nucleus_image/pipeline.py

"""Nucleus-Image MLX Pipeline.

MoE DiT + VAE in MLX, text encoder in PyTorch (hybrid).
"""

import json
import time
from pathlib import Path
from typing import Optional

import mlx.core as mx
import mlx.nn as nn
import numpy as np
from PIL import Image
from huggingface_hub import snapshot_download

from .dit import NucleusMoEDiT
from .vae import VAEDecoder
from .scheduler import FlowMatchEulerScheduler


def patchify(x, patch_size=2):
    """[B, H, W, C] → [B, (H/p)*(W/p), C*p*p]

    Matches diffusers _pack_latents: token layout is [C, ph, pw] (channels first).
    Input x is NHWC. We rearrange to [B, H/p, W/p, C, p, p] then flatten.
    """
    B, H, W, C = x.shape
    p = patch_size
    x = x.reshape(B, H // p, p, W // p, p, C)
    # [B, H/p, p, W/p, p, C] → [B, H/p, W/p, C, p, p]
    x = x.transpose(0, 1, 3, 5, 2, 4)
    return x.reshape(B, (H // p) * (W // p), C * p * p)


def unpatchify(x, h, w, patch_size=2):
    """[B, N, C*p*p] → [B, H, W, C]

    Inverse of patchify. Token layout is [C, ph, pw].
    """
    B, N, D = x.shape
    p = patch_size
    C = D // (p * p)
    hp, wp = h // p, w // p
    x = x.reshape(B, hp, wp, C, p, p)
    # [B, hp, wp, C, p, p] → [B, hp, p, wp, p, C]
    x = x.transpose(0, 1, 4, 2, 5, 3)
    return x.reshape(B, h, w, C)


class NucleusImagePipeline:

    def __init__(self, dit, vae, scheduler, latents_mean, latents_std):
        self.dit = dit
        self.vae = vae
        self.scheduler = scheduler
        self.latents_mean = latents_mean
        self.latents_std = latents_std

    @staticmethod
    def from_pretrained(model_id="NucleusAI/Nucleus-Image", quantize=None):
        path = Path(snapshot_download(model_id))

        # Config
        with open(path / "transformer" / "config.json") as f:
            dit_config = json.load(f)
        with open(path / "vae" / "config.json") as f:
            vae_config = json.load(f)

        # DiT
        print("Loading DiT...")
        dit = NucleusMoEDiT(dit_config)
        dit_weights = {}
        for f in sorted((path / "transformer").glob("*.safetensors")):
            dit_weights.update(mx.load(str(f)))
        dit.load_weights(list(dit_weights.items()))
        if quantize:
            print(f"Quantizing DiT to {quantize}-bit...")
            nn.quantize(dit, bits=quantize)

        # VAE
        print("Loading VAE...")
        vae = VAEDecoder()
        raw_vae = mx.load(str(path / "vae" / "diffusion_pytorch_model.safetensors"))
        vae_w = {}
        for k, v in raw_vae.items():
            if k.startswith("encoder.") or k.startswith("quant_conv"): continue
            if k.startswith("latents_") or k in ("spatial_scale_factor", "temporal_scale_factor"): continue
            if k.startswith("bn."): continue
            if "weight" in k and v.ndim == 5:
                D = v.shape[2]
                # CausalConv3d: for T=1 input with padding=(2*p, 0), only the
                # LAST temporal slice of the kernel contributes
                v = v[:, :, -1, :, :] if D > 1 else v.squeeze(2)
                v = v.transpose(0, 2, 3, 1)
            elif "weight" in k and v.ndim == 4:
                v = v.transpose(0, 2, 3, 1)
            if "gamma" in k:
                v = v.squeeze()
            vae_w[k] = v
        vae.load_weights(list(vae_w.items()))

        # Latent stats
        latents_mean = mx.array(vae_config["latents_mean"])
        latents_std = mx.array(vae_config["latents_std"])

        scheduler = FlowMatchEulerScheduler()

        return NucleusImagePipeline(dit, vae, scheduler, latents_mean, latents_std)

    def generate(self, text_embeddings=None, neg_text_embeddings=None,
                 height=1024, width=1024, num_inference_steps=50,
                 guidance_scale=4.0, seed=None):
        t_start = time.time()

        latent_h = height // 8  # VAE is 8x
        latent_w = width // 8

        if text_embeddings is None:
            text_embeddings = mx.zeros((1, 1, 4096))
        text_bth = mx.expand_dims(text_embeddings, 0) if text_embeddings.ndim == 2 else text_embeddings

        do_cfg = guidance_scale > 1.0
        if do_cfg and neg_text_embeddings is None:
            neg_text_embeddings = mx.zeros_like(text_bth)

        if seed is not None:
            mx.random.seed(seed)

        # Generate noise in latent space, then patchify
        latents = mx.random.normal((1, latent_h, latent_w, 16))
        tokens = patchify(latents, patch_size=2)

        import numpy as np

        # Calculate dynamic shift based on image sequence length
        image_seq_len = tokens.shape[1]
        base_seq_len = 256
        max_seq_len = 4096
        base_shift = 0.5
        max_shift = 1.15
        # Linear interpolation of shift based on sequence length
        m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
        b = base_shift - m * base_seq_len
        mu = image_seq_len * m + b

        sigmas = np.linspace(1.0, 1.0 / num_inference_steps, num_inference_steps)
        # Apply shift: sigma_shifted = exp(mu) * sigma / (1 + (exp(mu) - 1) * sigma)
        shift = np.exp(mu)
        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)

        self.scheduler.sigmas = mx.concatenate([mx.array(sigmas), mx.array([0.0])])
        self.scheduler.timesteps = mx.array(sigmas) * 1000

        for i, t in enumerate(self.scheduler.timesteps):
            # Normalize: divide by num_train_timesteps (1000) matching diffusers pipeline
            # Transformer receives sigma (0-1), Timesteps(scale=1000) handles the rest
            t_normalized = mx.array([t.item() / 1000.0])

            pred = self.dit(tokens, t_normalized, text_bth)

            if do_cfg:
                neg_pred = self.dit(tokens, t_normalized, neg_text_embeddings)
                # CFG with norm rescaling
                comb = neg_pred + guidance_scale * (pred - neg_pred)
                cond_norm = mx.sqrt(mx.sum(pred * pred, axis=-1, keepdims=True) + 1e-8)
                noise_norm = mx.sqrt(mx.sum(comb * comb, axis=-1, keepdims=True) + 1e-8)
                pred = comb * (cond_norm / noise_norm)

            # Negate prediction (from diffusers pipeline line 597)
            pred = -pred

            tokens = self.scheduler.step(pred, i, tokens)

        mx.eval(tokens)
        denoise_time = time.time() - t_start

        # Unpatchify
        latents = unpatchify(tokens, latent_h, latent_w, patch_size=2)

        # Denormalize: latents * std + mean
        # diffusers computes: latents_std_inv = 1/config_std, then latents / std_inv = latents * config_std
        mean = self.latents_mean.reshape(1, 1, 1, -1)
        std = self.latents_std.reshape(1, 1, 1, -1)
        latents = latents * std + mean

        # VAE decode
        images = self.vae(latents)
        mx.eval(images)
        total_time = time.time() - t_start

        print(f"  Denoise: {denoise_time:.1f}s | Decode: {total_time - denoise_time:.1f}s | Total: {total_time:.1f}s")

        images = mx.clip(images, -1, 1)
        images = ((images + 1) / 2 * 255).astype(mx.uint8)
        return Image.fromarray(np.array(images[0]))