Add model class for running transformer with precomputed text latents

diffusion/models/models.py

@@ -20,7 +20,7 @@
 from diffusion.models.pixel_diffusion import PixelDiffusion
 from diffusion.models.precomputed_text_latent_diffusion import PrecomputedTextLatentDiffusion
 from diffusion.models.stable_diffusion import StableDiffusion
-from diffusion.models.t2i_transformer import ComposerTextToImageMMDiT
+from diffusion.models.t2i_transformer import ComposerPrecomputedTextLatentsToImageMMDiT, ComposerTextToImageMMDiT
 from diffusion.models.text_encoder import MultiTextEncoder, MultiTokenizer
 from diffusion.models.transformer import DiffusionTransformer
 from diffusion.schedulers.schedulers import ContinuousTimeScheduler
@@ -1010,6 +1010,164 @@ def text_to_image_transformer(
     return model
 
 
+def precomputed_text_latents_to_image_transformer(
+    vae_model_name: str = 'madebyollin/sdxl-vae-fp16-fix',
+    autoencoder_path: Optional[str] = None,
+    autoencoder_local_path: str = '/tmp/autoencoder_weights.pt',
+    include_text_encoders: bool = False,
+    text_encoder_dtype: str = 'bfloat16',
+    cache_dir: str = '/tmp/hf_files',
+    num_layers: int = 28,
+    max_image_side: int = 1280,
+    conditioning_features: int = 768,
+    conditioning_max_sequence_length: int = 512 + 77,
+    num_register_tokens: int = 0,
+    patch_size: int = 2,
+    latent_mean: Union[float, Tuple, str] = 0.0,
+    latent_std: Union[float, Tuple, str] = 7.67754318618,
+    timestep_mean: float = 0.0,
+    timestep_std: float = 1.0,
+    timestep_shift: float = 1.0,
+    image_key: str = 'image',
+    t5_latent_key: str = 'T5_LATENTS',
+    t5_mask_key: str = 'T5_ATTENTION_MASK',
+    clip_latent_key: str = 'CLIP_LATENTS',
+    clip_mask_key: str = 'CLIP_ATTENTION_MASK',
+    clip_pooled_key: str = 'CLIP_POOLED',
+    pretrained: bool = False,
+):
+    """Text to image transformer training setup.
+
+    Args:
+        vae_model_name (str): Name of the VAE model to load. Defaults to 'madebyollin/sdxl-vae-fp16-fix'.
+        autoencoder_path (optional, str): Path to autoencoder weights if using custom autoencoder. If not specified,
+            will use the vae from `model_name`. Default `None`.
+        include_text_encoders (bool): Whether to include text encoders in the model. Should only do this for running
+            inference. Default: `False`.
+        text_encoder_dtype (str): The dtype to use for the text encoder. One of [`float32`, `float16`, `bfloat16`].
+            Default: `bfloat16`.
+        cache_dir (str): Directory to cache the model in if using `include_text_encoders`. Default: `'/tmp/hf_files'`.
+        autoencoder_local_path (optional, str): Path to autoencoder weights. Default: `/tmp/autoencoder_weights.pt`.
+        num_layers (int): Number of layers in the transformer. Number of heads and layer width are determined by
+            this according to `num_features = 64 * num_layers`, and `num_heads = num_layers`. Default: `28`.
+        max_image_side (int): Maximum side length of the image. Default: `1280`.
+        conditioning_features (int): Number of features in the conditioning transformer. Default: `768`.
+        conditioning_max_sequence_length (int): Maximum sequence length for the conditioning transformer. Default: `77`.
+        num_register_tokens (int): Number of additional register tokens to use. Default: `0`.
+        patch_size (int): Patch size for the transformer. Default: `2`.
+        latent_mean (float, Tuple, str): The mean of the autoencoder latents. Either a float for a single value,
+            a tuple of means, or or `'latent_statistics'` to try to use the value from the autoencoder
+            checkpoint. Defaults to `0.0`.
+        latent_std (float, Tuple, str): The std. dev. of the autoencoder latents. Either a float for a single value,
+            a tuple of std_devs, or or `'latent_statistics'` to try to use the value from the autoencoder
+            checkpoint. Defaults to `1/0.13025`.
+        timestep_mean (float): The mean of the timesteps. Default: `0.0`.
+        timestep_std (float): The std. dev. of the timesteps. Default: `1.0`.
+        timestep_shift (float): The shift of the timesteps. Default: `1.0`.
+        image_key (str): The key for the image in the batch. Default: `image`.
+        t5_latent_key (str): The key to use for the T5 latents in the precomputed latents. Default: `'T5_LATENTS'`.
+        t5_mask_key (str): The key to use for the T5 attention mask in the precomputed latents. Default: `'T5_ATTENTION_MASK'`.
+        clip_latent_key (str): The key to use for the CLIP latents in the precomputed latents. Default: `'CLIP_LATENTS'`.
+        clip_mask_key (str): The key to use for the CLIP attention mask in the precomputed latents. Default: `'CLIP_ATTENTION_MASK'`.
+        clip_pooled_key (str): The key to use for the CLIP pooled in the precomputed latents. Default: `'CLIP_POOLED'`.
+        pretrained (bool): Whether to load pretrained weights. Not used. Defaults to False.
+    """
+    latent_mean, latent_std = _parse_latent_statistics(latent_mean), _parse_latent_statistics(latent_std)
+
+    precision = torch.float16
+    # Make the autoencoder
+    if autoencoder_path is None:
+        if latent_mean == 'latent_statistics' or latent_std == 'latent_statistics':
+            raise ValueError('Cannot use tracked latent_statistics when using the pretrained vae.')
+        downsample_factor = 8
+        autoencoder_channels = 4
+        # Use the pretrained vae
+        try:
+            vae = AutoencoderKL.from_pretrained(vae_model_name, subfolder='vae', torch_dtype=precision)
+        except:  # for handling SDXL vae fp16 fixed checkpoint
+            vae = AutoencoderKL.from_pretrained(vae_model_name, torch_dtype=precision)
