cortiq_engine/

videogen.rs

//! End-to-end MiniMax-H3 text→(video + synchronized stereo audio):
//! Qwen3-VL prompt encode → 4-step dual-schedule flow sampling over the
//! packed DiT → ViT3D video decode and BigVGAN audio decode.
//!
//! Stages load and drop one at a time, as the image pipeline next door
//! does: peak resident is one component, not their sum.
//!
//! ## Two clocks, four steps
//!
//! The sampler walks the VIDEO sigma grid — `simple` at shift 12, which
//! at four steps is 1, 0.973, 0.923, 0.8, 0 — and the audio stream is
//! integrated on its own remap of that grid (shift 3). Stepping both on
//! the video grid is what a stock sampler does, and it is fine at
//! twenty steps and audibly wrong at four: `Δσ_a` and `Δσ_v` differ by
//! a factor of three over the last interval, and no per-step slope
//! correction fixes a step that large. Hence `--stock-sampler`, which
//! reproduces the broken behaviour on purpose, for comparison.

use crate::audiovae::AudioVae;
use crate::mmh3::{Layout, MiniMaxH3, time_shift_sigma};
use crate::qwen3te::{ImageSpan, Qwen3Encoder};
use crate::qwen3vis::{self, VisionTower};
use crate::sampler::SplitMix64;
use crate::tokenizer::Tokenizer;
use crate::vae3d::VideoVae;
use crate::vae3d::VideoVaeEncoder;
use std::path::Path;
use std::sync::Arc;

pub const FPS: usize = 24;
pub const AUDIO_LATENT_FPS: usize = 40;

pub struct AnimParams {
    pub width: usize,
    pub height: usize,
    /// Frames at 24 fps; snapped up to the model's 17k+5 grid.
    pub frames: usize,
    pub steps: usize,
    pub seed: u64,
    /// Integrate the audio on the video's grid, as a stock sampler
    /// would. Wrong at four steps; kept for A/B.
    pub stock_sampler: bool,
    pub max_tokens: usize,
    /// RGB in [0, 1] as `[3, h, w]` with its size — the clip's first
    /// frame, and/or its last.
    pub first_frame: Option<(Vec<f32>, usize, usize)>,
    pub last_frame: Option<(Vec<f32>, usize, usize)>,
    /// Extra reference frames with the pixel index each one stands for:
    /// video-to-video as this architecture actually takes it — every
    /// frame is a condition row pinned to its own time coordinate, the
    /// same machinery `--first-frame`/`--last-frame` use for two.
    pub mid_frames: Vec<((Vec<f32>, usize, usize), usize)>,
    /// A LoRA adapter (.safetensors) applied at runtime, and how hard.
    pub lora: Option<String>,
    pub lora_strength: f32,
    /// The published latent upscaler (.safetensors) and the factor to
    /// apply: the denoised latent is resized by the learned net and the
    /// VAE decodes at the larger size, so the 5 B-parameter decode →
    /// pixel resize → encode round trip never happens.
    pub upscale: Option<String>,
    pub upscale_by: f32,
}

/// The vision-block token ids the H3 presentation flanks a picture with.
const VISION_START: u32 = 151_652;
const VISION_END: u32 = 151_653;

