0.26.2

The default save location is now none so if keys are not specified the
corresponding checkpoint type is not saved.

Models and checkpoints are now both saved with version number and the
config used to create them in order to simplify loading.

Documentation was fixed to be in line with current usage.

README.md

@@ -628,6 +628,82 @@ images = dalle2(
 
 Now you'll just have to worry about training the Prior and the Decoder!
 
+## Inpainting
+
+Inpainting is also built into the `Decoder`. You simply have to pass in the `inpaint_image` and `inpaint_mask` (boolean tensor where `True` indicates which regions of the inpaint image to keep)
+
+This repository uses the formulation put forth by <a href="https://arxiv.org/abs/2201.09865">Lugmayr et al. in Repaint</a>
+
+```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)
+
+unet = Unet(
+    dim = 16,
+    image_embed_dim = 512,
+    cond_dim = 128,
+    channels = 3,
+    dim_mults = (1, 1, 1, 1)
+).cuda()
+
+
+# decoder, which contains the unet(s) and clip
+
+decoder = Decoder(
+    clip = clip,
+    unet = (unet,),               # insert both unets in order of low resolution to highest resolution (you can have as many stages as you want here)
+    image_sizes = (256,),         # resolutions, 256 for first unet, 512 for second. these must be unique and in ascending order (matches with the unets passed in)
+    timesteps = 1000,
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5
+).cuda()
+
+# mock images (get a lot of this)
+
+images = torch.randn(4, 3, 256, 256).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()
+
+# do the above for many steps for both unets
+
+mock_image_embed = torch.randn(1, 512).cuda()
+
+# then to do inpainting
+
+inpaint_image = torch.randn(1, 3, 256, 256).cuda()      # (batch, channels, height, width)
+inpaint_mask = torch.ones(1, 256, 256).bool().cuda()    # (batch, height, width)
+
+inpainted_images = decoder.sample(
+    image_embed = mock_image_embed,
+    inpaint_image = inpaint_image,    # just pass in the inpaint image
+    inpaint_mask = inpaint_mask       # and the mask
+)
+
+inpainted_images.shape # (1, 3, 256, 256)
+```
+
 ## Experimental
 
 ### DALL-E2 with Latent Diffusion
@@ -991,26 +1067,12 @@ dataset = ImageEmbeddingDataset(
 )
 ```
 
-### Scripts (wip)
+### Scripts
 
 #### `train_diffusion_prior.py`
 
 For detailed information on training the diffusion prior, please refer to the [dedicated readme](prior.md)
 
-## CLI (wip)
-
-```bash
-$ 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 CLI (wip)
-
-<a href="https://github.com/lucidrains/stylegan2-pytorch">template</a>
-
 ## Todo
 
 - [x] finish off gaussian diffusion class for latent embedding - allow for prediction of epsilon
@@ -1048,11 +1110,10 @@ Once built, images will be saved to the same directory the command is invoked
 - [x] bring in skip-layer excitations (from lightweight gan paper) to see if it helps for either decoder of unet or vqgan-vae training (doesnt work well)
 - [x] test out grid attention in cascading ddpm locally, decide whether to keep or remove https://arxiv.org/abs/2204.01697 (keeping, seems to be fine)
 - [x] allow for unet to be able to condition non-cross attention style as well
-- [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet (test out unet² in ddpm repo) - consider https://github.com/lucidrains/uformer-pytorch attention-based unet
-- [ ] speed up inference, read up on papers (ddim or diffusion-gan, etc)
-- [ ] figure out if possible to augment with external memory, as described in https://arxiv.org/abs/2204.11824
+- [x] speed up inference, read up on papers (ddim)
+- [x] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
+- [ ] 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
-- [ ] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
 
 ## Citations
 
@@ -1170,4 +1231,14 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
+```bibtex
+@article{Lugmayr2022RePaintIU,
+    title   = {RePaint: Inpainting using Denoising Diffusion Probabilistic Models},
+    author  = {Andreas Lugmayr and Martin Danelljan and Andr{\'e}s Romero and Fisher Yu and Radu Timofte and Luc Van Gool},
+    journal = {ArXiv},
+    year    = {2022},
+    volume  = {abs/2201.09865}
+}
+```
+
 *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>

configs/README.md

@@ -74,9 +74,6 @@ Settings for controlling the training hyperparameters.
