allow for full customization of number of resnet blocks per down or upsampling layers in unet, as in imagen

README.md

@@ -24,6 +24,8 @@ As of 5/23/22, it is no longer SOTA. SOTA will be <a href="https://github.com/lu
 
 *ongoing at 21k steps*
 
+- <a href="https://twitter.com/Buntworthy/status/1529475416775434240?t=0GEge3Kr9I36cjcUVCQUTg">Justin Pinkney</a> successfully trained the diffusion prior in the repository for his CLIP to Stylegan2 text-to-image application
+
 ## Pre-Trained Models
 - LAION is training prior models. Checkpoints are available on <a href="https://huggingface.co/zenglishuci/conditioned-prior">🤗huggingface</a> and the training statistics are available on <a href="https://wandb.ai/nousr_laion/conditioned-prior/reports/LAION-DALLE2-PyTorch-Prior--VmlldzoyMDI2OTIx">🐝WANDB</a>.
 - Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/jkrtg0so?workspace=user-veldrovive">In-progress test run</a> 🚧
@@ -1048,6 +1050,7 @@ This library would not have gotten to this working state without the help of
 - <a href="https://github.com/rom1504">Romain</a> for the pull request reviews and project management
 - <a href="https://github.com/Ciaohe">He Cao</a> and <a href="https://github.com/xiankgx">xiankgx</a> for the Q&A and for identifying of critical bugs
 - <a href="https://github.com/crowsonkb">Katherine</a> for her advice
+- <a href="https://stability.ai/">Stability AI</a> for the generous sponsorship
 
 ... and many others. Thank you! 🙏
 
@@ -1140,7 +1143,7 @@ This library would not have gotten to this working state without the help of
 ```bibtex
 @inproceedings{Tu2022MaxViTMV,
     title   = {MaxViT: Multi-Axis Vision Transformer},
-    author  = {Zhe-Wei Tu and Hossein Talebi and Han Zhang and Feng Yang and Peyman Milanfar and Alan Conrad Bovik and Yinxiao Li},
+    author  = {Zhengzhong Tu and Hossein Talebi and Han Zhang and Feng Yang and Peyman Milanfar and Alan Conrad Bovik and Yinxiao Li},
     year    = {2022}
 }
 ```
@@ -1196,7 +1199,7 @@ This library would not have gotten to this working state without the help of
 ```
 
 ```bibtex
-@misc{Saharia2022,
+@misc{Saharia2022,https://stability.ai/
     title   = {Imagen: unprecedented photorealism × deep level of language understanding},
     author  = {Chitwan Saharia*, William Chan*, Saurabh Saxena†, Lala Li†, Jay Whang†, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho†, David Fleet†, Mohammad Norouzi*},
     year    = {2022}

configs/train_prior_config.example.json

@@ -0,0 +1,70 @@
+{
+    "prior": {
+        "clip": {
+            "make": "x-clip",
+            "model": "ViT-L/14",
+            "base_model_kwargs": {
+                "dim_text": 768,
+                "dim_image": 768,
+                "dim_latent": 768
+            }
+        },
+        "net": {
+            "dim": 768,
+            "depth": 12,
+            "num_timesteps": 1000,
+            "num_time_embeds": 1,
+            "num_image_embeds": 1,
+            "num_text_embeds": 1,
+            "dim_head": 64,
+            "heads": 12,
+            "ff_mult": 4,
+            "norm_out": true,
+            "attn_dropout": 0.0,
+            "ff_dropout": 0.0,
+            "final_proj": true,
+            "normformer": true,
+            "rotary_emb": true
+        },
+        "image_embed_dim": 768,
+        "image_size": 224,
+        "image_channels": 3,
+        "timesteps": 1000,
+        "cond_drop_prob": 0.1,
+        "loss_type": "l2",
+        "predict_x_start": true,
+        "beta_schedule": "cosine",
+        "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,
+        "splits": {
+            "train": 0.9,
+            "val": 1e-7,
+            "test": 0.0999999
+        }
+    },
+    "train": {
+        "epochs": 1,
+        "lr": 1.1e-4,
+        "wd": 6.02e-2,
+        "max_grad_norm": 0.5,
+        "use_ema": true,
+        "amp": false,
+        "save_every": 10000
+    },
+    "load": {
+        "source": null,
+        "resume": false
+    },
+    "tracker": {
+        "tracker_type": "wandb",
+        "data_path": "./prior_checkpoints",
+        "wandb_entity": "laion",
+        "wandb_project": "diffusion-prior",
+        "verbose": true
+    }
+}

