mirror of
https://github.com/invoke-ai/InvokeAI.git
synced 2026-04-23 03:00:31 -04:00
detect lora files with .pt suffix
This commit is contained in:
Lincoln Stein
@@ -25,11 +25,20 @@ from invokeai.backend.modules.parameters import parameters_to_command
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import invokeai.frontend.dist as frontend
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from ldm.generate import Generate
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from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
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from ldm.invoke.conditioning import get_tokens_for_prompt_object, get_prompt_structure, split_weighted_subprompts, \
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get_tokenizer
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from ldm.invoke.conditioning import (
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get_tokens_for_prompt_object,
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get_prompt_structure,
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split_weighted_subprompts,
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get_tokenizer,
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)
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from ldm.invoke.generator.diffusers_pipeline import PipelineIntermediateState
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from ldm.invoke.generator.inpaint import infill_methods
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from ldm.invoke.globals import Globals, global_converted_ckpts_dir, global_models_dir, global_lora_models_dir
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from ldm.invoke.globals import (
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Globals,
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global_converted_ckpts_dir,
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global_models_dir,
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global_lora_models_dir,
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)
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from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
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from compel.prompt_parser import Blend
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from ldm.invoke.merge_diffusers import merge_diffusion_models
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@@ -192,8 +201,7 @@ class InvokeAIWebServer:
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(width, height) = pil_image.size
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thumbnail_path = save_thumbnail(
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pil_image, os.path.basename(
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file_path), self.thumbnail_image_path
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pil_image, os.path.basename(file_path), self.thumbnail_image_path
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)
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response = {
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@@ -223,7 +231,7 @@ class InvokeAIWebServer:
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server="flask_socketio",
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width=1600,
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height=1000,
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port=self.port
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port=self.port,
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).run()
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except KeyboardInterrupt:
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import sys
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@@ -264,16 +272,14 @@ class InvokeAIWebServer:
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# location for "finished" images
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self.result_path = args.outdir
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# temporary path for intermediates
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self.intermediate_path = os.path.join(
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self.result_path, "intermediates/")
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self.intermediate_path = os.path.join(self.result_path, "intermediates/")
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# path for user-uploaded init images and masks
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self.init_image_path = os.path.join(self.result_path, "init-images/")
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self.mask_image_path = os.path.join(self.result_path, "mask-images/")
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# path for temp images e.g. gallery generations which are not committed
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self.temp_image_path = os.path.join(self.result_path, "temp-images/")
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# path for thumbnail images
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self.thumbnail_image_path = os.path.join(
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self.result_path, "thumbnails/")
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self.thumbnail_image_path = os.path.join(self.result_path, "thumbnails/")
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# txt log
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self.log_path = os.path.join(self.result_path, "invoke_log.txt")
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# make all output paths
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@@ -298,21 +304,22 @@ class InvokeAIWebServer:
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config["infill_methods"] = infill_methods()
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socketio.emit("systemConfig", config)
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@socketio.on('searchForModels')
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@socketio.on("searchForModels")
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def handle_search_models(search_folder: str):
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try:
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if not search_folder:
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socketio.emit(
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"foundModels",
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{'search_folder': None, 'found_models': None},
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{"search_folder": None, "found_models": None},
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)
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else:
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search_folder, found_models = self.generate.model_manager.search_models(
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search_folder)
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(
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search_folder,
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found_models,
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) = self.generate.model_manager.search_models(search_folder)
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socketio.emit(
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"foundModels",
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{'search_folder': search_folder,
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'found_models': found_models},
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{"search_folder": search_folder, "found_models": found_models},
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)
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except Exception as e:
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self.handle_exceptions(e)
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@@ -321,11 +328,11 @@ class InvokeAIWebServer:
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@socketio.on("addNewModel")
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def handle_add_model(new_model_config: dict):
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try:
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model_name = new_model_config['name']
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del new_model_config['name']
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model_name = new_model_config["name"]
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del new_model_config["name"]
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model_attributes = new_model_config
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if len(model_attributes['vae']) == 0:
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del model_attributes['vae']
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if len(model_attributes["vae"]) == 0:
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del model_attributes["vae"]
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update = False
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current_model_list = self.generate.model_manager.list_models()
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if model_name in current_model_list:
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@@ -334,14 +341,20 @@ class InvokeAIWebServer:
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print(f">> Adding New Model: {model_name}")
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self.generate.model_manager.add_model(
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model_name=model_name, model_attributes=model_attributes, clobber=True)
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model_name=model_name,
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model_attributes=model_attributes,
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clobber=True,
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)
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self.generate.model_manager.commit(opt.conf)
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new_model_list = self.generate.model_manager.list_models()
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socketio.emit(
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"newModelAdded",
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{"new_model_name": model_name,
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"model_list": new_model_list, 'update': update},
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{
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"new_model_name": model_name,
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"model_list": new_model_list,
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"update": update,
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},
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)
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print(f">> New Model Added: {model_name}")
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except Exception as e:
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@@ -356,8 +369,10 @@ class InvokeAIWebServer:
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updated_model_list = self.generate.model_manager.list_models()
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socketio.emit(
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"modelDeleted",
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{"deleted_model_name": model_name,
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"model_list": updated_model_list},
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{
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"deleted_model_name": model_name,
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"model_list": updated_model_list,
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},
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)