 | `validation_samples` | No | `None` | The number of samples to use for validation. None mean the entire validation set. |
 | `use_ema` | No | `True` | Whether to use exponential moving average models for sampling. |
 | `ema_beta` | No | `0.99` | The ema coefficient. |
-| `save_all` | No | `False` | If True, preserves a checkpoint for every epoch. |
-| `save_latest` | No | `True` | If True, overwrites the `latest.pth` every time the model is saved. |
-| `save_best` | No | `True` | If True, overwrites the `best.pth` every time the model has a lower validation loss than all previous models. |
 | `unet_training_mask` | No | `None` | A boolean array of the same length as the number of unets. If false, the unet is frozen. A value of `None` trains all unets. |
 
 **<ins>Evaluate</ins>:**
@@ -163,9 +160,10 @@ All save locations have these configuration options
 | Option | Required | Default | Description |
 | ------ | -------- | ------- | ----------- |
 | `save_to` | Yes | N/A | Must be `local`, `huggingface`, or `wandb`. |
-| `save_latest_to` | No | `latest.pth` | Sets the relative path to save the latest model to. |
-| `save_best_to` | No | `best.pth` | Sets the relative path to save the best model to every time the model has a lower validation loss than all previous models. |
-| `save_type` | No | `'checkpoint'` | The type of save. `'checkpoint'` saves a checkpoint, `'model'` saves a model without any fluff (Saves with ema if ema is enabled). |
+| `save_latest_to` | No | `None` | Sets the relative path to save the latest model to. |
+| `save_best_to` | No | `None` | Sets the relative path to save the best model to every time the model has a lower validation loss than all previous models. |
+| `save_meta_to` | No | `None` | The path to save metadata files in. This includes the config files used to start the training. |
+| `save_type` | No | `checkpoint` | The type of save. `checkpoint` saves a checkpoint, `model` saves a model without any fluff (Saves with ema if ema is enabled). |
 
 If using `local`
 | Option | Required | Default | Description |
@@ -177,7 +175,6 @@ If using `huggingface`
 | ------ | -------- | ------- | ----------- |
 | `save_to` | Yes | N/A | Must be `huggingface`. |
 | `huggingface_repo` | Yes | N/A | The huggingface repository to save to. |
-| `huggingface_base_path` | Yes | N/A | The base path that checkpoints will be saved under. |
 | `token_path` | No | `None` | If logging in with the huggingface cli is not possible, point to a token file instead. |
 
 If using `wandb`

configs/train_decoder_config.example.json

@@ -56,9 +56,6 @@
         "use_ema": true,
         "ema_beta": 0.99,
         "amp": false,
-        "save_all": false,
-        "save_latest": true,
-        "save_best": true,
         "unet_training_mask": [true]
     },
     "evaluate": {
@@ -96,14 +93,15 @@
         },
 
         "save": [{
-            "save_to": "wandb"
+            "save_to": "wandb",
+            "save_latest_to": "latest.pth"
         }, {
             "save_to": "huggingface",
             "huggingface_repo": "Veldrovive/test_model",
 
-            "save_all": true,
-            "save_latest": true,
-            "save_best": true,
+            "save_latest_to": "path/to/model_dir/latest.pth",
+            "save_best_to": "path/to/model_dir/best.pth",
+            "save_meta_to": "path/to/directory/for/assorted/files",
 
             "save_type": "model"
         }]

configs/train_decoder_config.test.json

@@ -61,9 +61,6 @@
         "use_ema": true,
         "ema_beta": 0.99,
         "amp": false,
-        "save_all": false,
-        "save_latest": true,
-        "save_best": true,
         "unet_training_mask": [true]
     },
     "evaluate": {
@@ -96,7 +93,8 @@
         },
 
        "save": [{
-            "save_to": "local"
+            "save_to": "local",
+            "save_latest_to": "latest.pth"
         }]
     }
 }

dalle2_pytorch/dalle2_pytorch.py

@@ -52,10 +52,10 @@ def first(arr, d = None):
 
 def maybe(fn):
     @wraps(fn)
-    def inner(x):
+    def inner(x, *args, **kwargs):
         if not exists(x):
             return x
-        return fn(x)
+        return fn(x, *args, **kwargs)
     return inner
 
 def default(val, d):
@@ -63,13 +63,13 @@ def default(val, d):
         return val
     return d() if callable(d) else d
 
-def cast_tuple(val, length = None):
+def cast_tuple(val, length = None, validate = True):
     if isinstance(val, list):
         val = tuple(val)
 
     out = val if isinstance(val, tuple) else ((val,) * default(length, 1))
 
-    if exists(length):
+    if exists(length) and validate:
         assert len(out) == length
 
     return out
@@ -494,6 +494,9 @@ class NoiseScheduler(nn.Module):
         self.has_p2_loss_reweighting = p2_loss_weight_gamma > 0.
