add gradient checkpointing for all resnet blocks

This allows pylance to do proper type hinting and makes developing
extensions to the package much easier

README.md

@@ -371,6 +371,7 @@ 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),
@@ -395,7 +396,7 @@ decoder = Decoder(
 ).cuda()
 
 for unet_number in (1, 2):
-    loss = decoder(images, unet_number = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much
+    loss = 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.backward()
 
 # do above for many steps
@@ -626,8 +627,96 @@ 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
+
+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
@@ -784,25 +873,23 @@ unet1 = Unet(
     text_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
-    dim_mults=(1, 2, 4, 8)
+    dim_mults=(1, 2, 4, 8),
+    cond_on_text_encodings = True,
 ).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,
-    condition_on_text_encodings = True
+    timesteps = 1000
 ).cuda()
 
 decoder_trainer = DecoderTrainer(
@@ -827,8 +914,8 @@ 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(4, 512).cuda()
-images = decoder_trainer.sample(mock_image_embed, text = text) # (4, 3, 256, 256)
+mock_image_embed = torch.randn(32, 512).cuda()
+images = decoder_trainer.sample(image_embed = mock_image_embed, text = text) # (4, 3, 256, 256)
 ```
 
 ### Diffusion Prior Training
@@ -991,26 +1078,12 @@ 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
@@ -1049,8 +1122,9 @@ Once built, images will be saved to the same directory the command is invoked
 - [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
 - [x] speed up inference, read up on papers (ddim)
-- [ ] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
-- [ ] 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
+- [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
 - [ ] interface out the vqgan-vae so a pretrained one can be pulled off the shelf to validate latent diffusion + DALL-E2
 
 ## Citations
@@ -1169,4 +1243,14 @@ 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

@@ -69,14 +69,12 @@ Settings for controlling the training hyperparameters.
 | `wd` | No | `0.01` | The weight decay. |
 | `max_grad_norm`| No | `0.5` | The grad norm clipping. |
 | `save_every_n_samples` | No | `100000` | Samples will be generated and a checkpoint will be saved every `save_every_n_samples` samples. |
+| `cond_scale` | No | `1.0` | Conditioning scale to use for sampling. Can also be an array of values, one for each unet. |
 | `device` | No | `cuda:0` | The device to train on. |
 | `epoch_samples` | No | `None` | Limits the number of samples iterated through in each epoch. This must be set if resampling. None means no limit. |
 | `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 +161,10 @@ 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 +176,6 @@ 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"
         }]
     }
 }

configs/train_prior_config.example.json

@@ -1,18 +1,14 @@
 {
     "prior": {
         "clip": {
-            "make": "x-clip",
-            "model": "ViT-L/14",
-            "base_model_kwargs": {
-                "dim_text": 768,
-                "dim_image": 768,
-                "dim_latent": 768
-            }
+            "make": "openai",
+            "model": "ViT-L/14"
         },
         "net": {
             "dim": 768,
             "depth": 12,
             "num_timesteps": 1000,
+            "max_text_len": 77,
             "num_time_embeds": 1,
             "num_image_embeds": 1,
             "num_text_embeds": 1,
@@ -20,8 +16,8 @@
             "heads": 12,
             "ff_mult": 4,
             "norm_out": true,
-            "attn_dropout": 0.0,
-            "ff_dropout": 0.0,
+            "attn_dropout": 0.05,
+            "ff_dropout": 0.05,
             "final_proj": true,
             "normformer": true,
             "rotary_emb": true
@@ -30,6 +26,7 @@
         "image_size": 224,
         "image_channels": 3,
         "timesteps": 1000,
+        "sample_timesteps": 64,
         "cond_drop_prob": 0.1,
         "loss_type": "l2",
         "predict_x_start": true,
@@ -37,34 +34,48 @@
         "condition_on_text_encodings": true
     },
     "data": {
-        "image_url": "https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/",
-        "text_url": "https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/",
-        "meta_url": "https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/laion2B-en-metadata/",
-        "batch_size": 256,
+        "batch_size": 128,
+        "num_data_points": 100000,
+        "eval_every_seconds": 1600,
+        "image_url": "<path to your images>",
+        "meta_url": "<path to your metadata>",
         "splits": {
-            "train": 0.9,
-            "val": 1e-7,
-            "test": 0.0999999
+            "train": 0.8,
+            "val":  0.1,
+            "test": 0.1
         }
     },
     "train": {
-        "epochs": 1,
+        "epochs": 5,
         "lr": 1.1e-4,
         "wd": 6.02e-2,
         "max_grad_norm": 0.5,
         "use_ema": true,
+        "ema_beta": 0.9999,
+        "ema_update_after_step": 50,
+        "warmup_steps": 50,
         "amp": false,
-        "save_every": 10000
-    },
-    "load": {
-        "source": null,
-        "resume": false
+        "save_every_seconds": 3600,
+        "eval_timesteps": [64, 1000],
+        "random_seed": 84513
     },
     "tracker": {
-        "tracker_type": "wandb",
-        "data_path": "./prior_checkpoints",
-        "wandb_entity": "laion",
-        "wandb_project": "diffusion-prior",
-        "verbose": true
+        "data_path": ".prior",
+        "overwrite_data_path": true,
+        "log": {
+            "log_type": "wandb",
+            "wandb_entity": "<your wandb username>",
+            "wandb_project": "prior_debugging",
+            "wandb_resume": false,
+            "verbose": true
+        },
+        "save": [
+            {
+                "save_to": "local",
+                "save_type": "checkpoint",
+                "save_latest_to": ".prior/latest_checkpoint.pth",
+                "save_best_to": ".prior/best_checkpoint.pth"
+            }
+        ]
     }
 }

dalle2_pytorch/dalle2_pytorch.py

@@ -8,6 +8,7 @@ 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
 
@@ -75,6 +76,8 @@ def cast_tuple(val, length = None, validate = True):
     return out
 
 def module_device(module):
+    if isinstance(module, nn.Identity):
+        return 'cpu' # It doesn't matter
     return next(module.parameters()).device
 
 def zero_init_(m):
@@ -106,6 +109,28 @@ 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):
@@ -337,6 +362,75 @@ 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):
@@ -514,6 +608,17 @@ class NoiseScheduler(nn.Module):
             extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
         )
 
+    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))
+
+        alpha = extract(self.sqrt_alphas_cumprod, from_t, shape)
+        sigma = extract(self.sqrt_one_minus_alphas_cumprod, from_t, shape)
+        alpha_next = extract(self.sqrt_alphas_cumprod, to_t, shape)
+        sigma_next = extract(self.sqrt_one_minus_alphas_cumprod, to_t, shape)
+
+        return x_from * (alpha_next / alpha) + noise * (sigma_next * alpha - sigma * alpha_next) / alpha
+
     def predict_start_from_noise(self, x_t, t, noise):
         return (
             extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
@@ -534,34 +639,40 @@ class NoiseScheduler(nn.Module):
 # diffusion prior
 
 class LayerNorm(nn.Module):
-    def __init__(self, dim, eps = 1e-5, stable = False):
+    def __init__(self, dim, eps = 1e-5, fp16_eps = 1e-3, 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 + self.eps).rsqrt() * self.g
+        return (x - mean) * (var + eps).rsqrt() * self.g
 
 class ChanLayerNorm(nn.Module):
-    def __init__(self, dim, eps = 1e-5, stable = False):
+    def __init__(self, dim, eps = 1e-5, fp16_eps = 1e-3, 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 + self.eps).rsqrt() * self.g
+        return (x - mean) * (var + eps).rsqrt() * self.g
 
 class Residual(nn.Module):
     def __init__(self, fn):
@@ -682,11 +793,12 @@ class Attention(nn.Module):
         dropout = 0.,
         causal = False,
         rotary_emb = None,
-        pb_relax_alpha = 128
+        cosine_sim = True,
+        cosine_sim_scale = 16
     ):
         super().__init__()
-        self.pb_relax_alpha = pb_relax_alpha
-        self.scale = dim_head ** -0.5 * (pb_relax_alpha ** -1)
+        self.scale = cosine_sim_scale if cosine_sim else (dim_head ** -0.5)
+        self.cosine_sim = cosine_sim
 
         self.heads = heads
         inner_dim = dim_head * heads
@@ -726,6 +838,13 @@ 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)
@@ -751,9 +870,6 @@ 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)
 
@@ -1344,7 +1460,8 @@ class ResnetBlock(nn.Module):
         *,
         cond_dim = None,
         time_cond_dim = None,
-        groups = 8
+        groups = 8,
+        cosine_sim_cross_attn = False
     ):
         super().__init__()
 
@@ -1364,7 +1481,8 @@ class ResnetBlock(nn.Module):
                 'b (h w) c',
                 CrossAttention(
                     dim = dim_out,
-                    context_dim = cond_dim
+                    context_dim = cond_dim,
+                    cosine_sim = cosine_sim_cross_attn
                 )
             )
 
@@ -1399,11 +1517,12 @@ class CrossAttention(nn.Module):
         heads = 8,
         dropout = 0.,
         norm_context = False,
-        pb_relax_alpha = 32 ** 2
+        cosine_sim = False,
+        cosine_sim_scale = 16
     ):
         super().__init__()
-        self.pb_relax_alpha = pb_relax_alpha
-        self.scale = dim_head ** -0.5 * (pb_relax_alpha ** -1)
+        self.cosine_sim = cosine_sim
+        self.scale = cosine_sim_scale if cosine_sim else (dim_head ** -0.5)
         self.heads = heads
         inner_dim = dim_head * heads
 
@@ -1439,7 +1558,10 @@ class CrossAttention(nn.Module):
         k = torch.cat((nk, k), dim = -2)
         v = torch.cat((nv, v), dim = -2)
 
-        q = q * self.scale
+        if self.cosine_sim:
+            q, k = map(l2norm, (q, k))
+
+        q, k = map(lambda t: t * math.sqrt(self.scale), (q, k))
 
         sim = einsum('b h i d, b h j d -> b h i j', q, k)
         max_neg_value = -torch.finfo(sim.dtype).max
@@ -1449,9 +1571,6 @@ 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)
@@ -1463,7 +1582,8 @@ class LinearAttention(nn.Module):
         self,
