start a file for all attention-related modules, use attention-based upsampling in the unets in dalle-2

added diffusion-gan thoughts

README.md

@@ -1,6 +1,6 @@
 <img src="./dalle2.png" width="450px"></img>
 
-## DALL-E 2 - Pytorch (wip)
+## DALL-E 2 - Pytorch
 
 Implementation of <a href="https://openai.com/dall-e-2/">DALL-E 2</a>, OpenAI's updated text-to-image synthesis neural network, in Pytorch.
 
@@ -10,11 +10,9 @@ The main novelty seems to be an extra layer of indirection with the prior networ
 
 This model is SOTA for text-to-image for now.
 
-It may also explore an extension of using <a href="https://huggingface.co/spaces/multimodalart/latentdiffusion">latent diffusion</a> in the decoder from Rombach et al.
-
 Please join <a href="https://discord.gg/xBPBXfcFHd"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a> if you are interested in helping out with the replication
 
-There was enough interest for a Jax version. It will be completed after the Pytorch version shows signs of life on my toy tasks. <a href="https://github.com/lucidrains/dalle2-jax">Placeholder repository</a>
+There was enough interest for a <a href="https://github.com/lucidrains/dalle2-jax">Jax version</a>. I will also eventually extend this to <a href="https://github.com/lucidrains/dalle2-video">text to video</a>, once the repository is in a good place.
 
 ## Install
 
@@ -109,7 +107,7 @@ unet = Unet(
 # decoder, which contains the unet and clip
 
 decoder = Decoder(
-    net = unet,
+    unet = unet,
     clip = clip,
     timesteps = 100,
     cond_drop_prob = 0.2
@@ -182,7 +180,75 @@ loss.backward()
 # now the diffusion prior can generate image embeddings from the text embeddings
 ```
 
-Finally, to generate the DALL-E2 images from text. Insert the trained `DiffusionPrior` as well as the `Decoder` (which both contains `CLIP`, a unet, and a causal transformer)
+In the paper, they actually used a <a href="https://cascaded-diffusion.github.io/">recently discovered technique</a>, from <a href="http://www.jonathanho.me/">Jonathan Ho</a> himself (original author of DDPMs, the core technique used in DALL-E v2) for high resolution image synthesis.
+
+This can easily be used within this framework as so
+
+```python
+import torch
+from dalle2_pytorch import Unet, Decoder, CLIP
+
+# trained clip from step 1
+
+clip = CLIP(
+    dim_text = 512,
+    dim_image = 512,
+    dim_latent = 512,
+    num_text_tokens = 49408,
+    text_enc_depth = 6,
+    text_seq_len = 256,
+    text_heads = 8,
+    visual_enc_depth = 6,
+    visual_image_size = 256,
+    visual_patch_size = 32,
+    visual_heads = 8
+).cuda()
+
+# 2 unets for the decoder (a la cascading DDPM)
+
+unet1 = Unet(
+    dim = 32,
+    image_embed_dim = 512,
+    cond_dim = 128,
+    channels = 3,
+    dim_mults = (1, 2, 4, 8)
+).cuda()
+
+unet2 = Unet(
+    dim = 32,
+    image_embed_dim = 512,
+    cond_dim = 128,
+    channels = 3,
+    dim_mults = (1, 2, 4, 8, 16)
+).cuda()
+
+# decoder, which contains the unet(s) and clip
+
+decoder = Decoder(
+    clip = clip,
+    unet = (unet1, unet2),            # insert both unets in order of low resolution to highest resolution (you can have as many stages as you want here)
+    image_sizes = (256, 512),         # resolutions, 256 for first unet, 512 for second. these must be unique and in ascending order (matches with the unets passed in)
+    timesteps = 1000,
+    cond_drop_prob = 0.2
+).cuda()
+
+# mock images (get a lot of this)
+
+images = torch.randn(4, 3, 512, 512).cuda()
+
+# feed images into decoder, specifying which unet you want to train
+# each unet can be trained separately, which is one of the benefits of the cascading DDPM scheme
+
+loss = decoder(images, unet_number = 1)
+loss.backward()
+
+loss = decoder(images, unet_number = 2)
+loss.backward()
+
+# do the above for many steps for both unets
+```
+
+Finally, to generate the DALL-E2 images from text. Insert the trained `DiffusionPrior` as well as the `Decoder` (which wraps `CLIP`, the causal transformer, and unet(s))
 
 ```python
 from dalle2_pytorch import DALLE2
@@ -261,7 +327,7 @@ loss.backward()
 
 # decoder (with unet)
 
-unet = Unet(
+unet1 = Unet(
     dim = 128,
     image_embed_dim = 512,
     cond_dim = 128,
@@ -269,15 +335,25 @@ unet = Unet(
     dim_mults=(1, 2, 4, 8)
 ).cuda()
 
+unet2 = Unet(
+    dim = 16,
+    image_embed_dim = 512,
+    cond_dim = 128,
+    channels = 3,
+    dim_mults = (1, 2, 4, 8, 16)
+).cuda()
+
 decoder = Decoder(
-    net = unet,
+    unet = (unet1, unet2),
+    image_sizes = (128, 256),
     clip = clip,
     timesteps = 100,
     cond_drop_prob = 0.2
 ).cuda()
 
-loss = decoder(images) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much
-loss.backward()
+for unet_number in (1, 2):
+    loss = decoder(images, unet_number = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much
+    loss.backward()
 
 # do above for many steps
 
@@ -291,14 +367,136 @@ images = dalle2(
     cond_scale = 2. # classifier free guidance strength (> 1 would strengthen the condition)
 )
 
-# save your image
+# save your image (in this example, of size 256x256)
 ```
 
 Everything in this readme should run without error
 
+You can also train the decoder on images of greater than the size (say 512x512) at which CLIP was trained (256x256). The images will be resized to CLIP image resolution for the image embeddings
+
 For the layperson, no worries, training will all be automated into a CLI tool, at least for small scale training.
 
-## CLI Usage (work in progress)
+## Experimental
+
+### DALL-E2 with Latent Diffusion
+
+This repository decides to take the next step and offer DALL-E2 combined with <a href="https://huggingface.co/spaces/multimodalart/latentdiffusion">latent diffusion</a>, from Rombach et al.
+
+You can use it as follows. Latent diffusion can be limited to just the first U-Net in the cascade, or to any number you wish.
+
+```python
+import torch
+from dalle2_pytorch import Unet, Decoder, CLIP, VQGanVAE
+
+# trained clip from step 1
+
+clip = CLIP(
+    dim_text = 512,
+    dim_image = 512,
+    dim_latent = 512,
+    num_text_tokens = 49408,
+    text_enc_depth = 1,
+    text_seq_len = 256,
+    text_heads = 8,
+    visual_enc_depth = 1,
+    visual_image_size = 256,
+    visual_patch_size = 32,
+    visual_heads = 8
+)
+
+# 3 unets for the decoder (a la cascading DDPM)
+
+# first two unets are doing latent diffusion
+# vqgan-vae must be trained before hand
+
+vae1 = VQGanVAE(
+    dim = 32,
+    image_size = 256,
+    layers = 3,
+    layer_mults = (1, 2, 4)
+)
+
+vae2 = VQGanVAE(
+    dim = 32,
+    image_size = 512,
+    layers = 3,
+    layer_mults = (1, 2, 4)
+)
+
+unet1 = Unet(
+    dim = 32,
+    image_embed_dim = 512,
+    cond_dim = 128,
+    channels = 3,
+    sparse_attn = True,
+    sparse_attn_window = 2,
+    dim_mults = (1, 2, 4, 8)
+)
+
+unet2 = Unet(
+    dim = 32,
+    image_embed_dim = 512,
+    channels = 3,
+    dim_mults = (1, 2, 4, 8, 16),
+    cond_on_image_embeds = True,
+    cond_on_text_encodings = False
+)
+
+unet3 = Unet(
+    dim = 32,
+    image_embed_dim = 512,
+    channels = 3,
+    dim_mults = (1, 2, 4, 8, 16),
+    cond_on_image_embeds = True,
+    cond_on_text_encodings = False,
+    attend_at_middle = False
+)
+
+# decoder, which contains the unet(s) and clip
+
+decoder = Decoder(
+    clip = clip,
+    vae = (vae1, vae2),                # latent diffusion for unet1 (vae1) and unet2 (vae2), but not for the last unet3
+    unet = (unet1, unet2, unet3),      # insert unets in order of low resolution to highest resolution (you can have as many stages as you want here)
+    image_sizes = (256, 512, 1024),    # resolutions, 256 for first unet, 512 for second, 1024 for third
+    timesteps = 100,
+    cond_drop_prob = 0.2
+).cuda()
+
+# mock images (get a lot of this)
+
+images = torch.randn(1, 3, 1024, 1024).cuda()
+
+# feed images into decoder, specifying which unet you want to train
+# each unet can be trained separately, which is one of the benefits of the cascading DDPM scheme
+
+with decoder.one_unet_in_gpu(1):
+    loss = decoder(images, unet_number = 1)
+    loss.backward()
+
+with decoder.one_unet_in_gpu(2):
+    loss = decoder(images, unet_number = 2)
+    loss.backward()
+
+with decoder.one_unet_in_gpu(3):
+    loss = decoder(images, unet_number = 3)
+    loss.backward()
+
+# do the above for many steps for both unets
+
+# then it will learn to generate images based on the CLIP image embeddings
+
+# chaining the unets from lowest resolution to highest resolution (thus cascading)
+
+mock_image_embed = torch.randn(1, 512).cuda()
+images = decoder.sample(mock_image_embed) # (1, 3, 1024, 1024)
+```
+
+## Training wrapper (wip)
+
+Offer training wrappers
+
+## CLI (wip)
 
