fix repaint

README.md

@@ -371,7 +371,6 @@ loss.backward()
 unet1 = Unet(
     dim = 128,
     image_embed_dim = 512,
-    text_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
     dim_mults=(1, 2, 4, 8),
@@ -396,7 +395,7 @@ decoder = Decoder(
 ).cuda()
 
 for unet_number in (1, 2):
-    loss = decoder(images, text = text, 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 = 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
@@ -627,18 +626,6 @@ images = dalle2(
 # save your image (in this example, of size 256x256)
 ```
 
-Alternatively, you can also use <a href="https://github.com/mlfoundations/open_clip">Open Clip</a>
-
-```bash
-$ pip install open-clip-torch
-```
-
-```python
-from dalle2_pytorch import OpenClipAdapter
-
-clip = OpenClipAdapter()
-```
-
 Now you'll just have to worry about training the Prior and the Decoder!
 
 ## Inpainting
@@ -873,23 +860,25 @@ unet1 = Unet(
     text_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
-    dim_mults=(1, 2, 4, 8),
-    cond_on_text_encodings = True,
+    dim_mults=(1, 2, 4, 8)
 ).cuda()
 
 unet2 = Unet(
     dim = 16,
     image_embed_dim = 512,
+    text_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
     dim_mults = (1, 2, 4, 8, 16),
+    cond_on_text_encodings = True
 ).cuda()
 
 decoder = Decoder(
     unet = (unet1, unet2),
     image_sizes = (128, 256),
     clip = clip,
-    timesteps = 1000
+    timesteps = 1000,
+    condition_on_text_encodings = True
 ).cuda()
 
 decoder_trainer = DecoderTrainer(
@@ -914,8 +903,8 @@ for unet_number in (1, 2):
 # after much training
 # you can sample from the exponentially moving averaged unets as so
 
-mock_image_embed = torch.randn(32, 512).cuda()
-images = decoder_trainer.sample(image_embed = mock_image_embed, text = text) # (4, 3, 256, 256)
+mock_image_embed = torch.randn(4, 512).cuda()
+images = decoder_trainer.sample(mock_image_embed, text = text) # (4, 3, 256, 256)
 ```
 
 ### Diffusion Prior Training
@@ -1123,8 +1112,7 @@ For detailed information on training the diffusion prior, please refer to the [d
 - [x] allow for unet to be able to condition non-cross attention style as well
 - [x] speed up inference, read up on papers (ddim)
 - [x] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
-- [x] add the final combination of upsample feature maps, used in unet squared, seems to have an effect in local experiments
-- [ ] consider elucidated dalle2 https://arxiv.org/abs/2206.00364
+- [ ] try out the nested unet from https://arxiv.org/abs/2005.09007 after hearing several positive testimonies from researchers, for segmentation anyhow
 - [ ] interface out the vqgan-vae so a pretrained one can be pulled off the shelf to validate latent diffusion + DALL-E2
 
 ## Citations

dalle2_pytorch/dalle2_pytorch.py

@@ -8,7 +8,6 @@ from pathlib import Path
 
 import torch
 import torch.nn.functional as F
-from torch.utils.checkpoint import checkpoint
 from torch import nn, einsum
 import torchvision.transforms as T
 
@@ -109,28 +108,6 @@ def pad_tuple_to_length(t, length, fillvalue = None):
         return t
     return (*t, *((fillvalue,) * remain_length))
 
-# checkpointing helper function
-
-def make_checkpointable(fn, **kwargs):
-    if isinstance(fn, nn.ModuleList):
-        return [maybe(make_checkpointable)(el, **kwargs) for el in fn]
-
-    condition = kwargs.pop('condition', None)
-
-    if exists(condition) and not condition(fn):
-        return fn
-
-    @wraps(fn)
-    def inner(*args):
-        input_needs_grad = any([isinstance(el, torch.Tensor) and el.requires_grad for el in args])
-
-        if not input_needs_grad:
-            return fn(*args)
-
-        return checkpoint(fn, *args)
-
-    return inner
-
 # for controlling freezing of CLIP
 
 def set_module_requires_grad_(module, requires_grad):
@@ -362,75 +339,6 @@ class OpenAIClipAdapter(BaseClipAdapter):
         image_embed = self.clip.encode_image(image)
         return EmbeddedImage(l2norm(image_embed.float()), None)
 
