cache sampler

modules/face/__init__.py

@@ -109,29 +109,32 @@ class Script(scripts.Script):
 
         processing.process_init(p)
         if mode == 'FaceID': # faceid runs as ipadapter in its own pipeline
-            from modules.face.faceid import face_id
             from modules.face.insightface import get_app
-            processed_images = face_id(p, app=get_app('buffalo_l'), source_image=source_image, model=ip_model, override=ip_override, cache=ip_cache, scale=ip_strength, structure=ip_structure) # run faceid pipeline
+            app = get_app('buffalo_l')
+            from modules.face.faceid import face_id
+            processed_images = face_id(p, app=app, source_image=source_image, model=ip_model, override=ip_override, cache=ip_cache, scale=ip_strength, structure=ip_structure) # run faceid pipeline
             processed = processing.Processed(p, images_list=processed_images, seed=p.seed, subseed=p.subseed, index_of_first_image=0) # manually created processed object
         elif mode == 'PhotoMaker': # photomaker creates pipeline and triggers original process_images
             from modules.face.photomaker import photo_maker
             processed = photo_maker(p, input_images=input_images, trigger=pm_trigger, strength=pm_strength, start=pm_start)
         elif mode == 'InstantID':
-            from modules.face.instantid import instant_id # instantid creates pipeline and triggers original process_images
             from modules.face.insightface import get_app
-            processed = instant_id(p, app=get_app('antelopev2'), source_image=source_image, strength=id_strength, conditioning=id_conditioning, cache=id_cache)
+            app=get_app('antelopev2')
+            from modules.face.instantid import instant_id # instantid creates pipeline and triggers original process_images
+            processed = instant_id(p, app=app, source_image=source_image, strength=id_strength, conditioning=id_conditioning, cache=id_cache)
 
         if processed is None: # run normal pipeline
             processed = processing.process_images(p)
 
         if mode == 'FaceSwap': # faceswap runs as postprocessing
-            from modules.face.faceswap import face_swap
             from modules.face.insightface import get_app
+            app=get_app('buffalo_l')
+            from modules.face.faceswap import face_swap
             if shared.opts.save_images_before_face_restoration and not p.do_not_save_samples:
                 for i, image in enumerate(processed.images):
                     info = processing.create_infotext(p, index=i)
                     images.save_image(image, path=p.outpath_samples, seed=p.all_seeds[i], prompt=p.all_prompts[i], info=info, p=p, suffix="-before-faceswap")
-            processed.images = face_swap(p, app=get_app('buffalo_l'), input_images=processed.images, source_image=source_image, cache=fs_cache)
+            processed.images = face_swap(p, app=app, input_images=processed.images, source_image=source_image, cache=fs_cache)
 
         processed.info = processed.infotext(p, 0)
         processed.infotexts = [processed.info]

modules/processing_diffusers.py

@@ -508,19 +508,16 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
             shared.log.debug('Moving to CPU: model=base')
             shared.sd_model.to(devices.cpu)
             devices.torch_gc()
-
-        update_sampler(shared.sd_refiner, second_pass=True)
-
         if shared.state.interrupted or shared.state.skipped:
             shared.sd_model = orig_pipeline
             return results
-
         if shared.opts.diffusers_move_refiner and not getattr(shared.sd_refiner, 'has_accelerate', False):
             shared.sd_refiner.to(devices.device)
         p.ops.append('refine')
         p.is_refiner_pass = True
         shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
         shared.sd_refiner = sd_models.set_diffuser_pipe(shared.sd_refiner, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
+        update_sampler(shared.sd_refiner, second_pass=True)
         for i in range(len(output.images)):
             image = output.images[i]
             noise_level = round(350 * p.denoising_strength)

modules/sd_samplers.py

@@ -10,6 +10,8 @@ all_samplers_map = {}
 samplers = all_samplers
 samplers_for_img2img = all_samplers
 samplers_map = {}
+loaded_config = None
+loaded_sampler = None
 
 
 def list_samplers(backend_name = shared.backend):
@@ -45,6 +47,7 @@ def visible_sampler_names():
 
 
 def create_sampler(name, model):
+    global loaded_config, loaded_sampler # pylint: disable=global-statement
     if name == 'Default' and hasattr(model, 'scheduler'):
         config = {k: v for k, v in model.scheduler.config.items() if not k.startswith('_')}
         shared.log.debug(f'Sampler default {type(model.scheduler).__name__}: {config}')
@@ -53,22 +56,26 @@ def create_sampler(name, model):
     if config is None:
         shared.log.error(f'Attempting to use unknown sampler: {name}')
         config = all_samplers[0]
+    sampler = loaded_sampler
     if shared.backend == shared.Backend.ORIGINAL:
-        sampler = config.constructor(model)
-        sampler.config = config
+        if config != loaded_config:
+            sampler = config.constructor(model)
+            sampler.config = config
+            sampler.name = name
+            loaded_config = config
+            shared.log.debug(f'Sampler: sampler="{name}" config={config.options}')
         sampler.initialize(p=None)
-        sampler.name = name
-        shared.log.debug(f'Sampler: sampler="{sampler.name}" config={sampler.config.options}')
-        return sampler
+        loaded_sampler = sampler
     elif shared.backend == shared.Backend.DIFFUSERS:
-        sampler = config.constructor(model)
-        if not hasattr(model, 'scheduler_config'):
-            model.scheduler_config = sampler.sampler.config.copy()
-        model.scheduler = sampler.sampler
-        shared.log.debug(f'Sampler: sampler="{sampler.name}" config={sampler.config}')
-        return sampler.sampler
-    else:
-        return None
+        if config != loaded_config:
+            sampler = config.constructor(model)
+            loaded_config = config
+            if not hasattr(model, 'scheduler_config'):
+                model.scheduler_config = sampler.sampler.config.copy()
+            shared.log.debug(f'Sampler: sampler="{sampler.name}" config={sampler.config}')
+            loaded_sampler = sampler.sampler
+        model.scheduler = loaded_sampler
+    return loaded_sampler
 
 
 def set_samplers():

webui.py

@@ -243,6 +243,10 @@ def start_ui():
 
     global local_url # pylint: disable=global-statement
     stdout = io.StringIO()
+    allowed_paths = [os.path.dirname(__file__)]
+    if cmd_opts.data_dir is not None and os.path.isdir(cmd_opts.data_dir):
+        allowed_paths.append(cmd_opts.data_dir)
+    shared.log.debug(f'Root paths: {allowed_paths}')
     with contextlib.redirect_stdout(stdout):
         app, local_url, share_url = shared.demo.launch( # app is FastAPI(Starlette) instance
             share=cmd_opts.share,
@@ -258,7 +262,7 @@ def start_ui():
             show_api=False,
             quiet=True,
             favicon_path='html/logo.ico',
-            allowed_paths=[os.path.dirname(__file__), cmd_opts.data_dir],
+            allowed_paths=allowed_paths,
             app_kwargs=fastapi_args,
             _frontend=True and cmd_opts.share,
         )