 ```bash
 $ dream 'sharing a sunset at the summit of mount everest with my dog'
@@ -306,9 +504,7 @@ $ dream 'sharing a sunset at the summit of mount everest with my dog'
 
 Once built, images will be saved to the same directory the command is invoked
 
-## Training wrapper (wip)
-
-Offer training wrappers
+<a href="https://github.com/lucidrains/big-sleep">template</a>
 
 ## Training CLI (wip)
 
@@ -321,11 +517,21 @@ Offer training wrappers
 - [x] make sure it works end to end to produce an output tensor, taking a single gradient step
 - [x] augment unet so that it can also be conditioned on text encodings (although in paper they hinted this didn't make much a difference)
 - [x] figure out all the current bag of tricks needed to make DDPMs great (starting with the blur trick mentioned in paper)
-- [ ] build the cascading ddpm by having Decoder class manage multiple unets at different resolutions
+- [x] build the cascading ddpm by having Decoder class manage multiple unets at different resolutions
+- [x] add efficient attention in unet
+- [x] be able to finely customize what to condition on (text, image embed) for specific unet in the cascade (super resolution ddpms near the end may not need too much conditioning)
+- [x] offload unets not being trained on to CPU for memory efficiency (for training each resolution unets separately)
+- [x] build out latent diffusion architecture, with the vq-reg variant (vqgan-vae), make it completely optional and compatible with cascading ddpms
+- [x] for decoder, allow ability to customize objective (predict epsilon vs x0), in case latent diffusion does better with prediction of x0
+- [ ] spend one day cleaning up tech debt in decoder
+- [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet
+- [ ] copy the cascading ddpm code to a separate repo (perhaps https://github.com/lucidrains/denoising-diffusion-pytorch) as the main contribution of dalle2 really is just the prior network
+- [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
 - [ ] train on a toy task, offer in colab
-- [ ] add attention to unet - apply some personal tricks with efficient attention - use the sparse attention mechanism from https://github.com/lucidrains/vit-pytorch#maxvit
-- [ ] build out latent diffusion architecture in separate file, as it is not faithful to dalle-2 (but offer it as as setting)
-- [ ] consider U2-net for decoder https://arxiv.org/abs/2005.09007 (also in separate file as experimental) build out https://github.com/lucidrains/x-unet
+- [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
+- [ ] bring in tools to train vqgan-vae
+- [ ] bring in vit-vqgan https://arxiv.org/abs/2110.04627 for the latent diffusion
+- [ ] experiment with https://arxiv.org/abs/2112.11435 as upsampler, test in https://github.com/lucidrains/lightweight-gan first
 
 ## Citations
 
@@ -357,20 +563,27 @@ Offer training wrappers
 
 ```bibtex
 @inproceedings{Liu2022ACF,
-    title   = {A ConvNet for the 2020s},
+    title   = {A ConvNet for the 2020https://arxiv.org/abs/2112.11435s},
     author  = {Zhuang Liu and Hanzi Mao and Chaozheng Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
     year    = {2022}
 }
 ```
 
 ```bibtex
-@misc{zhang2019root,
-    title   = {Root Mean Square Layer Normalization},
-    author  = {Biao Zhang and Rico Sennrich},
-    year    = {2019},
-    eprint  = {1910.07467},
-    archivePrefix = {arXiv},
-    primaryClass = {cs.LG}
+@inproceedings{Tu2022MaxViTMV,
+    title   = {MaxViT: Multi-Axis Vision Transformer},
+    author  = {Zhe-Wei Tu and Hossein Talebi and Han Zhang and Feng Yang and Peyman Milanfar and Alan Conrad Bovik and Yinxiao Li},
+    year    = {2022}
+}
+```
+
+```bibtex
+@article{Arar2021LearnedQF,
+    title   = {Learned Queries for Efficient Local Attention},
+    author  = {Moab Arar and Ariel Shamir and Amit H. Bermano},
+    journal = {ArXiv},
+    year    = {2021},
+    volume  = {abs/2112.11435}
 }
 ```
 

dalle2_pytorch/__init__.py

@@ -1,2 +1,4 @@
 from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder
+
+from dalle2_pytorch.vqgan_vae import VQGanVAE
 from x_clip import CLIP

dalle2_pytorch/attention.py

@@ -0,0 +1,125 @@
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+
+from einops import rearrange, repeat
+
+class LayerNormChan(nn.Module):
+    def __init__(
+        self,
+        dim,
+        eps = 1e-5
+    ):
+        super().__init__()
+        self.eps = eps
+        self.gamma = nn.Parameter(torch.ones(1, dim, 1, 1))
+
+    def forward(self, x):
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
+        mean = torch.mean(x, dim = 1, keepdim = True)
+        return (x - mean) / (var + self.eps).sqrt() * self.gamma
+
+# attention-based upsampling
+# from https://arxiv.org/abs/2112.11435
+
+class QueryAndAttend(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        num_queries = 1,
+        dim_head = 32,
+        heads = 8,
+        window_size = 3
+    ):
+        super().__init__()
+        self.scale = dim_head ** -0.5
+        inner_dim = dim_head * heads
+        self.heads = heads
+        self.dim_head = dim_head
+        self.window_size = window_size
+        self.num_queries = num_queries
+
+        self.rel_pos_bias = nn.Parameter(torch.randn(heads, num_queries, window_size * window_size, 1, 1))
+
+        self.queries = nn.Parameter(torch.randn(heads, num_queries, dim_head))
+        self.to_kv = nn.Conv2d(dim, dim_head * 2, 1, bias = False)
+        self.to_out = nn.Conv2d(inner_dim, dim, 1, bias = False)
+
+    def forward(self, x):
+        """
+        einstein notation
+        b - batch
+        h - heads
+        l - num queries
+        d - head dimension
+        x - height
+        y - width
+        j - source sequence for attending to (kernel size squared in this case)
+        """
+
+        wsz, heads, dim_head, num_queries = self.window_size, self.heads, self.dim_head, self.num_queries
+        batch, _, height, width = x.shape
+
+        is_one_query = self.num_queries == 1
+
+        # queries, keys, values
+
+        q = self.queries * self.scale
+        k, v = self.to_kv(x).chunk(2, dim = 1)
+
+        # similarities
+
+        sim = einsum('h l d, b d x y -> b h l x y', q, k)
+        sim = rearrange(sim, 'b ... x y -> b (...) x y')
+
+        # unfold the similarity scores, with float(-inf) as padding value
+
+        mask_value = -torch.finfo(sim.dtype).max
+        sim = F.pad(sim, ((wsz // 2,) * 4), value = mask_value)
+        sim = F.unfold(sim, kernel_size = wsz)
+        sim = rearrange(sim, 'b (h l j) (x y) -> b h l j x y', h = heads, l = num_queries, x = height, y = width)
+
+        # rel pos bias
+
+        sim = sim + self.rel_pos_bias
+
+        # numerically stable attention
+
+        sim = sim - sim.amax(dim = -3, keepdim = True).detach()
+        attn = sim.softmax(dim = -3)
+
+        # unfold values
+
+        v = F.pad(v, ((wsz // 2,) * 4), value = 0.)
+        v = F.unfold(v, kernel_size = wsz)
+        v = rearrange(v, 'b (d j) (x y) -> b d j x y', d = dim_head, x = height, y = width)
+
+        # aggregate values
+
+        out = einsum('b h l j x y, b d j x y -> b l h d x y', attn, v)
+
+        # combine heads
+
+        out = rearrange(out, 'b l h d x y -> (b l) (h d) x y')
+        out = self.to_out(out)
+        out = rearrange(out, '(b l) d x y -> b l d x y', b = batch)
+
+        # return original input if one query
+
+        if is_one_query:
+            out = rearrange(out, 'b 1 ... -> b ...')
+
+        return out
+
+class QueryAttnUpsample(nn.Module):
+    def __init__(self, dim, **kwargs):
+        super().__init__()
+        self.norm = LayerNormChan(dim)
+        self.qna = QueryAndAttend(dim = dim, num_queries = 4, **kwargs)
+
+    def forward(self, x):
+        x = self.norm(x)
+        out = self.qna(x)
+        out = rearrange(out, 'b (w1 w2) c h w -> b c (h w1) (w w2)', w1 = 2, w2 = 2)
+        return out

dalle2_pytorch/cli.py

@@ -1,9 +1,51 @@
 import click
+import torch
+import torchvision.transforms as T
+from pathlib import Path
+
+from dalle2_pytorch import DALLE2, Decoder, DiffusionPrior
+
+def safeget(dictionary, keys, default = None):
+    return reduce(lambda d, key: d.get(key, default) if isinstance(d, dict) else default, keys.split('.'), dictionary)
+
+def simple_slugify(text, max_length = 255):
+    return text.replace("-", "_").replace(",", "").replace(" ", "_").replace("|", "--").strip('-_')[:max_length]
+
+def get_pkg_version():
+    from pkg_resources import get_distribution
+    return get_distribution('dalle2_pytorch').version
 
 def main():
     pass
 
 @click.command()
+@click.option('--model', default = './dalle2.pt', help = 'path to trained DALL-E2 model')
+@click.option('--cond_scale', default = 2, help = 'conditioning scale (classifier free guidance) in decoder')
 @click.argument('text')
-def dream(text):
-    return image
+def dream(
+    model,
+    cond_scale,
+    text
+):
+    model_path = Path(model)
+    full_model_path = str(model_path.resolve())
+    assert model_path.exists(), f'model not found at {full_model_path}'
+    loaded = torch.load(str(model_path))
+
+    version = safeget(loaded, 'version')
+    print(f'loading DALL-E2 from {full_model_path}, saved at version {version} - current package version is {get_pkg_version()}')
+
+    prior_init_params = safeget(loaded, 'init_params.prior')
+    decoder_init_params = safeget(loaded, 'init_params.decoder')
+    model_params = safeget(loaded, 'model_params')
+
+    prior = DiffusionPrior(**prior_init_params)
+    decoder = Decoder(**decoder_init_params)
+
+    dalle2 = DALLE2(prior, decoder)
+    dalle2.load_state_dict(model_params)
+
+    image = dalle2(text, cond_scale = cond_scale)
+
+    pil_image = T.ToPILImage()(image)
+    return pil_image.save(f'./{simple_slugify(text)}.png')

dalle2_pytorch/dalle2_pytorch.py

@@ -1,6 +1,8 @@
 import math
 from tqdm import tqdm
 from inspect import isfunction
+from functools import partial
+from contextlib import contextmanager
 
 import torch
 import torch.nn.functional as F
@@ -11,9 +13,11 @@ from einops.layers.torch import Rearrange
 from einops_exts import rearrange_many, repeat_many, check_shape
 from einops_exts.torch import EinopsToAndFrom
 