dalle2_pytorch/dalle2_pytorch.py

@@ -1346,6 +1346,7 @@ class Unet(nn.Module):
         init_dim = None,
         init_conv_kernel_size = 7,
         resnet_groups = 8,
+        num_resnet_blocks = 1,
         init_cross_embed_kernel_sizes = (3, 7, 15),
         cross_embed_downsample = False,
         cross_embed_downsample_kernel_sizes = (2, 4),
@@ -1431,6 +1432,7 @@ class Unet(nn.Module):
         # resnet block klass
 
         resnet_groups = cast_tuple(resnet_groups, len(in_out))
+        num_resnet_blocks = cast_tuple(num_resnet_blocks, len(in_out))
 
         assert len(resnet_groups) == len(in_out)
 
@@ -1446,7 +1448,7 @@ class Unet(nn.Module):
         self.ups = nn.ModuleList([])
         num_resolutions = len(in_out)
 
-        for ind, ((dim_in, dim_out), groups) in enumerate(zip(in_out, resnet_groups)):
+        for ind, ((dim_in, dim_out), groups, layer_num_resnet_blocks) in enumerate(zip(in_out, resnet_groups, num_resnet_blocks)):
             is_first = ind == 0
             is_last = ind >= (num_resolutions - 1)
             layer_cond_dim = cond_dim if not is_first else None
@@ -1454,7 +1456,7 @@ class Unet(nn.Module):
             self.downs.append(nn.ModuleList([
                 ResnetBlock(dim_in, dim_out, time_cond_dim = time_cond_dim, groups = groups),
                 Residual(LinearAttention(dim_out, **attn_kwargs)) if sparse_attn else nn.Identity(),
-                ResnetBlock(dim_out, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups),
+                nn.ModuleList([ResnetBlock(dim_out, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups) for _ in range(layer_num_resnet_blocks)]),
                 downsample_klass(dim_out) if not is_last else nn.Identity()
             ]))
 
@@ -1464,14 +1466,14 @@ class Unet(nn.Module):
         self.mid_attn = EinopsToAndFrom('b c h w', 'b (h w) c', Residual(Attention(mid_dim, **attn_kwargs))) if attend_at_middle else None
         self.mid_block2 = ResnetBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim, groups = resnet_groups[-1])
 
-        for ind, ((dim_in, dim_out), groups) in enumerate(zip(reversed(in_out[1:]), reversed(resnet_groups))):
+        for ind, ((dim_in, dim_out), groups, layer_num_resnet_blocks) in enumerate(zip(reversed(in_out[1:]), reversed(resnet_groups), reversed(num_resnet_blocks))):
             is_last = ind >= (num_resolutions - 2)
             layer_cond_dim = cond_dim if not is_last else None
 
             self.ups.append(nn.ModuleList([
                 ResnetBlock(dim_out * 2, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups),
                 Residual(LinearAttention(dim_in, **attn_kwargs)) if sparse_attn else nn.Identity(),
-                ResnetBlock(dim_in, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups),
+                nn.ModuleList([ResnetBlock(dim_in, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups)  for _ in range(layer_num_resnet_blocks)]),
                 Upsample(dim_in)
             ]))
 
@@ -1628,10 +1630,13 @@ class Unet(nn.Module):
 
         hiddens = []
 
