force einops 0.6.1 or greater and call allow_ops_in_compiled_graph

fix the previous commit which assumes open_clip is installed

README.md

@@ -634,10 +634,12 @@ Alternatively, you can also use <a href="https://github.com/mlfoundations/open_c
 $ pip install open-clip-torch
 ```
 
+Ex. using the <a href="https://laion.ai/blog/large-openclip/">SOTA Open Clip</a> model trained by <a href="https://github.com/rom1504">Romain</a>
+
 ```python
 from dalle2_pytorch import OpenClipAdapter
 
-clip = OpenClipAdapter()
+clip = OpenClipAdapter('ViT-H/14')
 ```
 
 Now you'll just have to worry about training the Prior and the Decoder!
@@ -1066,7 +1068,7 @@ dataloader = create_image_embedding_dataloader(
 )
 for img, emb in dataloader:
     print(img.shape)  # torch.Size([32, 3, 256, 256])
-    print(emb.shape)  # torch.Size([32, 512])
+    print(emb["img"].shape)  # torch.Size([32, 512])
     # Train decoder only as shown above
 
 # Or create a dataset without a loader so you can configure it manually
@@ -1296,4 +1298,14 @@ For detailed information on training the diffusion prior, please refer to the [d
 }
 ```
 
+```bibtex
+@article{Salimans2022ProgressiveDF,
+    title   = {Progressive Distillation for Fast Sampling of Diffusion Models},
+    author  = {Tim Salimans and Jonathan Ho},
+    journal = {ArXiv},
+    year    = {2022},
+    volume  = {abs/2202.00512}
+}
+```
+
 *Creating noise from data is easy; creating data from noise is generative modeling.* - <a href="https://arxiv.org/abs/2011.13456">Yang Song's paper</a>

dalle2_pytorch/__init__.py

@@ -1,6 +1,13 @@
+import torch
+from packaging import version
+
+if version.parse(torch.__version__) >= version.parse('2.0.0'):
+    from einops._torch_specific import allow_ops_in_compiled_graph
+    allow_ops_in_compiled_graph()
+
 from dalle2_pytorch.version import __version__
 from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder
-from dalle2_pytorch.dalle2_pytorch import OpenAIClipAdapter
+from dalle2_pytorch.dalle2_pytorch import OpenAIClipAdapter, OpenClipAdapter
 from dalle2_pytorch.trainer import DecoderTrainer, DiffusionPriorTrainer
 
 from dalle2_pytorch.vqgan_vae import VQGanVAE

dalle2_pytorch/dalle2_pytorch.py

@@ -12,10 +12,8 @@ from torch.utils.checkpoint import checkpoint
 from torch import nn, einsum
 import torchvision.transforms as T
 
-from einops import rearrange, repeat, reduce
+from einops import rearrange, repeat, reduce, pack, unpack
 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 import gaussian_blur2d
 import kornia.augmentation as K
@@ -100,6 +98,9 @@ def eval_decorator(fn):
         return out
     return inner
 
+def is_float_dtype(dtype):
+    return any([dtype == float_dtype for float_dtype in (torch.float64, torch.float32, torch.float16, torch.bfloat16)])
+
 def is_list_str(x):
     if not isinstance(x, (list, tuple)):
         return False
@@ -314,7 +315,10 @@ class OpenAIClipAdapter(BaseClipAdapter):
         self.eos_id = 49407 # for handling 0 being also '!'
 
         text_attention_final = self.find_layer('ln_final')
+
+        self.dim_latent_ = text_attention_final.weight.shape[0]
         self.handle = text_attention_final.register_forward_hook(self._hook)
+
         self.clip_normalize = preprocess.transforms[-1]
         self.cleared = False
 
@@ -333,7 +337,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
 
     @property
     def dim_latent(self):
-        return 512
+        return self.dim_latent_
 