-from kornia.filters.gaussian import GaussianBlur2d
+from kornia.filters import gaussian_blur2d
 
 from dalle2_pytorch.tokenizer import tokenizer
+from dalle2_pytorch.vqgan_vae import NullVQGanVAE, VQGanVAE
+from dalle2_pytorch.attention import QueryAttnUpsample
 
 # use x-clip
 
@@ -29,6 +33,9 @@ def default(val, d):
         return val
     return d() if isfunction(d) else d
 
+def cast_tuple(val, length = 1):
+    return val if isinstance(val, tuple) else ((val,) * length)
+
 def eval_decorator(fn):
     def inner(model, *args, **kwargs):
         was_training = model.training
@@ -43,6 +50,12 @@ def is_list_str(x):
         return False
     return all([type(el) == str for el in x])
 
+def pad_tuple_to_length(t, length, fillvalue = None):
+    remain_length = length - len(t)
+    if remain_length <= 0:
+        return t
+    return (*t, *((fillvalue,) * remain_length))
+
 # for controlling freezing of CLIP
 
 def set_module_requires_grad_(module, requires_grad):
@@ -64,6 +77,15 @@ def freeze_model_and_make_eval_(model):
 def l2norm(t):
     return F.normalize(t, dim = -1)
 
+def resize_image_to(t, image_size, mode = 'bilinear'): # take a look at https://github.com/assafshocher/ResizeRight
+    shape = cast_tuple(image_size, 2)
+    orig_image_size = t.shape[-2:]
+
+    if orig_image_size == shape:
+        return t
+
+    return F.interpolate(t, size = shape, mode = mode)
+
 # classifier free guidance functions
 
 def prob_mask_like(shape, prob, device):
@@ -92,8 +114,8 @@ def cosine_beta_schedule(timesteps, s = 0.008):
     as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
     """
     steps = timesteps + 1
-    x = torch.linspace(0, steps, steps)
-    alphas_cumprod = torch.cos(((x / steps) + s) / (1 + s) * torch.pi * 0.5) ** 2
+    x = torch.linspace(0, timesteps, steps)
+    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2
     alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
     betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
     return torch.clip(betas, 0, 0.999)
@@ -123,23 +145,27 @@ def sigmoid_beta_schedule(timesteps):
 
 # diffusion prior
 
-class RMSNorm(nn.Module):
+class LayerNorm(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.ones(dim))
+        self.register_buffer("beta", torch.zeros(dim))
+
+    def forward(self, x):
+        return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta)
+
+
+class ChanLayerNorm(nn.Module):
     def __init__(self, dim, eps = 1e-5):
         super().__init__()
         self.eps = eps
-        self.scale = dim ** 0.5
-        self.gamma = nn.Parameter(torch.ones(dim))
+        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
 
     def forward(self, x):
-        squared_sum = (x ** 2).sum(dim = -1, keepdim = True)
-        inv_norm = torch.rsqrt(squared_sum + self.eps)
-        return x * inv_norm * self.gamma * self.scale
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
+        mean = torch.mean(x, dim = 1, keepdim = True)
+        return (x - mean) / (var + self.eps).sqrt() * self.g
 
-class ChanRMSNorm(RMSNorm):
-    def forward(self, x):
-        squared_sum = (x ** 2).sum(dim = 1, keepdim = True)
-        inv_norm = torch.rsqrt(squared_sum + self.eps)
-        return x * inv_norm * rearrange(self.gamma, 'c -> 1 c 1 1') * self.scale
 
 class Residual(nn.Module):
     def __init__(self, fn):
@@ -235,10 +261,10 @@ def FeedForward(dim, mult = 4, dropout = 0., post_activation_norm = False):
 
     inner_dim = int(mult * dim)
     return nn.Sequential(
-        RMSNorm(dim),
+        LayerNorm(dim),
         nn.Linear(dim, inner_dim * 2, bias = False),
         SwiGLU(),
-        RMSNorm(inner_dim) if post_activation_norm else nn.Identity(),
+        LayerNorm(inner_dim) if post_activation_norm else nn.Identity(),
         nn.Dropout(dropout),
         nn.Linear(inner_dim, dim, bias = False)
     )
@@ -261,7 +287,8 @@ class Attention(nn.Module):
         inner_dim = dim_head * heads
 
         self.causal = causal
-        self.norm = RMSNorm(dim)
+        self.norm = LayerNorm(dim)
+        self.post_norm = LayerNorm(dim)     # sandwich norm from Coqview paper + Normformer
         self.dropout = nn.Dropout(dropout)
 
         self.null_kv = nn.Parameter(torch.randn(2, dim_head))
@@ -317,7 +344,8 @@ class Attention(nn.Module):
         out = einsum('b h i j, b j d -> b h i d', attn, v)
 
         out = rearrange(out, 'b h n d -> b n (h d)')
-        return self.to_out(out)
+        out = self.to_out(out)
+        return self.post_norm(out)
 
 class CausalTransformer(nn.Module):
     def __init__(
@@ -330,7 +358,8 @@ class CausalTransformer(nn.Module):
         ff_mult = 4,
         norm_out = False,
         attn_dropout = 0.,
-        ff_dropout = 0.
+        ff_dropout = 0.,
+        final_proj = True
     ):
         super().__init__()
         self.rel_pos_bias = RelPosBias(heads = heads)
@@ -342,7 +371,8 @@ class CausalTransformer(nn.Module):
                 FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout)
             ]))
 
-        self.norm = RMSNorm(dim) if norm_out else nn.Identity()  # unclear in paper whether they projected after the classic layer norm for the final denoised image embedding, or just had the transformer output it directly: plan on offering both options
+        self.norm = LayerNorm(dim) if norm_out else nn.Identity()  # unclear in paper whether they projected after the classic layer norm for the final denoised image embedding, or just had the transformer output it directly: plan on offering both options
+        self.project_out = nn.Linear(dim, dim, bias = False) if final_proj else nn.Identity()
 
     def forward(
         self,
@@ -357,7 +387,8 @@ class CausalTransformer(nn.Module):
             x = attn(x, mask = mask, attn_bias = attn_bias) + x
             x = ff(x) + x
 
-        return self.norm(x)
+        out = self.norm(x)
+        return self.project_out(out)
 
 class DiffusionPriorNetwork(nn.Module):
     def __init__(
@@ -450,11 +481,11 @@ class DiffusionPrior(nn.Module):
         net,
         *,
         clip,
-        timesteps=1000,
-        cond_drop_prob=0.2,
-        loss_type="l1",
-        predict_x0=True,
-        beta_schedule="cosine",
+        timesteps = 1000,
+        cond_drop_prob = 0.2,
+        loss_type = "l1",
+        predict_x_start = True,
+        beta_schedule = "cosine",
     ):
         super().__init__()
         assert isinstance(clip, CLIP)
@@ -467,7 +498,7 @@ class DiffusionPrior(nn.Module):
         self.image_size = clip.image_size
         self.cond_drop_prob = cond_drop_prob
 
-        self.predict_x0 = predict_x0
+        self.predict_x_start = predict_x_start
         # in paper, they do not predict the noise, but predict x0 directly for image embedding, claiming empirically better results. I'll just offer both.
 
         if beta_schedule == "cosine":
@@ -484,7 +515,7 @@ class DiffusionPrior(nn.Module):
             raise NotImplementedError()
 
         alphas = 1. - betas
-        alphas_cumprod = torch.cumprod(alphas, axis=0)
+        alphas_cumprod = torch.cumprod(alphas, axis = 0)
         alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value = 1.)
 
         timesteps, = betas.shape
@@ -517,12 +548,14 @@ class DiffusionPrior(nn.Module):
         self.register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
         self.register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
 
+    @torch.no_grad()
     def get_image_embed(self, image):
         image_encoding = self.clip.visual_transformer(image)
         image_cls = image_encoding[:, 0]
         image_embed = self.clip.to_visual_latent(image_cls)
         return l2norm(image_embed)
 
+    @torch.no_grad()
     def get_text_cond(self, text):
         text_encodings = self.clip.text_transformer(text)
         text_cls, text_encodings = text_encodings[:, 0], text_encodings[:, 1:]
@@ -552,14 +585,16 @@ class DiffusionPrior(nn.Module):
         return posterior_mean, posterior_variance, posterior_log_variance_clipped
 
     def p_mean_variance(self, x, t, text_cond, clip_denoised: bool):
-        if self.predict_x0:
-            x_recon = self.net(x, t, **text_cond)
+        pred = self.net(x, t, **text_cond)
+
+        if self.predict_x_start:
+            x_recon = pred
             # not 100% sure of this above line - for any spectators, let me know in the github issues (or through a pull request) if you know how to correctly do this
             # i'll be rereading https://arxiv.org/abs/2111.14822, where i think a similar approach is taken
         else:
-            x_recon = self.predict_start_from_noise(x, t = t, noise = self.net(x, t, **text_cond))
+            x_recon = self.predict_start_from_noise(x, t = t, noise = pred)
 