/// Resize `[3, h, w]` RGB to the canvas. The first frame is a geometry
/// anchor and is stretched; the last one follows and is cover-cropped,
/// which is what the reference node does with each.
pub fn fit_to_canvas(
    rgb: &[f32],
    h: usize,
    w: usize,
    out_h: usize,
    out_w: usize,
    crop: bool,
) -> Vec<f32> {
    // Cover-crop picks the largest centred rectangle of the source with
    // the target's aspect; a stretch takes the whole thing.
    let (sx0, sy0, sw, sh) = if crop {
        let (tw, th) = (out_w as f64, out_h as f64);
        let scale = (w as f64 / tw).min(h as f64 / th);
        let (cw, ch) = ((tw * scale).round() as usize, (th * scale).round() as usize);
        ((w - cw) / 2, (h - ch) / 2, cw.max(1), ch.max(1))
    } else {
        (0, 0, w, h)
    };
    let mut out = vec![0f32; 3 * out_h * out_w];
    for c in 0..3 {
        for y in 0..out_h {
            let sy = ((y as f64 + 0.5) * sh as f64 / out_h as f64 - 0.5).max(0.0);
            let y0 = sy.floor() as usize;
            let y1 = (y0 + 1).min(sh - 1);
            let fy = (sy - y0 as f64) as f32;
            for x in 0..out_w {
                let sx = ((x as f64 + 0.5) * sw as f64 / out_w as f64 - 0.5).max(0.0);
                let x0 = sx.floor() as usize;
                let x1 = (x0 + 1).min(sw - 1);
                let fx = (sx - x0 as f64) as f32;
                let p = |yy: usize, xx: usize| rgb[(c * h + sy0 + yy) * w + sx0 + xx];
                let top = p(y0, x0) * (1.0 - fx) + p(y0, x1) * fx;
                let bot = p(y1, x0) * (1.0 - fx) + p(y1, x1) * fx;
                out[(c * out_h + y) * out_w + x] = top * (1.0 - fy) + bot * fy;
            }
        }
    }
    out
}

impl Default for AnimParams {
    fn default() -> Self {
        Self {
            width: 512,
            height: 288,
            frames: 39,
            steps: 4,
            seed: 42,
            stock_sampler: false,
            max_tokens: 512,
            first_frame: None,
            last_frame: None,
            mid_frames: Vec::new(),
            lora: None,
            lora_strength: 1.0,
            upscale: None,
            upscale_by: 2.0,
        }
    }
}

/// The rendered result: RGB in [0, 1] as `[3, frames, h, w]`, and
/// stereo f32 in [-1, 1] as `[2, samples]`.
pub struct Anim {
    pub rgb: Vec<f32>,
    pub frames: usize,
    pub height: usize,
    pub width: usize,
    pub audio: Vec<f32>,
    pub samples: usize,
    pub sample_rate: usize,
}

/// Frame counts snap UP to 17k+5 — the grid the temporal VAE and the
/// DiT's frame-span pattern agree on.
pub fn align_frames(n: usize) -> usize {
    let mut n = n.max(5);
    while n % 17 != 5 {
        n += 1;
    }
    n
}

pub fn video_latent_t(frames: usize) -> usize {
    if frames <= 5 {
        2
    } else {
        (frames - 5) / 17 * 5 + 2
    }
}

/// `(frames, latent_t, audio_t)` for a requested length.
pub fn temporal_shape(len: usize) -> (usize, usize, usize) {
    let frames = align_frames(len);
    let audio_t = ((frames as f64 / FPS as f64) * AUDIO_LATENT_FPS as f64).round() as usize;
    (frames, video_latent_t(frames), audio_t)
}

/// The `simple` scheduler over `ModelSamplingDiscreteFlow(shift)`: the
/// 1000-entry sigma table sampled at even strides, terminal 0 appended.
pub fn sigmas(steps: usize, shift: f64) -> Vec<f64> {
    let table = 1000usize;
    let mut out: Vec<f64> = (0..steps)
        .map(|x| {
            let idx = table - 1 - x * table / steps;
            let t = (idx + 1) as f64 / table as f64;
            shift * t / (1.0 + (shift - 1.0) * t)
        })
        .collect();
    out.push(0.0);
    out
}

/// Shared with the DiT's condition-row noise blend.
pub fn gauss_pub(n: usize, seed: u64) -> Vec<f32> {
    gauss(n, seed)
}

fn gauss(n: usize, seed: u64) -> Vec<f32> {
    let mut rng = SplitMix64::new(seed);
    let mut u = || (rng.next_u64() >> 11) as f64 / (1u64 << 53) as f64;
    let mut out = Vec::with_capacity(n);
    while out.len() < n {
        let (a, b) = (u().max(1e-300), u());
        let r = (-2.0 * a.ln()).sqrt();
        let ang = 2.0 * std::f64::consts::PI * b;
        out.push((r * ang.cos()) as f32);
        if out.len() < n {
            out.push((r * ang.sin()) as f32);
        }
    }
    out
}