         dim,
         dim_head = 32,
-        heads = 8
+        heads = 8,
+        **kwargs
     ):
         super().__init__()
         self.scale = dim_head ** -0.5
@@ -1481,6 +1601,7 @@ 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)
@@ -1490,6 +1611,9 @@ 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)
@@ -1525,6 +1649,38 @@ 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,
@@ -1545,6 +1701,8 @@ 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,
@@ -1562,6 +1720,8 @@ 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__()
@@ -1664,7 +1824,7 @@ class Unet(nn.Module):
 
         # attention related params
 
-        attn_kwargs = dict(heads = attn_heads, dim_head = attn_dim_head)
+        attn_kwargs = dict(heads = attn_heads, dim_head = attn_dim_head, cosine_sim = cosine_sim_self_attn)
 
         self_attn = cast_tuple(self_attn, num_stages)
 
@@ -1687,9 +1847,13 @@ 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 = ResnetBlock(init_dim, init_dim, time_cond_dim = time_cond_dim, groups = top_level_resnet_group) if memory_efficient else None
+        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
 
         # layers
 
@@ -1697,7 +1861,8 @@ class Unet(nn.Module):
         self.ups = nn.ModuleList([])
         num_resolutions = len(in_out)
 
-        skip_connect_dims = [] # keeping track of skip connection dimensions
+        skip_connect_dims = []          # keeping track of skip connection dimensions
+        upsample_combiner_dims = []     # keeping track of dimensions for final upsample feature map combiner
 
         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
@@ -1715,17 +1880,17 @@ class Unet(nn.Module):
 
             self.downs.append(nn.ModuleList([
                 downsample_klass(dim_in, dim_out = dim_out) if memory_efficient else None,
-                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)]),
+                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)]),
                 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 = ResnetBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim, groups = resnet_groups[-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_attn = create_self_attn(mid_dim)
-        self.mid_block2 = ResnetBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim, groups = resnet_groups[-1])
+        self.mid_block2 = resnet_block(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)
@@ -1739,14 +1904,27 @@ 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([
-                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)]),
+                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)]),
                 attention,
                 upsample_klass(dim_out, dim_in) if not is_last or memory_efficient else nn.Identity()
             ]))
 
-        self.final_resnet_block = ResnetBlock(dim * 2, dim, time_cond_dim = time_cond_dim, groups = top_level_resnet_group)
+        # 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)
 
         out_dim_in = dim + (channels if lowres_cond else 0)
 
@@ -1754,6 +1932,10 @@ 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(
@@ -1770,7 +1952,7 @@ 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 \
-            cond_on_lowres_noise == self.cond_on_lowres_noise and \
+            lowres_noise_cond == self.lowres_noise_cond and \
             channels_out == self.channels_out:
             return self
 
@@ -1811,7 +1993,8 @@ class Unet(nn.Module):
         image_cond_drop_prob = 0.,
         text_cond_drop_prob = 0.,
         blur_sigma = None,
-        blur_kernel_size = None
+        blur_kernel_size = None,
+        disable_checkpoint = False
     ):
         batch_size, device = x.shape[0], x.device
 
@@ -1933,16 +2116,29 @@ 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(self.init_resnet_block):
-            x = self.init_resnet_block(x, t)
+        if exists(init_resnet_block):
+            x = init_resnet_block(x, t)
 
         # go through the layers of the unet, down and up
 
-        hiddens = []
+        down_hiddens = []
+        up_hiddens = []
 
-        for pre_downsample, init_block, resnet_blocks, attn, post_downsample in self.downs:
+        for pre_downsample, init_block, resnet_blocks, attn, post_downsample in downs:
             if exists(pre_downsample):
                 x = pre_downsample(x)
 
@@ -1950,24 +2146,24 @@ class Unet(nn.Module):
 
             for resnet_block in resnet_blocks:
                 x = resnet_block(x, t, c)
-                hiddens.append(x)
+                down_hiddens.append(x.contiguous())
 
             x = attn(x)
-            hiddens.append(x.contiguous())
+            down_hiddens.append(x.contiguous())
 
             if exists(post_downsample):
                 x = post_downsample(x)
 
-        x = self.mid_block1(x, t, mid_c)
+        x = mid_block1(x, t, mid_c)
 
-        if exists(self.mid_attn):
-            x = self.mid_attn(x)
+        if exists(mid_attn):
+            x = mid_attn(x)
 
-        x = self.mid_block2(x, t, mid_c)
+        x = mid_block2(x, t, mid_c)
 
-        connect_skip = lambda fmap: torch.cat((fmap, hiddens.pop() * self.skip_connect_scale), dim = 1)
+        connect_skip = lambda fmap: torch.cat((fmap, down_hiddens.pop() * self.skip_connect_scale), dim = 1)
 
-        for init_block, resnet_blocks, attn, upsample in self.ups:
+        for init_block, resnet_blocks, attn, upsample in ups:
             x = connect_skip(x)
             x = init_block(x, t, c)
 
@@ -1976,11 +2172,15 @@ 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 = self.final_resnet_block(x, t)
+        x = final_resnet_block(x, t)
 
         if exists(lowres_cond_img):
             x = torch.cat((x, lowres_cond_img), dim = 1)
@@ -2115,7 +2315,7 @@ 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
@@ -2326,7 +2526,7 @@ class Decoder(nn.Module):
 
     @property
     def condition_on_text_encodings(self):
-        return any([unet.cond_on_text_encodings for unet in self.unets])
+        return any([unet.cond_on_text_encodings for unet in self.unets if isinstance(unet, Unet)])
 
     def get_unet(self, unet_number):
         assert 0 < unet_number <= self.num_unets
@@ -2415,36 +2615,102 @@ class Decoder(nn.Module):
         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, lowres_noise_level = None):
+    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,
+        inpaint_resample_times = 5
+    ):
         device = self.device
 
         b = shape[0]
         img = torch.randn(shape, device = device)
 
+        is_inpaint = exists(inpaint_image)
+        resample_times = inpaint_resample_times if is_inpaint else 1
+
+        if is_inpaint:
+            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):
-            img = self.p_sample(
-                unet,
-                img,
-                torch.full((b,), i, device = device, dtype = torch.long),
-                image_embed = image_embed,
-                text_encodings = text_encodings,
-                cond_scale = cond_scale,
-                lowres_cond_img = lowres_cond_img,
-                lowres_noise_level = lowres_noise_level,
-                predict_x_start = predict_x_start,
-                noise_scheduler = noise_scheduler,
-                learned_variance = learned_variance,
-                clip_denoised = clip_denoised
-            )
+        for time in tqdm(reversed(range(0, noise_scheduler.num_timesteps)), desc = 'sampling loop time step', total = noise_scheduler.num_timesteps):
+            is_last_timestep = time == 0
+
+            for r in reversed(range(0, resample_times)):
+                is_last_resample_step = r == 0
+
+                times = torch.full((b,), time, device = device, dtype = torch.long)
+
+                if is_inpaint:
+                    # 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,
+                    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 is_inpaint and not (is_last_timestep or is_last_resample_step):
+                    # in repaint, you renoise and resample up to 10 times every step
+                    img = noise_scheduler.q_sample_from_to(img, times - 1, times)
+
+        if is_inpaint:
+            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, lowres_noise_level = None):
+    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,
+        inpaint_resample_times = 5
+    ):
         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]
@@ -2452,39 +2718,68 @@ class Decoder(nn.Module):
         times = list(reversed(times.int().tolist()))
         time_pairs = list(zip(times[:-1], times[1:]))
 
+        is_inpaint = exists(inpaint_image)
+        resample_times = inpaint_resample_times if is_inpaint else 1
+
+        if is_inpaint:
+            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:
             lowres_cond_img = maybe(self.normalize_img)(lowres_cond_img)
 
         for time, time_next in tqdm(time_pairs, desc = 'sampling loop time step'):
-            alpha = alphas[time]
-            alpha_next = alphas[time_next]
+            is_last_timestep = time_next == 0
 
-            time_cond = torch.full((batch,), time, device = device, dtype = torch.long)
+            for r in reversed(range(0, resample_times)):
+                is_last_resample_step = r == 0
 
-            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)
+                alpha = alphas[time]
+                alpha_next = alphas[time_next]
 
-            if learned_variance:
-                pred, _ = pred.chunk(2, dim = 1)
+                time_cond = torch.full((batch,), time, device = device, dtype = torch.long)
 
-            if 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
+                if is_inpaint:
+                    # 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)
 
-            if clip_denoised:
-                x_start = self.dynamic_threshold(x_start)
+                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)
 
-            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 time_next > 0 else 0.
+                if learned_variance:
+                    pred, _ = pred.chunk(2, dim = 1)
 
-            img = x_start * alpha_next.sqrt() + \
-                  c1 * noise + \
-                  c2 * pred_noise
+                if 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
+
+                if clip_denoised:
+                    x_start = self.dynamic_threshold(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.
