sd_model.py

from typing import Optional

from dataclasses import dataclass, field
from diffusers.models import AutoencoderKL, UNet2DConditionModel


import torch
from torch import nn


from dataclasses import dataclass



@dataclass
class BaseModelConfig:
    pass


from diffusers import AutoencoderKL, UNet2DConditionModel
from trainer.noise_schedulers.scheduling_ddpm_zerosnr import DDPMScheduler

from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.training_utils import EMAModel

from diffusers.utils import logging

from diffusers.utils.hub_utils import PushToHubMixin

from diffusers.models.modeling_utils import ModelMixin

from diffusers.configuration_utils import ConfigMixin

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

# from hydra.utils import instantiate
from peft import get_peft_model

from layers import PositionalEncodingPermute1D
from einops import rearrange, repeat

from typing import Optional
from omegaconf import II


@dataclass
class LoraConfig:
    _target_: str = "peft.LoraConfig"
    r: int = 8
    lora_alpha: int =32
    target_modules: list = field(default_factory=lambda: ["to_q", "to_v", "query", "value"])
    lora_dropout: float =0.0
    bias: str ="none"


@dataclass
class SDModelConfig(BaseModelConfig):
    _target_: str = "sd_model.SDModel"
    pretrained_model_name_or_path: str = "runwayml/stable-diffusion-v1-5"
    conditioning_dropout_prob: float = 0.05
    use_ema: bool = True
    concat_all_steps: bool = False
    positional_encoding_type: Optional[str] = "sinusoidal"
    positional_encoding_length: Optional[int] = None
    image_positional_encoding_type: Optional[str] = None #"sinusoidal"
    image_positional_encoding_length: Optional[int] = None
    broadcast_positional_encoding: bool = True
    sequence_length: Optional[int] = 6
    text_sequence_length: Optional[int] = 7
    use_lora: bool = False
    # lora_cfg: Any = LoraConfig()
    zero_snr: bool = True
    # seed: int = 42 # TODO: inherit from higher config
    # lora: LoraConfig = LoraConfig(
    #     )
    
    
class SDModel(ModelMixin, ConfigMixin, PushToHubMixin):
    def __init__(self, cfg: SDModelConfig = None) -> None:
        super().__init__()
        
        if cfg is None: # workaround for default
            self.cfg = SDModelConfig()
        else:
            self.cfg = cfg

        print(self.cfg)
        
        self.noise_scheduler = DDPMScheduler.from_pretrained(
            self.cfg.pretrained_model_name_or_path, 
            subfolder="scheduler",
            zero_snr=self.cfg.zero_snr)
        
        
        
        self.text_encoder = CLIPTextModel.from_pretrained(
            self.cfg.pretrained_model_name_or_path, subfolder="text_encoder", 
        )
        self.tokenizer = CLIPTokenizer.from_pretrained(
            self.cfg.pretrained_model_name_or_path, subfolder="tokenizer"
        )
        
        self.vae = AutoencoderKL.from_pretrained(self.cfg.pretrained_model_name_or_path, subfolder="vae")
        self.unet = UNet2DConditionModel.from_pretrained(
            self.cfg.pretrained_model_name_or_path, subfolder="unet"
        )
        
        in_channels = 8 # TODO make part of cfg
        out_channels = self.unet.conv_in.out_channels
        self.unet.register_to_config(in_channels=in_channels)

        with torch.no_grad(): 
            new_conv_in = nn.Conv2d(
                in_channels, out_channels, self.unet.conv_in.kernel_size, self.unet.conv_in.stride, self.unet.conv_in.padding
            )
            new_conv_in.weight.zero_()
            new_conv_in.weight[:, :4, :, :].copy_(self.unet.conv_in.weight) # copy the pretrained weights, leave the rest as zero
            new_conv_in.bias.copy_(self.unet.conv_in.bias) # EXTREMELY IMPORTANT MODIFICATION FROM INITIAL DIFFUSERS CODE
            self.unet.conv_in = new_conv_in
            
        self.init_pos()
        self.init_image_pos()
        
        
        if self.cfg.use_lora:
            config = LoraConfig(
                    r=8,
                    lora_alpha=32,
                    target_modules=["to_q", "to_v", "query", "value"],
                    lora_dropout=0.0,
                    bias="none",
                )
            self.unet = get_peft_model(self.unet, config)
            self.unet.conv_in.requires_grad_(True)  # NOTE: this makes the whole input conv trainable, not just the new parameters! consider if that's what you really want
            self.unet.print_trainable_parameters()
            print(self.unet)
            
        self.vae.requires_grad_(False)
        self.text_encoder.requires_grad_(False)
        