/// Text→(video, audio) from a packaged `.cmf`.
pub fn generate(
    path: &Path,
    prompt: &str,
    p: &AnimParams,
    mut progress: impl FnMut(&str, usize, usize),
) -> Result<Anim, String> {
    if p.width % 32 != 0 || p.height % 32 != 0 {
        return Err("width/height must be multiples of 32".into());
    }
    // The wgpu wide-GEMM arm was measured WRONG on one driver stack
    // (RTX PRO 6000: step-1 velocity rms off, step-2 NaN) and byte-
    // healthy on another (2×RTX 5090, coop and plain arms within 0.5%
    // of each other and 3.5% of the host render). Trust is therefore
    // PER-STACK, decided by a parity probe on this file's own first
    // qkv weight at DiT-scale activations — not by a hardcoded verdict
    // either way. CMF_MMH3_GPU=1/0 still forces.
    let use_gpu = match std::env::var("CMF_MMH3_GPU").ok().as_deref() {
        Some("1") => true,
        Some("0") => false,
        _ => mmh3_gpu_parity_probe(path).unwrap_or(false),
    };
    if use_gpu {
        generate_inner(path, prompt, p, &mut progress)
    } else {
        crate::gpu::cpu_scope(|| generate_inner(path, prompt, p, &mut progress))
    }
}

/// GPU-vs-host parity on the packed DiT's first attention projection:
/// the real q4tp bytes, activations spanning the modulation range
/// (±2000 mixed with ±2), rms gate at 1e-2 — the measured failure was
/// ~24%, honest drift is ~1e-5, so the gate has a decade of margin on
/// each side. Any refusal (no adapter, dtype outside the kernel) is a
/// clean "no": the host path is never wrong, only slower.
fn mmh3_gpu_parity_probe(path: &Path) -> Result<bool, String> {
    let model = Arc::new(
        cortiq_core::CmfModel::open(path).map_err(|e| format!("{}: {e}", path.display()))?,
    );
    // Any codec, not just q4tp: the probe's job is to check THIS file's
    // device arm against the host, and a container packed as q8_2f has one
    // too. Matching on dtype sent every non-q4tp build to the CPU for the
    // whole render — measured at 357 s against 60 on the same card.
    let Some(idx) = model.tensors.iter().position(|t| {
        t.name.starts_with("dit.") && t.name.ends_with("attn.qkv_proj.weight")
    }) else {
        tracing::info!("mmh3 GPU parity probe: no qkv weight — host path");
        return Ok(false);
    };
    let entry = &model.tensors[idx];
    let (rows, cols) = (entry.shape[0], entry.shape[1]);
    let b = 64usize;
    let mut xs = vec![0f32; b * cols];
    for (i, v) in xs.iter_mut().enumerate() {
        let base = ((i * 37 + 11) % 1009) as f32 / 1009.0 - 0.5;
        *v = base * if i % 7 == 0 { 2000.0 } else { 2.0 };
    }
    let mut gpu = vec![0f32; b * rows];
    if std::env::var("CMF_GPU_DEBUG").is_ok() {
        eprintln!(
            "mmh3 probe env: CMF_GPU={:?} enabled={} avail={}",
            std::env::var("CMF_GPU").ok(),
            crate::gpu::enabled(),
            crate::gpu::backend_available(),
        );
    }
    let qt = crate::qtensor::QTensor::from_model(&model, &entry.name.clone())?;
    if !qt.device_matmat(&xs, b, &mut gpu) {
        tracing::info!(
            "mmh3 GPU parity probe: {:?} device GEMM refused ({rows}x{cols}) — host path",
            entry.dtype
        );
        if std::env::var("CMF_GPU_DEBUG").is_ok() {
            eprintln!("mmh3 probe: device GEMM refused {rows}x{cols} for {:?}", entry.dtype);
        }
        return Ok(false);
    }
    let host = {
        let name = entry.name.clone();
        let proj = crate::dit::Proj::from_model(&model, &name)?;
        let mut out = vec![0f32; b * rows];
        crate::gpu::cpu_scope(|| proj.matmat(&xs, b, &mut out, None));
        out
    };
    let mut num = 0f64;
    let mut den = 0f64;
    for (g, h) in gpu.iter().zip(&host) {
        num += ((g - h) as f64).powi(2);
        den += (*h as f64).powi(2);
    }
    let rel = (num / den.max(1e-30)).sqrt();
    let ok = rel < 1e-2;
    tracing::info!(
        "mmh3 GPU parity probe: rel rms {rel:.2e} → {}",
        if ok { "device" } else { "host" }
    );
    if std::env::var("CMF_GPU_DEBUG").is_ok() {
        eprintln!("mmh3 GPU parity probe: rel rms {rel:.2e}");
    }
    Ok(ok)
}

