Merge branch 'dev' into master

modules/api/api.py

@@ -14,7 +14,7 @@ from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -22,7 +22,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
-from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights
+from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights, checkpoint_aliases
 from modules.sd_vae import vae_dict
 from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
@@ -30,13 +30,7 @@ from modules import devices
 from typing import Dict, List, Any
 import piexif
 import piexif.helper
-
-
-def upscaler_to_index(name: str):
-    try:
-        return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") from e
+from contextlib import closing
 
 
 def script_name_to_index(name, scripts):
@@ -84,6 +78,8 @@ def encode_pil_to_base64(image):
             image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
 
         elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
+            if image.mode == "RGBA":
+                image = image.convert("RGB")
             parameters = image.info.get('parameters', None)
             exif_bytes = piexif.dump({
                 "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") }
@@ -209,6 +205,11 @@ class Api:
         self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
         self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
 
+        if shared.cmd_opts.api_server_stop:
+            self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
+            self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"])
+            self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"])
+
         self.default_script_arg_txt2img = []
         self.default_script_arg_img2img = []
 
@@ -324,19 +325,19 @@ class Api:
         args.pop('save_images', None)
 
         with self.queue_lock:
-            p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
-            p.scripts = script_runner
-            p.outpath_grids = opts.outdir_txt2img_grids
-            p.outpath_samples = opts.outdir_txt2img_samples
+            with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:
+                p.scripts = script_runner
+                p.outpath_grids = opts.outdir_txt2img_grids
+                p.outpath_samples = opts.outdir_txt2img_samples
 
-            shared.state.begin()
-            if selectable_scripts is not None:
-                p.script_args = script_args
-                processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
-            else:
-                p.script_args = tuple(script_args) # Need to pass args as tuple here
-                processed = process_images(p)
-            shared.state.end()
+                shared.state.begin(job="scripts_txt2img")
+                if selectable_scripts is not None:
+                    p.script_args = script_args
+                    processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
+                else:
+                    p.script_args = tuple(script_args) # Need to pass args as tuple here
+                    processed = process_images(p)
+                shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
@@ -380,20 +381,20 @@ class Api:
         args.pop('save_images', None)
 
         with self.queue_lock:
-            p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
-            p.init_images = [decode_base64_to_image(x) for x in init_images]
-            p.scripts = script_runner
-            p.outpath_grids = opts.outdir_img2img_grids
-            p.outpath_samples = opts.outdir_img2img_samples
+            with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
+                p.init_images = [decode_base64_to_image(x) for x in init_images]
+                p.scripts = script_runner
+                p.outpath_grids = opts.outdir_img2img_grids
+                p.outpath_samples = opts.outdir_img2img_samples
 
-            shared.state.begin()
-            if selectable_scripts is not None:
-                p.script_args = script_args
-                processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
-            else:
-                p.script_args = tuple(script_args) # Need to pass args as tuple here
-                processed = process_images(p)
-            shared.state.end()
+                shared.state.begin(job="scripts_img2img")
+                if selectable_scripts is not None:
+                    p.script_args = script_args
+                    processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
+                else:
+                    p.script_args = tuple(script_args) # Need to pass args as tuple here
+                    processed = process_images(p)
+                shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
@@ -517,6 +518,10 @@ class Api:
         return options
 
     def set_config(self, req: Dict[str, Any]):
+        checkpoint_name = req.get("sd_model_checkpoint", None)
+        if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
+            raise RuntimeError(f"model {checkpoint_name!r} not found")
+
         for k, v in req.items():
             shared.opts.set(k, v)
 
@@ -598,44 +603,42 @@ class Api:
 
     def create_embedding(self, args: dict):
         try:
-            shared.state.begin()
+            shared.state.begin(job="create_embedding")
             filename = create_embedding(**args) # create empty embedding
             sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
-            shared.state.end()
             return models.CreateResponse(info=f"create embedding filename: {filename}")
         except AssertionError as e:
-            shared.state.end()
             return models.TrainResponse(info=f"create embedding error: {e}")
+        finally:
+            shared.state.end()
+
 
     def create_hypernetwork(self, args: dict):
         try:
-            shared.state.begin()
+            shared.state.begin(job="create_hypernetwork")
             filename = create_hypernetwork(**args) # create empty embedding
-            shared.state.end()
             return models.CreateResponse(info=f"create hypernetwork filename: {filename}")
         except AssertionError as e:
-            shared.state.end()
             return models.TrainResponse(info=f"create hypernetwork error: {e}")
+        finally:
+            shared.state.end()
 
     def preprocess(self, args: dict):
         try:
-            shared.state.begin()
+            shared.state.begin(job="preprocess")
             preprocess(**args) # quick operation unless blip/booru interrogation is enabled
             shared.state.end()
-            return models.PreprocessResponse(info = 'preprocess complete')
+            return models.PreprocessResponse(info='preprocess complete')
         except KeyError as e:
-            shared.state.end()
             return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
-        except AssertionError as e:
-            shared.state.end()
+        except Exception as e:
             return models.PreprocessResponse(info=f"preprocess error: {e}")
-        except FileNotFoundError as e:
+        finally:
             shared.state.end()
-            return models.PreprocessResponse(info=f'preprocess error: {e}')
 
     def train_embedding(self, args: dict):
         try:
-            shared.state.begin()
+            shared.state.begin(job="train_embedding")
             apply_optimizations = shared.opts.training_xattention_optimizations
             error = None
             filename = ''
@@ -648,15 +651,15 @@ class Api:
             finally:
                 if not apply_optimizations:
                     sd_hijack.apply_optimizations()
-                shared.state.end()
             return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
-        except AssertionError as msg:
-            shared.state.end()
+        except Exception as msg:
             return models.TrainResponse(info=f"train embedding error: {msg}")
+        finally:
+            shared.state.end()
 
     def train_hypernetwork(self, args: dict):
         try:
-            shared.state.begin()
+            shared.state.begin(job="train_hypernetwork")
             shared.loaded_hypernetworks = []
             apply_optimizations = shared.opts.training_xattention_optimizations
             error = None
@@ -674,9 +677,10 @@ class Api:
                     sd_hijack.apply_optimizations()
                 shared.state.end()
             return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
-        except AssertionError:
+        except Exception as exc:
+            return models.TrainResponse(info=f"train embedding error: {exc}")
+        finally:
             shared.state.end()
-            return models.TrainResponse(info=f"train embedding error: {error}")
 
     def get_memory(self):
         try:
@@ -716,3 +720,16 @@ class Api:
     def launch(self, server_name, port):
         self.app.include_router(self.router)
         uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive)
+
+    def kill_webui(self):
+        restart.stop_program()
+
+    def restart_webui(self):
+        if restart.is_restartable():
+            restart.restart_program()
+        return Response(status_code=501)
+
+    def stop_webui(request):
+        shared.state.server_command = "stop"
+        return Response("Stopping.")
+