Update list of scheduler available for inferences (#1298)

apps/stable_diffusion/scripts/img2img.py

@@ -34,16 +34,11 @@ def main():
         args.scheduler = "DDIM"
         args.hf_model_id = "runwayml/stable-diffusion-v1-5"
         image, args.width, args.height = resize_stencil(image)
-    elif args.scheduler != "PNDM":
-        if "Shark" in args.scheduler:
-            print(
-                f"SharkEulerDiscrete scheduler not supported. Switching to PNDM scheduler"
-            )
-            args.scheduler = "PNDM"
-        else:
-            sys.exit(
-                "Img2Img works best with PNDM scheduler. Other schedulers are not supported yet."
-            )
+    elif "Shark" in args.scheduler:
+        print(
+            f"Shark schedulers are not supported. Switching to EulerDiscrete scheduler"
+        )
+        args.scheduler = "EulerDiscrete"
     cpu_scheduling = not args.scheduler.startswith("Shark")
     dtype = torch.float32 if args.precision == "fp32" else torch.half
     set_init_device_flags()

apps/stable_diffusion/web/ui/img2img_ui.py

@@ -14,7 +14,7 @@ from apps.stable_diffusion.web.ui.utils import (
     nodlogo_loc,
     get_custom_model_path,
     get_custom_model_files,
-    scheduler_list,
+    scheduler_list_cpu_only,
     predefined_models,
     cancel_sd,
 )
@@ -119,16 +119,11 @@ def img2img_inf(
         args.scheduler = "DDIM"
         args.hf_model_id = "runwayml/stable-diffusion-v1-5"
         image, width, height = resize_stencil(image)
-    elif args.scheduler != "PNDM":
-        if "Shark" in args.scheduler:
-            print(
-                f"SharkEulerDiscrete scheduler not supported. Switching to PNDM scheduler"
-            )
-            args.scheduler = "PNDM"
-        else:
-            sys.exit(
-                "Img2Img works best with PNDM scheduler. Other schedulers are not supported yet."
-            )
+    elif "Shark" in args.scheduler:
+        print(
+            f"Shark schedulers are not supported. Switching to EulerDiscrete scheduler"
+        )
+        args.scheduler = "EulerDiscrete"
     cpu_scheduling = not args.scheduler.startswith("Shark")
     args.precision = precision
     dtype = torch.float32 if precision == "fp32" else torch.half
@@ -308,7 +303,7 @@ def img2img_api(
         InputData["seed"],
         batch_count=1,
         batch_size=1,
-        scheduler="PNDM",
+        scheduler="EulerDiscrete",
         custom_model="None",
         hf_model_id="stabilityai/stable-diffusion-2-1-base",
         precision="fp16",
@@ -407,8 +402,8 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
                         scheduler = gr.Dropdown(
                             elem_id="scheduler",
                             label="Scheduler",
-                            value="PNDM",
-                            choices=scheduler_list,
+                            value="EulerDiscrete",
+                            choices=scheduler_list_cpu_only,
                         )
                         with gr.Group():
                             save_metadata_to_png = gr.Checkbox(

apps/stable_diffusion/web/ui/inpaint_ui.py

@@ -9,7 +9,7 @@ from apps.stable_diffusion.web.ui.utils import (
     nodlogo_loc,
     get_custom_model_path,
     get_custom_model_files,
-    scheduler_list,
+    scheduler_list_cpu_only,
     predefined_paint_models,
     cancel_sd,
 )
@@ -89,8 +89,8 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
                         scheduler = gr.Dropdown(
                             elem_id="scheduler",
                             label="Scheduler",
-                            value="PNDM",
-                            choices=scheduler_list,
+                            value="EulerDiscrete",
+                            choices=scheduler_list_cpu_only,
                         )
                         with gr.Group():
                             save_metadata_to_png = gr.Checkbox(

apps/stable_diffusion/web/ui/lora_train_ui.py

@@ -10,7 +10,7 @@ from apps.stable_diffusion.web.ui.utils import (
     get_custom_model_path,
     get_custom_model_files,
     get_custom_vae_or_lora_weights,
-    scheduler_list_txt2img,
+    scheduler_list,
     predefined_models,
 )
 