fn generate_inner(
    path: &Path,
    prompt: &str,
    p: &AnimParams,
    progress: &mut dyn FnMut(&str, usize, usize),
) -> Result<Anim, String> {
    let model = Arc::new(
        cortiq_core::CmfModel::open(path).map_err(|e| format!("{}: {e}", path.display()))?,
    );
    let (frames_total, latent_t, audio_t) = temporal_shape(p.frames);
    let (lat_h, lat_w) = (p.height / 16, p.width / 16);

    // ── prompt ──
    // The H3 presentation is raw text: no chat template, no BOS, no
    // special tokens at all.
    // Stage clock. Half of a render is not the DiT — at 8 steps the
    // denoiser is 63 s of 116 — and until this line existed there was
    // no way to see which half anything went to.
    let t_stage = std::time::Instant::now();
    let mut marks: Vec<(&str, f32)> = Vec::new();
    let lap = |marks: &mut Vec<(&'static str, f32)>, name: &'static str| {
        let prev: f32 = marks.iter().map(|(_, v)| v).sum();
        marks.push((name, t_stage.elapsed().as_secs_f32() - prev));
    };
    let vocab = model
        .vocab
        .as_deref()
        .ok_or("packaged .cmf has no embedded tokenizer")?;
    let tok = Tokenizer::from_bytes(vocab).map_err(|e| format!("tokenizer: {e}"))?;
    // fl2va: every keyframe is presented as "<Picture i>: " and a
    // vision block BEFORE the prompt, and separately conditions the DiT
    // as a latent. Both halves come from the same picture.
    let mut keyframes: Vec<(&(Vec<f32>, usize, usize), usize)> = p
        .first_frame
        .iter()
        .map(|f| (f, 0usize))
        .chain(p.mid_frames.iter().map(|(f, i)| (f, (*i).min(frames_total - 1))))
        .chain(p.last_frame.iter().map(|f| (f, frames_total - 1)))
        .collect();
    // Condition rows are placed by time coordinate, so they have to arrive
    // in time order and only once per frame — two references pinned to the
    // same pixel index would be two rows claiming one moment.
    keyframes.sort_by_key(|(_, i)| *i);
    keyframes.dedup_by_key(|(_, i)| *i);
    let mut ids: Vec<u32> = Vec::new();
    let mut spans: Vec<ImageSpan> = Vec::new();
    let mut embeds: Vec<Vec<f32>> = Vec::new();
    let mut deepstack: Vec<Vec<f32>> = Vec::new();
    let mut cond: Vec<Vec<f32>> = Vec::new();
    let mut tags: Vec<u8> = Vec::new();

