MMDiT implementation and text-to-image training with rectified flows (#…

…155)

diffusion/inference/__init__.py

@@ -3,6 +3,6 @@
 
 """Inference endpoint."""
 
-from diffusion.inference.inference_model import StableDiffusionInference, StableDiffusionXLInference
+from diffusion.inference.inference_model import ModelInference, StableDiffusionInference, StableDiffusionXLInference
 
-__all__ = ['StableDiffusionInference', 'StableDiffusionXLInference']
+__all__ = ['ModelInference', 'StableDiffusionInference', 'StableDiffusionXLInference']

diffusion/inference/inference_model.py

@@ -11,6 +11,7 @@
 from composer.utils.file_helpers import get_file
 from PIL import Image
 
+import diffusion.models
 from diffusion.models import stable_diffusion_2, stable_diffusion_xl
 
 # Local checkpoint params
@@ -225,3 +226,80 @@ def predict(self, model_requests: List[Dict[str, Any]]):
             base64_encoded_image = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
             png_images.append(base64_encoded_image)
         return png_images
+
+
+class ModelInference():
+    """Generic inference endpoint class for diffusion models with a model.generate() method.
+
+    Args:
+        model_name (str): Name of the model from `diffusion.models` to load. Ex: for stable diffusion xl, use 'stable_diffusion_xl'.
+        local_checkpoint_path (str): Path to the local checkpoint. Default: '/tmp/model.pt'.
+        strict (bool): Whether to load the model weights strictly. Default: False.
+        **model_kwargs: Keyword arguments to pass to the model initialization.
+    """
+
+    def __init__(self, model_name, local_checkpoint_path: str = LOCAL_CHECKPOINT_PATH, strict=False, **model_kwargs):
+        self.device = torch.cuda.current_device()
+        model_factory = getattr(diffusion.models, model_name)
+        model = model_factory(**model_kwargs)
+
+        if 'pretrained' in model_kwargs and model_kwargs['pretrained']:
+            pass
+        else:
+            state_dict = torch.load(local_checkpoint_path)
+            for key in list(state_dict['state']['model'].keys()):
+                if 'val_metrics.' in key:
+                    del state_dict['state']['model'][key]
+            model.load_state_dict(state_dict['state']['model'], strict=strict)
+        model.to(self.device)
+        self.model = model.eval()
+
+    def predict(self, model_requests: List[Dict[str, Any]]):
+        prompts = []
+        negative_prompts = []
+        generate_kwargs = {}
+
+        # assumes the same generate_kwargs across all samples
+        for req in model_requests:
+            if 'input' not in req:
+                raise RuntimeError('"input" must be provided to generate call')
+            inputs = req['input']
+
+            # Prompts and negative prompts if available
+            if isinstance(inputs, str):
+                prompts.append(inputs)
+            elif isinstance(inputs, Dict):
+                if 'prompt' not in inputs:
+                    raise RuntimeError('"prompt" must be provided to generate call if using a dict as input')
+                prompts.append(inputs['prompt'])
+                if 'negative_prompt' in inputs:
+                    negative_prompts.append(inputs['negative_prompt'])
+            else:
+                raise RuntimeError(f'Input must be of type string or dict, but it is type: {type(inputs)}')
+
+            generate_kwargs = req['parameters']
+
+        # Check for prompts
+        if len(prompts) == 0:
+            raise RuntimeError('No prompts provided, must be either a string or dictionary with "prompt"')
+
+        # Check negative prompt length
+        if len(negative_prompts) == 0:
+            negative_prompts = None
+        elif len(prompts) != len(negative_prompts):
+            raise RuntimeError('There must be the same number of negative prompts as prompts.')
+
+        # Generate images
+        with torch.cuda.amp.autocast(True):
+            imgs = self.model.generate(prompt=prompts, negative_prompt=negative_prompts, **generate_kwargs).cpu()
+
+        # Send as bytes
+        png_images = []
+        for i in range(imgs.shape[0]):
+            img = (imgs[i].permute(1, 2, 0).numpy() * 255).round().astype('uint8')
+            pil_image = Image.fromarray(img, 'RGB')
+            img_byte_arr = io.BytesIO()
+            pil_image.save(img_byte_arr, format='PNG')
+            base64_encoded_image = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
+            png_images.append(base64_encoded_image)
+        return png_images

diffusion/models/__init__.py

@@ -4,7 +4,8 @@
 """Diffusion models."""
 