-class OpenClipAdapter(BaseClipAdapter):
-    def __init__(
-        self,
-        name = 'ViT-B/32',
-        pretrained = 'laion400m_e32'
-    ):
-        import open_clip
-        clip, _, preprocess = open_clip.create_model_and_transforms(name, pretrained = pretrained)
-
-        super().__init__(clip)
-        self.eos_id = 49407
-
-        text_attention_final = self.find_layer('ln_final')
-        self.handle = text_attention_final.register_forward_hook(self._hook)
-        self.clip_normalize = preprocess.transforms[-1]
-        self.cleared = False
-
-    def find_layer(self,  layer):
-        modules = dict([*self.clip.named_modules()])
-        return modules.get(layer, None)
-
-    def clear(self):
-        if self.cleared:
-            return
-
-        self.handle()
-
-    def _hook(self, _, inputs, outputs):
-        self.text_encodings = outputs
-
-    @property
-    def dim_latent(self):
-        return 512
-
-    @property
-    def image_size(self):
-        return self.clip.visual.image_size
-
-    @property
-    def image_channels(self):
-        return 3
-
-    @property
-    def max_text_len(self):
-        return self.clip.context_length
-
-    @torch.no_grad()
-    def embed_text(self, text):
-        text = text[..., :self.max_text_len]
-
-        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)
-        assert not self.cleared
-
-        text_embed = self.clip.encode_text(text)
-        text_encodings = self.text_encodings
-        text_encodings = text_encodings.masked_fill(~text_mask[..., None], 0.)
-        del self.text_encodings
-        return EmbeddedText(l2norm(text_embed.float()), text_encodings.float())
-
-    @torch.no_grad()
-    def embed_image(self, image):
-        assert not self.cleared
-        image = self.validate_and_resize_image(image)
-        image = self.clip_normalize(image)
-        image_embed = self.clip.encode_image(image)
-        return EmbeddedImage(l2norm(image_embed.float()), None)
-
 # classifier free guidance functions
 
 def prob_mask_like(shape, prob, device):
@@ -639,40 +547,34 @@ class NoiseScheduler(nn.Module):
 # diffusion prior
 
 class LayerNorm(nn.Module):
-    def __init__(self, dim, eps = 1e-5, fp16_eps = 1e-3, stable = False):
+    def __init__(self, dim, eps = 1e-5, stable = False):
         super().__init__()
         self.eps = eps
-        self.fp16_eps = fp16_eps
         self.stable = stable
         self.g = nn.Parameter(torch.ones(dim))
 
     def forward(self, x):
-        eps = self.eps if x.dtype == torch.float32 else self.fp16_eps
-
         if self.stable:
             x = x / x.amax(dim = -1, keepdim = True).detach()
 
         var = torch.var(x, dim = -1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = -1, keepdim = True)
-        return (x - mean) * (var + eps).rsqrt() * self.g
+        return (x - mean) * (var + self.eps).rsqrt() * self.g
 
 class ChanLayerNorm(nn.Module):
-    def __init__(self, dim, eps = 1e-5, fp16_eps = 1e-3, stable = False):
+    def __init__(self, dim, eps = 1e-5, stable = False):
         super().__init__()
         self.eps = eps
-        self.fp16_eps = fp16_eps
         self.stable = stable
         self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
 
     def forward(self, x):
-        eps = self.eps if x.dtype == torch.float32 else self.fp16_eps
-
         if self.stable:
             x = x / x.amax(dim = 1, keepdim = True).detach()
 
         var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = 1, keepdim = True)
-        return (x - mean) * (var + eps).rsqrt() * self.g
+        return (x - mean) * (var + self.eps).rsqrt() * self.g
 
 class Residual(nn.Module):
     def __init__(self, fn):
@@ -793,12 +695,11 @@ class Attention(nn.Module):
         dropout = 0.,
         causal = False,
         rotary_emb = None,
-        cosine_sim = True,
-        cosine_sim_scale = 16
+        pb_relax_alpha = 128
     ):
         super().__init__()
-        self.scale = cosine_sim_scale if cosine_sim else (dim_head ** -0.5)
-        self.cosine_sim = cosine_sim
+        self.pb_relax_alpha = pb_relax_alpha
+        self.scale = dim_head ** -0.5 * (pb_relax_alpha ** -1)
 
         self.heads = heads
         inner_dim = dim_head * heads
@@ -838,13 +739,6 @@ class Attention(nn.Module):
         k = torch.cat((nk, k), dim = -2)
         v = torch.cat((nv, v), dim = -2)
 