    if !keyframes.is_empty() {
        let tower = VisionTower::from_cmf(&model)?;
        // An activation harvest (CMF_TE_ONLY) never denoises, and the
        // VAE latent is the DiT's food alone — the frame's 3-D conv
        // encode is 99.5 s of a 102.5 s M4 run. Skip it.
        let te_only = std::env::var("CMF_TE_ONLY").as_deref() == Ok("1");
        let venc = if te_only {
            None
        } else {
            Some(VideoVaeEncoder::from_cmf(&model)?)
        };
        for (i, (frame, _)) in keyframes.iter().enumerate() {
            let (src, sh, sw) = *frame;
            // The picture the DiT sees is on the generation canvas; the
            // one Qwen sees keeps its own resolution policy.
            let fitted = fit_to_canvas(src, *sh, *sw, p.height, p.width, i > 0);
            if let Some(venc) = &venc {
                let (z, _, _) = venc.encode_frame(
                    &fitted.iter().map(|&v| v * 2.0 - 1.0).collect::<Vec<_>>(),
                    p.height,
                    p.width,
                );
                cond.push(z);
            }

            for t in tok.encode(&format!("<Picture {}>: ", i + 1)) {
                ids.push(t);
                tags.push(1);
            }
            let (patches, gh, gw) = qwen3vis::preprocess(
                &fitted,
                p.height,
                p.width,
                tower.patch_size,
                tower.temporal_patch,
                tower.merge,
            );
            let (merged, deep) = tower.forward(&patches, gh, gw);
            let n_img = merged.len() / tower.out_hidden;
            // The whole block carries the VIDEO tag, the flanking
            // markers included.
            ids.push(VISION_START);
            tags.push(0);
            let start = ids.len();
            for _ in 0..n_img {
                ids.push(VISION_START); // a placeholder the embed replaces
                tags.push(0);
            }
            ids.push(VISION_END);
            tags.push(0);
            spans.push(ImageSpan {
                start,
                len: n_img,
                merged_h: gh / tower.merge,
                merged_w: gw / tower.merge,
            });
            embeds.push(merged);
            if deepstack.is_empty() {
                deepstack = deep;
            } else {
                for (a, b) in deepstack.iter_mut().zip(deep) {
                    a.extend_from_slice(&b);
                }
            }
        }
    }
    for t in tok.encode(prompt) {
        ids.push(t);
        tags.push(1);
    }
    if ids.is_empty() {
        ids.push(151643); // the pad id, as the reference does for ""
        tags.push(1);
    }
    ids.truncate(p.max_tokens);
    tags.truncate(ids.len());
    lap(&mut marks, "prepare");
    // The prompt encoder is a one-shot pass over 12 GB of weights; on a
    // machine the file does not fit it streams from disk and its GEMMs
    // run over any contention budget for reasons that are not contention.
    // The kill stays disarmed until the encode is done (users on 24 GB
    // Macs had to patch it out to keep the denoise loop on the GPU).
    crate::gpu::mm_kill_arm(false);
    progress("encode", 0, 1);
    let states = {
        let enc = Qwen3Encoder::from_cmf(&model)?;
        enc.encode_with_images(&ids, &spans, &embeds, &deepstack)
    };
    // `CMF_TE_DUMP=<path>`: the conditioning as `[u64 n][u64 width]`
    // then f32 rows. A stand-in encoder is only as good as the stream
    // it hands the DiT, and that is measurable against the teacher's
    // dump on the same prompt WITHOUT rendering a frame.
    if let Ok(p) = std::env::var("CMF_TE_DUMP") {
        let w = states.len() / ids.len().max(1);
        let mut b = Vec::with_capacity(16 + states.len() * 4);
        b.extend_from_slice(&(ids.len() as u64).to_le_bytes());
        b.extend_from_slice(&(w as u64).to_le_bytes());
        for v in &states {
            b.extend_from_slice(&v.to_le_bytes());
        }
        std::fs::write(&p, &b).map_err(|e| format!("CMF_TE_DUMP {p}: {e}"))?;
        eprintln!("te dump: {} tokens x {w} -> {p}", ids.len());
    }
    progress("encode", 1, 1);
    lap(&mut marks, "text encode");
    crate::gpu::mm_kill_arm(true);
    // `CMF_TE_ONLY=1`: stop after the dump — an activation-harvest run
    // (the ClipProj refit) wants hundreds of encodes and zero renders.
    if std::env::var("CMF_TE_ONLY").as_deref() == Ok("1") {
        return Err("CMF_TE_ONLY: encode dumped, render skipped".into());
    }
    // The prompt encoder and vision tower ran their once-per-generation
    // pass; release their page cache so the denoise loop's DiT does not
    // fight 12+ GB of dead weights for RAM. On a 24 GB Mac with the
    // 25.7 GB full-encoder fl2va file this is the difference between
    // denoise steps at DiT speed and 320 s/step of SSD thrash.
    {
        let dropped = model.advise_done(|n| n.starts_with("model.") || n.starts_with("vis."));
        if dropped > 0 {
            tracing::info!(
                "encoder pages released after prompt encode: {} MB",
                dropped / (1024 * 1024)
            );
        }
    }

