Efficient Unified Image Generation Models

# Illustrative sketch (not from the paper)
import torch
from torch import nn

# 1. Base 6B DiT (single-stream) placeholder
class DiT(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.layers = nn.ModuleList([nn.Linear(dim, dim) for _ in range(12)])  # simplified

    def forward(self, x, prompt):
        # combine prompt (high‑level) with pixel tokens
        for layer in self.layers:
            x = layer(x)
        return x

# 2. Prompt Enhancer decouples reasoning from rendering
class PromptEnhancer(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Linear(768, 768)  # placeholder

    def forward(self, text):
        return self.encoder(text)

# 3. Progressive training stages (semantic → high‑res)
def progressive_train(model, enhancer, dataloader):
    for stage in ["semantic", "high_res"]:
        for images, texts in dataloader:
            prompt = enhancer(texts)
            loss = ((model(images, prompt) - images) ** 2).mean()
            loss.backward()
            # optimizer step omitted for brevity

# 4. Parallel expert RL (mock)
def parallel_expert_rl(model):
    # run multiple expert policies in parallel and collect rewards
    pass

# 5. Multi‑teacher on‑policy distillation (mock)
def multi_teacher_distill(student, teachers):
    # student learns from teacher outputs
    pass

# 6. Structural pruning & few‑step distillation (mock)
def prune_and_distill(model):
    # remove low‑importance weights and distill with few sampling steps
    pass

// Illustrative sketch (not from the paper)
const torch = require('torch-js'); // placeholder for PyTorch-like API

// 1. Base 6B DiT placeholder
class DiT {
  constructor(dim) {
    this.layers = Array.from({ length: 12 }, () => torch.nn.Linear(dim, dim));
  }
  forward(x, prompt) {
    for (const layer of this.layers) {
      x = layer.apply(x);
    }
    return x;
  }
}

// 2. Prompt Enhancer
class PromptEnhancer {
  constructor() {
    this.encoder = torch.nn.Linear(768, 768);
  }
  forward(text) {
    return this.encoder.apply(text);
  }
}

// 3. Progressive training loop
function progressiveTrain(model, enhancer, dataloader) {
  for (const stage of ['semantic', 'high_res']) {
    for (const batch of dataloader) {
      const { images, texts } = batch;
      const prompt = enhancer.forward(texts);
      const output = model.forward(images, prompt);
      const loss = torch.mean(torch.square(output.sub(images)));
      loss.backward();
      // optimizer step omitted
    }
  }
}

// 4. Parallel expert RL (stub)
function parallelExpertRL(model) {
  // run multiple expert policies in parallel
}

// 5. Multi‑teacher distillation (stub)
function multiTeacherDistill(student, teachers) {
  // student learns from teacher outputs
}

// 6. Pruning and few‑step distillation (stub)
function pruneAndDistill(model) {
  // structural pruning + few‑step distillation
}