+
+                img = x_start * alpha_next.sqrt() + \
+                      c1 * noise + \
+                      c2 * pred_noise
+
+                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)
+
+        if exists(inpaint_image):
+            img = (img * ~inpaint_mask) + (inpaint_image * inpaint_mask)
 
         img = self.unnormalize_img(img)
         return img
@@ -2578,13 +2873,18 @@ class Decoder(nn.Module):
     @eval_decorator
     def sample(
         self,
+        image = None,
         image_embed = None,
         text = None,
         text_encodings = None,
         batch_size = 1,
         cond_scale = 1.,
+        start_at_unet_number = 1,
         stop_at_unet_number = None,
         distributed = False,
+        inpaint_image = None,
+        inpaint_mask = None,
+        inpaint_resample_times = 5
     ):
         assert self.unconditional or exists(image_embed), 'image embed must be present on sampling from decoder unless if trained unconditionally'
 
@@ -2598,17 +2898,29 @@ 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
+        if start_at_unet_number > 1:
+            # Then we are not generating the first image and one must have been passed in
+            assert exists(image), 'image must be passed in if starting at unet number > 1'
+            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)):
+            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 and not distributed else null_context()
+            context = self.one_unet_in_gpu(unet = unet) if is_cuda else null_context()
 
             with context:
+                # prepare low resolution conditioning for upsamplers
+
                 lowres_cond_img = lowres_noise_level = None
                 shape = (batch_size, channel, image_size, image_size)
 
@@ -2619,12 +2931,16 @@ class Decoder(nn.Module):
                         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,
@@ -2638,7 +2954,10 @@ class Decoder(nn.Module):
                     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,
+                    inpaint_resample_times = inpaint_resample_times
                 )
 
                 img = vae.decode(img)
@@ -2753,7 +3072,7 @@ 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, text = text_cond, cond_scale = cond_scale)
+        images = self.decoder.sample(image_embed = 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/dataloaders/prior_loader.py

@@ -67,6 +67,15 @@ class PriorEmbeddingDataset(IterableDataset):
     def __str__(self):
         return f"<PriorEmbeddingDataset: start: {self.start}, stop: {self.stop}, len: {self.__len__()}>"
 
+    def set_start(self, start):
+        """
+        Adjust the starting point within the reader, useful for resuming an epoch
+        """
+        self.start = start
+
+    def get_start(self):
+        return self.start
+
     def get_sample(self):
         """
         pre-proocess data from either reader into a common format

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 +522,38 @@ 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.accelerator.unwrap_model(prior)
+                # Remove CLIP if it is part of the model
+                original_clip = prior.clip
+                prior.clip = None
+                model_state_dict = prior.state_dict()
+                prior.clip = original_clip
             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
+                original_clip = decoder.clip
+                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()
+                decoder.clip = original_clip
             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/train_configs.py

@@ -1,7 +1,7 @@
 import json
 from torchvision import transforms as T
 from pydantic import BaseModel, validator, root_validator
-from typing import List, Iterable, Optional, Union, Tuple, Dict, Any
+from typing import List, Optional, Union, Tuple, Dict, Any, TypeVar
 
 from x_clip import CLIP as XCLIP
 from coca_pytorch import CoCa
@@ -25,11 +25,9 @@ def exists(val):
 def default(val, d):
     return val if exists(val) else d
 
-def ListOrTuple(inner_type):
-    return Union[List[inner_type], Tuple[inner_type]]
-
-def SingularOrIterable(inner_type):
-    return Union[inner_type, ListOrTuple(inner_type)]
+InnerType = TypeVar('InnerType')
+ListOrTuple = Union[List[InnerType], Tuple[InnerType]]
+SingularOrIterable = Union[InnerType, ListOrTuple[InnerType]]
 
 # general pydantic classes
 
@@ -145,6 +143,9 @@ class DiffusionPriorNetworkConfig(BaseModel):
     normformer: bool = False
     rotary_emb: bool = True
 
+    class Config:
+        extra = "allow"
+
     def create(self):
         kwargs = self.dict()
         return DiffusionPriorNetwork(**kwargs)
@@ -187,23 +188,26 @@ class DiffusionPriorTrainConfig(BaseModel):
     use_ema: bool = True
     ema_beta: float = 0.99
     amp: bool = False
-    save_every: int = 10000 # what steps to save on
+    warmup_steps: int = None             # number of warmup steps
+    save_every_seconds: int = 3600       # how often to save
+    eval_timesteps: List[int] = [64]     # which sampling timesteps to evaluate with
+    best_validation_loss: float = 1e9    # the current best valudation loss observed
+    current_epoch: int = 0               # the current epoch
+    num_samples_seen: int = 0            # the current number of samples seen
+    random_seed: int = 0                 # manual seed for torch
 
 class DiffusionPriorDataConfig(BaseModel):
-    image_url: str     # path to embeddings folder
-    meta_url: str      # path to metadata (captions) for images
-    splits: TrainSplitConfig
-    batch_size: int = 64
-
-class DiffusionPriorLoadConfig(BaseModel):
-    source: str = None
-    resume: bool = False
+    image_url: str                   # path to embeddings folder
+    meta_url: str                    # path to metadata (captions) for images
+    splits: TrainSplitConfig         # define train, validation, test splits for your dataset
+    batch_size: int                  # per-gpu batch size used to train the model
+    num_data_points: int = 25e7      # total number of datapoints to train on
+    eval_every_seconds: int = 3600   # validation statistics will be performed this often
 
 class TrainDiffusionPriorConfig(BaseModel):
     prior: DiffusionPriorConfig
     data: DiffusionPriorDataConfig
     train: DiffusionPriorTrainConfig
-    load: DiffusionPriorLoadConfig
     tracker: TrackerConfig
 
     @classmethod
@@ -216,13 +220,13 @@ class TrainDiffusionPriorConfig(BaseModel):
 
 class UnetConfig(BaseModel):
     dim: int
-    dim_mults: ListOrTuple(int)
+    dim_mults: ListOrTuple[int]
     image_embed_dim: int = None
     text_embed_dim: int = None
     cond_on_text_encodings: bool = None
     cond_dim: int = None
     channels: int = 3
-    self_attn: ListOrTuple(int)
+    self_attn: ListOrTuple[int]
     attn_dim_head: int = 32
     attn_heads: int = 16
     init_cross_embed: bool = True
@@ -231,16 +235,16 @@ class UnetConfig(BaseModel):
         extra = "allow"
 
 class DecoderConfig(BaseModel):
-    unets: ListOrTuple(UnetConfig)
+    unets: ListOrTuple[UnetConfig]
     image_size: int = None
-    image_sizes: ListOrTuple(int) = None
+    image_sizes: ListOrTuple[int] = None
     clip: Optional[AdapterConfig]   # The clip model to use if embeddings are not provided
     channels: int = 3
     timesteps: int = 1000
-    sample_timesteps: Optional[SingularOrIterable(int)] = None
+    sample_timesteps: Optional[SingularOrIterable[int]] = None
     loss_type: str = 'l2'
-    beta_schedule: ListOrTuple(str) = 'cosine'
-    learned_variance: bool = True
+    beta_schedule: ListOrTuple[str] = None  # None means all cosine
+    learned_variance: SingularOrIterable[bool] = True
     image_cond_drop_prob: float = 0.1
     text_cond_drop_prob: float = 0.5
 
@@ -299,20 +303,22 @@ class DecoderDataConfig(BaseModel):
 
 class DecoderTrainConfig(BaseModel):
     epochs: int = 20
-    lr: SingularOrIterable(float) = 1e-4
-    wd: SingularOrIterable(float) = 0.01
-    warmup_steps: Optional[SingularOrIterable(int)] = None
+    lr: SingularOrIterable[float] = 1e-4
+    wd: SingularOrIterable[float] = 0.01
+    warmup_steps: Optional[SingularOrIterable[int]] = None
     find_unused_parameters: bool = True
-    max_grad_norm: SingularOrIterable(float) = 0.5
+    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
+    cond_scale: Union[float, List[float]] = 1.0
     device: str = 'cuda:0'
     epoch_samples: int = None                      # Limits the number of samples per epoch. None means no limit. Required if resample_train is true as otherwise the number of samples per epoch is infinite.
     validation_samples: int = None                 # Same as above but for validation.
+    save_immediately: bool = False
     use_ema: bool = True
     ema_beta: float = 0.999
     amp: bool = False
-    unet_training_mask: ListOrTuple(bool) = None   # If None, use all unets
+    unet_training_mask: ListOrTuple[bool] = None   # If None, use all unets
 
 class DecoderEvaluateConfig(BaseModel):
     n_evaluation_samples: int = 1000
@@ -321,12 +327,6 @@ class DecoderEvaluateConfig(BaseModel):
     KID: Dict[str, Any] = None
     LPIPS: Dict[str, Any] = None
 
-class DecoderLoadConfig(BaseModel):
-    source: str = None                      # Supports file and wandb
-    run_path: str = ''                      # Used only if source is wandb
-    file_path: str = ''                     # The local filepath if source is file. If source is wandb, the relative path to the model file in wandb.
-    resume: bool = False                    # If using wandb, whether to resume the run
-
 class TrainDecoderConfig(BaseModel):
     decoder: DecoderConfig
     data: DecoderDataConfig

dalle2_pytorch/trainer.py

@@ -174,26 +174,24 @@ class DiffusionPriorTrainer(nn.Module):
     def __init__(
         self,
         diffusion_prior,
+        accelerator = None,
         use_ema = True,
         lr = 3e-4,
         wd = 1e-2,
         eps = 1e-6,
         max_grad_norm = None,
-        amp = False,
         group_wd_params = True,
-        device = None,
-        accelerator = None,
-        verbose = True,
+        warmup_steps = 1,
         **kwargs
     ):
         super().__init__()
         assert isinstance(diffusion_prior, DiffusionPrior)
-        assert not exists(accelerator) or isinstance(accelerator, Accelerator)
+
         ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
+        accelerator_kwargs, kwargs = groupby_prefix_and_trim('accelerator_', kwargs)
 
-        # verbosity
-
-        self.verbose = verbose
+        if not exists(accelerator):
+            accelerator = Accelerator(**accelerator_kwargs)
 
         # assign some helpful member vars
 
@@ -202,23 +200,31 @@ class DiffusionPriorTrainer(nn.Module):
 
         # setting the device
 
-        if not exists(accelerator) and not exists(device):
-            diffusion_prior_device = next(diffusion_prior.parameters()).device
-            self.print(f'accelerator not given, and device not specified: defaulting to device of diffusion prior parameters - {diffusion_prior_device}')
-            self.device = diffusion_prior_device
-        else:
-            self.device = accelerator.device if exists(accelerator) else device
-            diffusion_prior.to(self.device)
+        self.device = accelerator.device
+        diffusion_prior.to(self.device)
 
         # save model
 
         self.diffusion_prior = diffusion_prior
 
-        # optimizer and mixed precision stuff
+        # mixed precision checks
 
-        self.amp = amp
+        if (
+            exists(self.accelerator) 
+            and self.accelerator.distributed_type == DistributedType.DEEPSPEED 
+            and self.diffusion_prior.clip is not None
+            ):
+            # Then we need to make sure clip is using the correct precision or else deepspeed will error
+            cast_type_map = {
+                "fp16": torch.half,
+                "bf16": torch.bfloat16,
+                "no": torch.float
+            }
+            precision_type = cast_type_map[accelerator.mixed_precision]
+            assert precision_type == torch.float, "DeepSpeed currently only supports float32 precision when using on the fly embedding generation from clip"
+            self.diffusion_prior.clip.to(precision_type)
 
-        self.scaler = GradScaler(enabled = amp)
+        # optimizer stuff
 
         self.optim_kwargs = dict(lr=lr, wd=wd, eps=eps, group_wd_params=group_wd_params)
 
@@ -227,17 +233,21 @@ class DiffusionPriorTrainer(nn.Module):
             **self.optim_kwargs,
             **kwargs
         )
+        
+        self.scheduler = LambdaLR(self.optimizer, lr_lambda = lambda _: 1.0)
+        
+        self.warmup_scheduler = warmup.LinearWarmup(self.optimizer, warmup_period = warmup_steps) if exists(warmup_steps) else None
 
         # distribute the model if using HFA
-        if exists(self.accelerator):
-            self.diffusion_prior, self.optimizer = self.accelerator.prepare(self.diffusion_prior, self.optimizer)
+
+        self.diffusion_prior, self.optimizer, self.scheduler = self.accelerator.prepare(self.diffusion_prior, self.optimizer, self.scheduler)
 
         # exponential moving average stuff
 
         self.use_ema = use_ema
 
         if self.use_ema:
-            self.ema_diffusion_prior = EMA(self.unwrap_model(self.diffusion_prior), **ema_kwargs)
+            self.ema_diffusion_prior = EMA(self.accelerator.unwrap_model(self.diffusion_prior), **ema_kwargs)
 
         # gradient clipping if needed
 
@@ -247,67 +257,24 @@ class DiffusionPriorTrainer(nn.Module):
 
         self.register_buffer('step', torch.tensor([0], device = self.device))
 
-    # accelerator wrappers
-
-    def print(self, msg):
-        if not self.verbose:
-            return
-
-        if exists(self.accelerator):
-            self.accelerator.print(msg)
-        else:
-            print(msg)
-
-    def unwrap_model(self, model):
-        if exists(self.accelerator):
-            return self.accelerator.unwrap_model(model)
-        else:
-            return model
-
-    def wait_for_everyone(self):
-        if exists(self.accelerator):
-            self.accelerator.wait_for_everyone()
-
-    def is_main_process(self):
-        if exists(self.accelerator):
-            return self.accelerator.is_main_process
-        else:
-            return True
-
-    def clip_grad_norm_(self, *args):
-        if exists(self.accelerator):
-            return self.accelerator.clip_grad_norm_(*args)
-        else:
-            return torch.nn.utils.clip_grad_norm_(*args)
-
-    def backprop(self, x):
-        if exists(self.accelerator):
-            self.accelerator.backward(x)
-        else:
-            try:
-                x.backward()
-            except Exception as e:
-                self.print(f"Caught error in backprop call: {e}")
-
     # utility
 
     def save(self, path, overwrite = True, **kwargs):
-        # ensure we sync gradients before continuing
-        self.wait_for_everyone()
 
         # only save on the main process
-        if self.is_main_process():
-            self.print(f"Saving checkpoint at step: {self.step.item()}")
+        if self.accelerator.is_main_process:
+            print(f"Saving checkpoint at step: {self.step.item()}")
             path = Path(path)
             assert not (path.exists() and not overwrite)
             path.parent.mkdir(parents = True, exist_ok = True)
 
+            # FIXME: LambdaLR can't be saved due to pickling issues
             save_obj = dict(
-                scaler = self.scaler.state_dict(),
                 optimizer = self.optimizer.state_dict(),
-                model = self.unwrap_model(self.diffusion_prior).state_dict(), # unwrap the model from distribution if applicable
+                warmup_scheduler = self.warmup_scheduler,
+                model = self.accelerator.unwrap_model(self.diffusion_prior).state_dict(),
                 version = version.parse(__version__),
-                step = self.step.item(),
+                step = self.step,
                 **kwargs
             )
 
@@ -320,14 +287,14 @@ class DiffusionPriorTrainer(nn.Module):
 
             torch.save(save_obj, str(path))
 
-    def load(self, path, overwrite_lr = True, strict = True):
+    def load(self, path_or_state, overwrite_lr = True, strict = True):
         """
         Load a checkpoint of a diffusion prior trainer.
