diff --git a/modules/processing.py b/modules/processing.py index c622ff337..2fda7f332 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -874,7 +874,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: else: if opts.sd_vae_decode_method != 'Full': p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method - with hypertile_context_unet(p.sd_model.first_stage_model, aspect_ratio=p.width / p.height, tile_size=largest_tile_size_available(p.width, p.height), is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts): + with hypertile_context_unet(p.sd_model.model, aspect_ratio=p.width / p.height, tile_size=largest_tile_size_available(p.width, p.height), is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts): x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True) x_samples_ddim = torch.stack(x_samples_ddim).float() @@ -1145,7 +1145,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = self.rng.next() tile_size = largest_tile_size_available(self.width, self.height) with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts): - with hypertile_context_unet(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts): + with hypertile_context_unet(self.sd_model.model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts): devices.torch_gc() samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) del x @@ -1247,7 +1247,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): tile_size = largest_tile_size_available(target_width, target_height) aspect_ratio = self.width / self.height with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts): - with hypertile_context_unet(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts): + with hypertile_context_unet(self.sd_model.model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts): samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning) sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio()) @@ -1535,7 +1535,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): aspect_ratio = self.width / self.height tile_size = largest_tile_size_available(self.width, self.height) with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts): - with hypertile_context_unet(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts): + with hypertile_context_unet(self.sd_model.model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts): devices.torch_gc() samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)