@@ -83,7 +83,7 @@ with gr.Blocks(title="Lora Training") as lora_train_web:
                             elem_id="scheduler",
                             label="Scheduler",
                             value=args.scheduler,
-                            choices=scheduler_list_txt2img,
+                            choices=scheduler_list,
                         )
                     with gr.Row():
                         height = gr.Slider(

apps/stable_diffusion/web/ui/outpaint_ui.py

@@ -9,7 +9,7 @@ from apps.stable_diffusion.web.ui.utils import (
     nodlogo_loc,
     get_custom_model_path,
     get_custom_model_files,
-    scheduler_list,
+    scheduler_list_cpu_only,
     predefined_paint_models,
     cancel_sd,
 )
@@ -86,8 +86,8 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
                         scheduler = gr.Dropdown(
                             elem_id="scheduler",
                             label="Scheduler",
-                            value="PNDM",
-                            choices=scheduler_list,
+                            value="EulerDiscrete",
+                            choices=scheduler_list_cpu_only,
                         )
                         with gr.Group():
                             save_metadata_to_png = gr.Checkbox(

apps/stable_diffusion/web/ui/txt2img_ui.py

@@ -9,7 +9,7 @@ from apps.stable_diffusion.web.ui.utils import (
     nodlogo_loc,
     get_custom_model_path,
     get_custom_model_files,
-    scheduler_list_txt2img,
+    scheduler_list,
     predefined_models,
     cancel_sd,
 )
@@ -276,7 +276,7 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
                             elem_id="scheduler",
                             label="Scheduler",
                             value=args.scheduler,
-                            choices=scheduler_list_txt2img,
+                            choices=scheduler_list,
                         )
                         with gr.Group():
                             save_metadata_to_png = gr.Checkbox(

apps/stable_diffusion/web/ui/upscaler_ui.py

@@ -9,7 +9,7 @@ from apps.stable_diffusion.web.ui.utils import (
     nodlogo_loc,
     get_custom_model_path,
     get_custom_model_files,
-    scheduler_list,
+    scheduler_list_cpu_only,
     predefined_upscaler_models,
 )
 
@@ -86,7 +86,7 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
                             elem_id="scheduler",
                             label="Scheduler",
                             value="DDIM",
-                            choices=scheduler_list,
+                            choices=scheduler_list_cpu_only,
                         )
                         with gr.Group():
                             save_metadata_to_png = gr.Checkbox(

apps/stable_diffusion/web/ui/utils.py

@@ -32,13 +32,7 @@ custom_model_filetypes = (
     "*.safetensors",
 )  # the tuple of file types
 
-scheduler_list = [
-    "DDIM",
-    "PNDM",
-    "DPMSolverMultistep",
-    "EulerAncestralDiscrete",
-]
-scheduler_list_txt2img = [
+scheduler_list_cpu_only = [
     "DDIM",
     "PNDM",
     "LMSDiscrete",
@@ -46,6 +40,8 @@ scheduler_list_txt2img = [
     "DPMSolverMultistep",
     "EulerDiscrete",
     "EulerAncestralDiscrete",
+]
+scheduler_list = scheduler_list_cpu_only + [
     "SharkEulerDiscrete",
 ]
 

apps/stable_diffusion/web/utils/png_metadata.py

@@ -2,7 +2,7 @@ import re
 from pathlib import Path
 from apps.stable_diffusion.web.ui.utils import (
     get_custom_model_pathfile,
-    scheduler_list_txt2img,
+    scheduler_list,
     predefined_models,
 )
 
@@ -124,7 +124,7 @@ def import_png_metadata(
         if "Prompt" in metadata:
             prompt = metadata["Prompt"]
         if "Sampler" in metadata:
-            if metadata["Sampler"] in scheduler_list_txt2img:
+            if metadata["Sampler"] in scheduler_list:
                 sampler = metadata["Sampler"]
             else:
                 print(