     @property
     def image_size(self):
@@ -354,6 +358,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
         is_eos_id = (text == self.eos_id)
         text_mask_excluding_eos = is_eos_id.cumsum(dim = -1) == 0
         text_mask = F.pad(text_mask_excluding_eos, (1, -1), value = True)
+        text_mask = text_mask & (text != 0)
         assert not self.cleared
 
         text_embed = self.clip.encode_text(text)
@@ -383,6 +388,8 @@ class OpenClipAdapter(BaseClipAdapter):
         self.eos_id = 49407
 
         text_attention_final = self.find_layer('ln_final')
+        self._dim_latent = text_attention_final.weight.shape[0]
+
         self.handle = text_attention_final.register_forward_hook(self._hook)
         self.clip_normalize = preprocess.transforms[-1]
         self.cleared = False
@@ -402,11 +409,14 @@ class OpenClipAdapter(BaseClipAdapter):
 
     @property
     def dim_latent(self):
-        return 512
+        return self._dim_latent
 
     @property
     def image_size(self):
-        return self.clip.visual.image_size
+        image_size = self.clip.visual.image_size
+        if isinstance(image_size, tuple):
+            return max(image_size)
+        return image_size
 
     @property
     def image_channels(self):
@@ -423,6 +433,7 @@ class OpenClipAdapter(BaseClipAdapter):
         is_eos_id = (text == self.eos_id)
         text_mask_excluding_eos = is_eos_id.cumsum(dim = -1) == 0
         text_mask = F.pad(text_mask_excluding_eos, (1, -1), value = True)
+        text_mask = text_mask & (text != 0)
         assert not self.cleared
 
         text_embed = self.clip.encode_text(text)
@@ -608,7 +619,7 @@ class NoiseScheduler(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 q_sample(self, x_start, t, noise=None):
+    def q_sample(self, x_start, t, noise = None):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
         return (
@@ -616,6 +627,12 @@ class NoiseScheduler(nn.Module):
             extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
         )
 
+    def calculate_v(self, x_start, t, noise = None):
+        return (
+            extract(self.sqrt_alphas_cumprod, t, x_start.shape) * noise -
+            extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * x_start
+        )
+
     def q_sample_from_to(self, x_from, from_t, to_t, noise = None):
         shape = x_from.shape
         noise = default(noise, lambda: torch.randn_like(x_from))
@@ -627,6 +644,12 @@ class NoiseScheduler(nn.Module):
 
         return x_from * (alpha_next / alpha) + noise * (sigma_next * alpha - sigma * alpha_next) / alpha
 
+    def predict_start_from_v(self, x_t, t, v):
+        return (
+            extract(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
+            extract(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
+        )
+
     def predict_start_from_noise(self, x_t, t, noise):
         return (
             extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
@@ -644,6 +667,23 @@ class NoiseScheduler(nn.Module):
             return loss
         return loss * extract(self.p2_loss_weight, times, loss.shape)
 
+# rearrange image to sequence
+
+class RearrangeToSequence(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, x):
+        x = rearrange(x, 'b c ... -> b ... c')
+        x, ps = pack([x], 'b * c')
+
+        x = self.fn(x)
+
+        x, = unpack(x, ps, 'b * c')
+        x = rearrange(x, 'b ... c -> b c ...')
+        return x
+
 # diffusion prior
 
 class LayerNorm(nn.Module):
@@ -842,7 +882,7 @@ class Attention(nn.Module):
 
         # add null key / value for classifier free guidance in prior net
 
-        nk, nv = repeat_many(self.null_kv.unbind(dim = -2), 'd -> b 1 d', b = b)
+        nk, nv = map(lambda t: repeat(t, 'd -> b 1 d', b = b), self.null_kv.unbind(dim = -2))
         k = torch.cat((nk, k), dim = -2)
         v = torch.cat((nv, v), dim = -2)
 
@@ -962,6 +1002,8 @@ class DiffusionPriorNetwork(nn.Module):
             Rearrange('b (n d) -> b n d', n = num_text_embeds)
         )
 
+        self.continuous_embedded_time = not exists(num_timesteps)
+
         self.to_time_embeds = nn.Sequential(
             nn.Embedding(num_timesteps, dim * num_time_embeds) if exists(num_timesteps) else nn.Sequential(SinusoidalPosEmb(dim), MLP(dim, dim * num_time_embeds)), # also offer a continuous version of timestep embeddings, with a 2 layer MLP
             Rearrange('b (n d) -> b n d', n = num_time_embeds)
@@ -1089,12 +1131,15 @@ class DiffusionPriorNetwork(nn.Module):
         # whether text embedding is used for conditioning depends on whether text encodings are available for attention (for classifier free guidance, even though it seems from the paper it was not used in the prior ddpm, as the objective is different)
         # but let's just do it right
 