-        if clip_denoised:
+        if clip_denoised and not self.predict_x_start:
             x_recon.clamp_(-1., 1.)
 
         model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
@@ -585,7 +620,41 @@ class DiffusionPrior(nn.Module):
             img = self.p_sample(img, torch.full((b,), i, device = device, dtype = torch.long), text_cond = text_cond)
         return img
 
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+
+        return (
+            extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+            extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
+        )
+
+    def p_losses(self, image_embed, t, text_cond, noise = None):
+        noise = default(noise, lambda: torch.randn_like(image_embed))
+
+        image_embed_noisy = self.q_sample(x_start = image_embed, t = t, noise = noise)
+
+        x_recon = self.net(
+            image_embed_noisy,
+            t,
+            cond_drop_prob = self.cond_drop_prob,
+            **text_cond
+        )
+
+        to_predict = noise if not self.predict_x_start else image_embed
+
+        if self.loss_type == 'l1':
+            loss = F.l1_loss(to_predict, x_recon)
+        elif self.loss_type == 'l2':
+            loss = F.mse_loss(to_predict, x_recon)
+        elif self.loss_type == "huber":
+            loss = F.smooth_l1_loss(to_predict, x_recon)
+        else:
+            raise NotImplementedError()
+
+        return loss
+
     @torch.no_grad()
+    @eval_decorator
     def sample(self, text, num_samples_per_batch = 2):
         # in the paper, what they did was
         # sample 2 image embeddings, choose the top 1 similarity, as judged by CLIP
@@ -610,39 +679,6 @@ class DiffusionPrior(nn.Module):
         top_image_embeds = image_embeds.gather(1, top_sim_indices)
         return rearrange(top_image_embeds, 'b 1 d -> b d')
 
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-
-        return (
-            extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-            extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
-        )
-
-    def p_losses(self, image_embed, t, text_cond, noise = None):
-        noise = default(noise, lambda: torch.randn_like(image_embed))
-
-        image_embed_noisy = self.q_sample(x_start = image_embed, t = t, noise = noise)
-
-        x_recon = self.net(
-            image_embed_noisy,
-            t,
-            cond_drop_prob = self.cond_drop_prob,
-            **text_cond
-        )
-
-        to_predict = noise if not self.predict_x0 else image_embed
-
-        if self.loss_type == 'l1':
-            loss = F.l1_loss(to_predict, x_recon)
-        elif self.loss_type == 'l2':
-            loss = F.mse_loss(to_predict, x_recon)
-        elif self.loss_type == "huber":
-            loss = F.smooth_l1_loss(to_predict, x_recon)
-        else:
-            raise NotImplementedError()
-
-        return loss
-
     def forward(self, text, image, *args, **kwargs):
         b, device, img_size, = image.shape[0], image.device, self.image_size
         check_shape(image, 'b c h w', h = img_size, w = img_size, c = self.channels)
@@ -657,7 +693,7 @@ class DiffusionPrior(nn.Module):
 # decoder
 
 def Upsample(dim):
-    return nn.ConvTranspose2d(dim, dim, 4, 2, 1)
+    return QueryAttnUpsample(dim)
 
 def Downsample(dim):
     return nn.Conv2d(dim, dim, 4, 2, 1)
@@ -705,7 +741,7 @@ class ConvNextBlock(nn.Module):
 
         inner_dim = int(dim_out * mult)
         self.net = nn.Sequential(
-            ChanRMSNorm(dim) if norm else nn.Identity(),
+            ChanLayerNorm(dim) if norm else nn.Identity(),
             nn.Conv2d(dim, inner_dim, 3, padding = 1),
             nn.GELU(),
             nn.Conv2d(inner_dim, dim_out, 3, padding = 1)
@@ -741,8 +777,8 @@ class CrossAttention(nn.Module):
 
         context_dim = default(context_dim, dim)
 
-        self.norm = RMSNorm(dim)
-        self.norm_context = RMSNorm(context_dim)
+        self.norm = LayerNorm(dim)
+        self.norm_context = LayerNorm(context_dim)
         self.dropout = nn.Dropout(dropout)
 
         self.null_kv = nn.Parameter(torch.randn(2, dim_head))
@@ -784,6 +820,20 @@ class CrossAttention(nn.Module):
         out = rearrange(out, 'b h n d -> b n (h d)')
         return self.to_out(out)
 
+class GridAttention(nn.Module):
+    def __init__(self, *args, window_size = 8, **kwargs):
+        super().__init__()
+        self.window_size = window_size
+        self.attn = Attention(*args, **kwargs)
+
+    def forward(self, x):
+        h, w = x.shape[-2:]
+        wsz = self.window_size
+        x = rearrange(x, 'b c (w1 h) (w2 w) -> (b h w) (w1 w2) c', w1 = wsz, w2 = wsz)
+        out = self.attn(x)
+        out = rearrange(out, '(b h w) (w1 w2) c -> b c (w1 h) (w2 w)', w1 = wsz, w2 = wsz, h = h // wsz, w = w // wsz)
+        return out
+
 class Unet(nn.Module):
     def __init__(
         self,
@@ -792,20 +842,29 @@ class Unet(nn.Module):
         image_embed_dim,
         cond_dim = None,
         num_image_tokens = 4,
+        num_time_tokens = 2,
         out_dim = None,
         dim_mults=(1, 2, 4, 8),
         channels = 3,
+        attn_dim_head = 32,
+        attn_heads = 8,
         lowres_cond = False, # for cascading diffusion - https://cascaded-diffusion.github.io/
-        lowres_cond_upsample_mode = 'bilinear',
-        blur_sigma = 0.1
+        sparse_attn = False,
+        sparse_attn_window = 8,  # window size for sparse attention
+        attend_at_middle = True, # whether to have a layer of attention at the bottleneck (can turn off for higher resolution in cascading DDPM, before bringing in efficient attention)
+        cond_on_text_encodings = False,
+        cond_on_image_embeds = False,
     ):
         super().__init__()
+        # save locals to take care of some hyperparameters for cascading DDPM
+
+        self._locals = locals()
+        del self._locals['self']
+        del self._locals['__class__']
 
         # for eventual cascading diffusion
 
         self.lowres_cond = lowres_cond
-        self.lowres_cond_upsample_mode = lowres_cond_upsample_mode
-        self.lowres_cond_blur = GaussianBlur2d((3, 3), (blur_sigma, blur_sigma))
 
         # determine dimensions
 
@@ -824,8 +883,8 @@ class Unet(nn.Module):
             SinusoidalPosEmb(dim),
             nn.Linear(dim, dim * 4),
             nn.GELU(),
-            nn.Linear(dim * 4, cond_dim),
-            Rearrange('b d -> b 1 d')
+            nn.Linear(dim * 4, cond_dim * num_time_tokens),
+            Rearrange('b (r d) -> b r d', r = num_time_tokens)
         )
 
         self.image_to_cond = nn.Sequential(
@@ -835,11 +894,21 @@ class Unet(nn.Module):
 
         self.text_to_cond = nn.LazyLinear(cond_dim)
 
+        # finer control over whether to condition on image embeddings and text encodings
+        # so one can have the latter unets in the cascading DDPMs only focus on super-resoluting
+
+        self.cond_on_text_encodings = cond_on_text_encodings
+        self.cond_on_image_embeds = cond_on_image_embeds
+
         # for classifier free guidance
 
         self.null_image_embed = nn.Parameter(torch.randn(1, num_image_tokens, cond_dim))
         self.null_text_embed = nn.Parameter(torch.randn(1, 1, cond_dim))
 
+        # attention related params
+
+        attn_kwargs = dict(heads = attn_heads, dim_head = attn_dim_head)
+
         # layers
 
         self.downs = nn.ModuleList([])
@@ -847,27 +916,32 @@ class Unet(nn.Module):
         num_resolutions = len(in_out)
 
         for ind, (dim_in, dim_out) in enumerate(in_out):
+            is_first = ind == 0
             is_last = ind >= (num_resolutions - 1)
+            layer_cond_dim = cond_dim if not is_first else None
 
             self.downs.append(nn.ModuleList([
                 ConvNextBlock(dim_in, dim_out, norm = ind != 0),
-                ConvNextBlock(dim_out, dim_out, cond_dim = cond_dim),
+                Residual(GridAttention(dim_out, window_size = sparse_attn_window, **attn_kwargs)) if sparse_attn else nn.Identity(),
+                ConvNextBlock(dim_out, dim_out, cond_dim = layer_cond_dim),
                 Downsample(dim_out) if not is_last else nn.Identity()
             ]))
 
         mid_dim = dims[-1]
 
         self.mid_block1 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim)
-        self.mid_attn = EinopsToAndFrom('b c h w', 'b (h w) c', Residual(Attention(mid_dim)))
+        self.mid_attn = EinopsToAndFrom('b c h w', 'b (h w) c', Residual(Attention(mid_dim, **attn_kwargs))) if attend_at_middle else None
         self.mid_block2 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim)
 
         for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
-            is_last = ind >= (num_resolutions - 1)
+            is_last = ind >= (num_resolutions - 2)
+            layer_cond_dim = cond_dim if not is_last else None
 
             self.ups.append(nn.ModuleList([
-                ConvNextBlock(dim_out * 2, dim_in, cond_dim = cond_dim),
-                ConvNextBlock(dim_in, dim_in, cond_dim = cond_dim),
-                Upsample(dim_in) if not is_last else nn.Identity()
+                ConvNextBlock(dim_out * 2, dim_in, cond_dim = layer_cond_dim),
+                Residual(GridAttention(dim_in, window_size = sparse_attn_window, **attn_kwargs)) if sparse_attn else nn.Identity(),
+                ConvNextBlock(dim_in, dim_in, cond_dim = layer_cond_dim),
+                Upsample(dim_in)
             ]))
 
         out_dim = default(out_dim, channels)
@@ -876,6 +950,20 @@ class Unet(nn.Module):
             nn.Conv2d(dim, out_dim, 1)
         )
 