 
         Will load the entire trainer, including the optimizer and EMA.
 
         Params:
-            - path (str): a path to the DiffusionPriorTrainer checkpoint file
+            - path_or_state (str | torch): a path to the DiffusionPriorTrainer checkpoint file
             - overwrite_lr (bool): wether or not to overwrite the stored LR with the LR specified in the new trainer
             - strict (bool): kwarg for `torch.nn.Module.load_state_dict`, will force an exact checkpoint match
 
@@ -336,56 +303,56 @@ class DiffusionPriorTrainer(nn.Module):
         """
 
         # all processes need to load checkpoint. no restriction here
-        path = Path(path)
-        assert path.exists()
+        if isinstance(path_or_state, str):
+            path = Path(path_or_state)
+            assert path.exists()
+            loaded_obj = torch.load(str(path), map_location=self.device)
 
-        loaded_obj = torch.load(str(path), map_location=self.device)
+        elif isinstance(path_or_state, dict):
+            loaded_obj = path_or_state
 
         if version.parse(__version__) != loaded_obj['version']:
             print(f'loading saved diffusion prior at version {loaded_obj["version"]} but current package version is at {__version__}')
 
         # unwrap the model when loading from checkpoint
-        self.unwrap_model(self.diffusion_prior).load_state_dict(loaded_obj['model'], strict = strict)
-        self.step.copy_(torch.ones_like(self.step) * loaded_obj['step'])
-
-        self.scaler.load_state_dict(loaded_obj['scaler'])
+        self.accelerator.unwrap_model(self.diffusion_prior).load_state_dict(loaded_obj['model'], strict = strict)
+        self.step.copy_(torch.ones_like(self.step, device=self.device) * loaded_obj['step'].to(self.device))
         self.optimizer.load_state_dict(loaded_obj['optimizer'])
 
+        # set warmupstep
+        if exists(self.warmup_scheduler):
+            self.warmup_scheduler.last_step = self.step.item()
+
+        # ensure new lr is used if different from old one
         if overwrite_lr:
             new_lr = self.optim_kwargs["lr"]
 
-            self.print(f"Overriding LR to be {new_lr}")
-
             for group in self.optimizer.param_groups:
-                group["lr"] = new_lr
+                group["lr"] = new_lr if group["lr"] > 0.0 else 0.0
 
         if self.use_ema:
             assert 'ema' in loaded_obj
             self.ema_diffusion_prior.load_state_dict(loaded_obj['ema'], strict = strict)
-            # below not be necessary, but I had a suspicion that this wasn't being loaded correctly
+            # below might not be necessary, but I had a suspicion that this wasn't being loaded correctly
             self.ema_diffusion_prior.ema_model.load_state_dict(loaded_obj["ema_model"])
 
-        # sync and inform
-        self.wait_for_everyone()
-        self.print(f"Loaded model")
-
         return loaded_obj
 
     # model functionality
 
     def update(self):
-        # only continue with updates until all ranks finish
-        self.wait_for_everyone()
 
         if exists(self.max_grad_norm):
-            self.scaler.unscale_(self.optimizer)
-            # utilize HFA clipping where applicable
-            self.clip_grad_norm_(self.diffusion_prior.parameters(), self.max_grad_norm)
-
-        self.scaler.step(self.optimizer)
-        self.scaler.update()
+            self.accelerator.clip_grad_norm_(self.diffusion_prior.parameters(), self.max_grad_norm)
+        
+        self.optimizer.step()
         self.optimizer.zero_grad()
 
+        # accelerator will ocassionally skip optimizer steps in a "dynamic loss scaling strategy"
+        if not self.accelerator.optimizer_step_was_skipped:
+            with self.warmup_scheduler.dampening():
+                self.scheduler.step()
+
         if self.use_ema:
             self.ema_diffusion_prior.update()
 
@@ -414,7 +381,7 @@ class DiffusionPriorTrainer(nn.Module):
     @cast_torch_tensor
     @prior_sample_in_chunks
     def embed_text(self, *args, **kwargs):
-        return self.unwrap_model(self.diffusion_prior).clip.embed_text(*args, **kwargs)
+        return self.accelerator.unwrap_model(self.diffusion_prior).clip.embed_text(*args, **kwargs)
 
     @cast_torch_tensor
     def forward(
@@ -426,16 +393,14 @@ class DiffusionPriorTrainer(nn.Module):
         total_loss = 0.
 
         for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
-            with autocast(enabled = self.amp):
+            with self.accelerator.autocast():
                 loss = self.diffusion_prior(*chunked_args, **chunked_kwargs)
                 loss = loss * chunk_size_frac
 
             total_loss += loss.item()
 
-            # backprop with accelerate if applicable
-
             if self.training:
-                self.backprop(self.scaler.scale(loss))
+                self.accelerator.backward(loss)
 
         return total_loss
 
@@ -498,23 +463,27 @@ class DecoderTrainer(nn.Module):
         warmup_schedulers = []
 
         for unet, unet_lr, unet_wd, unet_eps, unet_warmup_steps in zip(decoder.unets, lr, wd, eps, warmup_steps):
-            optimizer = get_optimizer(
-                unet.parameters(),
-                lr = unet_lr,
-                wd = unet_wd,
-                eps = unet_eps,
-                group_wd_params = group_wd_params,
-                **kwargs
-            )
+            if isinstance(unet, nn.Identity):
+                optimizers.append(None)
+                schedulers.append(None)
+                warmup_schedulers.append(None)
+            else:
+                optimizer = get_optimizer(
+                    unet.parameters(),
+                    lr = unet_lr,
+                    wd = unet_wd,
+                    eps = unet_eps,
+                    group_wd_params = group_wd_params,
+                    **kwargs
+                )
 
-            optimizers.append(optimizer)
+                optimizers.append(optimizer)
+                scheduler = LambdaLR(optimizer, lr_lambda = lambda step: 1.0)
 
-            scheduler = LambdaLR(optimizer, lr_lambda = lambda step: 1.0)
+                warmup_scheduler = warmup.LinearWarmup(optimizer, warmup_period = unet_warmup_steps) if exists(unet_warmup_steps) else None
+                warmup_schedulers.append(warmup_scheduler)
 
-            warmup_scheduler = warmup.LinearWarmup(optimizer, warmup_period = unet_warmup_steps) if exists(unet_warmup_steps) else None
-            warmup_schedulers.append(warmup_scheduler)
-
-            schedulers.append(scheduler)
+                schedulers.append(scheduler)
 
             if self.use_ema:
                 self.ema_unets.append(EMA(unet, **ema_kwargs))
@@ -590,7 +559,8 @@ class DecoderTrainer(nn.Module):
         for ind in range(0, self.num_unets):
             optimizer_key = f'optim{ind}'
             optimizer = getattr(self, optimizer_key)
-            save_obj = {**save_obj, optimizer_key: self.accelerator.unwrap_model(optimizer).state_dict()}
+            state_dict = optimizer.state_dict() if optimizer is not None else None
+            save_obj = {**save_obj, optimizer_key: state_dict}
 
         if self.use_ema:
             save_obj = {**save_obj, 'ema': self.ema_unets.state_dict()}
@@ -612,8 +582,8 @@ class DecoderTrainer(nn.Module):
             optimizer_key = f'optim{ind}'
             optimizer = getattr(self, optimizer_key)
             warmup_scheduler = self.warmup_schedulers[ind]
-
-            self.accelerator.unwrap_model(optimizer).load_state_dict(loaded_obj[optimizer_key])
+            if optimizer is not None:
+                optimizer.load_state_dict(loaded_obj[optimizer_key])
 
             if exists(warmup_scheduler):
                 warmup_scheduler.last_step = last_step
@@ -714,23 +684,32 @@ class DecoderTrainer(nn.Module):
         *args,
         unet_number = None,
         max_batch_size = None,
+        return_lowres_cond_image=False,
         **kwargs
     ):
         unet_number = self.validate_and_return_unet_number(unet_number)
 
         total_loss = 0.