+        if self.continuous_embedded_time:
+            diffusion_timesteps = diffusion_timesteps.type(dtype)
+
         time_embed = self.to_time_embeds(diffusion_timesteps)
 
         learned_queries = repeat(self.learned_query, 'd -> b 1 d', b = batch)
 
         if self.self_cond:
-            learned_queries = torch.cat((image_embed, self_cond), dim = -2)
+            learned_queries = torch.cat((self_cond, learned_queries), dim = -2)
 
         tokens = torch.cat((
             text_encodings,
@@ -1130,6 +1175,7 @@ class DiffusionPrior(nn.Module):
         image_cond_drop_prob = None,
         loss_type = "l2",
         predict_x_start = True,
+        predict_v = False,
         beta_schedule = "cosine",
         condition_on_text_encodings = True,  # the paper suggests this is needed, but you can turn it off for your CLIP preprocessed text embed -> image embed training
         sampling_clamp_l2norm = False,       # whether to l2norm clamp the image embed at each denoising iteration (analogous to -1 to 1 clipping for usual DDPMs)
@@ -1181,6 +1227,7 @@ class DiffusionPrior(nn.Module):
         # in paper, they do not predict the noise, but predict x0 directly for image embedding, claiming empirically better results. I'll just offer both.
 
         self.predict_x_start = predict_x_start
+        self.predict_v = predict_v # takes precedence over predict_x_start
 
         # @crowsonkb 's suggestion - https://github.com/lucidrains/DALLE2-pytorch/issues/60#issue-1226116132
 
@@ -1210,7 +1257,9 @@ class DiffusionPrior(nn.Module):
 
         pred = self.net.forward_with_cond_scale(x, t, cond_scale = cond_scale, self_cond = self_cond, **text_cond)
 
-        if self.predict_x_start:
+        if self.predict_v:
+            x_start = self.noise_scheduler.predict_start_from_v(x, t = t, v = pred)
+        elif self.predict_x_start:
             x_start = pred
         else:
             x_start = self.noise_scheduler.predict_start_from_noise(x, t = t, noise = pred)
@@ -1281,12 +1330,16 @@ class DiffusionPrior(nn.Module):
 
             pred = self.net.forward_with_cond_scale(image_embed, time_cond, self_cond = self_cond, cond_scale = cond_scale, **text_cond)
 
-            if self.predict_x_start:
+            # derive x0
+
+            if self.predict_v:
+                x_start = self.noise_scheduler.predict_start_from_v(image_embed, t = time_cond, v = pred)
+            elif self.predict_x_start:
                 x_start = pred
-                pred_noise = self.noise_scheduler.predict_noise_from_start(image_embed, t = time_cond, x0 = pred)
             else:
                 x_start = self.noise_scheduler.predict_start_from_noise(image_embed, t = time_cond, noise = pred)
-                pred_noise = pred
+
+            # clip x0 before maybe predicting noise
 
             if not self.predict_x_start:
                 x_start.clamp_(-1., 1.)
@@ -1294,6 +1347,10 @@ class DiffusionPrior(nn.Module):
             if self.predict_x_start and self.sampling_clamp_l2norm:
                 x_start = self.l2norm_clamp_embed(x_start)
 
+            # predict noise
+
+            pred_noise = self.noise_scheduler.predict_noise_from_start(image_embed, t = time_cond, x0 = x_start)
+
             if time_next < 0:
                 image_embed = x_start
                 continue
@@ -1347,7 +1404,12 @@ class DiffusionPrior(nn.Module):
         if self.predict_x_start and self.training_clamp_l2norm:
             pred = self.l2norm_clamp_embed(pred)
 
