set firstpass w/h to 0 by default and rever to old behavior when any are 0

2 files changed, 32 insertions, 21 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 100a259f..a75b9f84 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -501,17 +501,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 

 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

     sampler = None

-    firstphase_width = 0

-    firstphase_height = 0

-    firstphase_width_truncated = 0

-    firstphase_height_truncated = 0

 

-    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs):

+    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):

         super().__init__(**kwargs)

         self.enable_hr = enable_hr

         self.denoising_strength = denoising_strength

         self.firstphase_width = firstphase_width

         self.firstphase_height = firstphase_height

+        self.truncate_x = 0

+        self.truncate_y = 0

 

     def init(self, all_prompts, all_seeds, all_subseeds):

         if self.enable_hr:

@@ -520,6 +518,32 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:

                 state.job_count = state.job_count * 2

 

+            if self.firstphase_width == 0 or self.firstphase_height == 0:

+                desired_pixel_count = 512 * 512

+                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

+                firstphase_width_truncated = int(scale * self.width)

+                firstphase_height_truncated = int(scale * self.height)

+

+            else:

+                self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"

+

+                width_ratio = self.width / self.firstphase_width

+                height_ratio = self.height / self.firstphase_height

+

+                if width_ratio > height_ratio:

+                    firstphase_width_truncated = self.firstphase_width

+                    firstphase_height_truncated = self.firstphase_width * self.height / self.width

+                else:

+                    firstphase_width_truncated = self.firstphase_height * self.width / self.height

+                    firstphase_height_truncated = self.firstphase_height

+

+            self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f

+            self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f

+

+

     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)

 

@@ -528,23 +552,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)

             return samples

 

-        self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"

-

         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)

 

-        truncate_x = 0

-        truncate_y = 0

-        width_ratio = self.width/self.firstphase_width

-        height_ratio = self.height/self.firstphase_height

-

-        if width_ratio > height_ratio:

-            truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)

-

-        elif width_ratio < height_ratio:

-            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[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]

 

         decoded_samples = decode_first_stage(self.sd_model, samples)

 

diff --git a/modules/ui.py b/modules/ui.py
index 6d193955..a1d18be9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -567,8 +567,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)

 

                 with gr.Row(visible=False) as hr_options:

-                    firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)

-                    firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)

+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass width", value=0)

+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass height", value=0)

                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)

 

                 with gr.Row(equal_height=True):