-
-        
-        using_amp = self.accelerator.mixed_precision != 'no'
-
+        cond_images = []
         for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
             with self.accelerator.autocast():
-                loss = self.decoder(*chunked_args, unet_number = unet_number, **chunked_kwargs)
+                loss_obj = self.decoder(*chunked_args, unet_number = unet_number, return_lowres_cond_image=return_lowres_cond_image, **chunked_kwargs)
+                # loss_obj may be a tuple with loss and cond_image
+                if return_lowres_cond_image:
+                    loss, cond_image = loss_obj
+                else:
+                    loss = loss_obj
+                    cond_image = None
                 loss = loss * chunk_size_frac
+                if cond_image is not None:
+                    cond_images.append(cond_image)
 
             total_loss += loss.item()
 
             if self.training:
                 self.accelerator.backward(loss)
 
-        return total_loss
+        if return_lowres_cond_image:
+            return total_loss, torch.stack(cond_images)
+        else:
+            return total_loss

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.25.2'
+__version__ = '1.5.0'

setup.py

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

train_decoder.py

@@ -1,5 +1,6 @@
 from pathlib import Path
 from typing import List
+from datetime import timedelta
 
 from dalle2_pytorch.trainer import DecoderTrainer
 from dalle2_pytorch.dataloaders import create_image_embedding_dataloader
@@ -11,11 +12,12 @@ from clip import tokenize
 
 import torchvision
 import torch
+from torch import nn
 from torchmetrics.image.fid import FrechetInceptionDistance
 from torchmetrics.image.inception import InceptionScore
 from torchmetrics.image.kid import KernelInceptionDistance
 from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-from accelerate import Accelerator, DistributedDataParallelKwargs
+from accelerate import Accelerator, DistributedDataParallelKwargs, InitProcessGroupKwargs
 from accelerate.utils import dataclasses as accelerate_dataclasses
 import webdataset as wds
 import click
@@ -132,7 +134,7 @@ def get_example_data(dataloader, device, n=5):
             break
     return list(zip(images[:n], img_embeddings[:n], text_embeddings[:n], captions[:n]))
 
-def generate_samples(trainer, example_data, condition_on_text_encodings=False, text_prepend="", match_image_size=True):
+def generate_samples(trainer, example_data, start_unet=1, end_unet=None, condition_on_text_encodings=False, cond_scale=1.0, device=None, text_prepend="", match_image_size=True):
     """
     Takes example data and generates images from the embeddings
     Returns three lists: real images, generated images, and captions
@@ -157,6 +159,13 @@ def generate_samples(trainer, example_data, condition_on_text_encodings=False, t
             # Then we are using precomputed text embeddings
             text_embeddings = torch.stack(text_embeddings)
             sample_params["text_encodings"] = text_embeddings
+    sample_params["start_at_unet_number"] = start_unet
+    sample_params["stop_at_unet_number"] = end_unet
+    if start_unet > 1:
+        # If we are only training upsamplers
+        sample_params["image"] = torch.stack(real_images)
+    if device is not None:
+        sample_params["_device"] = device
     samples = trainer.sample(**sample_params)
     generated_images = list(samples)
     captions = [text_prepend + txt for txt in txts]
@@ -165,15 +174,15 @@ def generate_samples(trainer, example_data, condition_on_text_encodings=False, t
         real_images = [resize_image_to(image, generated_image_size, clamp_range=(0, 1)) for image in real_images]
     return real_images, generated_images, captions
 
-def generate_grid_samples(trainer, examples, condition_on_text_encodings=False, text_prepend=""):
+def generate_grid_samples(trainer, examples, start_unet=1, end_unet=None, condition_on_text_encodings=False, cond_scale=1.0, device=None, text_prepend=""):
     """
     Generates samples and uses torchvision to put them in a side by side grid for easy viewing
     """
-    real_images, generated_images, captions = generate_samples(trainer, examples, condition_on_text_encodings, text_prepend)
+    real_images, generated_images, captions = generate_samples(trainer, examples, start_unet, end_unet, condition_on_text_encodings, cond_scale, device, text_prepend)
     grid_images = [torchvision.utils.make_grid([original_image, generated_image]) for original_image, generated_image in zip(real_images, generated_images)]
     return grid_images, captions
                     
-def evaluate_trainer(trainer, dataloader, device, condition_on_text_encodings=False, n_evaluation_samples=1000, FID=None, IS=None, KID=None, LPIPS=None):
+def evaluate_trainer(trainer, dataloader, device,  start_unet, end_unet, condition_on_text_encodings=False, cond_scale=1.0, inference_device=None, n_evaluation_samples=1000, FID=None, IS=None, KID=None, LPIPS=None):
     """
     Computes evaluation metrics for the decoder
     """
@@ -183,7 +192,7 @@ def evaluate_trainer(trainer, dataloader, device, condition_on_text_encodings=Fa
     if len(examples) == 0:
         print("No data to evaluate. Check that your dataloader has shards.")
         return metrics
-    real_images, generated_images, captions = generate_samples(trainer, examples, condition_on_text_encodings)
+    real_images, generated_images, captions = generate_samples(trainer, examples, start_unet, end_unet, condition_on_text_encodings, cond_scale, inference_device)
     real_images = torch.stack(real_images).to(device=device, dtype=torch.float)
     generated_images = torch.stack(generated_images).to(device=device, dtype=torch.float)
     # Convert from [0, 1] to [0, 255] and from torch.float to torch.uint8
@@ -259,11 +268,13 @@ def train(
     evaluate_config=None,
     epoch_samples = None,  # If the training dataset is resampling, we have to manually stop an epoch
     validation_samples = None,
+    save_immediately=False,
     epochs = 20,
     n_sample_images = 5,
     save_every_n_samples = 100000,
     unet_training_mask=None,
     condition_on_text_encodings=False,
+    cond_scale=1.0,
     **kwargs
 ):
     """
@@ -271,6 +282,21 @@ def train(
     """
     is_master = accelerator.process_index == 0
 
+    if not exists(unet_training_mask):
+        # Then the unet mask should be true for all unets in the decoder
+        unet_training_mask = [True] * len(decoder.unets)
+    assert len(unet_training_mask) == len(decoder.unets), f"The unet training mask should be the same length as the number of unets in the decoder. Got {len(unet_training_mask)} and {trainer.num_unets}"
+    trainable_unet_numbers = [i+1 for i, trainable in enumerate(unet_training_mask) if trainable]
+    first_trainable_unet = trainable_unet_numbers[0]
+    last_trainable_unet = trainable_unet_numbers[-1]
+    def move_unets(unet_training_mask):
+        for i in range(len(decoder.unets)):
+            if not unet_training_mask[i]:
+                # Replace the unet from the module list with a nn.Identity(). This training script never uses unets that aren't being trained so this is fine.
+                decoder.unets[i] = nn.Identity().to(inference_device)
+    # Remove non-trainable unets
+    move_unets(unet_training_mask)
+
     trainer = DecoderTrainer(
         decoder=decoder,
         accelerator=accelerator,
@@ -285,6 +311,7 @@ def train(
     sample = 0
     samples_seen = 0
     val_sample = 0
+    step = lambda: int(trainer.num_steps_taken(unet_number=first_trainable_unet))
 
     if tracker.can_recall:
         start_epoch, validation_losses, next_task, recalled_sample, samples_seen = recall_trainer(tracker, trainer)
@@ -296,13 +323,6 @@ def train(
         accelerator.print(f"Starting training from task {next_task} at sample {sample} and validation sample {val_sample}")
     trainer.to(device=inference_device)
 
-    if not exists(unet_training_mask):
-        # Then the unet mask should be true for all unets in the decoder
-        unet_training_mask = [True] * trainer.num_unets
-    first_training_unet = min(index for index, mask in enumerate(unet_training_mask) if mask)
-    step = lambda: int(trainer.num_steps_taken(unet_number=first_training_unet+1))
-    assert len(unet_training_mask) == trainer.num_unets, f"The unet training mask should be the same length as the number of unets in the decoder. Got {len(unet_training_mask)} and {trainer.num_unets}"
-
     accelerator.print(print_ribbon("Generating Example Data", repeat=40))
     accelerator.print("This can take a while to load the shard lists...")
     if is_master:
@@ -360,7 +380,7 @@ def train(
                             tokenized_texts = tokenize(txt, truncate=True)
                             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)})"
                             forward_params['text'] = tokenized_texts
-                    loss = trainer.forward(img, **forward_params, unet_number=unet)
+                    loss = trainer.forward(img, **forward_params, unet_number=unet, _device=inference_device)
                     trainer.update(unet_number=unet)
                     unet_losses_tensor[i % TRAIN_CALC_LOSS_EVERY_ITERS, unet-1] = loss
                 
@@ -373,10 +393,10 @@ def train(
                     unet_all_losses = accelerator.gather(unet_losses_tensor)
                     mask = unet_all_losses != 0
                     unet_average_loss = (unet_all_losses * mask).sum(dim=0) / mask.sum(dim=0)
-                    loss_map = { f"Unet {index} Training Loss": loss.item() for index, loss in enumerate(unet_average_loss) if loss != 0 }
+                    loss_map = { f"Unet {index} Training Loss": loss.item() for index, loss in enumerate(unet_average_loss) if unet_training_mask[index] }
 
                     # gather decay rate on each UNet
-                    ema_decay_list = {f"Unet {index} EMA Decay": ema_unet.get_current_decay() for index, ema_unet in enumerate(trainer.ema_unets)}
+                    ema_decay_list = {f"Unet {index} EMA Decay": ema_unet.get_current_decay() for index, ema_unet in enumerate(trainer.ema_unets) if unet_training_mask[index]}
 
                     log_data = {
                         "Epoch": epoch,
@@ -391,7 +411,7 @@ def train(
                     if is_master:
                         tracker.log(log_data, step=step())
 
-                if is_master and last_snapshot + save_every_n_samples < sample:  # This will miss by some amount every time, but it's not a big deal... I hope
+                if is_master and (last_snapshot + save_every_n_samples < sample or (save_immediately and i == 0)):  # This will miss by some amount every time, but it's not a big deal... I hope
                     # It is difficult to gather this kind of info on the accelerator, so we have to do it on the master
                     print("Saving snapshot")