         register_buffer('p2_loss_weight', (p2_loss_weight_k + alphas_cumprod / (1 - alphas_cumprod)) ** -p2_loss_weight_gamma)
 
+    def sample_random_times(self, batch):
+        return torch.randint(0, self.num_timesteps, (batch,), device = self.betas.device, dtype = torch.long)
+
     def q_posterior(self, x_start, x_t, t):
         posterior_mean = (
             extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
@@ -519,7 +522,7 @@ class NoiseScheduler(nn.Module):
 
     def predict_noise_from_start(self, x_t, t, x0):
         return (
-            (x0 - extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t) / \
+            (extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - x0) / \
             extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
         )
 
@@ -1243,7 +1246,7 @@ class DiffusionPrior(nn.Module):
         # timestep conditioning from ddpm
 
         batch, device = image_embed.shape[0], image_embed.device
-        times = torch.randint(0, self.noise_scheduler.num_timesteps, (batch,), device = device, dtype = torch.long)
+        times = self.noise_scheduler.sample_random_times(batch)
 
         # scale image embed (Katherine)
 
@@ -1539,9 +1542,10 @@ class Unet(nn.Module):
         self_attn = False,
         attn_dim_head = 32,
         attn_heads = 16,
-        lowres_cond = False, # for cascading diffusion - https://cascaded-diffusion.github.io/
+        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,
-        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)
+        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,
         cond_on_image_embeds = False,
@@ -1629,6 +1633,17 @@ class Unet(nn.Module):
             self.text_to_cond = nn.Linear(text_embed_dim, cond_dim)
             self.text_embed_dim = text_embed_dim
 
+        # low resolution noise conditiong, based on Imagen's upsampler training technique
+
+        self.lowres_noise_cond = lowres_noise_cond
+
+        self.to_lowres_noise_cond = nn.Sequential(
+            SinusoidalPosEmb(dim),
+            nn.Linear(dim, time_cond_dim),
+            nn.GELU(),
+            nn.Linear(time_cond_dim, time_cond_dim)
+        ) if lowres_noise_cond else None
+
         # 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
 
@@ -1745,15 +1760,17 @@ class Unet(nn.Module):
         self,
         *,
         lowres_cond,
+        lowres_noise_cond,
         channels,
         channels_out,
         cond_on_image_embeds,
-        cond_on_text_encodings
+        cond_on_text_encodings,
     ):
         if lowres_cond == self.lowres_cond and \
             channels == self.channels and \
             cond_on_image_embeds == self.cond_on_image_embeds and \
             cond_on_text_encodings == self.cond_on_text_encodings and \
+            cond_on_lowres_noise == self.cond_on_lowres_noise and \
             channels_out == self.channels_out:
             return self
 
@@ -1762,7 +1779,8 @@ class Unet(nn.Module):
             channels = channels,
             channels_out = channels_out,
             cond_on_image_embeds = cond_on_image_embeds,
-            cond_on_text_encodings = cond_on_text_encodings
+            cond_on_text_encodings = cond_on_text_encodings,
+            lowres_noise_cond = lowres_noise_cond
         )
 
         return self.__class__(**{**self._locals, **updated_kwargs})
@@ -1788,6 +1806,7 @@ class Unet(nn.Module):
         *,
         image_embed,
         lowres_cond_img = None,
+        lowres_noise_level = None,
         text_encodings = None,
         image_cond_drop_prob = 0.,
         text_cond_drop_prob = 0.,
@@ -1816,6 +1835,13 @@ class Unet(nn.Module):
         time_tokens = self.to_time_tokens(time_hiddens)
         t = self.to_time_cond(time_hiddens)
 
+        # low res noise conditioning (similar to time above)
+
+        if exists(lowres_noise_level):
+            assert exists(self.to_lowres_noise_cond), 'lowres_noise_cond must be set to True on instantiation of the unet in order to conditiong on lowres noise'
+            lowres_noise_level = lowres_noise_level.type_as(x)
+            t = t + self.to_lowres_noise_cond(lowres_noise_level)
+
         # conditional dropout
 
         image_keep_mask = prob_mask_like((batch_size,), 1 - image_cond_drop_prob, device = device)
@@ -1965,25 +1991,48 @@ class LowresConditioner(nn.Module):
     def __init__(
         self,
         downsample_first = True,
+        use_blur = True,
         blur_prob = 0.5,
         blur_sigma = 0.6,
         blur_kernel_size = 3,
-        input_image_range = None
+        use_noise = False,
+        input_image_range = None,
+        normalize_img_fn = identity,
+        unnormalize_img_fn = identity
     ):
         super().__init__()
         self.downsample_first = downsample_first
         self.input_image_range = input_image_range
 
+        self.use_blur = use_blur
         self.blur_prob = blur_prob
         self.blur_sigma = blur_sigma
         self.blur_kernel_size = blur_kernel_size
 
+        self.use_noise = use_noise
+        self.normalize_img = normalize_img_fn
+        self.unnormalize_img = unnormalize_img_fn
+        self.noise_scheduler = NoiseScheduler(beta_schedule = 'linear', timesteps = 1000, loss_type = 'l2') if use_noise else None
+
+    def noise_image(self, cond_fmap, noise_levels = None):
+        assert exists(self.noise_scheduler)
+
+        batch = cond_fmap.shape[0]
+        cond_fmap = self.normalize_img(cond_fmap)
+
+        random_noise_levels = default(noise_levels, lambda: self.noise_scheduler.sample_random_times(batch))