-        # whether to use cosine sim
-
-        if self.cosine_sim:
-            q, k = map(l2norm, (q, k))
-
-        q, k = map(lambda t: t * math.sqrt(self.scale), (q, k))
-
         # calculate query / key similarities
 
         sim = einsum('b h i d, b j d -> b h i j', q, k)
@@ -870,6 +764,9 @@ class Attention(nn.Module):
 
         # attention
 
+        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
+        sim = sim * self.pb_relax_alpha
+
         attn = sim.softmax(dim = -1)
         attn = self.dropout(attn)
 
@@ -1460,8 +1357,7 @@ class ResnetBlock(nn.Module):
         *,
         cond_dim = None,
         time_cond_dim = None,
-        groups = 8,
-        cosine_sim_cross_attn = False
+        groups = 8
     ):
         super().__init__()
 
@@ -1481,8 +1377,7 @@ class ResnetBlock(nn.Module):
                 'b (h w) c',
                 CrossAttention(
                     dim = dim_out,
-                    context_dim = cond_dim,
-                    cosine_sim = cosine_sim_cross_attn
+                    context_dim = cond_dim
                 )
             )
 
@@ -1517,12 +1412,11 @@ class CrossAttention(nn.Module):
         heads = 8,
         dropout = 0.,
         norm_context = False,
-        cosine_sim = False,
-        cosine_sim_scale = 16
+        pb_relax_alpha = 32 ** 2
     ):
         super().__init__()
-        self.cosine_sim = cosine_sim
-        self.scale = cosine_sim_scale if cosine_sim else (dim_head ** -0.5)
+        self.pb_relax_alpha = pb_relax_alpha
+        self.scale = dim_head ** -0.5 * (pb_relax_alpha ** -1)
         self.heads = heads
         inner_dim = dim_head * heads
 
@@ -1558,10 +1452,7 @@ class CrossAttention(nn.Module):
         k = torch.cat((nk, k), dim = -2)
         v = torch.cat((nv, v), dim = -2)
 
-        if self.cosine_sim:
-            q, k = map(l2norm, (q, k))
-
-        q, k = map(lambda t: t * math.sqrt(self.scale), (q, k))
+        q = q * self.scale
 
         sim = einsum('b h i d, b h j d -> b h i j', q, k)
         max_neg_value = -torch.finfo(sim.dtype).max
@@ -1571,6 +1462,9 @@ class CrossAttention(nn.Module):
             mask = rearrange(mask, 'b j -> b 1 1 j')
             sim = sim.masked_fill(~mask, max_neg_value)
 
+        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
+        sim = sim * self.pb_relax_alpha
+
         attn = sim.softmax(dim = -1)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
@@ -1582,8 +1476,7 @@ class LinearAttention(nn.Module):
         self,
         dim,
         dim_head = 32,
-        heads = 8,
-        **kwargs
+        heads = 8
     ):
         super().__init__()
         self.scale = dim_head ** -0.5
@@ -1601,7 +1494,6 @@ class LinearAttention(nn.Module):
 
     def forward(self, fmap):
         h, x, y = self.heads, *fmap.shape[-2:]
-        seq_len = x * y
 
         fmap = self.norm(fmap)
         q, k, v = self.to_qkv(fmap).chunk(3, dim = 1)
@@ -1611,9 +1503,6 @@ class LinearAttention(nn.Module):
         k = k.softmax(dim = -2)
 
         q = q * self.scale
-        v = l2norm(v)
-
-        k, v = map(lambda t: t / math.sqrt(seq_len), (k, v))
 
         context = einsum('b n d, b n e -> b d e', k, v)
         out = einsum('b n d, b d e -> b n e', q, context)
@@ -1649,38 +1538,6 @@ class CrossEmbedLayer(nn.Module):
         fmaps = tuple(map(lambda conv: conv(x), self.convs))
         return torch.cat(fmaps, dim = 1)
 