                     last_snapshot = sample
@@ -399,7 +419,7 @@ def train(
                     save_trainer(tracker, trainer, epoch, sample, next_task, validation_losses, samples_seen)
                     if exists(n_sample_images) and n_sample_images > 0:
                         trainer.eval()
-                        train_images, train_captions = generate_grid_samples(trainer, train_example_data, condition_on_text_encodings, "Train: ")
+                        train_images, train_captions = generate_grid_samples(trainer, train_example_data, first_trainable_unet, last_trainable_unet, condition_on_text_encodings, cond_scale, inference_device, "Train: ")
                         tracker.log_images(train_images, captions=train_captions, image_section="Train Samples", step=step())
                 
                 if epoch_samples is not None and sample >= epoch_samples:
@@ -449,8 +469,9 @@ def train(
                         else:
                             # Then we need to pass the text instead
                             tokenized_texts = tokenize(txt, truncate=True)
+                            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)})"
                             forward_params['text'] = tokenized_texts
-                    loss = trainer.forward(img.float(), **forward_params, unet_number=unet)
+                    loss = trainer.forward(img.float(), **forward_params, unet_number=unet, _device=inference_device)
                     average_val_loss_tensor[0, unet-1] += loss
 
                 if i % VALID_CALC_LOSS_EVERY_ITERS == 0:
@@ -477,7 +498,7 @@ def train(
         if next_task == 'eval':
             if exists(evaluate_config):
                 accelerator.print(print_ribbon(f"Starting Evaluation {epoch}", repeat=40))
-                evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, **evaluate_config.dict(), condition_on_text_encodings=condition_on_text_encodings)
+                evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, first_trainable_unet, last_trainable_unet, inference_device=inference_device, **evaluate_config.dict(), condition_on_text_encodings=condition_on_text_encodings, cond_scale=cond_scale)
                 if is_master:
                     tracker.log(evaluation, step=step())
             next_task = 'sample'
@@ -488,15 +509,15 @@ def train(
                 # Generate examples and save the model if we are the master
                 # Generate sample images
                 print(print_ribbon(f"Sampling Set {epoch}", repeat=40))
-                test_images, test_captions = generate_grid_samples(trainer, test_example_data, condition_on_text_encodings, "Test: ")
-                train_images, train_captions = generate_grid_samples(trainer, train_example_data, condition_on_text_encodings, "Train: ")
+                test_images, test_captions = generate_grid_samples(trainer, test_example_data,  first_trainable_unet, last_trainable_unet, condition_on_text_encodings, cond_scale, inference_device, "Test: ")
+                train_images, train_captions = generate_grid_samples(trainer, train_example_data,  first_trainable_unet, last_trainable_unet, condition_on_text_encodings, cond_scale, inference_device, "Train: ")
                 tracker.log_images(test_images, captions=test_captions, image_section="Test Samples", step=step())
                 tracker.log_images(train_images, captions=train_captions, image_section="Train Samples", step=step())
 
                 print(print_ribbon(f"Starting Saving {epoch}", repeat=40))
                 is_best = False
                 if all_average_val_losses is not None:
-                    average_loss = all_average_val_losses.mean(dim=0).item()
+                    average_loss = all_average_val_losses.mean(dim=0).sum() / sum(unet_training_mask)
                     if len(validation_losses) == 0 or average_loss < min(validation_losses):
                         is_best = True
                     validation_losses.append(average_loss)
@@ -513,6 +534,7 @@ 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):
@@ -521,7 +543,8 @@ def initialize_training(config: TrainDecoderConfig, config_path):
 
     # Set up accelerator for configurable distributed training
     ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=config.train.find_unused_parameters)
-    accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
+    init_kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=60*60))
+    accelerator = Accelerator(kwargs_handlers=[ddp_kwargs, init_kwargs])
 
     if accelerator.num_processes > 1:
         # We are using distributed training and want to immediately ensure all can connect

train_diffusion_prior.py

@@ -1,31 +1,23 @@
-# TODO: add start, num_data_points, eval_every and group to config
-# TODO: switch back to repo's wandb
-
-START = 0
-NUM_DATA_POINTS = 250e6
-EVAL_EVERY = 1000
-GROUP = "distributed"
-
-import os
 import click
-import wandb
-
 import torch
+
 from torch import nn
-from torch.utils.data import DataLoader
-
-import numpy as np
-
+from typing import List
 from accelerate import Accelerator
+from accelerate.utils import set_seed
+from torch.utils.data import DataLoader
+from embedding_reader import EmbeddingReader
+from accelerate.utils import dataclasses as accelerate_dataclasses
 
-from dalle2_pytorch.dataloaders import get_reader, make_splits
 from dalle2_pytorch.utils import Timer
+from dalle2_pytorch.trackers import Tracker
+from dalle2_pytorch import DiffusionPriorTrainer
+from dalle2_pytorch.dataloaders import get_reader, make_splits
 from dalle2_pytorch.train_configs import (
+    DiffusionPriorConfig,
     DiffusionPriorTrainConfig,
     TrainDiffusionPriorConfig,
 )
-from dalle2_pytorch.trackers import BaseTracker, WandbTracker
-from dalle2_pytorch import DiffusionPriorTrainer
 
 
 # helpers
@@ -38,8 +30,19 @@ def exists(val):
     return val is not None
 
 
+def all_between(values: list, lower_bound, upper_bound):
+    for value in values:
+        if value < lower_bound or value > upper_bound:
+            return False
+
+    return True
+
+
 def make_model(
-    prior_config, train_config, device: str = None, accelerator: Accelerator = None
+    prior_config: DiffusionPriorConfig,
+    train_config: DiffusionPriorTrainConfig,
+    device: str = None,
+    accelerator: Accelerator = None,
 ):
     # create model from config
     diffusion_prior = prior_config.create()
@@ -54,71 +57,214 @@ def make_model(
         use_ema=train_config.use_ema,
         device=device,
         accelerator=accelerator,
+        warmup_steps=train_config.warmup_steps,
     )
 
     return trainer
 
 
+def create_tracker(
+    accelerator: Accelerator,
+    config: TrainDiffusionPriorConfig,
+    config_path: str,
+    dummy: bool = False,
+) -> Tracker:
+    tracker_config = config.tracker
+
+    accelerator_config = {
+        "Distributed": accelerator.distributed_type
+        != accelerate_dataclasses.DistributedType.NO,
+        "DistributedType": accelerator.distributed_type,
+        "NumProcesses": accelerator.num_processes,
+        "MixedPrecision": accelerator.mixed_precision,
+    }
+
+    tracker: Tracker = tracker_config.create(
+        config, accelerator_config, dummy_mode=dummy
+    )
+
+    tracker.save_config(config_path, config_name="prior_config.json")
+
+    return tracker
+
+
+def pad_gather_reduce(trainer: DiffusionPriorTrainer, x, method="mean"):
+    """
+    pad a value or tensor across all processes and gather
+
+    params:
+        - trainer: a trainer that carries an accelerator object
+        - x: a number or torch tensor to reduce
+        - method: "mean", "sum", "max", "min"
+
+    return:
+        - the average tensor after maskin out 0's
+        - None if the gather resulted in an empty tensor
+    """
+
+    assert method in [
+        "mean",
+        "sum",
+        "max",
+        "min",
+    ], "This function has limited capabilities [sum, mean, max, min]"
+    assert type(x) is not None, "Cannot reduce a None type object"
+
+    # wait for everyone to arrive here before gathering
+
+    if type(x) is not torch.Tensor:
+        x = torch.tensor([x])
+
+    # verify that the tensor is on the proper device
+    x = x.to(trainer.device)
+
+    # pad across processes
+    padded_x = trainer.accelerator.pad_across_processes(x, dim=0)
+
+    # gather across all procesess
+    gathered_x = trainer.accelerator.gather(padded_x)
+
+    # mask out zeros
+    masked_x = gathered_x[gathered_x != 0]
+
+    # if the tensor is empty, warn and return None
+    if len(masked_x) == 0:
+        click.secho(
+            f"The call to this method resulted in an empty tensor after masking out zeros. The gathered tensor was this: {gathered_x} and the original value passed was: {x}.",
+            fg="red",
+        )
+        return None
+
+    if method == "mean":
+        return torch.mean(masked_x)
+    elif method == "sum":
+        return torch.sum(masked_x)
+    elif method == "max":
+        return torch.max(masked_x)
+    elif method == "min":
+        return torch.min(masked_x)
+
+
+def save_trainer(
+    tracker: Tracker,
+    trainer: DiffusionPriorTrainer,
+    is_latest: bool,
+    is_best: bool,
+    epoch: int,
+    samples_seen: int,
+    best_validation_loss: float,
+):
+    """
+    Logs the model with an appropriate method depending on the tracker
+    """
+    trainer.accelerator.wait_for_everyone()
+
+    if trainer.accelerator.is_main_process:
+        click.secho(
+            f"RANK:{trainer.accelerator.process_index} | Saving Model | Best={is_best} | Latest={is_latest}",
+            fg="magenta",
+        )
+
+    tracker.save(
+        trainer=trainer,
+        is_best=is_best,
+        is_latest=is_latest,
+        epoch=int(epoch),
+        samples_seen=int(samples_seen),
+        best_validation_loss=best_validation_loss,
+    )
+
+
+def recall_trainer(tracker: Tracker, trainer: DiffusionPriorTrainer):
+    """
+    Loads the model with an appropriate method depending on the tracker
+    """
+
+    if trainer.accelerator.is_main_process:
+        click.secho(f"Loading model from {type(tracker.loader).__name__}", fg="yellow")
+
+    state_dict = tracker.recall()
+
+    trainer.load(state_dict, strict=True)
+
+    return (
+        int(state_dict.get("epoch", 0)),
+        state_dict.get("best_validation_loss", 0),
+        int(state_dict.get("samples_seen", 0)),
+    )
+
+
 # eval functions
 
 
-def eval_model(
+def report_validation_loss(
     trainer: DiffusionPriorTrainer,
     dataloader: DataLoader,
     text_conditioned: bool,
+    use_ema: bool,
+    tracker: Tracker,
+    split: str,
+    tracker_folder: str,
     loss_type: str,
-    tracker_context: str,
-    tracker: BaseTracker = None,
-    use_ema: bool = True,
 ):
-    trainer.eval()
-    if trainer.is_main_process():
-        click.secho(f"Measuring performance on {tracker_context}", fg="green", blink=True)
+    """
+    Compute the validation loss on a given subset of data.
