Merge branch 'master' into gradient-clipping

1 files changed, 9 insertions, 7 deletions

diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index 01074291..9739545c 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -17,13 +17,14 @@ class Script(scripts.Script):
         return is_img2img

 

     def ui(self, is_img2img):

-        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image to twice the dimensions; use width and height sliders to set tile size</p>")

+        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")

         overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)

+        scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0)

         upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")

 

-        return [info, overlap, upscaler_index]

+        return [info, overlap, upscaler_index, scale_factor]

 

-    def run(self, p, _, overlap, upscaler_index):

+    def run(self, p, _, overlap, upscaler_index, scale_factor):

         processing.fix_seed(p)

         upscaler = shared.sd_upscalers[upscaler_index]

 

@@ -34,9 +35,10 @@ class Script(scripts.Script):
         seed = p.seed

 

         init_img = p.init_images[0]

-        

-        if(upscaler.name != "None"): 

-            img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)

+        init_img = images.flatten(init_img, opts.img2img_background_color)

+

+        if upscaler.name != "None":

+            img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)

         else:

             img = init_img

 

@@ -69,7 +71,7 @@ class Script(scripts.Script):
             work_results = []

             for i in range(batch_count):

                 p.batch_size = batch_size

-                p.init_images = work[i*batch_size:(i+1)*batch_size]

+                p.init_images = work[i * batch_size:(i + 1) * batch_size]

 

                 state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"

                 processed = processing.process_images(p)