+        cond_fmap = self.noise_scheduler.q_sample(cond_fmap, t = random_noise_levels, noise = torch.randn_like(cond_fmap))
+
+        cond_fmap = self.unnormalize_img(cond_fmap)
+        return cond_fmap, random_noise_levels
+
     def forward(
         self,
         cond_fmap,
         *,
         target_image_size,
         downsample_image_size = None,
+        should_blur = True,
         blur_sigma = None,
         blur_kernel_size = None
     ):
@@ -1993,7 +2042,7 @@ class LowresConditioner(nn.Module):
         # blur is only applied 50% of the time
         # section 3.1 in https://arxiv.org/abs/2106.15282
 
-        if random.random() < self.blur_prob:
+        if self.use_blur and should_blur and random.random() < self.blur_prob:
 
             # when training, blur the low resolution conditional image
 
@@ -2015,8 +2064,21 @@ class LowresConditioner(nn.Module):
 
             cond_fmap = gaussian_blur2d(cond_fmap, cast_tuple(blur_kernel_size, 2), cast_tuple(blur_sigma, 2))
 
+        # resize to target image size
+
         cond_fmap = resize_image_to(cond_fmap, target_image_size, clamp_range = self.input_image_range, nearest = True)
-        return cond_fmap
+
+        # noise conditioning, as done in Imagen
+        # as a replacement for the BSR noising, and potentially replace blurring for first stage too
+
+        random_noise_levels = None
+
+        if self.use_noise:
+            cond_fmap, random_noise_levels = self.noise_image(cond_fmap)
+
+        # return conditioning feature map, as well as the augmentation noise levels
+
+        return cond_fmap, random_noise_levels
 
 class Decoder(nn.Module):
     def __init__(
@@ -2037,10 +2099,13 @@ class Decoder(nn.Module):
         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)
+        use_noise_for_lowres_cond = False,          # whether to use Imagen-like noising for low resolution conditioning  
+        use_blur_for_lowres_cond = True,            # whether to use the blur conditioning used in the original cascading ddpm paper, as well as DALL-E2
         lowres_downsample_first = True,             # cascading ddpm - resizes to lower resolution, then to next conditional resolution + blur
         blur_prob = 0.5,                            # cascading ddpm - when training, the gaussian blur is only applied 50% of the time
         blur_sigma = 0.6,                           # cascading ddpm - blur sigma
         blur_kernel_size = 3,                       # cascading ddpm - blur kernel size
+        lowres_noise_sample_level = 0.2,            # in imagen paper, they use a 0.2 noise level at sample time for low resolution conditioning
         clip_denoised = True,
         clip_x_start = True,
         clip_adapter_overrides = dict(),
@@ -2050,7 +2115,7 @@ class Decoder(nn.Module):
         unconditional = False,                      # set to True for generating images without conditioning
         auto_normalize_img = True,                  # whether to take care of normalizing the image from [0, 1] to [-1, 1] and back automatically - you can turn this off if you want to pass in the [-1, 1] ranged image yourself from the dataloader
         use_dynamic_thres = False,                  # from the Imagen paper
-        dynamic_thres_percentile = 0.9,
+        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
@@ -2088,10 +2153,17 @@ class Decoder(nn.Module):
 
         self.channels = channels
 
+
+        # normalize and unnormalize image functions
+
+        self.normalize_img = normalize_neg_one_to_one if auto_normalize_img else identity
+        self.unnormalize_img = unnormalize_zero_to_one if auto_normalize_img else identity
+
         # verify conditioning method
 
         unets = cast_tuple(unet)
         num_unets = len(unets)
+        self.num_unets = num_unets
 
         self.unconditional = unconditional
 
@@ -2107,12 +2179,28 @@ class Decoder(nn.Module):
         self.learned_variance_constrain_frac = learned_variance_constrain_frac # whether to constrain the output of the network (the interpolation fraction) from 0 to 1
         self.vb_loss_weight = vb_loss_weight
 
+        # default and validate conditioning parameters
+
+        use_noise_for_lowres_cond = cast_tuple(use_noise_for_lowres_cond, num_unets - 1, validate = False)
+        use_blur_for_lowres_cond = cast_tuple(use_blur_for_lowres_cond, num_unets - 1, validate = False)
+
+        if len(use_noise_for_lowres_cond) < num_unets:
+            use_noise_for_lowres_cond = (False, *use_noise_for_lowres_cond)
+
+        if len(use_blur_for_lowres_cond) < num_unets:
+            use_blur_for_lowres_cond = (False, *use_blur_for_lowres_cond)
+
+        assert not use_noise_for_lowres_cond[0], 'first unet will never need low res noise conditioning'
+        assert not use_blur_for_lowres_cond[0], 'first unet will never need low res blur conditioning'
+
+        assert num_unets == 1 or all((use_noise or use_blur) for use_noise, use_blur in zip(use_noise_for_lowres_cond[1:], use_blur_for_lowres_cond[1:]))
+
         # construct unets and vaes
 
         self.unets = nn.ModuleList([])
         self.vaes = nn.ModuleList([])
 
-        for ind, (one_unet, one_vae, one_unet_learned_var) in enumerate(zip(unets, vaes, learned_variance)):
+        for ind, (one_unet, one_vae, one_unet_learned_var, lowres_noise_cond) in enumerate(zip(unets, vaes, learned_variance, use_noise_for_lowres_cond)):
             assert isinstance(one_unet, Unet)
             assert isinstance(one_vae, (VQGanVAE, NullVQGanVAE))
 
@@ -2124,6 +2212,7 @@ class Decoder(nn.Module):
 