+    """
 
-    with torch.no_grad():
-        total_loss = 0.0
-        total_samples = 0.0
+    if trainer.accelerator.is_main_process:
+        click.secho(
+            f"Measuring performance on {use_ema}-{split} split",
+            fg="green",
+            blink=True,
+        )
 
-        for image_embeddings, text_data in dataloader:
-            image_embeddings = image_embeddings.to(trainer.device)
-            text_data = text_data.to(trainer.device)
+    total_loss = torch.zeros(1, dtype=torch.float, device=trainer.device)
 
-            batches = image_embeddings.shape[0]
+    for image_embeddings, text_data in dataloader:
+        image_embeddings = image_embeddings.to(trainer.device)
+        text_data = text_data.to(trainer.device)
 
-            input_args = dict(image_embed=image_embeddings)
+        input_args = dict(image_embed=image_embeddings)
 
-            if text_conditioned:
-                input_args = dict(**input_args, text=text_data)
-            else:
-                input_args = dict(**input_args, text_embed=text_data)
+        if text_conditioned:
+            input_args = dict(**input_args, text=text_data)
+        else:
+            input_args = dict(**input_args, text_embed=text_data)
 
-            if use_ema:
-                loss = trainer.ema_diffusion_prior(**input_args)
-            else:
-                loss = trainer(**input_args)
+        if use_ema:
+            loss = trainer.ema_diffusion_prior(**input_args)
+        else:
+            loss = trainer(**input_args)
 
-            total_loss += loss * batches
-            total_samples += batches
+        total_loss += loss
 
-        avg_loss = total_loss / total_samples
+    # compute the average loss across all processes
 
-        stats = {f"{tracker_context}-{loss_type}": avg_loss}
-        trainer.print(stats)
+    avg_loss = pad_gather_reduce(trainer, total_loss, method="mean")
+    stats = {f"{tracker_folder}/{loss_type}-loss": avg_loss}
 
-        if exists(tracker):
-            tracker.log(stats, step=trainer.step.item() + 1)
+    # print and log results on main process
+    tracker.log(stats, step=trainer.step.item() + 1)
+
+    return avg_loss
 
 
 def report_cosine_sims(
     trainer: DiffusionPriorTrainer,
     dataloader: DataLoader,
     text_conditioned: bool,
-    tracker: BaseTracker,
-    tracker_context: str = "validation",
+    tracker: Tracker,
+    split: str,
+    timesteps: int,
+    tracker_folder: str,
 ):
     trainer.eval()
-    if trainer.is_main_process():
-        click.secho("Measuring Cosine-Similarity", fg="green", blink=True)
+    if trainer.accelerator.is_main_process:
+        click.secho(
+            f"Measuring Cosine-Similarity on {split} split with {timesteps} timesteps",
+            fg="green",
+            blink=True,
+        )
 
     for test_image_embeddings, text_data in dataloader:
         test_image_embeddings = test_image_embeddings.to(trainer.device)
@@ -127,9 +273,7 @@ def report_cosine_sims(
         # we are text conditioned, we produce an embedding from the tokenized text
         if text_conditioned:
             text_embedding, text_encodings = trainer.embed_text(text_data)
-            text_cond = dict(
-                text_embed=text_embedding, text_encodings=text_encodings
-            )
+            text_cond = dict(text_embed=text_embedding, text_encodings=text_encodings)
         else:
             text_embedding = text_data
             text_cond = dict(text_embed=text_embedding)
@@ -150,8 +294,7 @@ def report_cosine_sims(
             text_encodings_shuffled = None
 
         text_cond_shuffled = dict(
-            text_embed=text_embed_shuffled,
-            text_encodings=text_encodings_shuffled
+            text_embed=text_embed_shuffled, text_encodings=text_encodings_shuffled
         )
 
         # prepare the text embedding
@@ -164,7 +307,9 @@ def report_cosine_sims(
 
         # predict on the unshuffled text embeddings
         predicted_image_embeddings = trainer.p_sample_loop(
-            test_image_embeddings.shape, text_cond
+            test_image_embeddings.shape,
+            text_cond,
+            timesteps=timesteps,
         )
 
         predicted_image_embeddings = (
@@ -174,7 +319,9 @@ def report_cosine_sims(
 
         # predict on the shuffled embeddings
         predicted_unrelated_embeddings = trainer.p_sample_loop(
-            test_image_embeddings.shape, text_cond_shuffled
+            test_image_embeddings.shape,
+            text_cond_shuffled,
+            timesteps=timesteps,
         )
 
         predicted_unrelated_embeddings = (
@@ -183,32 +330,97 @@ def report_cosine_sims(
         )
 
         # calculate similarities
-        original_similarity = cos(text_embed, test_image_embeddings).cpu().numpy()
-        predicted_similarity = cos(text_embed, predicted_image_embeddings).cpu().numpy()
-        unrelated_similarity = (
-            cos(text_embed, predicted_unrelated_embeddings).cpu().numpy()
+        orig_sim = pad_gather_reduce(
+            trainer, cos(text_embed, test_image_embeddings), method="mean"
         )
-        predicted_img_similarity = (
-            cos(test_image_embeddings, predicted_image_embeddings).cpu().numpy()
+        pred_sim = pad_gather_reduce(
+            trainer, cos(text_embed, predicted_image_embeddings), method="mean"
+        )
+        unrel_sim = pad_gather_reduce(
+            trainer, cos(text_embed, predicted_unrelated_embeddings), method="mean"
+        )
+        pred_img_sim = pad_gather_reduce(
+            trainer,
+            cos(test_image_embeddings, predicted_image_embeddings),
+            method="mean",
         )
 
         stats = {
-            f"{tracker_context}/baseline similarity": np.mean(original_similarity),
-            f"{tracker_context}/similarity with text": np.mean(predicted_similarity),
-            f"{tracker_context}/similarity with original image": np.mean(
-                predicted_img_similarity
-            ),
-            f"{tracker_context}/similarity with unrelated caption": np.mean(unrelated_similarity),
-            f"{tracker_context}/difference from baseline similarity": np.mean(
-                predicted_similarity - original_similarity
-            ),
+            f"{tracker_folder}/baseline similarity [steps={timesteps}]": orig_sim,
+            f"{tracker_folder}/similarity with text [steps={timesteps}]": pred_sim,
+            f"{tracker_folder}/similarity with original image [steps={timesteps}]": pred_img_sim,
+            f"{tracker_folder}/similarity with unrelated caption [steps={timesteps}]": unrel_sim,
+            f"{tracker_folder}/difference from baseline similarity [steps={timesteps}]": pred_sim
+            - orig_sim,
         }
 
-        for k, v in stats.items():
-            trainer.print(f"{tracker_context}/{k}: {v}")
+        tracker.log(stats, step=trainer.step.item() + 1)
 
-        if exists(tracker):
-            tracker.log(stats, step=trainer.step.item() + 1)
+
+def eval_model(
+    trainer: DiffusionPriorTrainer,
+    dataloader: DataLoader,
+    text_conditioned: bool,
+    split: str,
+    tracker: Tracker,
+    use_ema: bool,
+    report_cosine: bool,
+    report_loss: bool,
+    timesteps: List[int],
+    loss_type: str = None,
+):
+    """
+    Run evaluation on a model and track metrics
+
+    returns: loss if requested
+    """
+    trainer.eval()
+
+    use_ema = "ema" if use_ema else "online"
+    tracker_folder = f"metrics/{use_ema}-{split}"
+
+    # detemine if valid timesteps are passed
+
+    min_timesteps = trainer.accelerator.unwrap_model(
+        trainer.diffusion_prior
+    ).sample_timesteps
+    max_timesteps = trainer.accelerator.unwrap_model(
+        trainer.diffusion_prior
+    ).noise_scheduler.num_timesteps
+
+    assert all_between(
+        timesteps, lower_bound=min_timesteps, upper_bound=max_timesteps
+    ), f"all timesteps values must be between {min_timesteps} and {max_timesteps}: got {timesteps}"
+
+    # measure cosine metrics across various eta and timesteps
+
+    if report_cosine:
+        for timestep in timesteps:
+            report_cosine_sims(
+                trainer,
+                dataloader=dataloader,
+                text_conditioned=text_conditioned,
+                tracker=tracker,
+                split=split,
+                timesteps=timestep,
+                tracker_folder=tracker_folder,
+            )
+
+    # measure loss on a seperate split of data
+
+    if report_loss:
+        loss = report_validation_loss(
+            trainer=trainer,
+            dataloader=dataloader,
+            text_conditioned=text_conditioned,
+            use_ema=use_ema,
+            tracker=tracker,
+            split=split,
+            tracker_folder=tracker_folder,
+            loss_type=loss_type,
+        )
+
+        return loss
 
 
 # training script
@@ -216,182 +428,327 @@ def report_cosine_sims(
 
 def train(
     trainer: DiffusionPriorTrainer,
+    tracker: Tracker,
     train_loader: DataLoader,
     eval_loader: DataLoader,
     test_loader: DataLoader,
     config: DiffusionPriorTrainConfig,
 ):
-    # distributed tracking with wandb
-    if trainer.accelerator.num_processes > 1:
-        os.environ["WANDB_START_METHOD"] = "thread"
+    # init timers
+    save_timer = Timer()  # when to save
+    samples_timer = Timer()  # samples/sec
+    validation_profiler = Timer()  # how long is validation taking
+    validation_countdown = Timer()  # when to perform evalutation
 
-    tracker = wandb.init(
-        name=f"RANK:{trainer.device}",
-        entity=config.tracker.wandb_entity,
-        project=config.tracker.wandb_project,
-        config=config.dict(),
-        group=GROUP,
-    )
+    # keep track of best validation loss
 
-    # sync after tracker init
-    trainer.wait_for_everyone()
-
-    # init a timer
-    timer = Timer()
+    best_validation_loss = config.train.best_validation_loss
+    samples_seen = config.train.num_samples_seen
 
     # do training
-    for img, txt in train_loader:
-        trainer.train()
-        current_step = trainer.step.item() + 1
 
-        # place data on device
-        img = img.to(trainer.device)
-        txt = txt.to(trainer.device)
+    start_epoch = config.train.current_epoch
 
-        # pass to model
-        loss = trainer(text=txt, image_embed=img)
+    for epoch in range(start_epoch, config.train.epochs):
+        # if we finished out an old epoch, reset the distribution to be a full epoch
+        tracker.log({"tracking/epoch": epoch}, step=trainer.step.item())
 
-        # display & log loss (will only print from main process)
-        trainer.print(f"Step {current_step}: Loss {loss}")
+        if train_loader.dataset.get_start() > 0 and epoch == start_epoch+1:
+            if trainer.accelerator.is_main_process:
+                click.secho(f"Finished resumed epoch...resetting dataloader.")