-        for block1, sparse_attn, block2, downsample in self.downs:
-            x = block1(x, c, t)
+        for init_block, sparse_attn, resnet_blocks, downsample in self.downs:
+            x = init_block(x, c, t)
             x = sparse_attn(x)
-            x = block2(x, c, t)
+
+            for resnet_block in resnet_blocks:
+                x = resnet_block(x, c, t)
+
             hiddens.append(x)
             x = downsample(x)
 
@@ -1642,11 +1647,14 @@ class Unet(nn.Module):
 
         x = self.mid_block2(x, mid_c, t)
 
-        for block1, sparse_attn, block2, upsample in self.ups:
+        for init_block, sparse_attn, resnet_blocks, upsample in self.ups:
             x = torch.cat((x, hiddens.pop()), dim=1)
-            x = block1(x, c, t)
+            x = init_block(x, c, t)
             x = sparse_attn(x)
-            x = block2(x, c, t)
+
+            for resnet_block in resnet_blocks:
+                x = resnet_block(x, c, t)
+
             x = upsample(x)
 
         return self.final_conv(x)

dalle2_pytorch/optimizer.py

@@ -21,8 +21,6 @@ def get_optimizer(
     if wd == 0:
         return Adam(params, lr = lr, betas = betas, eps = eps)
 
-    params = set(params)
-
     if group_wd_params:
         wd_params, no_wd_params = separate_weight_decayable_params(params)
 

dalle2_pytorch/train_configs.py

@@ -3,7 +3,18 @@ from torchvision import transforms as T
 from pydantic import BaseModel, validator, root_validator
 from typing import List, Iterable, Optional, Union, Tuple, Dict, Any
 
-from dalle2_pytorch.dalle2_pytorch import Unet, Decoder, DiffusionPrior, DiffusionPriorNetwork
+from x_clip import CLIP as XCLIP
+from coca_pytorch import CoCa
+
+from dalle2_pytorch.dalle2_pytorch import (
+    CoCaAdapter,
+    OpenAIClipAdapter,
+    Unet,
+    Decoder,
+    DiffusionPrior,
+    DiffusionPriorNetwork,
+    XClipAdapter,
+)
 
 # helper functions
 
@@ -16,7 +27,44 @@ def default(val, d):
 def ListOrTuple(inner_type):
     return Union[List[inner_type], Tuple[inner_type]]
 
-# pydantic classes
+# general pydantic classes
+
+class TrainSplitConfig(BaseModel):
+    train: float = 0.75
+    val: float = 0.15
+    test: float = 0.1
+
+    @root_validator
+    def validate_all(cls, fields):
+        actual_sum = sum([*fields.values()])
+        if actual_sum != 1.:
+            raise ValueError(f'{fields.keys()} must sum to 1.0. Found: {actual_sum}')
+        return fields
+
+class TrackerConfig(BaseModel):
+    tracker_type: str = 'console'           # Decoder currently supports console and wandb
+    data_path: str = './models'             # The path where files will be saved locally
+    init_config: Dict[str, Any] = None
+    wandb_entity: str = ''                  # Only needs to be set if tracker_type is wandb
+    wandb_project: str = ''
+    verbose: bool = False                   # Whether to print console logging for non-console trackers
+
+# diffusion prior pydantic classes
+
+class AdapterConfig(BaseModel):
+    make: str = "openai"
+    model: str = "ViT-L/14"
+    base_model_kwargs: Dict[str, Any] = None
+
+    def create(self):
+        if self.make == "openai":
+            return OpenAIClipAdapter(self.model)
+        elif self.make == "x-clip":
+            return XClipAdapter(XCLIP(**self.base_model_kwargs))
+        elif self.make == "coca":
+            return CoCaAdapter(CoCa(**self.base_model_kwargs))
+        else:
+            raise AttributeError("No adapter with that name is available.")
 
 class DiffusionPriorNetworkConfig(BaseModel):
     dim: int
@@ -35,8 +83,12 @@ class DiffusionPriorNetworkConfig(BaseModel):
     normformer: bool = False
     rotary_emb: bool = True
 