 from diffusion.models.models import (build_autoencoder, build_diffusers_autoencoder, continuous_pixel_diffusion,
-                                     discrete_pixel_diffusion, stable_diffusion_2, stable_diffusion_xl)
+                                     discrete_pixel_diffusion, stable_diffusion_2, stable_diffusion_xl,
+                                     text_to_image_transformer)
 from diffusion.models.noop import NoOpModel
 from diffusion.models.pixel_diffusion import PixelDiffusion
 from diffusion.models.stable_diffusion import StableDiffusion
@@ -19,4 +20,5 @@
     'stable_diffusion_2',
     'stable_diffusion_xl',
     'StableDiffusion',
+    'text_to_image_transformer',
 ]

diffusion/models/models.py

@@ -4,6 +4,7 @@
 """Constructors for diffusion models."""
 
 import logging
+import math
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -17,7 +18,9 @@
 from diffusion.models.layers import ClippedAttnProcessor2_0, ClippedXFormersAttnProcessor, zero_module
 from diffusion.models.pixel_diffusion import PixelDiffusion
 from diffusion.models.stable_diffusion import StableDiffusion
+from diffusion.models.t2i_transformer import ComposerTextToImageMMDiT
 from diffusion.models.text_encoder import MultiTextEncoder, MultiTokenizer
+from diffusion.models.transformer import DiffusionTransformer
 from diffusion.schedulers.schedulers import ContinuousTimeScheduler
 from diffusion.schedulers.utils import shift_noise_schedule
 
@@ -496,6 +499,135 @@ def stable_diffusion_xl(
     return model
 
 
+def text_to_image_transformer(
+    tokenizer_names: Union[str, Tuple[str, ...]] = ('stabilityai/stable-diffusion-xl-base-1.0/tokenizer'),
+    text_encoder_names: Union[str, Tuple[str, ...]] = ('stabilityai/stable-diffusion-xl-base-1.0/text_encoder'),
+    vae_model_name: str = 'madebyollin/sdxl-vae-fp16-fix',
+    autoencoder_path: Optional[str] = None,
+    autoencoder_local_path: str = '/tmp/autoencoder_weights.pt',
+    num_layers: int = 28,
+    max_image_side: int = 1280,
+    conditioning_features: int = 768,
+    conditioning_max_sequence_length: int = 77,
+    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',
+    caption_key: str = 'captions',
+    caption_mask_key: str = 'attention_mask',
+    pretrained: bool = False,
+):
+    """Text to image transformer training setup.
+
+    Args:
+        tokenizer_names (str, Tuple[str, ...]): HuggingFace name(s) of the tokenizer(s) to load.
+            Default: ``('stabilityai/stable-diffusion-xl-base-1.0/tokenizer')``.
+        text_encoder_names (str, Tuple[str, ...]): HuggingFace name(s) of the text encoder(s) to load.
+            Default: ``('stabilityai/stable-diffusion-xl-base-1.0/text_encoder')``.
+        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`.
+        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`.
+        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`.
+        caption_key (str): The key for the captions in the batch. Default: `captions`.
+        caption_mask_key (str): The key for the caption mask in the batch. Default: `attention_mask`.
+        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)
+
+    if (isinstance(tokenizer_names, tuple) or
+            isinstance(text_encoder_names, tuple)) and len(tokenizer_names) != len(text_encoder_names):
+        raise ValueError('Number of tokenizer_names and text_encoder_names must be equal')
+
+    # Make the tokenizer and text encoder
+    tokenizer = MultiTokenizer(tokenizer_names_or_paths=tokenizer_names)
+    text_encoder = MultiTextEncoder(model_names=text_encoder_names, encode_latents_in_fp16=True, pretrained_sdxl=False)
+
+    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=conditioning_features,
+                                       conditioning_max_sequence_length=conditioning_max_sequence_length,
+                                       conditioning_dimension=1,
+                                       expansion_factor=4)
+    # Make the composer model
+    model = ComposerTextToImageMMDiT(model=transformer,
+                                     autoencoder=vae,
+                                     text_encoder=text_encoder,
+                                     tokenizer=tokenizer,
+                                     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,
+                                     caption_key=caption_key,
+                                     caption_mask_key=caption_mask_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,