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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
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