-        target = noise if not self.predict_x_start else image_embed
+        if self.predict_v:
+            target = self.noise_scheduler.calculate_v(image_embed, times, noise)
+        elif self.predict_x_start:
+            target = image_embed
+        else:
+            target = noise
 
         loss = self.noise_scheduler.loss_fn(pred, target)
         return loss
@@ -1417,7 +1479,7 @@ class DiffusionPrior(nn.Module):
         **kwargs
     ):
         assert exists(text) ^ exists(text_embed), 'either text or text embedding must be supplied'
-        assert exists(image) ^ exists(image_embed), 'either text or text embedding must be supplied'
+        assert exists(image) ^ exists(image_embed), 'either image or image embedding must be supplied'
         assert not (self.condition_on_text_encodings and (not exists(text_encodings) and not exists(text))), 'text encodings must be present if you specified you wish to condition on it on initialization'
 
         if exists(image):
@@ -1523,6 +1585,8 @@ class SinusoidalPosEmb(nn.Module):
 
     def forward(self, x):
         dtype, device = x.dtype, x.device
+        assert is_float_dtype(dtype), 'input to sinusoidal pos emb must be a float type'
+
         half_dim = self.dim // 2
         emb = math.log(10000) / (half_dim - 1)
         emb = torch.exp(torch.arange(half_dim, device = device, dtype = dtype) * -emb)
@@ -1580,14 +1644,10 @@ class ResnetBlock(nn.Module):
         self.cross_attn = None
 
         if exists(cond_dim):
-            self.cross_attn = EinopsToAndFrom(
-                'b c h w',
-                'b (h w) c',
-                CrossAttention(
-                    dim = dim_out,
-                    context_dim = cond_dim,
-                    cosine_sim = cosine_sim_cross_attn
-                )
+            self.cross_attn = CrossAttention(
+                dim = dim_out,
+                context_dim = cond_dim,
+                cosine_sim = cosine_sim_cross_attn
             )
 
         self.block1 = Block(dim, dim_out, groups = groups, weight_standardization = weight_standardization)
@@ -1606,8 +1666,15 @@ class ResnetBlock(nn.Module):
 
         if exists(self.cross_attn):
             assert exists(cond)
+
+            h = rearrange(h, 'b c ... -> b ... c')
+            h, ps = pack([h], 'b * c')
+
             h = self.cross_attn(h, context = cond) + h
 
+            h, = unpack(h, ps, 'b * c')
+            h = rearrange(h, 'b ... c -> b c ...')
+
         h = self.block2(h)
         return h + self.res_conv(x)
 
@@ -1653,11 +1720,11 @@ class CrossAttention(nn.Module):
 
         q, k, v = (self.to_q(x), *self.to_kv(context).chunk(2, dim = -1))
 
-        q, k, v = rearrange_many((q, k, v), 'b n (h d) -> b h n d', h = self.heads)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), (q, k, v))
 
         # add null key / value for classifier free guidance in prior net
 
-        nk, nv = repeat_many(self.null_kv.unbind(dim = -2), 'd -> b h 1 d', h = self.heads,  b = b)
+        nk, nv = map(lambda t: repeat(t, 'd -> b h 1 d', h = self.heads,  b = b), self.null_kv.unbind(dim = -2))
 
         k = torch.cat((nk, k), dim = -2)
         v = torch.cat((nv, v), dim = -2)
@@ -1710,7 +1777,7 @@ class LinearAttention(nn.Module):
 
         fmap = self.norm(fmap)
         q, k, v = self.to_qkv(fmap).chunk(3, dim = 1)
-        q, k, v = rearrange_many((q, k, v), 'b (h c) x y -> (b h) (x y) c', h = h)
+        q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> (b h) (x y) c', h = h), (q, k, v))
 
         q = q.softmax(dim = -1)
         k = k.softmax(dim = -2)
@@ -1944,7 +2011,7 @@ class Unet(nn.Module):
 
         self_attn = cast_tuple(self_attn, num_stages)
 
-        create_self_attn = lambda dim: EinopsToAndFrom('b c h w', 'b (h w) c', Residual(Attention(dim, **attn_kwargs)))
+        create_self_attn = lambda dim: RearrangeToSequence(Residual(Attention(dim, **attn_kwargs)))
 
