jumbo patch

scripts/custom_code.py

@@ -77,7 +77,7 @@ return process_images(p)
         module.display = display
 
         indent = " " * indent_level
-        indented = code.replace('\n', '\n' + indent)
+        indented = code.replace('\n', f"\n{indent}")
         body = f"""def __webuitemp__():
 {indent}{indented}
 __webuitemp__()"""

scripts/loopback.py

@@ -84,7 +84,7 @@ class Script(scripts.Script):
                     p.color_corrections = initial_color_corrections
 
                 if append_interrogation != "None":
-                    p.prompt = original_prompt + ", " if original_prompt != "" else ""
+                    p.prompt = f"{original_prompt}, " if original_prompt else ""
                     if append_interrogation == "CLIP":
                         p.prompt += shared.interrogator.interrogate(p.init_images[0])
                     elif append_interrogation == "DeepBooru":

scripts/xyz_grid.py

@@ -227,7 +227,7 @@ axis_options = [
     AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
     AxisOptionTxt2Img("Fallback latent upscaler sampler", str, apply_fallback, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
     AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
-    AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),
+    AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
     AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
     AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
     AxisOption("Face restore", str, apply_face_restore, fmt=format_value),
@@ -446,7 +446,7 @@ class Script(scripts.Script):
         z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])
 
         def get_dropdown_update_from_params(axis,params):
-            val_key = axis + " Values"
+            val_key = f"{axis} Values"
             vals = params.get(val_key,"")
             valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
             return gr.update(value = valslist)