    // ── denoise ──
    let (mut video, audio) = {
        // The adapter is read here, after the encoder's pages are gone:
        // a rank-32 file for this DiT is 130 MB of f32 once expanded and
        // there is no reason for it to share a peak with 12 GB of text
        // tower on a 24 GB machine.
        let bank = match p.lora.as_deref() {
            None => None,
            Some(path) => {
                let k = crate::ltxlora::LoraBank::load(std::path::Path::new(path), p.lora_strength)?;
                Some(k)
            }
        };
        let dit = MiniMaxH3::from_cmf_lora(&model, bank.as_ref())?;
        if let Some(k) = &bank {
            let bound = dit.lora_bound();
            // Say what did NOT land. An adaLN branch on a curve-form
            // pack is the one real gap, and a user who sees "applied"
            // while half the adapter sat out has been lied to.
            let mut skipped: std::collections::BTreeMap<String, usize> = Default::default();
            for name in k.keys() {
                if !dit.lora_binds(name) {
                    let fam = name
                        .rsplit_once('.')
                        .map(|(_, t)| {
                            let head = name.split('.').next().unwrap_or("");
                            format!("{head}…{t}")
                        })
                        .unwrap_or_else(|| name.to_string());
                    *skipped.entry(fam).or_default() += 1;
                }
            }
            let tail = if skipped.is_empty() {
                String::new()
            } else {
                let parts: Vec<String> =
                    skipped.iter().map(|(k, v)| format!("{k} ×{v}")).collect();
                format!("; not applied: {}", parts.join(", "))
            };
            tracing::info!(
                "lora: rank {}, {} branches, {} bound at strength {}{}",
                k.rank(),
                k.len(),
                bound,
                p.lora_strength,
                tail
            );
            println!(
                "lora: rank {}, {}/{} branches bound{}",
                k.rank(),
                bound,
                k.len(),
                tail
            );
        }
        let kf: Vec<(usize, usize)> = keyframes
            .iter()
            .map(|&(_, idx)| (idx, frames_total))
            .collect();
        let layout = if kf.is_empty() {
            Layout::t2va(ids.len(), latent_t, lat_h, lat_w, audio_t)
        } else {
            Layout::fl2va(ids.len(), latent_t, lat_h, lat_w, audio_t, &kf, &tags)
        };
        let text = dit.refine_text(&states, ids.len());
        let mut v = gauss(dit.latents_dim * latent_t * lat_h * lat_w, p.seed);
        let mut a = gauss(dit.audio_dim * 2 * audio_t, p.seed ^ 0x9E37_79B9_7F4A_7C15);
        let sg = sigmas(p.steps, dit.shift_video);
        // `CMF_ANIM_PROF=1`: the per-step rms of both streams and of
        // their velocities. A run that is not denoising shows it here
        // long before anything is written out.
        let prof = std::env::var_os("CMF_ANIM_PROF").is_some();
        let rms = |x: &[f32]| {
            (x.iter().map(|&v| (v as f64) * (v as f64)).sum::<f64>() / x.len() as f64).sqrt()
        };
        if prof {
            eprintln!(
                "  text {} tok, refined rms {:.4}, sigmas {:?}",
                ids.len(),
                rms(&text),
                sg.iter()
                    .map(|v| (v * 1e4).round() / 1e4)
                    .collect::<Vec<_>>()
            );
        }
        for i in 0..p.steps {
            let (sv, sv_n) = (sg[i], sg[i + 1]);
            let (dv, da) = dit.forward(&layout, &text, &v, &a, sv, &cond);
            let step_v = (sv_n - sv) as f32;
            for (x, &d) in v.iter_mut().zip(&dv) {
                *x += step_v * d;
            }
            let step_a = if p.stock_sampler {
                step_v
            } else {
                (time_shift_sigma(sv_n, dit.shift_video, dit.shift_audio)
                    - time_shift_sigma(sv.max(1e-6), dit.shift_video, dit.shift_audio))
                    as f32
            };
            for (x, &d) in a.iter_mut().zip(&da) {
                *x += step_a * d;
            }
            if prof {
                eprintln!(
                    "  step {i}: sv {sv:.4}->{sv_n:.4} v_vel {:.4} a_vel {:.4} | video {:.4} audio {:.4}",
                    rms(&dv),
                    rms(&da),
                    rms(&v),
                    rms(&a)
                );
            }
            progress("denoise", i + 1, p.steps);
        }
        if let Some(rep) = dit.lora_report() {
            eprint!("{rep}");
        }
        (v, a)
    };