         # resnet block klass
 
@@ -2421,6 +2488,7 @@ class Decoder(nn.Module):
         loss_type = 'l2',
         beta_schedule = None,
         predict_x_start = False,
+        predict_v = False,
         predict_x_start_for_latent_diffusion = False,
         image_sizes = None,                         # for cascading ddpm, image size at each stage
         random_crop_sizes = None,                   # whether to random crop the image at that stage in the cascade (super resoluting convolutions at the end may be able to generalize on smaller crops)
@@ -2443,7 +2511,7 @@ class Decoder(nn.Module):
         dynamic_thres_percentile = 0.95,
         p2_loss_weight_gamma = 0.,                  # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time - 1. is recommended
         p2_loss_weight_k = 1,
-        ddim_sampling_eta = 1.                      # can be set to 0. for deterministic sampling afaict
+        ddim_sampling_eta = 0.                      # can be set to 0. for deterministic sampling afaict
     ):
         super().__init__()
 
@@ -2593,6 +2661,10 @@ class Decoder(nn.Module):
 
         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))
 
+        # predict v
+
+        self.predict_v = cast_tuple(predict_v, len(unets))
+
         # input image range
 
         self.input_image_range = (-1. if not auto_normalize_img else 0., 1.)
@@ -2673,11 +2745,16 @@ class Decoder(nn.Module):
         if exists(unet_number):
             unet = self.get_unet(unet_number)
 
+        # devices
+
+        cuda, cpu = torch.device('cuda'), torch.device('cpu')
+
         self.cuda()
 
         devices = [module_device(unet) for unet in self.unets]
-        self.unets.cpu()
-        unet.cuda()
+
+        self.unets.to(cpu)
+        unet.to(cuda)
 
         yield
 
@@ -2704,14 +2781,16 @@ class Decoder(nn.Module):
         x = x.clamp(-s, s) / s
         return x
 
-    def p_mean_variance(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, lowres_cond_img = None, self_cond = None, clip_denoised = True, predict_x_start = False, learned_variance = False, cond_scale = 1., model_output = None, lowres_noise_level = None):
+    def p_mean_variance(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, lowres_cond_img = None, self_cond = None, clip_denoised = True, predict_x_start = False, predict_v = False, learned_variance = False, cond_scale = 1., model_output = None, lowres_noise_level = None):
         assert not (cond_scale != 1. and not self.can_classifier_guidance), 'the decoder was not trained with conditional dropout, and thus one cannot use classifier free guidance (cond_scale anything other than 1)'
 
         model_output = default(model_output, lambda: 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, self_cond = self_cond, lowres_noise_level = lowres_noise_level))
 
         pred, var_interp_frac_unnormalized = self.parse_unet_output(learned_variance, model_output)
 
-        if predict_x_start:
+        if predict_v:
+            x_start = noise_scheduler.predict_start_from_v(x, t = t, v = pred)
+        elif predict_x_start:
             x_start = pred
         else:
             x_start = noise_scheduler.predict_start_from_noise(x, t = t, noise = pred)
@@ -2738,9 +2817,9 @@ class Decoder(nn.Module):
         return model_mean, posterior_variance, posterior_log_variance, x_start
 
     @torch.no_grad()
-    def p_sample(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, cond_scale = 1., lowres_cond_img = None, self_cond = None, predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_noise_level = None):
+    def p_sample(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, cond_scale = 1., lowres_cond_img = None, self_cond = None, predict_x_start = False, predict_v = False, learned_variance = False, clip_denoised = True, lowres_noise_level = None):
         b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance, x_start = 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, self_cond = self_cond, clip_denoised = clip_denoised, predict_x_start = predict_x_start, noise_scheduler = noise_scheduler, learned_variance = learned_variance, lowres_noise_level = lowres_noise_level)
+        model_mean, _, model_log_variance, x_start = 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, self_cond = self_cond, clip_denoised = clip_denoised, predict_x_start = predict_x_start, predict_v = predict_v, noise_scheduler = noise_scheduler, learned_variance = learned_variance, lowres_noise_level = lowres_noise_level)
         noise = torch.randn_like(x)
         # no noise when t == 0
         nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
@@ -2755,6 +2834,7 @@ class Decoder(nn.Module):
         image_embed,
         noise_scheduler,
         predict_x_start = False,
+        predict_v = False,
         learned_variance = False,
         clip_denoised = True,
         lowres_cond_img = None,
@@ -2813,6 +2893,7 @@ class Decoder(nn.Module):
                     lowres_cond_img = lowres_cond_img,
                     lowres_noise_level = lowres_noise_level,
                     predict_x_start = predict_x_start,
+                    predict_v = predict_v,
                     noise_scheduler = noise_scheduler,
                     learned_variance = learned_variance,
                     clip_denoised = clip_denoised
@@ -2838,6 +2919,7 @@ class Decoder(nn.Module):
         timesteps,
         eta = 1.,
         predict_x_start = False,
+        predict_v = False,
         learned_variance = False,
         clip_denoised = True,
         lowres_cond_img = None,
@@ -2897,16 +2979,24 @@ class Decoder(nn.Module):
 