+            train_loader.dataset.set_start(0)
 
-        # perform backprop & apply EMA updates
-        trainer.update()
+        for img, txt in train_loader:
+            # setup things every step
 
-        # track samples/sec/rank
-        samples_per_sec = img.shape[0] / timer.elapsed()
+            trainer.train()
+            current_step = trainer.step.item()
+            samples_timer.reset()
 
-        # samples seen
-        samples_seen = (
-            config.data.batch_size * trainer.accelerator.num_processes * current_step
-        )
+            # place data on device
 
-        # ema decay
-        ema_decay = trainer.ema_diffusion_prior.get_current_decay()
+            img = img.to(trainer.device)
+            txt = txt.to(trainer.device)
 
-        # Log on all processes for debugging
-        tracker.log(
-            {
-                "tracking/samples-sec": samples_per_sec,
-                "tracking/samples-seen": samples_seen,
-                "tracking/ema-decay": ema_decay,
-                "metrics/training-loss": loss,
-            },
-            step=current_step,
-        )
+            # pass to model
 
-        # Metric Tracking & Checkpointing (outside of timer's scope)
-        if current_step % EVAL_EVERY == 0:
-            eval_model(
-                trainer=trainer,
-                dataloader=eval_loader,
-                text_conditioned=config.prior.condition_on_text_encodings,
-                loss_type=config.prior.loss_type,
-                tracker_context="metrics/online-model-validation",
-                tracker=tracker,
-                use_ema=False,
+            loss = trainer(text=txt, image_embed=img)
+
+            # perform backprop & apply EMA updates
+
+            trainer.update()
+
+            # gather info about training step
+
+            all_loss = pad_gather_reduce(trainer, loss, method="mean")
+            num_samples = pad_gather_reduce(trainer, len(txt), method="sum")
+            samples_per_sec = num_samples / samples_timer.elapsed()
+            samples_seen += num_samples
+            ema_decay = trainer.ema_diffusion_prior.get_current_decay()
+
+            # log
+
+            tracker.log(
+                {
+                    "tracking/samples-sec": samples_per_sec,
+                    "tracking/samples-seen": samples_seen,
+                    "tracking/ema-decay": ema_decay,
+                    f"tracking/training-{config.prior.loss_type}": all_loss,
+                },
+                step=current_step,
             )
 
-            eval_model(
-                trainer=trainer,
-                dataloader=eval_loader,
-                text_conditioned=config.prior.condition_on_text_encodings,
-                loss_type=config.prior.loss_type,
-                tracker_context="metrics/ema-model-validation",
-                tracker=tracker,
-                use_ema=True,
+            # Metric Tracking @ Timed Intervals
+
+            eval_delta = pad_gather_reduce(
+                trainer, validation_countdown.elapsed(), method="min"
             )
 
-            report_cosine_sims(
-                trainer=trainer,
-                dataloader=eval_loader,
-                text_conditioned=config.prior.condition_on_text_encodings,
-                tracker=tracker,
-                tracker_context="metrics",
-            )
+            if eval_delta != None and eval_delta > config.data.eval_every_seconds:
+                # begin timing how long this takes
 
-        if current_step % config.train.save_every == 0:
-            trainer.save(f"{config.tracker.data_path}/chkpt_step_{current_step}.pth")
+                validation_profiler.reset()
 
-        # reset timer for next round
-        timer.reset()
+                # package kwargs for evaluation
+
+                eval_kwargs = {
+                    "trainer": trainer,
+                    "tracker": tracker,
+                    "text_conditioned": config.prior.condition_on_text_encodings,
+                    "timesteps": config.train.eval_timesteps,
+                }
+
+                # ONLINE MODEL : COSINE : LOSS : VALIDATION SPLIT
+
+                eval_model(
+                    dataloader=eval_loader,
+                    loss_type=config.prior.loss_type,
+                    split="validation",
+                    use_ema=False,
+                    report_cosine=False,
+                    report_loss=True,
+                    **eval_kwargs,
+                )
+
+                # EMA MODEL : COSINE : LOSS : VALIDATION DATA
+
+                ema_val_loss = eval_model(
+                    dataloader=eval_loader,
+                    loss_type=config.prior.loss_type,
+                    split="validation",
+                    use_ema=True,
+                    report_cosine=True,
+                    report_loss=True,
+                    **eval_kwargs,
+                )
+
+                tracker.log(
+                    {
+                        "tracking/validation length (minutes)": validation_profiler.elapsed()
+                        / 60
+                    }
+                )
+
+                # check if the ema validation is the lowest seen yet
+
+                if ema_val_loss < best_validation_loss:
+                    best_validation_loss = ema_val_loss
+
+                    #  go save the model as best
+
+                    save_trainer(
+                        trainer=trainer,
+                        tracker=tracker,
+                        is_best=True,
+                        is_latest=False,
+                        samples_seen=samples_seen,
+                        epoch=epoch,
+                        best_validation_loss=best_validation_loss,
+                    )
+
+                # reset timer for validaiton
+
+                validation_countdown.reset()
+
+            elif eval_delta is None:
+                click.secho(
+                    f"Error occured reading the eval time on rank: {trainer.device}",
+                    fg="yellow",
+                )
+
+            # save as latest model on schedule
+
+            save_delta = pad_gather_reduce(trainer, save_timer.elapsed(), method="min")
+
+            if save_delta != None and save_delta >= config.train.save_every_seconds:
+                save_trainer(
+                    trainer=trainer,
+                    tracker=tracker,
+                    is_best=False,
+                    is_latest=True,
+                    samples_seen=samples_seen,
+                    epoch=epoch,
+                    best_validation_loss=best_validation_loss,
+                )
+
+                save_timer.reset()
+
+            elif save_delta is None:
+                click.secho(
+                    f"Error occured reading the save time on rank: {trainer.device}",
+                    fg="yellow",
+                )
 
     # evaluate on test data
 
-    eval_model(
+    if trainer.accelerator.is_main_process:
+        click.secho(f"Starting Test", fg="red")
+
+    # save one last time as latest before beginning validation
+
+    save_trainer(
+        tracker=tracker,
+        trainer=trainer,
+        is_best=False,
+        is_latest=True,
+        samples_seen=samples_seen,
+        epoch=epoch,
+        best_validation_loss=best_validation_loss,
+    )
+
+    test_loss = eval_model(
         trainer=trainer,
         dataloader=test_loader,
         text_conditioned=config.prior.condition_on_text_encodings,
-        loss_type=config.prior.loss_type,
-        tracker_context="test",
+        split="test",
         tracker=tracker,
+        use_ema=True,
+        report_cosine=False,
+        report_loss=True,
+        timesteps=config.train.eval_timesteps,
+        loss_type=config.prior.loss_type,
     )
 
-    report_cosine_sims(
-        trainer,
-        test_loader,
-        config.prior.condition_on_text_encodings,
-        tracker,
-        tracker_context="test",
-    )
+    if test_loss < best_validation_loss:
+        best_validation_loss = test_loss
+
+        #  go save the model as best
+
+        save_trainer(
+            trainer=trainer,
+            tracker=tracker,
+            is_best=True,
+            is_latest=False,
+            samples_seen=samples_seen,
+            epoch=epoch,
+            best_validation_loss=test_loss,
+        )
 
 
-def initialize_training(config, accelerator=None):
+def initialize_training(config_file, accelerator):
     """
     Parse the configuration file, and prepare everything necessary for training
     """
+    # load the configuration file
+    if accelerator.is_main_process:
+        click.secho(f"Loading configuration from {config_file}", fg="green")
+
+    config = TrainDiffusionPriorConfig.from_json_path(config_file)
+
+    # seed
+
+    set_seed(config.train.random_seed)
 
     # get a device
 
-    if accelerator:
-        device = accelerator.device
-        click.secho(f"Accelerating on: {device}", fg="yellow")
-    else:
-        if torch.cuda.is_available():
-            click.secho("GPU detected, defaulting to cuda:0", fg="yellow")
-            device = "cuda:0"
-        else:
-            click.secho("No GPU detected...using cpu", fg="yellow")
-            device = "cpu"
+    device = accelerator.device
 
     # make the trainer (will automatically distribute if possible & configured)
 
-    trainer = make_model(config.prior, config.train, device, accelerator).to(device)
+    trainer: DiffusionPriorTrainer = make_model(
+        config.prior, config.train, device, accelerator
+    ).to(device)
+
+    # create a tracker
+
+    tracker = create_tracker(
+        accelerator, config, config_file, dummy=accelerator.process_index != 0
+    )
 
     # reload from chcekpoint
 
-    if config.load.resume == True:
-        click.secho(f"Loading checkpoint: {config.load.source}", fg="cyan")
-        trainer.load(config.load.source)
+    if tracker.can_recall:
+        current_epoch, best_validation_loss, samples_seen = recall_trainer(
+            tracker=tracker, trainer=trainer
+        )
+
+        # display best values
+        if trainer.accelerator.is_main_process:
+            click.secho(f"Current Epoch: {current_epoch} | Best Val Loss: {best_validation_loss} | Samples Seen: {samples_seen}", fg="yellow")
+
+        # update config to reflect recalled values
+        config.train.num_samples_seen = samples_seen
+        config.train.current_epoch = current_epoch
+        config.train.best_validation_loss = best_validation_loss
 
     # fetch and prepare data
 
-    if trainer.is_main_process():
-        click.secho("Grabbing data from source", fg="blue", blink=True)
+    if trainer.accelerator.is_main_process:
+        click.secho("Grabbing data...", fg="blue", blink=True)
 
+    trainer.accelerator.wait_for_everyone()
     img_reader = get_reader(
         text_conditioned=trainer.text_conditioned,
         img_url=config.data.image_url,
         meta_url=config.data.meta_url,
     )
 
+    # calculate start point within epoch
+
+    trainer.accelerator.wait_for_everyone()
+
     train_loader, eval_loader, test_loader = make_splits(
         text_conditioned=trainer.text_conditioned,
         batch_size=config.data.batch_size,
-        num_data_points=NUM_DATA_POINTS,
+        num_data_points=config.data.num_data_points,
         train_split=config.data.splits.train,
         eval_split=config.data.splits.val,
         image_reader=img_reader,
-        rank=accelerator.state.process_index if exists(accelerator) else 0,
-        world_size=accelerator.state.num_processes if exists(accelerator) else 1,
-        start=START,
+        rank=accelerator.state.process_index,
+        world_size=accelerator.state.num_processes,
+        start=0,
     )
 
-    # wait for everyone to load data before continuing
-    trainer.wait_for_everyone()
+    # update the start point to finish out the epoch on a resumed run
+
+    if tracker.can_recall:
+        samples_seen = config.train.num_samples_seen
+        length = (
+            config.data.num_data_points
+            if samples_seen <= img_reader.count
+            else img_reader.count
+        )
+        scaled_samples = length * config.train.current_epoch
+        start_point = (
+            scaled_samples - samples_seen if scaled_samples > samples_seen else samples_seen
+        )
+
+        if trainer.accelerator.is_main_process:
+            click.secho(f"Resuming at sample: {start_point}", fg="yellow")
+
+        train_loader.dataset.set_start(start_point)
 
     # start training
+
+    if trainer.accelerator.is_main_process:
+        click.secho(
+            f"Beginning Prior Training : Distributed={accelerator.state.distributed_type != accelerate_dataclasses.DistributedType.NO}",
+            fg="yellow",
+        )
+
     train(
         trainer=trainer,
+        tracker=tracker,
         train_loader=train_loader,
         eval_loader=eval_loader,
         test_loader=test_loader,
@@ -400,23 +757,13 @@ def initialize_training(config, accelerator=None):
 
 
 @click.command()
-@click.option("--hfa", default=True)
-@click.option("--config_path", default="configs/prior.json")
-def main(hfa, config_path):
-    # start HFA if requested
-    if hfa:
-        accelerator = Accelerator()
-    else:
-        accelerator = None
+@click.option("--config_file", default="configs/train_prior_config.example.json")
+def main(config_file):
+    # start HFA
+    accelerator = Accelerator()
 
-    # load the configuration file on main process
-    if not exists(accelerator) or accelerator.is_main_process:
-        click.secho(f"Loading configuration from {config_path}", fg="green")
-
-    config = TrainDiffusionPriorConfig.from_json_path(config_path)
-
-    # send config to get processed
-    initialize_training(config, accelerator)
+    # setup training
+    initialize_training(config_file, accelerator)
 
 
 if __name__ == "__main__":