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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 | |