Paralleliseret diffusion: Hvordan AI-billedgenerering bryder kvalitets- og opløsningsbarrierer

class ParallelizedDiffusionPipeline:
    def __init__(self, num_modules=8, tile_size=512):
        self.modules = [DiffusionModule() for _ in range(num_modules)]
        self.tile_size = tile_size  # pixels per flise
        self.attention_bridges = CrossSpatialAttention()
 
    def generate_image(self, prompt, resolution=(4096, 4096)):  # Ultra-høj opløsning
        tiles_per_dim = resolution[0] // self.tile_size
 
        # Initialiser latente repræsentationer for hver flise
        latents = [module.encode(prompt, idx) for idx, module in enumerate(self.modules)]
 
        # Parallel støjfjernelse med bidirektionale begrænsninger
        for step in range(denoising_steps):
            # Hvert modul behandler sin flise
            parallel_outputs = parallel_map(
                lambda m, l, idx: m.denoise_step(l, step, context=self.get_context(idx)),
                self.modules, latents, range(len(self.modules))
            )
 
            # Bidirektional attention sikrer konsistens
            latents = self.attention_bridges.sync(parallel_outputs)
 
        return self.stitch_tiles(latents, resolution)

class CrossSpatialAttention(nn.Module):
    def sync(self, tiles):
        # tiles: liste af latente repræsentationer [B, C, H, W]
 
        # Beregn parvis attention-scores
        attention_matrix = self.compute_attention_scores(tiles)
 
        # Anvend bidirektionale begrænsninger
        for i, tile in enumerate(tiles):
            context = []
            for j, other_tile in enumerate(tiles):
                if i != j:
                    weight = attention_matrix[i, j]
                    # Tilstødende fliser påvirker hinanden
                    context.append(weight * self.transform(other_tile))
 
            tiles[i] = tile + sum(context)
 
        return tiles

import torch
import torch.nn as nn
from torch.nn.parallel import DataParallel
 
class MiniParallelDiffusion:
    def __init__(self, base_model, num_tiles=4):
        self.tiles = num_tiles
        self.models = nn.ModuleList([base_model.clone() for _ in range(num_tiles)])
        self.sync_layer = nn.MultiheadAttention(embed_dim=512, num_heads=8)
 
    @torch.no_grad()
    def generate(self, prompt_embeds, total_resolution=(2048, 2048)):
        tile_size = total_resolution[0] // int(self.tiles ** 0.5)
 
        # Initialiser støj for hver flise
        noise = torch.randn(self.tiles, 512, tile_size, tile_size)
 
        for t in reversed(range(1000)):  # Støjfjernelsestrin
            # Parallel behandling
            denoised = []
            for i, model in enumerate(self.models):
                tile_out = model(noise[i], t, prompt_embeds)
                denoised.append(tile_out)
 
            # Synkroniseringstrin
            denoised_tensor = torch.stack(denoised)
            synced, _ = self.sync_layer(denoised_tensor, denoised_tensor, denoised_tensor)
 
            noise = self.scheduler.step(synced, t)
 
        return self.stitch_tiles(noise, total_resolution)