src/model.py

import os, sys, pdb

import diffusers
from transformers import AutoTokenizer, PretrainedConfig
from diffusers import AutoencoderKL, UNet2DConditionModel, DDPMScheduler


def make_1step_sched():
    noise_scheduler = DDPMScheduler.from_pretrained("stabilityai/sd-turbo", subfolder="scheduler")
    noise_scheduler_1step = DDPMScheduler.from_pretrained("stabilityai/sd-turbo", subfolder="scheduler")
    noise_scheduler_1step.set_timesteps(1, device="cuda")
    noise_scheduler_1step.alphas_cumprod = noise_scheduler_1step.alphas_cumprod.cuda()
    return noise_scheduler_1step


"""The forward method of the `Encoder` class."""
def my_vae_encoder_fwd(self, sample):
    sample = self.conv_in(sample)
    l_blocks = []
    # down
    for down_block in self.down_blocks:
        l_blocks.append(sample)
        sample = down_block(sample)
    # middle
    sample = self.mid_block(sample)
    sample = self.conv_norm_out(sample)
    sample = self.conv_act(sample)
    sample = self.conv_out(sample)
    self.current_down_blocks = l_blocks
    return sample


"""The forward method of the `Decoder` class."""
def my_vae_decoder_fwd(self,sample, latent_embeds = None):
    sample = self.conv_in(sample)
    upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
    # middle
    sample = self.mid_block(sample, latent_embeds)
    sample = sample.to(upscale_dtype)
    if not self.ignore_skip:
        skip_convs = [self.skip_conv_1, self.skip_conv_2, self.skip_conv_3, self.skip_conv_4]
        # up
        for idx, up_block in enumerate(self.up_blocks):
            skip_in = skip_convs[idx](self.incoming_skip_acts[::-1][idx] * self.gamma)
            # add skip
            sample = sample + skip_in
            sample = up_block(sample, latent_embeds)
    else:
        for idx, up_block in enumerate(self.up_blocks):
            sample = up_block(sample, latent_embeds)
    # post-process
    if latent_embeds is None:
        sample = self.conv_norm_out(sample)
    else:
        sample = self.conv_norm_out(sample, latent_embeds)
    sample = self.conv_act(sample)
    sample = self.conv_out(sample)
    return sample