+    # if the current settings for the unet are not correct
+    # for cascading DDPM, then reinit the unet with the right settings
+    def cast_model_parameters(
+        self,
+        *,
+        lowres_cond,
+        channels
+    ):
+        if lowres_cond == self.lowres_cond and channels == self.channels:
+            return self
+
+        updated_kwargs = {**self._locals, 'lowres_cond': lowres_cond, 'channels': channels}
+        return self.__class__(**updated_kwargs)
+
     def forward_with_cond_scale(
         self,
         *args,
@@ -898,20 +986,17 @@ class Unet(nn.Module):
         image_embed,
         lowres_cond_img = None,
         text_encodings = None,
-        cond_drop_prob = 0.
+        cond_drop_prob = 0.,
+        blur_sigma = None,
+        blur_kernel_size = None
     ):
         batch_size, device = x.shape[0], x.device
 
         # add low resolution conditioning, if present
 
-        assert not self.lowres_cond and not exists(lowres_cond_img), 'low resolution conditioning image must be present'
+        assert not (self.lowres_cond and not exists(lowres_cond_img)), 'low resolution conditioning image must be present'
 
         if exists(lowres_cond_img):
-            if self.training:
-                # when training, blur the low resolution conditional image
-                lowres_cond_img = self.lowres_cond_blur(lowres_cond_img)
-
-            lowres_cond_img = F.interpolate(lowres_cond_img, size = x.shape[-2:], mode = self.lowres_cond_upsample_mode)
             x = torch.cat((x, lowres_cond_img), dim = 1)
 
         # time conditioning
@@ -926,17 +1011,22 @@ class Unet(nn.Module):
         # mask out image embedding depending on condition dropout
         # for classifier free guidance
 
-        image_tokens = self.image_to_cond(image_embed)
+        image_tokens = None
 
-        image_tokens = torch.where(
-            cond_prob_mask,
-            image_tokens,
-            self.null_image_embed
-        )
+        if self.cond_on_image_embeds:
+            image_tokens = self.image_to_cond(image_embed)
+
+            image_tokens = torch.where(
+                cond_prob_mask,
+                image_tokens,
+                self.null_image_embed
+            )
 
         # take care of text encodings (optional)
 
-        if exists(text_encodings):
+        text_tokens = None
+
+        if exists(text_encodings) and self.cond_on_text_encodings:
             text_tokens = self.text_to_cond(text_encodings)
             text_tokens = torch.where(
                 cond_prob_mask,
@@ -946,56 +1036,164 @@ class Unet(nn.Module):
 
         # main conditioning tokens (c)
 
-        c = torch.cat((time_tokens, image_tokens), dim = -2)
+        c = time_tokens
+
+        if exists(image_tokens):
+            c = torch.cat((c, image_tokens), dim = -2)
 
         # text and image conditioning tokens (mid_c)
         # to save on compute, only do cross attention based conditioning on the inner most layers of the Unet
 
-        mid_c = c if not exists(text_encodings) else torch.cat((c, text_tokens), dim = -2)
+        mid_c = c if not exists(text_tokens) else torch.cat((c, text_tokens), dim = -2)
 
         # go through the layers of the unet, down and up
 
         hiddens = []
 
-        for convnext, convnext2, downsample in self.downs:
+        for convnext, sparse_attn, convnext2, downsample in self.downs:
             x = convnext(x, c)
+            x = sparse_attn(x)
             x = convnext2(x, c)
             hiddens.append(x)
             x = downsample(x)
 
         x = self.mid_block1(x, mid_c)
-        x = self.mid_attn(x)
+
+        if exists(self.mid_attn):
+            x = self.mid_attn(x)
+
         x = self.mid_block2(x, mid_c)
 
-        for convnext, convnext2, upsample in self.ups:
+        for convnext, sparse_attn, convnext2, upsample in self.ups:
             x = torch.cat((x, hiddens.pop()), dim=1)
             x = convnext(x, c)
+            x = sparse_attn(x)
             x = convnext2(x, c)
             x = upsample(x)
 
         return self.final_conv(x)
 
+class LowresConditioner(nn.Module):
+    def __init__(
+        self,
+        cond_upsample_mode = 'bilinear',
+        downsample_first = True,
+        blur_sigma = 0.1,
+        blur_kernel_size = 3,
+    ):
+        super().__init__()
+        self.cond_upsample_mode = cond_upsample_mode
+        self.downsample_first = downsample_first
+        self.blur_sigma = blur_sigma
+        self.blur_kernel_size = blur_kernel_size
+
+    def forward(
+        self,
+        cond_fmap,
+        *,
+        target_image_size,
+        downsample_image_size = None,
+        blur_sigma = None,
+        blur_kernel_size = None
+    ):
+        target_image_size = cast_tuple(target_image_size, 2)
+
+        if self.training and self.downsample_first and exists(downsample_image_size):
+            cond_fmap = resize_image_to(cond_fmap, target_image_size, mode = self.cond_upsample_mode)
+
+        if self.training:
+            # when training, blur the low resolution conditional image
+            blur_sigma = default(blur_sigma, self.blur_sigma)
+            blur_kernel_size = default(blur_kernel_size, self.blur_kernel_size)
+            cond_fmap = gaussian_blur2d(cond_fmap, cast_tuple(blur_kernel_size, 2), cast_tuple(blur_sigma, 2))
+
+        cond_fmap = resize_image_to(cond_fmap, target_image_size, mode = self.cond_upsample_mode)
+
+        return cond_fmap
+
 class Decoder(nn.Module):
     def __init__(
         self,
-        net,
+        unet,
         *,
         clip,
-        timesteps=1000,
-        cond_drop_prob=0.2,
-        loss_type="l1",
-        beta_schedule="cosine",
+        vae = None,
+        timesteps = 1000,
+        cond_drop_prob = 0.2,
+        loss_type = 'l1',
+        beta_schedule = 'cosine',
+        predict_x_start = False,
+        predict_x_start_for_latent_diffusion = False,
+        image_sizes = None,                         # for cascading ddpm, image size at each stage
+        lowres_cond_upsample_mode = 'bilinear',     # cascading ddpm - low resolution upsample mode
+        lowres_downsample_first = True,             # cascading ddpm - resizes to lower resolution, then to next conditional resolution + blur
+        blur_sigma = 0.1,                           # cascading ddpm - blur sigma
+        blur_kernel_size = 3,                       # cascading ddpm - blur kernel size
     ):
         super().__init__()
         assert isinstance(clip, CLIP)
         freeze_model_and_make_eval_(clip)
         self.clip = clip
-
-        self.net = net
+        self.clip_image_size = clip.image_size
         self.channels = clip.image_channels
-        self.image_size = clip.image_size
+
+        # automatically take care of ensuring that first unet is unconditional
+        # while the rest of the unets are conditioned on the low resolution image produced by previous unet
+
+        unets = cast_tuple(unet)
+        vaes = pad_tuple_to_length(cast_tuple(vae), len(unets), fillvalue = NullVQGanVAE(channels = self.channels))
+
+        self.unets = nn.ModuleList([])
+        self.vaes = nn.ModuleList([])
+
+        for ind, (one_unet, one_vae) in enumerate(zip(unets, vaes)):
+            assert isinstance(one_unet, Unet)
+            assert isinstance(one_vae, (VQGanVAE, NullVQGanVAE))
+
+            is_first = ind == 0
+            latent_dim = one_vae.encoded_dim if exists(one_vae) else None
+
+            unet_channels = default(latent_dim, self.channels)
+
+            one_unet = one_unet.cast_model_parameters(
+                lowres_cond = not is_first,
+                channels = unet_channels
+            )
+
+            self.unets.append(one_unet)
+            self.vaes.append(one_vae.copy_for_eval())
+
+        # unet image sizes
+
+        image_sizes = default(image_sizes, (clip.image_size,))
+        image_sizes = tuple(sorted(set(image_sizes)))
+
+        assert len(self.unets) == len(image_sizes), f'you did not supply the correct number of u-nets ({len(self.unets)}) for resolutions {image_sizes}'
+        self.image_sizes = image_sizes
+        self.sample_channels = cast_tuple(self.channels, len(image_sizes))
+
+        # predict x0 config
+
+        self.predict_x_start = cast_tuple(predict_x_start, len(unets)) if not predict_x_start_for_latent_diffusion else tuple(map(lambda t: isinstance(t, VQGanVAE), self.vaes))
+
+        # cascading ddpm related stuff
+
+        lowres_conditions = tuple(map(lambda t: t.lowres_cond, self.unets))
+        assert lowres_conditions == (False, *((True,) * (len(self.unets) - 1))), 'the first unet must be unconditioned (by low resolution image), and the rest of the unets must have `lowres_cond` set to True'
+
+        self.to_lowres_cond = LowresConditioner(
+            cond_upsample_mode = lowres_cond_upsample_mode,
+            downsample_first = lowres_downsample_first,
+            blur_sigma = blur_sigma,
+            blur_kernel_size = blur_kernel_size,
+        )
+
+        # classifier free guidance
+
         self.cond_drop_prob = cond_drop_prob
 