             one_unet = one_unet.cast_model_parameters(
                 lowres_cond = not is_first,
+                lowres_noise_cond = lowres_noise_cond,
                 cond_on_image_embeds = not unconditional and is_first,
                 cond_on_text_encodings = not unconditional and one_unet.cond_on_text_encodings,
                 channels = unet_channels,
@@ -2166,7 +2255,7 @@ class Decoder(nn.Module):
         image_sizes = default(image_sizes, (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}'
+        assert self.num_unets == len(image_sizes), f'you did not supply the correct number of u-nets ({self.num_unets}) for resolutions {image_sizes}'
         self.image_sizes = image_sizes
         self.sample_channels = cast_tuple(self.channels, len(image_sizes))
 
@@ -2186,15 +2275,30 @@ class Decoder(nn.Module):
         # 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'
+        assert lowres_conditions == (False, *((True,) * (num_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(
-            downsample_first = lowres_downsample_first,
-            blur_prob = blur_prob,
-            blur_sigma = blur_sigma,
-            blur_kernel_size = blur_kernel_size,
-            input_image_range = self.input_image_range
-        )
+        self.lowres_conds = nn.ModuleList([])
+
+        for unet_index, use_noise, use_blur in zip(range(num_unets), use_noise_for_lowres_cond, use_blur_for_lowres_cond):
+            if unet_index == 0:
+                self.lowres_conds.append(None)
+                continue
+
+            lowres_cond = LowresConditioner(
+                downsample_first = lowres_downsample_first,
+                use_blur = use_blur,
+                use_noise = use_noise,
+                blur_prob = blur_prob,
+                blur_sigma = blur_sigma,
+                blur_kernel_size = blur_kernel_size,
+                input_image_range = self.input_image_range,
+                normalize_img_fn = self.normalize_img,
+                unnormalize_img_fn = self.unnormalize_img
+            )
+
+            self.lowres_conds.append(lowres_cond)
+
+        self.lowres_noise_sample_level = lowres_noise_sample_level
 
         # classifier free guidance
 
@@ -2212,11 +2316,6 @@ class Decoder(nn.Module):
         self.use_dynamic_thres = use_dynamic_thres
         self.dynamic_thres_percentile = dynamic_thres_percentile
 
-        # normalize and unnormalize image functions
-
-        self.normalize_img = normalize_neg_one_to_one if auto_normalize_img else identity
-        self.unnormalize_img = unnormalize_zero_to_one if auto_normalize_img else identity
-
         # device tracker
 
         self.register_buffer('_dummy', torch.Tensor([True]), persistent = False)
@@ -2230,7 +2329,7 @@ class Decoder(nn.Module):
         return any([unet.cond_on_text_encodings for unet in self.unets])
 
     def get_unet(self, unet_number):
-        assert 0 < unet_number <= len(self.unets)
+        assert 0 < unet_number <= self.num_unets
         index = unet_number - 1
         return self.unets[index]
 
@@ -2272,10 +2371,10 @@ 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, clip_denoised = True, predict_x_start = False, learned_variance = False, cond_scale = 1., model_output = None):
+    def p_mean_variance(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, lowres_cond_img = None, clip_denoised = True, predict_x_start = 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)'
 
-        pred = 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))
+        pred = 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, lowres_noise_level = lowres_noise_level))
 
         if learned_variance:
             pred, var_interp_frac_unnormalized = pred.chunk(2, dim = 1)
@@ -2307,44 +2406,97 @@ class Decoder(nn.Module):
         return model_mean, posterior_variance, posterior_log_variance
 
     @torch.no_grad()
-    def p_sample(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, cond_scale = 1., lowres_cond_img = None, predict_x_start = False, learned_variance = False, clip_denoised = True):
+    def p_sample(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, cond_scale = 1., lowres_cond_img = None, predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_noise_level = None):
         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, noise_scheduler = noise_scheduler, learned_variance = learned_variance)
+        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_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)))
         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
 
     @torch.no_grad()
-    def p_sample_loop_ddpm(self, unet, shape, image_embed, noise_scheduler, predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_cond_img = None, text_encodings = None, cond_scale = 1, is_latent_diffusion = False):
+    def p_sample_loop_ddpm(
+        self,
+        unet,
+        shape,
+        image_embed,
+        noise_scheduler,
+        predict_x_start = False,
+        learned_variance = False,
+        clip_denoised = True,
+        lowres_cond_img = None,
+        text_encodings = None,
+        cond_scale = 1,
+        is_latent_diffusion = False,
+        lowres_noise_level = None,
+        inpaint_image = None,
+        inpaint_mask = None
+    ):
         device = self.device
 
         b = shape[0]
         img = torch.randn(shape, device = device)
 
+        if exists(inpaint_image):
+            inpaint_image = self.normalize_img(inpaint_image)
+            inpaint_image = resize_image_to(inpaint_image, shape[-1], nearest = True)
