remove scale latent and no-crop options from hires fix

support copy-pasting new parameters for hires fix

3 files changed, 35 insertions, 57 deletions

diff --git a/modules/processing.py b/modules/processing.py
index d9b0e0e7..100a259f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -506,14 +506,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     firstphase_width_truncated = 0

     firstphase_height_truncated = 0

 

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

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

         super().__init__(**kwargs)

         self.enable_hr = enable_hr

-        self.scale_latent = scale_latent

         self.denoising_strength = denoising_strength

         self.firstphase_width = firstphase_width

         self.firstphase_height = firstphase_height

-        self.crop_scale = crop_scale

 

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

         if self.enable_hr:

@@ -530,6 +528,8 @@ 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)

 

@@ -538,46 +538,36 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         width_ratio = self.width/self.firstphase_width

         height_ratio = self.height/self.firstphase_height

 

-        if self.crop_scale:

-            if width_ratio > height_ratio:

-                #Crop to landscape

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

+        if width_ratio > height_ratio:

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

 

-            elif width_ratio < height_ratio:

-                #Crop to portrait

-                truncate_x = int((self.height - self.firstphase_height) / 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[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]

 

-        

+        decoded_samples = decode_first_stage(self.sd_model, samples)

 

-        if self.scale_latent:

-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

+        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":

+            decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")

         else:

-            decoded_samples = decode_first_stage(self.sd_model, samples)

+            lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)

 

-            if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":

-                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")

-            else:

-                lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)

-

-                batch_images = []

-                for i, x_sample in enumerate(lowres_samples):

-                    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

-                    x_sample = x_sample.astype(np.uint8)

-                    image = Image.fromarray(x_sample)

-                    image = images.resize_image(0, image, self.width, self.height)

-                    image = np.array(image).astype(np.float32) / 255.0

-                    image = np.moveaxis(image, 2, 0)

-                    batch_images.append(image)

-

-                decoded_samples = torch.from_numpy(np.array(batch_images))

-                decoded_samples = decoded_samples.to(shared.device)

-                decoded_samples = 2. * decoded_samples - 1.

-

-            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))

+            batch_images = []

+            for i, x_sample in enumerate(lowres_samples):

+                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

+                x_sample = x_sample.astype(np.uint8)

+                image = Image.fromarray(x_sample)

+                image = images.resize_image(0, image, self.width, self.height)

+                image = np.array(image).astype(np.float32) / 255.0

+                image = np.moveaxis(image, 2, 0)

+                batch_images.append(image)

+

+            decoded_samples = torch.from_numpy(np.array(batch_images))

+            decoded_samples = decoded_samples.to(shared.device)

+            decoded_samples = 2. * decoded_samples - 1.

+

+        samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))

 

         shared.state.nextjob()

 

diff --git a/modules/txt2img.py b/modules/txt2img.py
index 447ec3d3..2381347f 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html

 

 

-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, crop_scale: bool, *args):

+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):

     p = StableDiffusionProcessingTxt2Img(

         sd_model=shared.sd_model,

         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,

@@ -30,12 +30,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         restore_faces=restore_faces,

         tiling=tiling,

         enable_hr=enable_hr,

-        scale_latent=scale_latent if enable_hr else None,

         denoising_strength=denoising_strength if enable_hr else None,

-		firstphase_width=firstphase_width if enable_hr else None,

-		firstphase_height=firstphase_height if enable_hr else None,

-        crop_scale=crop_scale if enable_hr else None,

-

+        firstphase_width=firstphase_width if enable_hr else None,

+        firstphase_height=firstphase_height if enable_hr else None,

     )

 

     if cmd_opts.enable_console_prompts:

diff --git a/modules/ui.py b/modules/ui.py
index f2d81f68..d66ddc14 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -540,16 +540,9 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)

 

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

-                    with gr.Column(scale=1.0):

-                        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)

-                        

-                    with gr.Column(scale=1.0):

-                        with gr.Row():

-                            crop_scale = gr.Checkbox(label='Crop when scaling', value=False)

-                            scale_latent = gr.Checkbox(label='Scale latent', value=False)

-                        with gr.Row():

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

+                    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)

+                    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):

                     batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)

@@ -610,11 +603,9 @@ def create_ui(wrap_gradio_gpu_call):
                     height,

                     width,

                     enable_hr,

-                    scale_latent,

                     denoising_strength,

                     firstphase_width,

                     firstphase_height,

-                    crop_scale,

                 ] + custom_inputs,

                 outputs=[

                     txt2img_gallery,

@@ -679,8 +670,8 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),

                 (enable_hr, lambda d: "Denoising strength" in d),

                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),

-                (firstphase_width, "First pass width"),

-                (firstphase_height, "First pass height"),

+                (firstphase_width, "First pass size-1"),

+                (firstphase_height, "First pass size-2"),

             ]

             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)

             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])