From b382de2d77c653c565840ce92d27aa668a1934d7 Mon Sep 17 00:00:00 2001 From: Buckzor Date: Thu, 13 Oct 2022 22:23:22 +0100 Subject: Fixed Scale ratio problem --- modules/processing.py | 25 +++++++++++-------------- 1 file changed, 11 insertions(+), 14 deletions(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 0246f5dd..d9b0e0e7 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -522,15 +522,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: state.job_count = state.job_count * 2 - #desired_pixel_count = self.firstphase_width * self.firstphase_height - #actual_pixel_count = self.width * self.height - #scale = math.sqrt(desired_pixel_count / actual_pixel_count) - - #self.firstphase_width = math.ceil(scale * self.width / 64) * 64 - #self.firstphase_height = math.ceil(scale * self.height / 64) * 64 - #self.firstphase_width_truncated = int(scale * self.width) - #self.firstphase_height_truncated = int(scale * self.height) - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) @@ -544,17 +535,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): truncate_x = 0 truncate_y = 0 + width_ratio = self.width/self.firstphase_width + height_ratio = self.height/self.firstphase_height if self.crop_scale: - if self.width/self.firstphase_width > self.height/self.firstphase_height: + if width_ratio > height_ratio: #Crop to landscape - truncate_y = (self.width - self.firstphase_width)//2 // opt_f + truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f) - elif self.width/self.firstphase_width < self.height/self.firstphase_height: + elif width_ratio < height_ratio: #Crop to portrait - truncate_x = (self.height - self.firstphase_height)//2 // opt_f + truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f) + + samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] + + - samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] + if self.scale_latent: samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") -- cgit v1.2.1