    lap(&mut marks, "denoise");

    // ── the learned latent resize, when one was handed in ──
    let (mut out_h, mut out_w) = (p.height, p.width);
    let (mut lat_h, mut lat_w) = (lat_h, lat_w);
    if let Some(path) = p.upscale.as_deref() {
        progress("upscale", 0, 1);
        let t = std::time::Instant::now();
        let ups = crate::mmh3ups::LatentUpscaler::load(std::path::Path::new(path))?;
        let z = crate::mmh3ups::Vol {
            c: video.len() / (latent_t * lat_h * lat_w),
            t: latent_t,
            h: lat_h,
            w: lat_w,
            data: video,
        };
        // Snap to the VAE's own 16-pixel grid: the net takes any target,
        // the decoder does not.
        let f = p.upscale_by.max(1.0);
        let nh = ((lat_h as f32 * f).round() as usize).max(lat_h);
        let nw = ((lat_w as f32 * f).round() as usize).max(lat_w);
        let big = ups.upscale(&z, nh, nw, None);
        tracing::info!(
            "latent upscale {}x{} -> {}x{} in {:.1}s",
            lat_h,
            lat_w,
            nh,
            nw,
            t.elapsed().as_secs_f64()
        );
        out_h = out_h * nh / lat_h;
        out_w = out_w * nw / lat_w;
        lat_h = nh;
        lat_w = nw;
        video = big.data;
        progress("upscale", 1, 1);
        lap(&mut marks, "upscale");
    }

    // ── decode ──
    progress("video vae", 0, 1);
    let (rgb, out_frames) = {
        let vae = VideoVae::from_cmf(&model)?;
        vae.decode(&video, latent_t, lat_h, lat_w)
    };
    progress("video vae", 1, 1);
    lap(&mut marks, "video vae");
    progress("audio vae", 0, 1);
    let (wave, samples, sr) = {
        let vae = AudioVae::from_cmf(&model)?;
        let c = audio.len() / (2 * audio_t);
        let (w, n) = vae.decode(&audio, c, audio_t);
        (w, n, vae.sample_rate)
    };
    progress("audio vae", 1, 1);
    lap(&mut marks, "audio vae");
    tracing::info!(
        "stages: {}",
        marks
            .iter()
            .map(|(n, v)| format!("{n} {v:.1}s"))
            .collect::<Vec<_>>()
            .join(" · ")
    );
    // Where a GEMM's wall time goes on unified memory: copies or kernel.
    // The answer decides whether fusing blocks or tuning the kernel is
    // the optimization worth doing.
    #[cfg(target_os = "macos")]
    if std::env::var("CMF_METAL_MMPROF").is_ok() {
        use std::sync::atomic::Ordering::Relaxed;
        let n = crate::gpu_metal::MM_N.load(Relaxed);
        eprintln!(
            "  q4tp mm x{n}: upload {:.1}s · submit+wait {:.1}s · readback {:.1}s",
            crate::gpu_metal::MM_UP.load(Relaxed) as f64 / 1e6,
            crate::gpu_metal::MM_GPU.load(Relaxed) as f64 / 1e6,
            crate::gpu_metal::MM_DN.load(Relaxed) as f64 / 1e6,
        );
    }