+    def create(self):
+        kwargs = self.dict()
+        return DiffusionPriorNetwork(**kwargs)
+
 class DiffusionPriorConfig(BaseModel):
-    # only clip-less diffusion prior config for now
+    clip: AdapterConfig
     net: DiffusionPriorNetworkConfig
     image_embed_dim: int
     image_size: int
@@ -46,15 +98,52 @@ class DiffusionPriorConfig(BaseModel):
     loss_type: str = 'l2'
     predict_x_start: bool = True
     beta_schedule: str = 'cosine'
-
-    def create(self):
-        kwargs = self.dict()
-        diffusion_prior_network = DiffusionPriorNetwork(**kwargs.pop('net'))
-        return DiffusionPrior(net = diffusion_prior_network, **kwargs)
+    condition_on_text_encodings: bool = True
 
     class Config:
         extra = "allow"
 
+    def create(self):
+        kwargs = self.dict()
+        clip = AdapterConfig(**kwargs.pop('clip')).create()
+        diffusion_prior_network = DiffusionPriorNetworkConfig(**kwargs.pop('net')).create()
+        return DiffusionPrior(net = diffusion_prior_network, clip=clip, **kwargs)
+
+class DiffusionPriorTrainConfig(BaseModel):
+    epochs: int = 1
+    lr: float = 1.1e-4
+    wd: float = 6.02e-2
+    max_grad_norm: float = 0.5
+    use_ema: bool = True
+    ema_beta: float = 0.99
+    amp: bool = False
+    save_every: int = 10000 # what steps to save on
+
+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
+
+class TrainDiffusionPriorConfig(BaseModel):
+    prior: DiffusionPriorConfig
+    data: DiffusionPriorDataConfig
+    train: DiffusionPriorTrainConfig
+    load: DiffusionPriorLoadConfig
+    tracker: TrackerConfig
+
+    @classmethod
+    def from_json_path(cls, json_path):
+        with open(json_path) as f:
+            config = json.load(f)
+        return cls(**config)
+
+# decoder pydantic classes
+
 class UnetConfig(BaseModel):
     dim: int
     dim_mults: ListOrTuple(int)
@@ -94,17 +183,6 @@ class DecoderConfig(BaseModel):
     class Config:
         extra = "allow"
 
-class TrainSplitConfig(BaseModel):
-    train: float = 0.75
-    val: float = 0.15
-    test: float = 0.1
-
-    @root_validator
-    def validate_all(cls, fields):
-        if sum([*fields.values()]) != 1.:
-            raise ValueError(f'{fields.keys()} must sum to 1.0')
-        return fields
-
 class DecoderDataConfig(BaseModel):
     webdataset_base_url: str     # path to a webdataset with jpg images
     embeddings_url: str          # path to .npy files with embeddings
@@ -160,14 +238,6 @@ class DecoderEvaluateConfig(BaseModel):
     KID: Dict[str, Any] = None
     LPIPS: Dict[str, Any] = None
 
-class TrackerConfig(BaseModel):
-    tracker_type: str = 'console'           # Decoder currently supports console and wandb
-    data_path: str = './models'             # The path where files will be saved locally
-    init_config: Dict[str, Any] = None
-    wandb_entity: str = ''                  # Only needs to be set if tracker_type is wandb
-    wandb_project: str = ''
-    verbose: bool = False                   # Whether to print console logging for non-console trackers
-
 class DecoderLoadConfig(BaseModel):
     source: str = None                      # Supports file and wandb
     run_path: str = ''                      # Used only if source is wandb

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.5.2',
+  version = '0.5.5',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',

train_decoder.py

@@ -347,7 +347,7 @@ def train(
         # Compute evaluation metrics
         if exists(evaluate_config):
             print(print_ribbon(f"Starting Evaluation {epoch}", repeat=40))
-            evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, **evaluate_config)
+            evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, **evaluate_config.dict())
             tracker.log(evaluation, step=step, verbose=True)
 
         # Generate sample images