                 pred, _ = self.parse_unet_output(learned_variance, unet_output)
 
-                if predict_x_start:
+                # predict x0
+
+                if predict_v:
+                    x_start = noise_scheduler.predict_start_from_v(img, t = time_cond, v = pred)
+                elif predict_x_start:
                     x_start = pred
-                    pred_noise = noise_scheduler.predict_noise_from_start(img, t = time_cond, x0 = pred)
                 else:
                     x_start = noise_scheduler.predict_start_from_noise(img, t = time_cond, noise = pred)
-                    pred_noise = pred
+
+                # maybe clip x0
 
                 if clip_denoised:
                     x_start = self.dynamic_threshold(x_start)
 
+                # predict noise
+
+                pred_noise = noise_scheduler.predict_noise_from_start(img, t = time_cond, x0 = x_start)
+
                 c1 = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
                 c2 = ((1 - alpha_next) - torch.square(c1)).sqrt()
                 noise = torch.randn_like(img) if not is_last_timestep else 0.
@@ -2939,7 +3029,7 @@ class Decoder(nn.Module):
 
         return self.p_sample_loop_ddim(*args, noise_scheduler = noise_scheduler, timesteps = timesteps, **kwargs)
 
-    def p_losses(self, unet, x_start, times, *, image_embed, noise_scheduler, lowres_cond_img = None, text_encodings = None, predict_x_start = False, noise = None, learned_variance = False, clip_denoised = False, is_latent_diffusion = False, lowres_noise_level = None):
+    def p_losses(self, unet, x_start, times, *, image_embed, noise_scheduler, lowres_cond_img = None, text_encodings = None, predict_x_start = False, predict_v = False, noise = None, learned_variance = False, clip_denoised = False, is_latent_diffusion = False, lowres_noise_level = None):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
         # normalize to [-1, 1]
@@ -2984,7 +3074,12 @@ class Decoder(nn.Module):
 
         pred, _ = self.parse_unet_output(learned_variance, unet_output)
 
-        target = noise if not predict_x_start else x_start
+        if predict_v:
+            target = noise_scheduler.calculate_v(x_start, times, noise)
+        elif predict_x_start:
+            target = x_start
+        else:
+            target = noise
 
         loss = noise_scheduler.loss_fn(pred, target, reduction = 'none')
         loss = reduce(loss, 'b ... -> b (...)', 'mean')
@@ -3042,7 +3137,8 @@ class Decoder(nn.Module):
         distributed = False,
         inpaint_image = None,
         inpaint_mask = None,
-        inpaint_resample_times = 5
+        inpaint_resample_times = 5,
+        one_unet_in_gpu_at_time = True
     ):
         assert self.unconditional or exists(image_embed), 'image embed must be present on sampling from decoder unless if trained unconditionally'
 
@@ -3065,16 +3161,17 @@ class Decoder(nn.Module):
             assert image.shape[0] == batch_size, 'image must have batch size of {} if starting at unet number > 1'.format(batch_size)
             prev_unet_output_size = self.image_sizes[start_at_unet_number - 2]
             img = resize_image_to(image, prev_unet_output_size, nearest = True)
+
         is_cuda = next(self.parameters()).is_cuda
 
         num_unets = self.num_unets
         cond_scale = cast_tuple(cond_scale, num_unets)
 