+            inpaint_mask = rearrange(inpaint_mask, 'b h w -> b 1 h w').float()
+            inpaint_mask = resize_image_to(inpaint_mask, shape[-1], nearest = True)
+            inpaint_mask = inpaint_mask.bool()
+
         if not is_latent_diffusion:
             lowres_cond_img = maybe(self.normalize_img)(lowres_cond_img)
 
         for i in tqdm(reversed(range(0, noise_scheduler.num_timesteps)), desc = 'sampling loop time step', total = noise_scheduler.num_timesteps):
+            times = torch.full((b,), i, device = device, dtype = torch.long)
+
+            if exists(inpaint_image):
+                # following the repaint paper
+                # https://arxiv.org/abs/2201.09865
+                noised_inpaint_image = noise_scheduler.q_sample(inpaint_image, t = times)
+                img = (img * ~inpaint_mask) + (noised_inpaint_image * inpaint_mask)
+
             img = self.p_sample(
                 unet,
                 img,
-                torch.full((b,), i, device = device, dtype = torch.long),
+                times,
                 image_embed = image_embed,
                 text_encodings = text_encodings,
                 cond_scale = cond_scale,
                 lowres_cond_img = lowres_cond_img,
+                lowres_noise_level = lowres_noise_level,
                 predict_x_start = predict_x_start,
                 noise_scheduler = noise_scheduler,
                 learned_variance = learned_variance,
                 clip_denoised = clip_denoised
             )
 
+        if exists(inpaint_image):
+            img = (img * ~inpaint_mask) + (inpaint_image * inpaint_mask)
+
         unnormalize_img = self.unnormalize_img(img)
         return unnormalize_img
 
     @torch.no_grad()
-    def p_sample_loop_ddim(self, unet, shape, image_embed, noise_scheduler, timesteps, eta = 1., predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_cond_img = None, text_encodings = None, cond_scale = 1, is_latent_diffusion = False):
+    def p_sample_loop_ddim(
+        self,
+        unet,
+        shape,
+        image_embed,
+        noise_scheduler,
+        timesteps,
+        eta = 1.,
+        predict_x_start = False,
+        learned_variance = False,
+        clip_denoised = True,
+        lowres_cond_img = None,
+        text_encodings = None,
+        cond_scale = 1,
+        is_latent_diffusion = False,
+        lowres_noise_level = None,
+        inpaint_image = None,
+        inpaint_mask = None
+    ):
         batch, device, total_timesteps, alphas, eta = shape[0], self.device, noise_scheduler.num_timesteps, noise_scheduler.alphas_cumprod_prev, self.ddim_sampling_eta
 
         times = torch.linspace(0., total_timesteps, steps = timesteps + 2)[:-1]
@@ -2352,6 +2504,13 @@ class Decoder(nn.Module):
         times = list(reversed(times.int().tolist()))
         time_pairs = list(zip(times[:-1], times[1:]))
 
+        if exists(inpaint_image):
+            inpaint_image = self.normalize_img(inpaint_image)
+            inpaint_image = resize_image_to(inpaint_image, shape[-1], nearest = True)
+            inpaint_mask = rearrange(inpaint_mask, 'b h w -> b 1 h w').float()
+            inpaint_mask = resize_image_to(inpaint_mask, shape[-1], nearest = True)
+            inpaint_mask = inpaint_mask.bool()
+
         img = torch.randn(shape, device = device)
 
         if not is_latent_diffusion:
@@ -2363,7 +2522,13 @@ class Decoder(nn.Module):
 
             time_cond = torch.full((batch,), time, device = device, dtype = torch.long)
 
-            pred = unet.forward_with_cond_scale(img, time_cond, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img)
+            if exists(inpaint_image):
+                # following the repaint paper
+                # https://arxiv.org/abs/2201.09865
+                noised_inpaint_image = noise_scheduler.q_sample(inpaint_image, t = time_cond)
+                img = (img * ~inpaint_mask) + (noised_inpaint_image * inpaint_mask)
+
+            pred = unet.forward_with_cond_scale(img, time_cond, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, lowres_noise_level = lowres_noise_level)
 
             if learned_variance:
                 pred, _ = pred.chunk(2, dim = 1)
@@ -2386,6 +2551,9 @@ class Decoder(nn.Module):
                   c1 * noise + \
                   c2 * pred_noise
 
+        if exists(inpaint_image):
+            img = (img * ~inpaint_mask) + (inpaint_image * inpaint_mask)
+
         img = self.unnormalize_img(img)
         return img
 
@@ -2402,7 +2570,7 @@ 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):
+    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):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
         # normalize to [-1, 1]
@@ -2421,6 +2589,7 @@ class Decoder(nn.Module):
             image_embed = image_embed,
             text_encodings = text_encodings,
             lowres_cond_img = lowres_cond_img,
+            lowres_noise_level = lowres_noise_level,
             image_cond_drop_prob = self.image_cond_drop_prob,
             text_cond_drop_prob = self.text_cond_drop_prob,
         )
@@ -2484,6 +2653,8 @@ class Decoder(nn.Module):
         cond_scale = 1.,
         stop_at_unet_number = None,
         distributed = False,
+        inpaint_image = None,
+        inpaint_mask = None
     ):
         assert self.unconditional or exists(image_embed), 'image embed must be present on sampling from decoder unless if trained unconditionally'
 
@@ -2497,29 +2668,41 @@ class Decoder(nn.Module):
         assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