+        # noise schedule
+
         if beta_schedule == "cosine":
             betas = cosine_beta_schedule(timesteps)
         elif beta_schedule == "linear":
@@ -1010,7 +1208,7 @@ class Decoder(nn.Module):
             raise NotImplementedError()
 
         alphas = 1. - betas
-        alphas_cumprod = torch.cumprod(alphas, axis=0)
+        alphas_cumprod = torch.cumprod(alphas, axis = 0)
         alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value = 1.)
 
         timesteps, = betas.shape
@@ -1043,11 +1241,33 @@ class Decoder(nn.Module):
         self.register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
         self.register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
 
+    def get_unet(self, unet_number):
+        assert 0 < unet_number <= len(self.unets)
+        index = unet_number - 1
+        return self.unets[index]
+
+    @contextmanager
+    def one_unet_in_gpu(self, unet_number = None, unet = None):
+        assert exists(unet_number) ^ exists(unet)
+
+        if exists(unet_number):
+            unet = self.get_unet(unet_number)
+
+        self.cuda()
+        self.unets.cpu()
+
+        unet.cuda()
+        yield
+        unet.cpu()
+
+    @torch.no_grad()
     def get_text_encodings(self, text):
         text_encodings = self.clip.text_transformer(text)
         return text_encodings[:, 1:]
 
+    @torch.no_grad()
     def get_image_embed(self, image):
+        image = resize_image_to(image, self.clip_image_size)
         image_encoding = self.clip.visual_transformer(image)
         image_cls = image_encoding[:, 0]
         image_embed = self.clip.to_visual_latent(image_cls)
@@ -1074,43 +1294,50 @@ class Decoder(nn.Module):
         posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
         return posterior_mean, posterior_variance, posterior_log_variance_clipped
 
-    def p_mean_variance(self, x, t, image_embed, text_encodings = None, clip_denoised = True, cond_scale = 1.):
-        x_recon = self.predict_start_from_noise(x, t = t, noise = self.net.forward_with_cond_scale(x, t, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale))
+    def p_mean_variance(self, unet, x, t, image_embed, text_encodings = None, lowres_cond_img = None, clip_denoised = True, predict_x_start = False, cond_scale = 1.):
+        pred = unet.forward_with_cond_scale(x, t, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img)
 
-        if clip_denoised:
+        if predict_x_start:
+            x_recon = pred
+        else:
+            x_recon = self.predict_start_from_noise(x, t = t, noise = pred)
+
+        if clip_denoised and not predict_x_start:
             x_recon.clamp_(-1., 1.)
 
         model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
         return model_mean, posterior_variance, posterior_log_variance
 
     @torch.no_grad()
-    def p_sample(self, x, t, image_embed, text_encodings = None, cond_scale = 1., clip_denoised = True, repeat_noise = False):
+    def p_sample(self, unet, x, t, image_embed, text_encodings = None, cond_scale = 1., lowres_cond_img = None, predict_x_start = False, clip_denoised = True, repeat_noise = False):
         b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x = x, t = t, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, clip_denoised = clip_denoised)
+        model_mean, _, model_log_variance = self.p_mean_variance(unet, x = x, t = t, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, clip_denoised = clip_denoised, predict_x_start = predict_x_start)
         noise = noise_like(x.shape, device, repeat_noise)
         # no noise when t == 0
         nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
 
     @torch.no_grad()
-    def p_sample_loop(self, shape, image_embed, text_encodings = None, cond_scale = 1):
+    def p_sample_loop(self, unet, shape, image_embed, predict_x_start = False, lowres_cond_img = None, text_encodings = None, cond_scale = 1):
         device = self.betas.device
 
         b = shape[0]
-        img = torch.randn(shape, device=device)
+        img = torch.randn(shape, device = device)
+
+        for i in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps):
+            img = self.p_sample(
+                unet,
+                img,
+                torch.full((b,), i, device = device, dtype = torch.long),
+                image_embed = image_embed,
+                text_encodings = text_encodings,
+                cond_scale = cond_scale,
+                lowres_cond_img = lowres_cond_img,
+                predict_x_start = predict_x_start
+            )
 
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='sampling loop time step', total=self.num_timesteps):
-            img = self.p_sample(img, torch.full((b,), i, device = device, dtype = torch.long), image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale)
         return img
 
-    @torch.no_grad()
-    def sample(self, image_embed, text = None, cond_scale = 1.):
-        batch_size = image_embed.shape[0]
-        image_size = self.image_size
-        channels = self.channels
-        text_encodings = self.get_text_encodings(text) if exists(text) else None
-        return self.p_sample_loop((batch_size, channels, image_size, image_size), image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale)
-
     def q_sample(self, x_start, t, noise=None):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
@@ -1119,41 +1346,111 @@ class Decoder(nn.Module):
             extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
         )
 
-    def p_losses(self, x_start, t, *, image_embed, text_encodings = None, noise = None):
+    def p_losses(self, unet, x_start, t, *, image_embed, lowres_cond_img = None, text_encodings = None, predict_x_start = False, noise = None):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
         x_noisy = self.q_sample(x_start = x_start, t = t, noise = noise)
 
-        x_recon = self.net(
+        x_recon = unet(
             x_noisy,
             t,
             image_embed = image_embed,
             text_encodings = text_encodings,
+            lowres_cond_img = lowres_cond_img,
             cond_drop_prob = self.cond_drop_prob
         )
 
+        target = noise if not predict_x_start else x_start
+
         if self.loss_type == 'l1':
-            loss = F.l1_loss(noise, x_recon)
+            loss = F.l1_loss(target, x_recon)
         elif self.loss_type == 'l2':
-            loss = F.mse_loss(noise, x_recon)
+            loss = F.mse_loss(target, x_recon)
         elif self.loss_type == "huber":
-            loss = F.smooth_l1_loss(noise, x_recon)
+            loss = F.smooth_l1_loss(target, x_recon)
         else:
             raise NotImplementedError()
 
         return loss
 
-    def forward(self, image, text = None):
-        b, device, img_size, = image.shape[0], image.device, self.image_size
-        check_shape(image, 'b c h w', h = img_size, w = img_size, c = self.channels)
+    @torch.no_grad()
+    @eval_decorator
+    def sample(self, image_embed, text = None, cond_scale = 1.):
+        batch_size = image_embed.shape[0]
+
+        text_encodings = self.get_text_encodings(text) if exists(text) else None
+
+        img = None
+
+        for unet, vae, channel, image_size, predict_x_start in tqdm(zip(self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start)):
+            with self.one_unet_in_gpu(unet = unet):
+                lowres_cond_img = None
+                shape = (batch_size, channel, image_size, image_size)
+
+                if unet.lowres_cond:
+                    lowres_cond_img = self.to_lowres_cond(img, target_image_size = image_size)
+
+                image_size = vae.get_encoded_fmap_size(image_size)
+                shape = (batch_size, vae.encoded_dim, image_size, image_size)
+
+                if exists(lowres_cond_img):
+                    lowres_cond_img = vae.encode(lowres_cond_img)
+
+                img = self.p_sample_loop(
+                    unet,
+                    shape,
+                    image_embed = image_embed,
+                    text_encodings = text_encodings,
+                    cond_scale = cond_scale,
+                    predict_x_start = predict_x_start,
+                    lowres_cond_img = lowres_cond_img
+                )
+
+                img = vae.decode(img)
+
+        return img
+
+    def forward(
+        self,
+        image,
+        text = None,
+        image_embed = None,
+        text_encodings = None,
+        unet_number = None
+    ):
+        assert not (len(self.unets) > 1 and not exists(unet_number)), f'you must specify which unet you want trained, from a range of 1 to {len(self.unets)}, if you are training cascading DDPM (multiple unets)'
+        unet_number = default(unet_number, 1)
+        unet_index = unet_number - 1
+
+        unet = self.get_unet(unet_number)
+
+        target_image_size = self.image_sizes[unet_index]
+        vae = self.vaes[unet_index]
+        predict_x_start = self.predict_x_start[unet_index]
+
+        b, c, h, w, device, = *image.shape, image.device
+
+        check_shape(image, 'b c h w', c = self.channels)
+        assert h >= target_image_size and w >= target_image_size
 
         times = torch.randint(0, self.num_timesteps, (b,), device = device, dtype = torch.long)
 
-        image_embed = self.get_image_embed(image)
-        text_encodings = self.get_text_encodings(text) if exists(text) else None
+        if not exists(image_embed):
+            image_embed = self.get_image_embed(image)
 
-        loss = self.p_losses(image, times, image_embed = image_embed, text_encodings = text_encodings)
-        return loss
+        text_encodings = self.get_text_encodings(text) if exists(text) and not exists(text_encodings) else None
+
+        lowres_cond_img = self.to_lowres_cond(image, target_image_size = target_image_size, downsample_image_size = self.image_sizes[unet_index - 1]) if unet_number > 1 else None
+        image = resize_image_to(image, target_image_size)
+
+        vae.eval()
+        with torch.no_grad():
+            image = vae.encode(image)
+
+            if exists(lowres_cond_img):
+                lowres_cond_img = vae.encode(lowres_cond_img)
+
+        return self.p_losses(unet, image, times, image_embed = image_embed, text_encodings = text_encodings, lowres_cond_img = lowres_cond_img, predict_x_start = predict_x_start)
 