        # use_ema = True
        # if use_ema:
        if self.cfg.use_ema:
            self.ema_unet = EMAModel(self.unet.parameters(), model_cls=UNet2DConditionModel, model_config=self.unet.config)
            
        self.generator = None 

    def init_pos(self):
        self.cfg.positional_encoding_length = self.cfg.text_sequence_length
        if not self.cfg.broadcast_positional_encoding:
            self.cfg.positional_encoding_length *= 77
        elif self.cfg.positional_encoding_type == 'sinusoidal':
            self.unet.pos = PositionalEncodingPermute1D(self.cfg.positional_encoding_length)
        elif self.cfg.positional_encoding_type is None or self.cfg.positional_encoding_type == 'None':
            self.unet.pos = nn.Identity()
        else:
            raise ValueError(f'Unknown positional encoding type {self.cfg.positional_encoding_type}')#torch.Generator(self.unet.device).manual_seed(42) # seed: int = 42 # TODO: inherit from higher config # device=self.unet.device
    
    def init_image_pos(self):
        self.cfg.image_positional_encoding_length = self.cfg.sequence_length
        if self.cfg.image_positional_encoding_type == 'sinusoidal':
            self.unet.image_pos = PositionalEncodingPermute1D(self.cfg.image_positional_encoding_length)
        elif self.cfg.image_positional_encoding_type is None:
            self.unet.image_pos = nn.Identity()
        else:
            raise ValueError(f'Unknown image positional encoding type {self.cfg.image_positional_encoding_type}')
        
    def tokenize_captions(self, captions):
        inputs = self.tokenizer(
            captions, max_length=self.tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
        )
        return inputs.input_ids
            
    def forward(self, batch): # replace with input_ids, edited_pixel_values, original_pixel_values
        batch_size = batch["input_ids"].shape[0]
        condition_image = batch["original_pixel_values"]
        input_ids = batch["input_ids"].to(self.text_encoder.device)
        # We want to learn the denoising process w.r.t the edited images which
        # are conditioned on the original image (which was edited) and the edit instruction.
        # So, first, convert images to latent space.
        edited_images = batch["edited_pixel_values"]#.to(self.cfg.weight_dtype) #TODO check dtype thing
        output_seq_length = edited_images.shape[1]
        # edited_images = edited_images.flatten(0,1)
        edited_images = rearrange(edited_images, 'b s c h w -> (b s) c h w')
        
        latents = self.vae.encode(edited_images).latent_dist.sample()
        latents = latents * self.vae.config.scaling_factor
        
        latents = rearrange(latents, '(b s) c h w -> b c (s h) w', s=output_seq_length)
        # latents = latents.unflatten(0,(batch_size,output_seq_length)).transpose(1,2).flatten(2,3) # TODO: change the (batch_size, 3) to (batch_size, output_seq_length)
        # Sample noise that we'll add to the latents
        noise = torch.randn_like(latents)
        bsz = latents.shape[0]
        # Sample a random timestep for each image
        timesteps = torch.randint(0, self.noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
        timesteps = timesteps.long()

        # Add noise to the latents according to the noise magnitude at each timestep
        # (this is the forward diffusion process)
        noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps)
        
        if self.cfg.image_positional_encoding_type is not None:            
            latents = self.apply_image_positional_encoding(noisy_latents, output_seq_length)
        
        if len(input_ids.shape) == 2:
            input_ids = input_ids.unsqueeze(0)

        encoder_hidden_states = self.input_ids_to_text_condition(input_ids)
        if self.cfg.positional_encoding_type is not None:
            encoder_hidden_states = self.apply_step_positional_encoding(encoder_hidden_states)

        # Get the additional image embedding for conditioning.
        # Instead of getting a diagonal Gaussian here, we simply take the mode.
        original_image_embeds = self.vae.encode(condition_image).latent_dist.mode() #.to(self.cfg.weight_dtype)).latent_dist.mode() #TODO check dtype thing

        # Conditioning dropout to support classifier-free guidance during inference. For more details
        # check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800.
        if self.cfg.conditioning_dropout_prob is not None:
            encoder_hidden_states, original_image_embeds = self.apply_conditioning_dropout(encoder_hidden_states, original_image_embeds)

        # original_image_embeds = original_image_embeds.repeat(1,1,2,1)
        # original_image_embeds = original_image_embeds.unsqueeze(2).expand(-1, -1, output_seq_length, -1, -1).reshape(batch_size, 4, 32*output_seq_length, 32)
        original_image_embeds = repeat(original_image_embeds, 'b c h w -> b c (s h) w', s=output_seq_length) # TODO unify with pipeline get_image_latents
        