-        for unet_number, unet, vae, channel, image_size, predict_x_start, learned_variance, noise_scheduler, lowres_cond, sample_timesteps, unet_cond_scale in tqdm(zip(range(1, num_unets + 1), self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start, self.learned_variance, self.noise_schedulers, self.lowres_conds, self.sample_timesteps, cond_scale)):
+        for unet_number, unet, vae, channel, image_size, predict_x_start, predict_v, learned_variance, noise_scheduler, lowres_cond, sample_timesteps, unet_cond_scale in tqdm(zip(range(1, num_unets + 1), self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start, self.predict_v, self.learned_variance, self.noise_schedulers, self.lowres_conds, self.sample_timesteps, cond_scale)):
             if unet_number < start_at_unet_number:
                 continue  # It's the easiest way to do it
 
-            context = self.one_unet_in_gpu(unet = unet) if is_cuda else null_context()
+            context = self.one_unet_in_gpu(unet = unet) if is_cuda and one_unet_in_gpu_at_time else null_context()
 
             with context:
                 # prepare low resolution conditioning for upsamplers
@@ -3106,6 +3203,7 @@ class Decoder(nn.Module):
                     text_encodings = text_encodings,
                     cond_scale = unet_cond_scale,
                     predict_x_start = predict_x_start,
+                    predict_v = predict_v,
                     learned_variance = learned_variance,
                     clip_denoised = not is_latent_diffusion,
                     lowres_cond_img = lowres_cond_img,
@@ -3145,11 +3243,12 @@ class Decoder(nn.Module):
         lowres_conditioner  = self.lowres_conds[unet_index]
         target_image_size   = self.image_sizes[unet_index]
         predict_x_start     = self.predict_x_start[unet_index]
+        predict_v           = self.predict_v[unet_index]
         random_crop_size    = self.random_crop_sizes[unet_index]
         learned_variance    = self.learned_variance[unet_index]
         b, c, h, w, device, = *image.shape, image.device
 
-        check_shape(image, 'b c h w', c = self.channels)
+        assert image.shape[1] == self.channels
         assert h >= target_image_size and w >= target_image_size
 
         times = torch.randint(0, noise_scheduler.num_timesteps, (b,), device = device, dtype = torch.long)
@@ -3183,7 +3282,7 @@ class Decoder(nn.Module):
             image = vae.encode(image)
             lowres_cond_img = maybe(vae.encode)(lowres_cond_img)
 
-        losses = 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, learned_variance = learned_variance, is_latent_diffusion = is_latent_diffusion, noise_scheduler = noise_scheduler, lowres_noise_level = lowres_noise_level)
+        losses = 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, predict_v = predict_v, learned_variance = learned_variance, is_latent_diffusion = is_latent_diffusion, noise_scheduler = noise_scheduler, lowres_noise_level = lowres_noise_level)
 
         if not return_lowres_cond_image:
             return losses

dalle2_pytorch/train_configs.py

@@ -4,11 +4,13 @@ from pydantic import BaseModel, validator, root_validator
 from typing import List, Optional, Union, Tuple, Dict, Any, TypeVar
 
 from x_clip import CLIP as XCLIP
+from open_clip import list_pretrained
 from coca_pytorch import CoCa
 
 from dalle2_pytorch.dalle2_pytorch import (
     CoCaAdapter,
     OpenAIClipAdapter,
+    OpenClipAdapter,
     Unet,
     Decoder,
     DiffusionPrior,
@@ -117,6 +119,10 @@ class AdapterConfig(BaseModel):
     def create(self):
         if self.make == "openai":
             return OpenAIClipAdapter(self.model)
+        elif self.make == "open_clip":
+            pretrained = dict(list_pretrained())
+            checkpoint = pretrained[self.model]
+            return OpenClipAdapter(name=self.model, pretrained=checkpoint)
         elif self.make == "x-clip":
             return XClipAdapter(XCLIP(**self.base_model_kwargs))
         elif self.make == "coca":
@@ -307,6 +313,7 @@ class DecoderTrainConfig(BaseModel):
     wd: SingularOrIterable[float] = 0.01
     warmup_steps: Optional[SingularOrIterable[int]] = None
     find_unused_parameters: bool = True
+    static_graph: bool = True
     max_grad_norm: SingularOrIterable[float] = 0.5
     save_every_n_samples: int = 100000
     n_sample_images: int = 6                       # The number of example images to produce when sampling the train and test dataset

dalle2_pytorch/trainer.py

@@ -236,7 +236,7 @@ class DiffusionPriorTrainer(nn.Module):
         )
 
         if exists(cosine_decay_max_steps):
-            self.scheduler = CosineAnnealingLR(optimizer, T_max = cosine_decay_max_steps)
+            self.scheduler = CosineAnnealingLR(self.optimizer, T_max = cosine_decay_max_steps)
         else:
             self.scheduler = LambdaLR(self.optimizer, lr_lambda = lambda _: 1.0)
         