-class UpsampleCombiner(nn.Module):
-    def __init__(
-        self,
-        dim,
-        *,
-        enabled = False,
-        dim_ins = tuple(),
-        dim_outs = tuple()
-    ):
-        super().__init__()
-        assert len(dim_ins) == len(dim_outs)
-        self.enabled = enabled
-
-        if not self.enabled:
-            self.dim_out = dim
-            return
-
-        self.fmap_convs = nn.ModuleList([Block(dim_in, dim_out) for dim_in, dim_out in zip(dim_ins, dim_outs)])
-        self.dim_out = dim + (sum(dim_outs) if len(dim_outs) > 0 else 0)
-
-    def forward(self, x, fmaps = None):
-        target_size = x.shape[-1]
-
-        fmaps = default(fmaps, tuple())
-
-        if not self.enabled or len(fmaps) == 0 or len(self.fmap_convs) == 0:
-            return x
-
-        fmaps = [resize_image_to(fmap, target_size) for fmap in fmaps]
-        outs = [conv(fmap) for fmap, conv in zip(fmaps, self.fmap_convs)]
-        return torch.cat((x, *outs), dim = 1)
-
 class Unet(nn.Module):
     def __init__(
         self,
@@ -1701,8 +1558,6 @@ class Unet(nn.Module):
         lowres_cond = False,             # for cascading diffusion - https://cascaded-diffusion.github.io/
         lowres_noise_cond = False,       # for conditioning on low resolution noising, based on Imagen
         sparse_attn = False,
-        cosine_sim_cross_attn = False,
-        cosine_sim_self_attn = False,
         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,
         max_text_len = 256,
@@ -1720,8 +1575,6 @@ class Unet(nn.Module):
         scale_skip_connection = False,
         pixel_shuffle_upsample = True,
         final_conv_kernel_size = 1,
-        combine_upsample_fmaps = False, # whether to combine the outputs of all upsample blocks, as in unet squared paper
-        checkpoint_during_training = False,
         **kwargs
     ):
         super().__init__()
@@ -1824,7 +1677,7 @@ class Unet(nn.Module):
 
         # attention related params
 
-        attn_kwargs = dict(heads = attn_heads, dim_head = attn_dim_head, cosine_sim = cosine_sim_self_attn)
+        attn_kwargs = dict(heads = attn_heads, dim_head = attn_dim_head)
 
         self_attn = cast_tuple(self_attn, num_stages)
 
@@ -1847,13 +1700,9 @@ class Unet(nn.Module):
 
         upsample_klass = NearestUpsample if not pixel_shuffle_upsample else PixelShuffleUpsample
 
-        # prepare resnet klass
-
-        resnet_block = partial(ResnetBlock, cosine_sim_cross_attn = cosine_sim_cross_attn)
-
         # give memory efficient unet an initial resnet block
 
-        self.init_resnet_block = resnet_block(init_dim, init_dim, time_cond_dim = time_cond_dim, groups = top_level_resnet_group) if memory_efficient else None
+        self.init_resnet_block = ResnetBlock(init_dim, init_dim, time_cond_dim = time_cond_dim, groups = top_level_resnet_group) if memory_efficient else None
 
         # layers
 
@@ -1861,8 +1710,7 @@ class Unet(nn.Module):
         self.ups = nn.ModuleList([])
         num_resolutions = len(in_out)
 
-        skip_connect_dims = []          # keeping track of skip connection dimensions
-        upsample_combiner_dims = []     # keeping track of dimensions for final upsample feature map combiner
+        skip_connect_dims = [] # keeping track of skip connection dimensions
 
         for ind, ((dim_in, dim_out), groups, layer_num_resnet_blocks, layer_self_attn) in enumerate(zip(in_out, resnet_groups, num_resnet_blocks, self_attn)):
             is_first = ind == 0
@@ -1880,17 +1728,17 @@ class Unet(nn.Module):
 
             self.downs.append(nn.ModuleList([
                 downsample_klass(dim_in, dim_out = dim_out) if memory_efficient else None,
-                resnet_block(dim_layer, dim_layer, time_cond_dim = time_cond_dim, groups = groups),
-                nn.ModuleList([resnet_block(dim_layer, dim_layer, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups) for _ in range(layer_num_resnet_blocks)]),
+                ResnetBlock(dim_layer, dim_layer, time_cond_dim = time_cond_dim, groups = groups),
+                nn.ModuleList([ResnetBlock(dim_layer, dim_layer, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups) for _ in range(layer_num_resnet_blocks)]),
                 attention,
                 downsample_klass(dim_layer, dim_out = dim_out) if not is_last and not memory_efficient else nn.Conv2d(dim_layer, dim_out, 1)
             ]))
 
         mid_dim = dims[-1]
 
-        self.mid_block1 = resnet_block(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim, groups = resnet_groups[-1])
+        self.mid_block1 = ResnetBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim, groups = resnet_groups[-1])
         self.mid_attn = create_self_attn(mid_dim)
-        self.mid_block2 = resnet_block(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim, groups = resnet_groups[-1])
+        self.mid_block2 = ResnetBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim, groups = resnet_groups[-1])
 
         for ind, ((dim_in, dim_out), groups, layer_num_resnet_blocks, layer_self_attn) in enumerate(zip(reversed(in_out), reversed(resnet_groups), reversed(num_resnet_blocks), reversed(self_attn))):
             is_last = ind >= (len(in_out) - 1)
@@ -1904,27 +1752,14 @@ class Unet(nn.Module):
             elif sparse_attn:
                 attention = Residual(LinearAttention(dim_out, **attn_kwargs))
 
