenceToVideo,
            MiniMaxH3SigmaShift
            ]


async def comfy_entrypoint() -> MiniMaxH3Extension:
    return MiniMaxH3Extension()