         assert not (not self.condition_on_text_encodings and exists(text_encodings)), 'decoder specified not to be conditioned on text, yet it is presented'
 
+        assert not (exists(inpaint_image) ^ exists(inpaint_mask)), 'inpaint_image and inpaint_mask (boolean mask of [batch, height, width]) must be both given for inpainting'
+
         img = None
         is_cuda = next(self.parameters()).is_cuda
 
-        num_unets = len(self.unets)
+        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, 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.sample_timesteps, cond_scale)):
+        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)):
 
             context = self.one_unet_in_gpu(unet = unet) if is_cuda and not distributed else null_context()
 
             with context:
-                lowres_cond_img = None
+                # prepare low resolution conditioning for upsamplers
+
+                lowres_cond_img = lowres_noise_level = None
                 shape = (batch_size, channel, image_size, image_size)
 
                 if unet.lowres_cond:
                     lowres_cond_img = resize_image_to(img, target_image_size = image_size, clamp_range = self.input_image_range, nearest = True)
 
+                    if lowres_cond.use_noise:
+                        lowres_noise_level = torch.full((batch_size,), int(self.lowres_noise_sample_level * 1000), dtype = torch.long, device = self.device)
+                        lowres_cond_img, _ = lowres_cond.noise_image(lowres_cond_img, lowres_noise_level)
+
+                # latent diffusion
+
                 is_latent_diffusion = isinstance(vae, VQGanVAE)
                 image_size = vae.get_encoded_fmap_size(image_size)
                 shape = (batch_size, vae.encoded_dim, image_size, image_size)
 
                 lowres_cond_img = maybe(vae.encode)(lowres_cond_img)
 
+                # denoising loop for image
+
                 img = self.p_sample_loop(
                     unet,
                     shape,
@@ -2530,9 +2713,12 @@ class Decoder(nn.Module):
                     learned_variance = learned_variance,
                     clip_denoised = not is_latent_diffusion,
                     lowres_cond_img = lowres_cond_img,
+                    lowres_noise_level = lowres_noise_level,
                     is_latent_diffusion = is_latent_diffusion,
                     noise_scheduler = noise_scheduler,
-                    timesteps = sample_timesteps
+                    timesteps = sample_timesteps,
+                    inpaint_image = inpaint_image,
+                    inpaint_mask = inpaint_mask
                 )
 
                 img = vae.decode(img)
@@ -2551,7 +2737,7 @@ class Decoder(nn.Module):
         unet_number = None,
         return_lowres_cond_image = False # whether to return the low resolution conditioning images, for debugging upsampler purposes
     ):
-        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)'
+        assert not (self.num_unets > 1 and not exists(unet_number)), f'you must specify which unet you want trained, from a range of 1 to {self.num_unets}, if you are training cascading DDPM (multiple unets)'
         unet_number = default(unet_number, 1)
         unet_index = unet_number - 1
 
@@ -2559,6 +2745,7 @@ class Decoder(nn.Module):
 
         vae                 = self.vaes[unet_index]
         noise_scheduler     = self.noise_schedulers[unet_index]
+        lowres_conditioner  = self.lowres_conds[unet_index]
         target_image_size   = self.image_sizes[unet_index]
         predict_x_start     = self.predict_x_start[unet_index]
         random_crop_size    = self.random_crop_sizes[unet_index]
@@ -2581,7 +2768,7 @@ class Decoder(nn.Module):
         assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
         assert not (not self.condition_on_text_encodings and exists(text_encodings)), 'decoder specified not to be conditioned on text, yet it is presented'
 
-        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
+        lowres_cond_img, lowres_noise_level = lowres_conditioner(image, target_image_size = target_image_size, downsample_image_size = self.image_sizes[unet_index - 1]) if exists(lowres_conditioner) else (None, None)
         image = resize_image_to(image, target_image_size, nearest = True)
 
         if exists(random_crop_size):
@@ -2599,7 +2786,7 @@ 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)
+        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)
 
         if not return_lowres_cond_image:
             return losses

dalle2_pytorch/trackers.py

@@ -4,13 +4,15 @@ import json
 from pathlib import Path
 import shutil
 from itertools import zip_longest
-from typing import Optional, List, Union
+from typing import Any, Optional, List, Union
 from pydantic import BaseModel
 
 import torch
-
+from dalle2_pytorch.dalle2_pytorch import Decoder, DiffusionPrior
 from dalle2_pytorch.utils import import_or_print_error
 from dalle2_pytorch.trainer import DecoderTrainer, DiffusionPriorTrainer
+from dalle2_pytorch.version import __version__
+from packaging import version
 
 # constants
 
@@ -21,16 +23,6 @@ DEFAULT_DATA_PATH = './.tracker-data'
 def exists(val):
     return val is not None
 
-# load file functions
-
-def load_wandb_file(run_path, file_path, **kwargs):
-    wandb = import_or_print_error('wandb', '`pip install wandb` to use the wandb recall function')
-    file_reference = wandb.restore(file_path, run_path=run_path)
-    return file_reference.name
-
-def load_local_file(file_path, **kwargs):
-    return file_path
-
 class BaseLogger:
     """
     An abstract class representing an object that can log data.
