model.py

from PIL import Image
from base64 import b64encode

import torch
from torch import autocast
from torch.nn import functional as F
from diffusers import StableDiffusionPipeline, AutoencoderKL
from diffusers import UNet2DConditionModel, PNDMScheduler, LMSDiscreteScheduler
from diffusers.schedulers.scheduling_ddim import DDIMScheduler
#from transformers import CLIPTextModel, CLIPTokenizer
from tqdm.auto import tqdm
from huggingface_hub import notebook_login
import torch.nn as nn

device = 'cpu'

from Multilingual_CLIP.multilingual_clip import Config_MCLIP
import transformers
import torch


class MultilingualCLIP(transformers.PreTrainedModel):
    config_class = Config_MCLIP.MCLIPConfig

    def __init__(self, config, *args, **kwargs):
        super().__init__(config, *args, **kwargs)
        self.transformer = transformers.AutoModel.from_pretrained(config.modelBase)
        self.LinearTransformation = torch.nn.Linear(in_features=config.transformerDimensions,
                                                    out_features=config.numDims)

    def forward(self, txt, tokenizer, device):
        txt_tok = tokenizer(txt, padding='max_length', max_length=77, truncation=True, return_tensors='pt').to(device)
        embs = self.transformer(**txt_tok)
        embs = embs[0]
        att = txt_tok['attention_mask']
        embs = (embs * att.unsqueeze(2)) / att.sum(dim=1)[:, None].unsqueeze(2)
        return self.LinearTransformation(embs)

    @classmethod
    def _load_state_dict_into_model(cls, model, state_dict, pretrained_model_name_or_path, _fast_init=True):
        model.load_state_dict(state_dict)
        return model, [], [], []


# Define the adaptation layer, 'checkpoint_9.pth'
class AdaptationLayer(nn.Module):
  def __init__(self, input_dim, output_dim):
    super(AdaptationLayer, self).__init__()
    self.fc1 = nn.Linear(input_dim, output_dim*2)
    torch.nn.init.kaiming_uniform_(self.fc1.weight, nonlinearity='relu')
    self.bn1 = nn.BatchNorm1d(77)

    self.fc2 = nn.Linear(input_dim*2, output_dim*2)
    torch.nn.init.kaiming_uniform_(self.fc2.weight, nonlinearity='relu')
    self.bn2 = nn.BatchNorm1d(77)

    self.fc3 = nn.Linear(input_dim*2, output_dim)
    torch.nn.init.kaiming_uniform_(self.fc3.weight, nonlinearity='relu')
    self.bn3 = nn.BatchNorm1d(77)

    self.fc4 = nn.Linear(input_dim, output_dim)
    torch.nn.init.kaiming_uniform_(self.fc4.weight, nonlinearity='relu')
    self.bn4 = nn.BatchNorm1d(77)

    self.fc5 = nn.Linear(input_dim, output_dim)

  def forward(self, x):
    x = nn.functional.normalize(x, p=2.0, dim=1, eps=1e-12, out=None)
    x = torch.relu(self.bn1(self.fc1(x)))
    x = torch.relu(self.bn2(self.fc2(x)))
    x = torch.relu(self.bn3(self.fc3(x)))
    x = torch.relu(self.bn4(self.fc4(x)))

    return self.fc5(x)
  

adapt_model  = AdaptationLayer(768,768)
adapt_model.to(device)
state_dict = torch.load('weights/checkpoint_9.pth')
adapt_model.load_state_dict(state_dict)

from Multilingual_CLIP.multilingual_clip import pt_multilingual_clip

texts = [
    'قطة تقرأ كتابا'
]

model_name = 'M-CLIP/LABSE-Vit-L-14'

# Load Model & Tokenizer
text_model = pt_multilingual_clip.MultilingualCLIP.from_pretrained(model_name)
text_model = text_model.to(device)
text_tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)

embeddings= text_model.forward(texts, text_tokenizer, device )
# 1. Load the autoencoder model which will be used to decode the latents into image space.
vae = AutoencoderKL.from_pretrained(
    'CompVis/stable-diffusion-v1-4', subfolder='vae', use_auth_token=True)
vae = vae.to(device)

# 2. Load the tokenizer and text encoder to tokenize and encode the text.
tokenizer = text_tokenizer
text_encoder = text_model

# 3. The UNet model for generating the latents.
unet = UNet2DConditionModel.from_pretrained(
    'CompVis/stable-diffusion-v1-4', subfolder='unet', use_auth_token=True)
unet = unet.to(device)

# 4. Create a scheduler for inference
scheduler = LMSDiscreteScheduler(
    beta_start=0.00085, beta_end=0.012,
    beta_schedule='scaled_linear', num_train_timesteps=1000)


def get_text_embeds(prompt):
  with torch.no_grad():
    text_embeddings = text_model(prompt, text_tokenizer, device)
    text_embeddings = adapt_model(text_embeddings)

  # Do the same for unconditional embeddings
  with torch.no_grad():
    uncond_embeddings = text_model([''] * len(prompt), text_tokenizer, device)
    uncond_embeddings = adapt_model(uncond_embeddings)

  # Cat for final embeddings
  text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
  return text_embeddings


def produce_latents(text_embeddings, height=512, width=512,
                    num_inference_steps=50, guidance_scale=7.5, latents=None):
  if latents is None:
    latents = torch.randn((text_embeddings.shape[0] // 2, unet.in_channels, \
                           height // 8, width // 8))
  latents = latents.to(device)

  scheduler.set_timesteps(num_inference_steps)
  latents = latents * scheduler.sigmas[0]

  with autocast('cpu'):
    for i, t in tqdm(enumerate(scheduler.timesteps)):
      # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
      latent_model_input = torch.cat([latents] * 2)
      sigma = scheduler.sigmas[i]
      latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)

      # predict the noise residual
      with torch.no_grad():
        noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings.to(device))['sample']

      # perform guidance
      noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
      noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

      # compute the previous noisy sample x_t -> x_t-1
      latents = scheduler.step(noise_pred, i, latents)['prev_sample']

  return latents


def decode_img_latents(latents):
  latents = 1 / 0.18215 * latents

  with torch.no_grad():
    imgs = vae.decode(latents)

  imgs = (imgs / 2 + 0.5).clamp(0, 1)
  imgs = imgs.detach().cpu().permute(0, 2, 3, 1).numpy()
  imgs = (imgs * 255).round().astype('uint8')
  pil_images = [Image.fromarray(image) for image in imgs]
  return pil_images

def prompt_to_img(prompts, height=512, width=512, num_inference_steps=50,
                  guidance_scale=7.5, latents=None):
  if isinstance(prompts, str):
    prompts = [prompts]

  # Prompts -> text embeds
  text_embeds = get_text_embeds(prompts)

  # Text embeds -> img latents
  latents = produce_latents(
      text_embeds, height=height, width=width, latents=latents,
      num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)

  # Img latents -> imgs
  imgs = decode_img_latents(latents)
  
  return imgs