 # main class
 
@@ -1180,6 +1477,7 @@ class DALLE2(nn.Module):
         cond_scale = 1.
     ):
         device = next(self.parameters()).device
+        one_text = isinstance(text, str) or (not is_list_str(text) and text.shape[0] == 1)
 
         if isinstance(text, str) or is_list_str(text):
             text = [text] if not isinstance(text, (list, tuple)) else text
@@ -1187,4 +1485,8 @@ class DALLE2(nn.Module):
 
         image_embed = self.prior.sample(text, num_samples_per_batch = self.prior_num_samples)
         images = self.decoder.sample(image_embed, cond_scale = cond_scale)
+
+        if one_text:
+            return images[0]
+
         return images

dalle2_pytorch/vqgan_vae.py

@@ -0,0 +1,566 @@
+import copy
+import math
+from math import sqrt
+from functools import partial, wraps
+
+from vector_quantize_pytorch import VectorQuantize as VQ
+
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+from torch.autograd import grad as torch_grad
+import torchvision
+
+from einops import rearrange, reduce, repeat
+
+from dalle2_pytorch.attention import QueryAttnUpsample
+
+# constants
+
+MList = nn.ModuleList
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+# decorators
+
+def eval_decorator(fn):
+    def inner(model, *args, **kwargs):
+        was_training = model.training
+        model.eval()
+        out = fn(model, *args, **kwargs)
+        model.train(was_training)
+        return out
+    return inner
+
+def remove_vgg(fn):
+    @wraps(fn)
+    def inner(self, *args, **kwargs):
+        has_vgg = hasattr(self, 'vgg')
+        if has_vgg:
+            vgg = self.vgg
+            delattr(self, 'vgg')
+
+        out = fn(self, *args, **kwargs)
+
+        if has_vgg:
+            self.vgg = vgg
+
+        return out
+    return inner
+
+# keyword argument helpers
+
+def pick_and_pop(keys, d):
+    values = list(map(lambda key: d.pop(key), keys))
+    return dict(zip(keys, values))
+
+def group_dict_by_key(cond, d):
+    return_val = [dict(),dict()]
+    for key in d.keys():
+        match = bool(cond(key))
+        ind = int(not match)
+        return_val[ind][key] = d[key]
+    return (*return_val,)
+
+def string_begins_with(prefix, str):
+    return str.startswith(prefix)
+
+def group_by_key_prefix(prefix, d):
+    return group_dict_by_key(partial(string_begins_with, prefix), d)
+
+def groupby_prefix_and_trim(prefix, d):
+    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
+    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+    return kwargs_without_prefix, kwargs
+
+# tensor helper functions
+
+def log(t, eps = 1e-10):
+    return torch.log(t + eps)
+
+def gradient_penalty(images, output, weight = 10):
+    batch_size = images.shape[0]
+    gradients = torch_grad(outputs = output, inputs = images,
+                           grad_outputs = torch.ones(output.size(), device = images.device),
+                           create_graph = True, retain_graph = True, only_inputs = True)[0]
+
+    gradients = rearrange(gradients, 'b ... -> b (...)')
+    return weight * ((gradients.norm(2, dim = 1) - 1) ** 2).mean()
+
+def l2norm(t):
+    return F.normalize(t, dim = -1)
+
+def leaky_relu(p = 0.1):
+    return nn.LeakyReLU(0.1)
+
+def stable_softmax(t, dim = -1, alpha = 32 ** 2):
+    t = t / alpha
+    t = t - torch.amax(t, dim = dim, keepdim = True).detach()
+    return (t * alpha).softmax(dim = dim)
+
+def safe_div(numer, denom, eps = 1e-8):
+    return numer / (denom + eps)
+
+# gan losses
+
+def hinge_discr_loss(fake, real):
+    return (F.relu(1 + fake) + F.relu(1 - real)).mean()
+
+def hinge_gen_loss(fake):
+    return -fake.mean()
+
+def bce_discr_loss(fake, real):
+    return (-log(1 - torch.sigmoid(fake)) - log(torch.sigmoid(real))).mean()
+
+def bce_gen_loss(fake):
+    return -log(torch.sigmoid(fake)).mean()
+
+def grad_layer_wrt_loss(loss, layer):
+    return torch_grad(
+        outputs = loss,
+        inputs = layer,
+        grad_outputs = torch.ones_like(loss),
+        retain_graph = True
+    )[0].detach()
+
+# vqgan vae
+
+class LayerNormChan(nn.Module):
+    def __init__(
+        self,
+        dim,
+        eps = 1e-5
+    ):
+        super().__init__()
+        self.eps = eps
+        self.gamma = nn.Parameter(torch.ones(1, dim, 1, 1))
+
+    def forward(self, x):
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
+        mean = torch.mean(x, dim = 1, keepdim = True)
+        return (x - mean) / (var + self.eps).sqrt() * self.gamma
+
+class Discriminator(nn.Module):
+    def __init__(
+        self,
+        dims,
+        channels = 3,
+        groups = 16,
+        init_kernel_size = 5
+    ):
+        super().__init__()
+        dim_pairs = zip(dims[:-1], dims[1:])
+
+        self.layers = MList([nn.Sequential(nn.Conv2d(channels, dims[0], init_kernel_size, padding = init_kernel_size // 2), leaky_relu())])
+
+        for dim_in, dim_out in dim_pairs:
+            self.layers.append(nn.Sequential(
+                nn.Conv2d(dim_in, dim_out, 4, stride = 2, padding = 1),
+                nn.GroupNorm(groups, dim_out),
+                leaky_relu()
+            ))
+
+        dim = dims[-1]
+        self.to_logits = nn.Sequential( # return 5 x 5, for PatchGAN-esque training
+            nn.Conv2d(dim, dim, 1),
+            leaky_relu(),
+            nn.Conv2d(dim, 1, 4)
+        )
+
+    def forward(self, x):
+        for net in self.layers:
+            x = net(x)
+
+        return self.to_logits(x)
+
+class ContinuousPositionBias(nn.Module):
+    """ from https://arxiv.org/abs/2111.09883 """
+
+    def __init__(self, *, dim, heads, layers = 2):
+        super().__init__()
+        self.net = MList([])
+        self.net.append(nn.Sequential(nn.Linear(2, dim), leaky_relu()))
+
+        for _ in range(layers - 1):
+            self.net.append(nn.Sequential(nn.Linear(dim, dim), leaky_relu()))
+
+        self.net.append(nn.Linear(dim, heads))
+        self.register_buffer('rel_pos', None, persistent = False)
+
+    def forward(self, x):
+        n, device = x.shape[-1], x.device
+        fmap_size = int(sqrt(n))
+
+        if not exists(self.rel_pos):
+            pos = torch.arange(fmap_size, device = device)
+            grid = torch.stack(torch.meshgrid(pos, pos, indexing = 'ij'))
+            grid = rearrange(grid, 'c i j -> (i j) c')
+            rel_pos = rearrange(grid, 'i c -> i 1 c') - rearrange(grid, 'j c -> 1 j c')
+            rel_pos = torch.sign(rel_pos) * torch.log(rel_pos.abs() + 1)
+            self.register_buffer('rel_pos', rel_pos, persistent = False)
+
+        rel_pos = self.rel_pos.float()
+
+        for layer in self.net:
+            rel_pos = layer(rel_pos)
+
+        bias = rearrange(rel_pos, 'i j h -> h i j')
+        return x + bias
+
+class GLUResBlock(nn.Module):
+    def __init__(self, chan, groups = 16):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Conv2d(chan, chan * 2, 3, padding = 1),
+            nn.GLU(dim = 1),
+            nn.GroupNorm(groups, chan),
+            nn.Conv2d(chan, chan * 2, 3, padding = 1),
+            nn.GLU(dim = 1),
+            nn.GroupNorm(groups, chan),
+            nn.Conv2d(chan, chan, 1)
+        )
+
+    def forward(self, x):
+        return self.net(x) + x
+
+class ResBlock(nn.Module):
+    def __init__(self, chan, groups = 16):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Conv2d(chan, chan, 3, padding = 1),
+            nn.GroupNorm(groups, chan),
+            leaky_relu(),
+            nn.Conv2d(chan, chan, 3, padding = 1),
+            nn.GroupNorm(groups, chan),
+            leaky_relu(),
+            nn.Conv2d(chan, chan, 1)
+        )
+
+    def forward(self, x):
+        return self.net(x) + x
+
+# vqgan attention layer
+class VQGanAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        dim_head = 64,
+        heads = 8,
+        dropout = 0.
+    ):
+        super().__init__()
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        inner_dim = heads * dim_head
+
+        self.dropout = nn.Dropout(dropout)
+        self.pre_norm = LayerNormChan(dim)
+
+        self.cpb = ContinuousPositionBias(dim = dim // 4, heads = heads)
+        self.to_qkv = nn.Conv2d(dim, inner_dim * 3, 1, bias = False)
+        self.to_out = nn.Conv2d(inner_dim, dim, 1, bias = False)
+
+    def forward(self, x):
+        h = self.heads
+        height, width, residual = *x.shape[-2:], x.clone()
+
+        x = self.pre_norm(x)
+
+        q, k, v = self.to_qkv(x).chunk(3, dim = 1)
+
+        q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = h), (q, k, v))
+
+        sim = einsum('b h c i, b h c j -> b h i j', q, k) * self.scale
+
+        sim = self.cpb(sim)
+
+        attn = stable_softmax(sim, dim = -1)
+        attn = self.dropout(attn)
+
+        out = einsum('b h i j, b h c j -> b h c i', attn, v)
+        out = rearrange(out, 'b h c (x y) -> b (h c) x y', x = height, y = width)
+        out = self.to_out(out)
+
+        return out + residual
+
+class NullVQGanVAE(nn.Module):
+    def __init__(
+        self,
+        *,
+        channels
+    ):
+        super().__init__()
+        self.encoded_dim = channels