@@ -234,7 +226,7 @@ class LocalLoader(BaseLoader):
 
     def init(self, logger: BaseLogger, **kwargs) -> None:
         # Makes sure the file exists to be loaded
-        if not self.file_path.exists():
+        if not self.file_path.exists() and not self.only_auto_resume:
             raise FileNotFoundError(f'Model not found at {self.file_path}')
 
     def recall(self) -> dict:
@@ -283,9 +275,9 @@ def create_loader(loader_type: str, data_path: str, **kwargs) -> BaseLoader:
 class BaseSaver:
     def __init__(self,
         data_path: str,
-        save_latest_to: Optional[Union[str, bool]] = 'latest.pth',
-        save_best_to: Optional[Union[str, bool]] = 'best.pth',
-        save_meta_to: str = './',
+        save_latest_to: Optional[Union[str, bool]] = None,
+        save_best_to: Optional[Union[str, bool]] = None,
+        save_meta_to: Optional[str] = None,
         save_type: str = 'checkpoint',
         **kwargs
     ):
@@ -295,10 +287,10 @@ class BaseSaver:
         self.save_best_to = save_best_to
         self.saving_best = save_best_to is not None and save_best_to is not False
         self.save_meta_to = save_meta_to
+        self.saving_meta = save_meta_to is not None
         self.save_type = save_type
         assert save_type in ['checkpoint', 'model'], '`save_type` must be one of `checkpoint` or `model`'
-        assert self.save_meta_to is not None, '`save_meta_to` must be provided'
-        assert self.saving_latest or self.saving_best, '`save_latest_to` or `save_best_to` must be provided'
+        assert self.saving_latest or self.saving_best or self.saving_meta, 'At least one saving option must be specified'
 
     def init(self, logger: BaseLogger, **kwargs) -> None:
         raise NotImplementedError
@@ -459,6 +451,11 @@ class Tracker:
             print(f'\n\nWARNING: RUN HAS BEEN AUTO-RESUMED WITH THE LOGGER TYPE {self.logger.__class__.__name__}.\nIf this was not your intention, stop this run and set `auto_resume` to `False` in the config.\n\n')
             print(f"New logger config: {self.logger.__dict__}")
         
+        self.save_metadata = dict(
+            version = version.parse(__version__)
+        )  # Data that will be saved alongside the checkpoint or model
+        self.blacklisted_checkpoint_metadata_keys = ['scaler', 'optimizer', 'model', 'version', 'step', 'steps']  # These keys would cause us to error if we try to save them as metadata
+
         assert self.logger is not None, '`logger` must be set before `init` is called'
         if self.dummy_mode:
             # The only thing we need is a loader
@@ -507,8 +504,15 @@ class Tracker:
         # Save the config under config_name in the root folder of data_path
         shutil.copy(current_config_path, self.data_path / config_name)
         for saver in self.savers:
-            remote_path = Path(saver.save_meta_to) / config_name
-            saver.save_file(current_config_path, str(remote_path))
+            if saver.saving_meta:
+                remote_path = Path(saver.save_meta_to) / config_name
+                saver.save_file(current_config_path, str(remote_path))
+
+    def add_save_metadata(self, state_dict_key: str, metadata: Any):
+        """
+        Adds a new piece of metadata that will be saved along with the model or decoder.
+        """
+        self.save_metadata[state_dict_key] = metadata
 
     def _save_state_dict(self, trainer: Union[DiffusionPriorTrainer, DecoderTrainer], save_type: str, file_path: str, **kwargs) -> Path:
         """
@@ -518,24 +522,34 @@ class Tracker:
         """
         assert save_type in ['checkpoint', 'model']
         if save_type == 'checkpoint':
-            trainer.save(file_path, overwrite=True, **kwargs)
+            # Create a metadata dict without the blacklisted keys so we do not error when we create the state dict
+            metadata = {k: v for k, v in self.save_metadata.items() if k not in self.blacklisted_checkpoint_metadata_keys}
+            trainer.save(file_path, overwrite=True, **kwargs, **metadata)
         elif save_type == 'model':
             if isinstance(trainer, DiffusionPriorTrainer):
                 prior = trainer.ema_diffusion_prior.ema_model if trainer.use_ema else trainer.diffusion_prior
-                state_dict = trainer.unwrap_model(prior).state_dict()
-                torch.save(state_dict, file_path)
+                prior: DiffusionPrior = trainer.unwrap_model(prior)
+                # Remove CLIP if it is part of the model
+                prior.clip = None
+                model_state_dict = prior.state_dict()
             elif isinstance(trainer, DecoderTrainer):
-                decoder = trainer.accelerator.unwrap_model(trainer.decoder)
+                decoder: Decoder = trainer.accelerator.unwrap_model(trainer.decoder)
+                # Remove CLIP if it is part of the model
+                decoder.clip = None
                 if trainer.use_ema:
                     trainable_unets = decoder.unets
                     decoder.unets = trainer.unets  # Swap EMA unets in
-                    state_dict = decoder.state_dict()
+                    model_state_dict = decoder.state_dict()
                     decoder.unets = trainable_unets  # Swap back
                 else:
-                    state_dict = decoder.state_dict()
-                torch.save(state_dict, file_path)
+                    model_state_dict = decoder.state_dict()
             else:
                 raise NotImplementedError('Saving this type of model with EMA mode enabled is not yet implemented. Actually, how did you get here?')
+            state_dict = {
+                **self.save_metadata,
+                'model': model_state_dict
+            }
+            torch.save(state_dict, file_path)
         return Path(file_path)
 
     def save(self, trainer, is_best: bool, is_latest: bool, **kwargs):

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.24.3'
+__version__ = '0.26.2'

train_decoder.py

@@ -513,6 +513,7 @@ def create_tracker(accelerator: Accelerator, config: TrainDecoderConfig, config_
     }
     tracker: Tracker = tracker_config.create(config, accelerator_config, dummy_mode=dummy)
     tracker.save_config(config_path, config_name='decoder_config.json')
+    tracker.add_save_metadata(state_dict_key='config', metadata=config.dict())
     return tracker
     
 def initialize_training(config: TrainDecoderConfig, config_path):