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.10.4'
+__version__ = '1.14.2'

dalle2_pytorch/vqgan_vae.py

@@ -11,8 +11,7 @@ import torch.nn.functional as F
 from torch.autograd import grad as torch_grad
 import torchvision
 
-from einops import rearrange, reduce, repeat
-from einops_exts import rearrange_many
+from einops import rearrange, reduce, repeat, pack, unpack
 from einops.layers.torch import Rearrange
 
 # constants
@@ -408,7 +407,7 @@ class Attention(nn.Module):
         x = self.norm(x)
 
         q, k, v = self.to_qkv(x).chunk(3, dim = -1)
-        q, k, v = rearrange_many((q, k, v), 'b n (h d) -> b h n d', h = h)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
 
         q = q * self.scale
         sim = einsum('b h i d, b h j d -> b h i j', q, k)

setup.py

@@ -26,11 +26,11 @@ setup(
   install_requires=[
     'accelerate',
     'click',
-    'clip-anytorch>=2.4.0',
+    'open-clip-torch>=2.0.0,<3.0.0',
+    'clip-anytorch>=2.5.2',
     'coca-pytorch>=0.0.5',
     'ema-pytorch>=0.0.7',
-    'einops>=0.4',
-    'einops-exts>=0.0.3',
+    'einops>=0.6.1',
     'embedding-reader',
     'kornia>=0.5.4',
     'numpy',

train_decoder.py

@@ -156,7 +156,7 @@ def generate_samples(trainer, example_data, clip=None, start_unet=1, end_unet=No
         if text_embeddings[0] is None:
             # Generate text embeddings from text
             assert clip is not None, "clip is None, but text_embeddings is None"
-            tokenized_texts = tokenize(txts, truncate=True)
+            tokenized_texts = tokenize(txts, truncate=True).to(device=device)
             text_embed, text_encodings = clip.embed_text(tokenized_texts)
             sample_params["text_encodings"] = text_encodings
         else:
@@ -229,8 +229,8 @@ def evaluate_trainer(trainer, dataloader, device, start_unet, end_unet, clip=Non
         metrics["KID_std"] = kid_std.item()
     if exists(LPIPS):
         # Convert from [0, 1] to [-1, 1]
-        renorm_real_images = real_images.mul(2).sub(1)
-        renorm_generated_images = generated_images.mul(2).sub(1)
+        renorm_real_images = real_images.mul(2).sub(1).clamp(-1,1)
+        renorm_generated_images = generated_images.mul(2).sub(1).clamp(-1,1)
         lpips = LearnedPerceptualImagePatchSimilarity(**LPIPS, dist_sync_fn=null_sync)
         lpips.to(device=device)
         lpips.update(renorm_real_images, renorm_generated_images)
@@ -480,7 +480,7 @@ def train(
                         else:
                             # Then we need to pass the text instead
                             assert clip is not None
-                            tokenized_texts = tokenize(txt, truncate=True)
+                            tokenized_texts = tokenize(txt, truncate=True).to(device=inference_device)
                             assert tokenized_texts.shape[0] == len(img), f"The number of texts ({tokenized_texts.shape[0]}) should be the same as the number of images ({len(img)})"
                             text_embed, text_encodings = clip.embed_text(tokenized_texts)
                             forward_params['text_encodings'] = text_encodings
@@ -556,7 +556,7 @@ def initialize_training(config: TrainDecoderConfig, config_path):
     torch.manual_seed(config.seed)
 
     # Set up accelerator for configurable distributed training
-    ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=config.train.find_unused_parameters)
+    ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=config.train.find_unused_parameters, static_graph=config.train.static_graph)
     init_kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=60*60))
     accelerator = Accelerator(kwargs_handlers=[ddp_kwargs, init_kwargs])