first pass at complete DALL-E2 + Latent Diffusion integration, latent diffusion on any layer(s) of the cascading ddpm in the decoder.

README.md

@@ -12,7 +12,7 @@ This model is SOTA for text-to-image for now.
 
 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 <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.
+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>. 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
 
@@ -246,6 +246,13 @@ loss = decoder(images, unet_number = 2)
 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, 512, 512)
 ```
 
 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))
@@ -376,11 +383,9 @@ You can also train the decoder on images of greater than the size (say 512x512)
 
 For the layperson, no worries, training will all be automated into a CLI tool, at least for small scale training.
 
-## Experimental
+## Experimental - DALL-E2 with Latent Diffusion
 
-### DALL-E2 with Latent Diffusion
+This repository decides to take the next step and offer DALL-E2 combined with latent diffusion, from Rombach et al.
-
-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.
 
@@ -404,10 +409,9 @@ clip = CLIP(
     visual_heads = 8
 )
 
-# 3 unets for the decoder (a la cascading DDPM)
+# 2 unets for the decoder (a la cascading DDPM)
 
-# first two unets are doing latent diffusion
+# 1st unet is doing latent diffusion
-# vqgan-vae must be trained before hand
 
 vae1 = VQGanVAE(
     dim = 32,
@@ -465,7 +469,7 @@ decoder = Decoder(
 
 # mock images (get a lot of this)
 
-images = torch.randn(1, 3, 1024, 1024).cuda()
+images = torch.randn(1, 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
@@ -478,10 +482,6 @@ 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
@@ -492,11 +492,7 @@ mock_image_embed = torch.randn(1, 512).cuda()
 images = decoder.sample(mock_image_embed) # (1, 3, 1024, 1024)
 ```
 
-## Training wrapper (wip)
+## CLI Usage (work in progress)
-
-Offer training wrappers
-
-## CLI (wip)
 
 ```bash
 $ dream 'sharing a sunset at the summit of mount everest with my dog'
@@ -504,7 +500,9 @@ $ 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
 
-<a href="https://github.com/lucidrains/big-sleep">template</a>
+## Training wrapper (wip)
+
+Offer training wrappers
 
 ## Training CLI (wip)
 
@@ -522,16 +520,11 @@ Once built, images will be saved to the same directory the command is invoked
 - [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
 - [ ] 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
 
@@ -563,12 +556,23 @@ Once built, images will be saved to the same directory the command is invoked
 