-            upsample_combiner_dims.append(dim_out)
-
             self.ups.append(nn.ModuleList([
-                resnet_block(dim_out + skip_connect_dim, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups),
-                nn.ModuleList([resnet_block(dim_out + skip_connect_dim, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups)  for _ in range(layer_num_resnet_blocks)]),
+                ResnetBlock(dim_out + skip_connect_dim, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups),
+                nn.ModuleList([ResnetBlock(dim_out + skip_connect_dim, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups)  for _ in range(layer_num_resnet_blocks)]),
                 attention,
                 upsample_klass(dim_out, dim_in) if not is_last or memory_efficient else nn.Identity()
             ]))
 
-        # whether to combine outputs from all upsample blocks for final resnet block
-
-        self.upsample_combiner = UpsampleCombiner(
-            dim = dim,
-            enabled = combine_upsample_fmaps,
-            dim_ins = upsample_combiner_dims,
-            dim_outs = (dim,) * len(upsample_combiner_dims)
-        )
-
-        # a final resnet block
-
-        self.final_resnet_block = resnet_block(self.upsample_combiner.dim_out + dim, dim, time_cond_dim = time_cond_dim, groups = top_level_resnet_group)
+        self.final_resnet_block = ResnetBlock(dim * 2, dim, time_cond_dim = time_cond_dim, groups = top_level_resnet_group)
 
         out_dim_in = dim + (channels if lowres_cond else 0)
 
@@ -1932,10 +1767,6 @@ class Unet(nn.Module):
 
         zero_init_(self.to_out) # since both OpenAI and @crowsonkb are doing it
 
-        # whether to checkpoint during training
-
-        self.checkpoint_during_training = checkpoint_during_training
-
     # 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(
@@ -1952,7 +1783,7 @@ class Unet(nn.Module):
             channels == self.channels and \
             cond_on_image_embeds == self.cond_on_image_embeds and \
             cond_on_text_encodings == self.cond_on_text_encodings and \
-            lowres_noise_cond == self.lowres_noise_cond and \
+            cond_on_lowres_noise == self.cond_on_lowres_noise and \
             channels_out == self.channels_out:
             return self
 
@@ -1993,8 +1824,7 @@ class Unet(nn.Module):
         image_cond_drop_prob = 0.,
         text_cond_drop_prob = 0.,
         blur_sigma = None,
-        blur_kernel_size = None,
-        disable_checkpoint = False
+        blur_kernel_size = None
     ):
         batch_size, device = x.shape[0], x.device
 
@@ -2116,29 +1946,16 @@ class Unet(nn.Module):
         c = self.norm_cond(c)
         mid_c = self.norm_mid_cond(mid_c)
 
-        # gradient checkpointing
-
-        can_checkpoint = self.training and self.checkpoint_during_training and not disable_checkpoint
-        apply_checkpoint_fn = make_checkpointable if can_checkpoint else identity
-
-        # make checkpointable modules
-
-        init_resnet_block, mid_block1, mid_attn, mid_block2, final_resnet_block = [maybe(apply_checkpoint_fn)(module) for module in (self.init_resnet_block, self.mid_block1, self.mid_attn, self.mid_block2, self.final_resnet_block)]
-
-        can_checkpoint_cond = lambda m: isinstance(m, ResnetBlock)
-        downs, ups = [maybe(apply_checkpoint_fn)(m, condition = can_checkpoint_cond) for m in (self.downs, self.ups)]
-
         # initial resnet block
 
-        if exists(init_resnet_block):
-            x = init_resnet_block(x, t)
+        if exists(self.init_resnet_block):
+            x = self.init_resnet_block(x, t)
 
         # go through the layers of the unet, down and up
 
-        down_hiddens = []
-        up_hiddens = []
+        hiddens = []
 
-        for pre_downsample, init_block, resnet_blocks, attn, post_downsample in downs:
+        for pre_downsample, init_block, resnet_blocks, attn, post_downsample in self.downs:
             if exists(pre_downsample):
                 x = pre_downsample(x)
 