+        self.layers = 0
+
+    def get_encoded_fmap_size(self, size):
+        return size
+
+    def copy_for_eval(self):
+        return self
+
+    def encode(self, x):
+        return x
+
+    def decode(self, x):
+        return x
+
+class VQGanVAE(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        image_size,
+        channels = 3,
+        layers = 4,
+        layer_mults = None,
+        l2_recon_loss = False,
+        use_hinge_loss = True,
+        num_resnet_blocks = 1,
+        vgg = None,
+        vq_codebook_size = 512,
+        vq_decay = 0.8,
+        vq_commitment_weight = 1.,
+        vq_kmeans_init = True,
+        vq_use_cosine_sim = True,
+        use_attn = True,
+        attn_dim_head = 64,
+        attn_heads = 8,
+        resnet_groups = 16,
+        attn_dropout = 0.,
+        first_conv_kernel_size = 5,
+        use_vgg_and_gan = True,
+        **kwargs
+    ):
+        super().__init__()
+        assert dim % resnet_groups == 0, f'dimension {dim} must be divisible by {resnet_groups} (groups for the groupnorm)'
+
+        vq_kwargs, kwargs = groupby_prefix_and_trim('vq_', kwargs)
+
+        self.image_size = image_size
+        self.channels = channels
+        self.layers = layers
+        self.fmap_size = image_size // (layers ** 2)
+        self.codebook_size = vq_codebook_size
+
+        self.encoders = MList([])
+        self.decoders = MList([])
+
+        layer_mults = default(layer_mults, list(map(lambda t: 2 ** t, range(layers))))
+        assert len(layer_mults) == layers, 'layer multipliers must be equal to designated number of layers'
+
+        layer_dims = [dim * mult for mult in layer_mults]
+        dims = (dim, *layer_dims)
+        codebook_dim = layer_dims[-1]
+
+        self.encoded_dim = dims[-1]
+
+        dim_pairs = zip(dims[:-1], dims[1:])
+
+        append = lambda arr, t: arr.append(t)
+        prepend = lambda arr, t: arr.insert(0, t)
+
+        if not isinstance(num_resnet_blocks, tuple):
+            num_resnet_blocks = (*((0,) * (layers - 1)), num_resnet_blocks)
+
+        if not isinstance(use_attn, tuple):
+            use_attn = (*((False,) * (layers - 1)), use_attn)
+
+        assert len(num_resnet_blocks) == layers, 'number of resnet blocks config must be equal to number of layers'
+        assert len(use_attn) == layers
+
+        for layer_index, (dim_in, dim_out), layer_num_resnet_blocks, layer_use_attn in zip(range(layers), dim_pairs, num_resnet_blocks, use_attn):
+            append(self.encoders, nn.Sequential(nn.Conv2d(dim_in, dim_out, 4, stride = 2, padding = 1), leaky_relu()))
+            prepend(self.decoders, nn.Sequential(QueryAttnUpsample(dim_out), nn.Conv2d(dim_out, dim_in, 3, padding = 1), leaky_relu()))
+
+            if layer_use_attn:
+                prepend(self.decoders, VQGanAttention(dim = dim_out, heads = attn_heads, dim_head = attn_dim_head, dropout = attn_dropout))
+
+            for _ in range(layer_num_resnet_blocks):
+                append(self.encoders, ResBlock(dim_out, groups = resnet_groups))
+                prepend(self.decoders, GLUResBlock(dim_out, groups = resnet_groups))
+
+            if layer_use_attn:
+                append(self.encoders, VQGanAttention(dim = dim_out, heads = attn_heads, dim_head = attn_dim_head, dropout = attn_dropout))
+
+        prepend(self.encoders, nn.Conv2d(channels, dim, first_conv_kernel_size, padding = first_conv_kernel_size // 2))
+        append(self.decoders, nn.Conv2d(dim, channels, 1))
+
+        self.vq = VQ(
+            dim = codebook_dim,
+            codebook_size = vq_codebook_size,
+            decay = vq_decay,
+            commitment_weight = vq_commitment_weight,
+            accept_image_fmap = True,
+            kmeans_init = vq_kmeans_init,
+            use_cosine_sim = vq_use_cosine_sim,
+            **vq_kwargs
+        )
+
+        # reconstruction loss
+
+        self.recon_loss_fn = F.mse_loss if l2_recon_loss else F.l1_loss
+
+        # turn off GAN and perceptual loss if grayscale
+
+        self.vgg = None
+        self.discr = None
+        self.use_vgg_and_gan = use_vgg_and_gan
+
+        if not use_vgg_and_gan:
+            return
+
+        # preceptual loss
+
+        if exists(vgg):
+            self.vgg = vgg
+        else:
+            self.vgg = torchvision.models.vgg16(pretrained = True)
+            self.vgg.classifier = nn.Sequential(*self.vgg.classifier[:-2])
+
+        # gan related losses
+
+        self.discr = Discriminator(dims = dims, channels = channels)
+
+        self.discr_loss = hinge_discr_loss if use_hinge_loss else bce_discr_loss
+        self.gen_loss = hinge_gen_loss if use_hinge_loss else bce_gen_loss
+
+    def get_encoded_fmap_size(self, image_size):
+        return image_size // (2 ** self.layers)
+
+    def copy_for_eval(self):
+        device = next(self.parameters()).device
+        vae_copy = copy.deepcopy(self.cpu())
+
+        if vae_copy.use_vgg_and_gan:
+            del vae_copy.discr
+            del vae_copy.vgg
+
+        vae_copy.eval()
+        return vae_copy.to(device)
+
+    @remove_vgg
+    def state_dict(self, *args, **kwargs):
+        return super().state_dict(*args, **kwargs)
+
+    @remove_vgg
+    def load_state_dict(self, *args, **kwargs):
+        return super().load_state_dict(*args, **kwargs)
+
+    @property
+    def codebook(self):
+        return self.vq.codebook
+
+    def encode(self, fmap):
+        for enc in self.encoders:
+            fmap = enc(fmap)
+
+        return fmap
+
+    def decode(self, fmap, return_indices_and_loss = False):
+        fmap, indices, commit_loss = self.vq(fmap)
+
+        for dec in self.decoders:
+            fmap = dec(fmap)
+
+        if not return_indices_and_loss:
+            return fmap
+
+        return fmap, indices, commit_loss
+
+    def forward(
+        self,
+        img,
+        return_loss = False,
+        return_discr_loss = False,
+        return_recons = False,
+        add_gradient_penalty = True
+    ):
+        batch, channels, height, width, device = *img.shape, img.device
+        assert height == self.image_size and width == self.image_size, 'height and width of input image must be equal to {self.image_size}'
+        assert channels == self.channels, 'number of channels on image or sketch is not equal to the channels set on this VQGanVAE'
+
+        fmap = self.encode(img)
+
+        fmap, indices, commit_loss = self.decode(fmap, return_indices_and_loss = True)
+
+        if not return_loss and not return_discr_loss:
+            return fmap
+
+        assert return_loss ^ return_discr_loss, 'you should either return autoencoder loss or discriminator loss, but not both'
+
+        # whether to return discriminator loss
+
+        if return_discr_loss:
+            assert exists(self.discr), 'discriminator must exist to train it'
+
+            fmap.detach_()
+            img.requires_grad_()
+
+            fmap_discr_logits, img_discr_logits = map(self.discr, (fmap, img))
+
+            discr_loss = self.discr_loss(fmap_discr_logits, img_discr_logits)
+
+            if add_gradient_penalty:
+                gp = gradient_penalty(img, img_discr_logits)
+                loss = discr_loss + gp
+
+            if return_recons:
+                return loss, fmap
+
+            return loss
+
+        # reconstruction loss
+
+        recon_loss = self.recon_loss_fn(fmap, img)
+
+        # early return if training on grayscale
+
+        if not self.use_vgg_and_gan:
+            if return_recons:
+                return recon_loss, fmap
+
+            return recon_loss
+
+        # perceptual loss
+
+        img_vgg_input = img
+        fmap_vgg_input = fmap
+
+        if img.shape[1] == 1:
+            # handle grayscale for vgg
+            img_vgg_input, fmap_vgg_input = map(lambda t: repeat(t, 'b 1 ... -> b c ...', c = 3), (img_vgg_input, fmap_vgg_input))
+
+        img_vgg_feats = self.vgg(img_vgg_input)
+        recon_vgg_feats = self.vgg(fmap_vgg_input)
+        perceptual_loss = F.mse_loss(img_vgg_feats, recon_vgg_feats)
+
+        # generator loss
+
+        gen_loss = self.gen_loss(self.discr(fmap))
+
+        # calculate adaptive weight
+
+        last_dec_layer = self.decoders[-1].weight
+
+        norm_grad_wrt_gen_loss = grad_layer_wrt_loss(gen_loss, last_dec_layer).norm(p = 2)
+        norm_grad_wrt_perceptual_loss = grad_layer_wrt_loss(perceptual_loss, last_dec_layer).norm(p = 2)
+
+        adaptive_weight = safe_div(norm_grad_wrt_perceptual_loss, norm_grad_wrt_gen_loss)
+        adaptive_weight.clamp_(max = 1e4)
+
+        # combine losses
+
+        loss = recon_loss + perceptual_loss + commit_loss + adaptive_weight * gen_loss
+
+        if return_recons:
+            return loss, fmap
+
+        return loss

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.18',
+  version = '0.0.45',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',
@@ -30,6 +30,7 @@ setup(
     'torch>=1.10',
     'torchvision',
     'tqdm',
+    'vector-quantize-pytorch',
     'x-clip>=0.4.4',
     'youtokentome'
   ],