+    else:
+        # Use a custom autoencoder
+        vae, latent_statistics = load_autoencoder(autoencoder_path, autoencoder_local_path, torch_dtype=precision)
+        if latent_statistics is None and (latent_mean == 'latent_statistics' or latent_std == 'latent_statistics'):
+            raise ValueError(
+                'Must specify latent scale when using a custom autoencoder without tracking latent statistics.')
+        if isinstance(latent_mean, str) and latent_mean == 'latent_statistics':
+            assert isinstance(latent_statistics, dict)
+            latent_mean = tuple(latent_statistics['latent_channel_means'])
+        if isinstance(latent_std, str) and latent_std == 'latent_statistics':
+            assert isinstance(latent_statistics, dict)
+            latent_std = tuple(latent_statistics['latent_channel_stds'])
+        downsample_factor = 2**(len(vae.config['channel_multipliers']) - 1)
+        autoencoder_channels = vae.config['latent_channels']
+    assert isinstance(vae, torch.nn.Module)
+    if isinstance(latent_mean, float):
+        latent_mean = (latent_mean,) * autoencoder_channels
+    if isinstance(latent_std, float):
+        latent_std = (latent_std,) * autoencoder_channels
+    assert isinstance(latent_mean, tuple) and isinstance(latent_std, tuple)
+    # Figure out the maximum input sequence length
+    input_max_sequence_length = math.ceil(max_image_side / (downsample_factor * patch_size))
+    # Make the transformer model
+    transformer = DiffusionTransformer(num_features=64 * num_layers,
+                                       num_heads=num_layers,
+                                       num_layers=num_layers,
+                                       input_features=autoencoder_channels * (patch_size**2),
+                                       input_max_sequence_length=input_max_sequence_length,
+                                       input_dimension=2,
+                                       conditioning_features=64 * num_layers,
+                                       conditioning_max_sequence_length=conditioning_max_sequence_length,
+                                       conditioning_dimension=1,
+                                       expansion_factor=4,
+                                       num_register_tokens=num_register_tokens)
+
+    # Optionally load the tokenizers and text encoders
+    t5_tokenizer, t5_encoder, clip_tokenizer, clip_encoder = None, None, None, None
+    if include_text_encoders:
+        dtype_map = {'float32': torch.float32, 'float16': torch.float16, 'bfloat16': torch.bfloat16}
+        dtype = dtype_map[text_encoder_dtype]
+        t5_tokenizer = AutoTokenizer.from_pretrained('google/t5-v1_1-xxl', cache_dir=cache_dir, local_files_only=True)
+        clip_tokenizer = AutoTokenizer.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0',
+                                                       subfolder='tokenizer',
+                                                       cache_dir=cache_dir,
+                                                       local_files_only=False)
+        t5_encoder = AutoModel.from_pretrained('google/t5-v1_1-xxl',
+                                               torch_dtype=dtype,
+                                               cache_dir=cache_dir,
+                                               local_files_only=False).encoder.eval()
+        clip_encoder = CLIPTextModel.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0',
+                                                     subfolder='text_encoder',
+                                                     torch_dtype=dtype,
+                                                     cache_dir=cache_dir,
+                                                     local_files_only=False).cuda().eval()
+
+    # Make the composer model
+    model = ComposerPrecomputedTextLatentsToImageMMDiT(model=transformer,
+                                                       autoencoder=vae,
+                                                       t5_tokenizer=t5_tokenizer,
+                                                       t5_encoder=t5_encoder,
+                                                       clip_tokenizer=clip_tokenizer,
+                                                       clip_encoder=clip_encoder,
+                                                       latent_mean=latent_mean,
+                                                       latent_std=latent_std,
+                                                       patch_size=patch_size,
+                                                       downsample_factor=downsample_factor,
+                                                       latent_channels=autoencoder_channels,
+                                                       timestep_mean=timestep_mean,
+                                                       timestep_std=timestep_std,
+                                                       timestep_shift=timestep_shift,
+                                                       image_key=image_key,
+                                                       t5_latent_key=t5_latent_key,
+                                                       t5_mask_key=t5_mask_key,
+                                                       clip_latent_key=clip_latent_key,
+                                                       clip_mask_key=clip_mask_key,
+                                                       clip_pooled_key=clip_pooled_key)
+
+    if torch.cuda.is_available():
+        model = DeviceGPU().module_to_device(model)
+    return model
+
+
 def build_autoencoder(input_channels: int = 3,
                       output_channels: int = 3,
                       hidden_channels: int = 128,