@@ -2146,24 +1963,24 @@ class Unet(nn.Module):
 
             for resnet_block in resnet_blocks:
                 x = resnet_block(x, t, c)
-                down_hiddens.append(x.contiguous())
+                hiddens.append(x)
 
             x = attn(x)
-            down_hiddens.append(x.contiguous())
+            hiddens.append(x.contiguous())
 
             if exists(post_downsample):
                 x = post_downsample(x)
 
-        x = mid_block1(x, t, mid_c)
+        x = self.mid_block1(x, t, mid_c)
 
-        if exists(mid_attn):
-            x = mid_attn(x)
+        if exists(self.mid_attn):
+            x = self.mid_attn(x)
 
-        x = mid_block2(x, t, mid_c)
+        x = self.mid_block2(x, t, mid_c)
 
-        connect_skip = lambda fmap: torch.cat((fmap, down_hiddens.pop() * self.skip_connect_scale), dim = 1)
+        connect_skip = lambda fmap: torch.cat((fmap, hiddens.pop() * self.skip_connect_scale), dim = 1)
 
-        for init_block, resnet_blocks, attn, upsample in ups:
+        for init_block, resnet_blocks, attn, upsample in self.ups:
             x = connect_skip(x)
             x = init_block(x, t, c)
 
@@ -2172,15 +1989,11 @@ class Unet(nn.Module):
                 x = resnet_block(x, t, c)
 
             x = attn(x)
-
-            up_hiddens.append(x.contiguous())
             x = upsample(x)
 
-        x = self.upsample_combiner(x, up_hiddens)
-
         x = torch.cat((x, r), dim = 1)
 
-        x = final_resnet_block(x, t)
+        x = self.final_resnet_block(x, t)
 
         if exists(lowres_cond_img):
             x = torch.cat((x, lowres_cond_img), dim = 1)
@@ -2776,7 +2589,7 @@ class Decoder(nn.Module):
                 if is_inpaint and not (is_last_timestep or is_last_resample_step):
                     # in repaint, you renoise and resample up to 10 times every step
                     time_next_cond = torch.full((batch,), time_next, device = device, dtype = torch.long)
-                    img = noise_scheduler.q_sample_from_to(img, time_next_cond, time_cond)
+                    img = noise_scheduler.q_sample_from_to(img, time_cond, time_next_cond)
 
         if exists(inpaint_image):
             img = (img * ~inpaint_mask) + (inpaint_image * inpaint_mask)
@@ -3072,7 +2885,7 @@ class DALLE2(nn.Module):
         image_embed = self.prior.sample(text, num_samples_per_batch = self.prior_num_samples, cond_scale = prior_cond_scale)
 
         text_cond = text if self.decoder_need_text_cond else None
-        images = self.decoder.sample(image_embed = image_embed, text = text_cond, cond_scale = cond_scale)
+        images = self.decoder.sample(image_embed, text = text_cond, cond_scale = cond_scale)
 
         if return_pil_images:
             images = list(map(self.to_pil, images.unbind(dim = 0)))

dalle2_pytorch/trainer.py

@@ -174,7 +174,7 @@ class DiffusionPriorTrainer(nn.Module):
     def __init__(
         self,
         diffusion_prior,
-        accelerator = None,
+        accelerator,
         use_ema = True,
         lr = 3e-4,
         wd = 1e-2,
@@ -186,12 +186,8 @@ class DiffusionPriorTrainer(nn.Module):
     ):
         super().__init__()
         assert isinstance(diffusion_prior, DiffusionPrior)
-
+        assert isinstance(accelerator, Accelerator)
         ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
-        accelerator_kwargs, kwargs = groupby_prefix_and_trim('accelerator_', kwargs)
-
-        if not exists(accelerator):
-            accelerator = Accelerator(**accelerator_kwargs)
 
         # assign some helpful member vars
 
@@ -304,7 +300,7 @@ class DiffusionPriorTrainer(nn.Module):
 
         # all processes need to load checkpoint. no restriction here
         if isinstance(path_or_state, str):
-            path = Path(path_or_state)
+            path = Path(path)
             assert path.exists()
             loaded_obj = torch.load(str(path), map_location=self.device)
 

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.5.0'
+__version__ = '1.0.4'

setup.py

@@ -26,7 +26,7 @@ setup(
   install_requires=[
     'accelerate',
     'click',
-    'clip-anytorch>=2.4.0',
+    'clip-anytorch',
     'coca-pytorch>=0.0.5',
     'ema-pytorch>=0.0.7',
     'einops>=0.4',