 ```bibtex
 @inproceedings{Liu2022ACF,
-    title   = {A ConvNet for the 2020https://arxiv.org/abs/2112.11435s},
+    title   = {A ConvNet for the 2020s},
     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}
+}
+```
+
 ```bibtex
 @inproceedings{Tu2022MaxViTMV,
     title   = {MaxViT: Multi-Axis Vision Transformer},
@@ -577,14 +581,4 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
-```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}
-}
-```
-
 *Creating noise from data is easy; creating data from noise is generative modeling.* - Yang Song's <a href="https://arxiv.org/abs/2011.13456">paper</a>

dalle2_pytorch/cli.py

@@ -1,51 +1,9 @@
 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(
+def dream(text):
-    model,
+    return 'not ready yet'
-    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

@@ -16,7 +16,6 @@ from einops_exts.torch import EinopsToAndFrom
 from kornia.filters import gaussian_blur2d
 
 from dalle2_pytorch.tokenizer import tokenizer
-from dalle2_pytorch.vqgan_vae import NullVQGanVAE, VQGanVAE
 
 # use x-clip
 
@@ -49,11 +48,11 @@ 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):
+def pad_tuple_to_length(t, length):
     remain_length = length - len(t)
     if remain_length <= 0:
         return t
-    return (*t, *((fillvalue,) * remain_length))
+    return (*t, *((None,) * remain_length))
 
 # for controlling freezing of CLIP
 
@@ -483,7 +482,7 @@ class DiffusionPrior(nn.Module):
         timesteps = 1000,
         cond_drop_prob = 0.2,
         loss_type = "l1",
-        predict_x_start = True,
+        predict_x0 = True,
         beta_schedule = "cosine",
     ):
         super().__init__()
@@ -497,7 +496,7 @@ class DiffusionPrior(nn.Module):
         self.image_size = clip.image_size
         self.cond_drop_prob = cond_drop_prob
 
-        self.predict_x_start = predict_x_start
+        self.predict_x0 = predict_x0
         # 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":
@@ -584,16 +583,14 @@ 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):
-        pred = self.net(x, t, **text_cond)
+        if self.predict_x0:
-
+            x_recon = 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 = pred)
+            x_recon = self.predict_start_from_noise(x, t = t, noise = self.net(x, t, **text_cond))
 
-        if clip_denoised and not self.predict_x_start:
+        if clip_denoised:
             x_recon.clamp_(-1., 1.)
 
         model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
@@ -639,7 +636,7 @@ class DiffusionPrior(nn.Module):
             **text_cond
         )
 
-        to_predict = noise if not self.predict_x_start else image_embed
+        to_predict = noise if not self.predict_x0 else image_embed
 
         if self.loss_type == 'l1':
             loss = F.l1_loss(to_predict, x_recon)
@@ -1121,8 +1118,6 @@ class Decoder(nn.Module):
         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
@@ -1140,15 +1135,12 @@ class Decoder(nn.Module):
         # 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))
+        vaes = pad_tuple_to_length(cast_tuple(vae), len(unets))
 
         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
 
@@ -1160,7 +1152,7 @@ class Decoder(nn.Module):
             )
 
             self.unets.append(one_unet)
-            self.vaes.append(one_vae.copy_for_eval())
+            self.vaes.append(one_vae.copy_for_eval() if exists(one_vae) else None)
 
         # unet image sizes
 
@@ -1171,10 +1163,6 @@ class Decoder(nn.Module):
         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))
@@ -1293,47 +1281,34 @@ 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, unet, x, t, image_embed, text_encodings = None, lowres_cond_img = None, clip_denoised = True, predict_x_start = False, cond_scale = 1.):
+    def p_mean_variance(self, unet, x, t, image_embed, text_encodings = None, lowres_cond_img = None, clip_denoised = True, 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)
+        pred_noise = 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)
+        x_recon = self.predict_start_from_noise(x, t = t, noise = pred_noise)
 
-        if predict_x_start:
+        if clip_denoised:
-            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, 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):
+    def p_sample(self, unet, x, t, image_embed, text_encodings = None, cond_scale = 1., lowres_cond_img = None, clip_denoised = True, repeat_noise = False):
         b, *_, device = *x.shape, x.device
-        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)
+        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)
         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, unet, shape, image_embed, predict_x_start = False, lowres_cond_img = None, text_encodings = None, cond_scale = 1):
+    def p_sample_loop(self, unet, shape, image_embed, lowres_cond_img = None, text_encodings = None, cond_scale = 1):
         device = self.betas.device
 
         b = shape[0]
         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(
+            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)
-                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
-            )
 
         return img
 
@@ -1345,7 +1320,7 @@ class Decoder(nn.Module):
             extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
         )
 
-    def p_losses(self, unet, x_start, t, *, image_embed, lowres_cond_img = None, text_encodings = None, predict_x_start = False, noise = None):
+    def p_losses(self, unet, x_start, t, *, image_embed, lowres_cond_img = None, text_encodings = None, noise = None):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
         x_noisy = self.q_sample(x_start = x_start, t = t, noise = noise)
@@ -1359,14 +1334,12 @@ class Decoder(nn.Module):
             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(target, x_recon)
+            loss = F.l1_loss(noise, x_recon)
         elif self.loss_type == 'l2':
-            loss = F.mse_loss(target, x_recon)
+            loss = F.mse_loss(noise, x_recon)
         elif self.loss_type == "huber":
-            loss = F.smooth_l1_loss(target, x_recon)
+            loss = F.smooth_l1_loss(noise, x_recon)
         else:
             raise NotImplementedError()
 
@@ -1381,7 +1354,7 @@ class Decoder(nn.Module):
 
         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)):
+        for unet, vae, channel, image_size in tqdm(zip(self.unets, self.vaes, self.sample_channels, self.image_sizes)):
             with self.one_unet_in_gpu(unet = unet):
                 lowres_cond_img = None
                 shape = (batch_size, channel, image_size, image_size)
@@ -1389,11 +1362,12 @@ class Decoder(nn.Module):
                 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)
+                if exists(vae):
-                shape = (batch_size, vae.encoded_dim, image_size, image_size)
+                    image_size //= (2 ** vae.layers)
+                    shape = (batch_size, vae.encoded_dim, image_size, image_size)
 
-                if exists(lowres_cond_img):
+                    if exists(lowres_cond_img):
-                    lowres_cond_img = vae.encode(lowres_cond_img)
+                        lowres_cond_img = vae.encode(lowres_cond_img)
 
                 img = self.p_sample_loop(
                     unet,
@@ -1401,11 +1375,11 @@ class Decoder(nn.Module):
                     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)
+                if exists(vae):
+                    img = vae.decode(img)
 
         return img
 
@@ -1425,7 +1399,6 @@ class Decoder(nn.Module):
 
         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
 
@@ -1442,14 +1415,15 @@ class Decoder(nn.Module):
         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()
+        if exists(vae):
-        with torch.no_grad():
+            vae.eval()
-            image = vae.encode(image)
+            with torch.no_grad():
+                image = vae.encode(image)
 
-            if exists(lowres_cond_img):
+                if exists(lowres_cond_img):
-                lowres_cond_img = vae.encode(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)
+        return self.p_losses(unet, image, times, image_embed = image_embed, text_encodings = text_encodings, lowres_cond_img = lowres_cond_img)
 
 # main class
 
@@ -1476,7 +1450,6 @@ 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
@@ -1484,8 +1457,4 @@ 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

@@ -243,112 +243,6 @@ class ResBlock(nn.Module):
     def forward(self, x):
         return self.net(x) + x
 
-# 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
-
-# vqgan attention layer
 class VQGanAttention(nn.Module):
     def __init__(
         self,
@@ -393,28 +287,6 @@ class VQGanAttention(nn.Module):
 
         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,
@@ -481,7 +353,7 @@ class VQGanVAE(nn.Module):
 
         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()))
+            prepend(self.decoders, nn.Sequential(nn.Upsample(scale_factor = 2, mode = 'bilinear', align_corners = False), 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))
@@ -535,9 +407,6 @@ class VQGanVAE(nn.Module):
         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())
@@ -583,8 +452,7 @@ class VQGanVAE(nn.Module):
         img,
         return_loss = False,
         return_discr_loss = False,
-        return_recons = 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}'
@@ -609,11 +477,11 @@ class VQGanVAE(nn.Module):
 
             fmap_discr_logits, img_discr_logits = map(self.discr, (fmap, img))
 
+            gp = gradient_penalty(img, img_discr_logits)
+
             discr_loss = self.discr_loss(fmap_discr_logits, img_discr_logits)
 
-            if add_gradient_penalty:
+            loss = discr_loss + gp
-                gp = gradient_penalty(img, img_discr_logits)
-                loss = discr_loss + gp
 
             if return_recons:
                 return loss, fmap

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.44',
+  version = '0.0.37',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',