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A100
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# https://github.com/GaParmar/img2img-turbo/blob/main/src/model.py
from diffusers import DDPMScheduler
def make_1step_sched():
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
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
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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