        # Concatenate the `original_image_embeds` with the `noisy_latents`.
        concatenated_noisy_latents = torch.cat([noisy_latents, original_image_embeds], dim=1)

        target = self.get_loss_target(latents, noise, timesteps)

        # Predict the noise residual and compute loss
        model_pred = self.unet(concatenated_noisy_latents, timesteps, encoder_hidden_states).sample
        return model_pred, target

    def get_loss_target(self, latents, noise, timesteps):
        # Get the target for loss depending on the prediction type
        if self.noise_scheduler.config.prediction_type == "epsilon":
            target = noise
        elif self.noise_scheduler.config.prediction_type == "v_prediction":
            target = self.noise_scheduler.get_velocity(latents, noise, timesteps)
        else:
            raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}")
        return target

    def apply_conditioning_dropout(self, encoder_hidden_states, original_image_embeds):
        bsz = original_image_embeds.shape[0] # changed from the comment in line 141 from latents, but should be same. TODO check
        random_p = torch.rand(bsz, device=encoder_hidden_states.device, generator=self.generator) # was originally latents.device, TODO check
            # Sample masks for the edit prompts.
        prompt_mask = random_p < 2 * self.cfg.conditioning_dropout_prob
        prompt_mask = prompt_mask.reshape(bsz, 1, 1)
            # Final text conditioning.
        null_conditioning = self.get_null_conditioning()
        encoder_hidden_states = torch.where(prompt_mask, null_conditioning, encoder_hidden_states)

            # Sample masks for the original images.
        image_mask_dtype = original_image_embeds.dtype
        image_mask = 1 - (
                (random_p >= self.cfg.conditioning_dropout_prob).to(image_mask_dtype)
                * (random_p < 3 * self.cfg.conditioning_dropout_prob).to(image_mask_dtype)
            )
        image_mask = image_mask.reshape(bsz, 1, 1, 1)
        # Final image conditioning.
        original_image_embeds = image_mask * original_image_embeds
        return encoder_hidden_states,original_image_embeds

    def get_null_conditioning(self):
        null_token = self.tokenize_captions([""]).to(self.text_encoder.device)
        # null_conditioning = self.input_ids_to_text_condition(null_token) # would apply positional encoding twice
        null_conditioning = self.text_encoder(null_token)[0] # TODO fuse with input_ids_to_text_condition
        if not self.cfg.concat_all_steps:
            null_conditioning = repeat(null_conditioning, 'b t l -> b (s t) l', s=self.cfg.text_sequence_length)
        return null_conditioning

    def input_ids_to_text_condition(self, input_ids):
        # Get the text embedding for conditioning.
        if self.cfg.concat_all_steps:
            encoder_hidden_states = self.text_encoder(input_ids)[0] # text padded to 77 tokens; encoder_hidden_states.shape = (bsz, 77, 768)
        else:
            input_ids = rearrange(input_ids, 'b s t->(b s) t')
            encoder_hidden_states = self.text_encoder(input_ids)[0] # text padded to 77 tokens; encoder_hidden_states.shape = (bsz, 77, 768) # TODO check why this doesn't match concatenating the encodings of the three tokens; the ones that don't match are the 769-1535 dims of the feature, for tokens 15-76
            
            # if args.use_positional_encoding: # old way: added before concat which doesn't make sense
            #     encoder_hidden_states = pos(encoder_hidden_states) + encoder_hidden_states
            encoder_hidden_states = rearrange(encoder_hidden_states, '(b s) t d->b (s t) d', s=self.cfg.text_sequence_length)

        return encoder_hidden_states

    def apply_step_positional_encoding(self, encoder_hidden_states):
        positional_encoding = self.unet.pos(encoder_hidden_states)
        if self.cfg.broadcast_positional_encoding:
            positional_encoding = repeat(positional_encoding, 'b s d -> b (s t) d', t=77) # TODO check this
        encoder_hidden_states = positional_encoding + encoder_hidden_states
        return encoder_hidden_states
    
    def apply_image_positional_encoding(self, latents, output_seq_length):
        original_latents_shape = latents.shape
        h = original_latents_shape[2]//output_seq_length
        latents = rearrange(latents, 'b c (s h) w -> b s (c h w)', s=output_seq_length)
        image_pos = self.unet.image_pos(latents)
        latents = latents + image_pos
        latents = rearrange(latents, 'b s (c h w) -> b c (s h) w', s=output_seq_length, c=original_latents_shape[1], h=h, w=original_latents_shape[3]) # confirmed that without the pos addition in between, this reshaping brings it back to the original tensor
        return latents
    
    def instantiate_pipeline(self):
        pass