    // The VAE emits latent_t·4 frames; the request snapped to 17k+5,
    // which is one fewer than a multiple of four plus the leading key
    // frame, so trim rather than pad.
    let keep = out_frames.min(frames_total);
    Ok(Anim {
        rgb: trim_frames(&rgb, out_frames, keep, out_h, out_w),
        frames: keep,
        height: out_h,
        width: out_w,
        audio: wave,
        samples,
        sample_rate: sr,
    })
}

fn trim_frames(rgb: &[f32], have: usize, keep: usize, h: usize, w: usize) -> Vec<f32> {
    if keep == have {
        return rgb.to_vec();
    }
    let mut out = vec![0f32; 3 * keep * h * w];
    for c in 0..3 {
        let s = c * have * h * w;
        let d = c * keep * h * w;
        out[d..d + keep * h * w].copy_from_slice(&rgb[s..s + keep * h * w]);
    }
    out
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn the_four_step_schedule_is_the_references() {
        let s = sigmas(4, 12.0);
        let want = [1.0, 0.972_973, 0.923_077, 0.8, 0.0];
        assert_eq!(s.len(), want.len());
        for (g, w) in s.iter().zip(&want) {
            assert!((g - w).abs() < 1e-6, "{s:?}");
        }
    }

    #[test]
    fn a_stretch_keeps_the_corners_and_a_crop_takes_the_middle() {
        // A 4x2 ramp: value rises left to right, so the corners name
        // themselves.
        let (h, w) = (2usize, 4usize);
        let mut rgb = vec![0f32; 3 * h * w];
        for c in 0..3 {
            for y in 0..h {
                for x in 0..w {
                    rgb[(c * h + y) * w + x] = x as f32 / (w - 1) as f32;
                }
            }
        }
        // Stretch to a square: the far edges survive.
        let s = fit_to_canvas(&rgb, h, w, 4, 4, false);
        assert!((s[0] - 0.0).abs() < 1e-6, "left edge");
        assert!((s[3] - 1.0).abs() < 1e-6, "right edge");
        // Cover-crop to a square takes the centre 2x2, so the extremes
        // are gone and the span is narrower.
        let c = fit_to_canvas(&rgb, h, w, 4, 4, true);
        let (lo, hi) = c[..16]
            .iter()
            .fold((f32::MAX, f32::MIN), |(a, b), &v| (a.min(v), b.max(v)));
        assert!(lo > 0.05, "crop kept the left edge: {lo}");
        assert!(hi < 0.95, "crop kept the right edge: {hi}");
    }

    #[test]
    fn frame_counts_snap_to_the_models_grid() {
        // The grid is 5 + 17k: 5, 22, 39, 56, … 124.
        assert_eq!(align_frames(1), 5);
        assert_eq!(align_frames(39), 39);
        assert_eq!(align_frames(41), 56);
        assert_eq!(align_frames(124), 124);
        assert_eq!(video_latent_t(124), 37);
        assert_eq!(video_latent_t(39), 12);
        let (f, lt, at) = temporal_shape(124);
        assert_eq!((f, lt, at), (124, 37, 207));
    }
}