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print(f">> Model Deleted: {model_name}")
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except Exception as e:
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@@ -382,41 +397,48 @@ class InvokeAIWebServer:
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except Exception as e:
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self.handle_exceptions(e)
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@socketio.on('convertToDiffusers')
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@socketio.on("convertToDiffusers")
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def convert_to_diffusers(model_to_convert: dict):
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try:
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if (model_info := self.generate.model_manager.model_info(model_name=model_to_convert['model_name'])):
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if 'weights' in model_info:
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ckpt_path = Path(model_info['weights'])
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original_config_file = Path(model_info['config'])
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model_name = model_to_convert['model_name']
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model_description = model_info['description']
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if model_info := self.generate.model_manager.model_info(
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model_name=model_to_convert["model_name"]
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):
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if "weights" in model_info:
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ckpt_path = Path(model_info["weights"])
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original_config_file = Path(model_info["config"])
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model_name = model_to_convert["model_name"]
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model_description = model_info["description"]
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else:
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self.socketio.emit(
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"error", {"message": "Model is not a valid checkpoint file"})
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"error", {"message": "Model is not a valid checkpoint file"}
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)
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else:
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self.socketio.emit(
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"error", {"message": "Could not retrieve model info."})
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"error", {"message": "Could not retrieve model info."}
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)
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if not ckpt_path.is_absolute():
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ckpt_path = Path(Globals.root, ckpt_path)
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if original_config_file and not original_config_file.is_absolute():
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original_config_file = Path(
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Globals.root, original_config_file)
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original_config_file = Path(Globals.root, original_config_file)
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diffusers_path = Path(
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ckpt_path.parent.absolute(),
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f'{model_name}_diffusers'
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ckpt_path.parent.absolute(), f"{model_name}_diffusers"
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)
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if model_to_convert['save_location'] == 'root':
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if model_to_convert["save_location"] == "root":
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diffusers_path = Path(
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global_converted_ckpts_dir(), f'{model_name}_diffusers')
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global_converted_ckpts_dir(), f"{model_name}_diffusers"
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)
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if model_to_convert['save_location'] == 'custom' and model_to_convert['custom_location'] is not None:
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if (
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model_to_convert["save_location"] == "custom"
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and model_to_convert["custom_location"] is not None
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):
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diffusers_path = Path(
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model_to_convert['custom_location'], f'{model_name}_diffusers')
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model_to_convert["custom_location"], f"{model_name}_diffusers"
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)
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if diffusers_path.exists():
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shutil.rmtree(diffusers_path)
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@@ -434,75 +456,89 @@ class InvokeAIWebServer:
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new_model_list = self.generate.model_manager.list_models()
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socketio.emit(
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"modelConverted",
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{"new_model_name": model_name,
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"model_list": new_model_list, 'update': True},
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{
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"new_model_name": model_name,
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"model_list": new_model_list,
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"update": True,
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},
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)
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print(f">> Model Converted: {model_name}")
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except Exception as e:
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self.handle_exceptions(e)
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@socketio.on('mergeDiffusersModels')
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@socketio.on("mergeDiffusersModels")
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def merge_diffusers_models(model_merge_info: dict):
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try:
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models_to_merge = model_merge_info['models_to_merge']
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models_to_merge = model_merge_info["models_to_merge"]
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model_ids_or_paths = [
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self.generate.model_manager.model_name_or_path(x) for x in models_to_merge]
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self.generate.model_manager.model_name_or_path(x)
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for x in models_to_merge
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]
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merged_pipe = merge_diffusion_models(
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model_ids_or_paths, model_merge_info['alpha'], model_merge_info['interp'], model_merge_info['force'])
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model_ids_or_paths,
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model_merge_info["alpha"],
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model_merge_info["interp"],
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model_merge_info["force"],
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)
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dump_path = global_models_dir() / 'merged_models'
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if model_merge_info['model_merge_save_path'] is not None:
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dump_path = Path(model_merge_info['model_merge_save_path'])
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dump_path = global_models_dir() / "merged_models"
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if model_merge_info["model_merge_save_path"] is not None:
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dump_path = Path(model_merge_info["model_merge_save_path"])
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os.makedirs(dump_path, exist_ok=True)
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dump_path = dump_path / model_merge_info['merged_model_name']
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dump_path = dump_path / model_merge_info["merged_model_name"]
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merged_pipe.save_pretrained(dump_path, safe_serialization=1)
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merged_model_config = dict(
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model_name=model_merge_info['merged_model_name'],
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model_name=model_merge_info["merged_model_name"],
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description=f'Merge of models {", ".join(models_to_merge)}',
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commit_to_conf=opt.conf
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commit_to_conf=opt.conf,
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)
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if vae := self.generate.model_manager.config[models_to_merge[0]].get("vae", None):
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print(
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f">> Using configured VAE assigned to {models_to_merge[0]}")
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if vae := self.generate.model_manager.config[models_to_merge[0]].get(
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"vae", None
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):
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print(f">> Using configured VAE assigned to {models_to_merge[0]}")
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merged_model_config.update(vae=vae)
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self.generate.model_manager.import_diffuser_model(
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dump_path, **merged_model_config)
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dump_path, **merged_model_config
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)
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new_model_list = self.generate.model_manager.list_models()
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socketio.emit(
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"modelsMerged",
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{"merged_models": models_to_merge,
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"merged_model_name": model_merge_info['merged_model_name'],
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"model_list": new_model_list, 'update': True},
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{
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"merged_models": models_to_merge,
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"merged_model_name": model_merge_info["merged_model_name"],
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"model_list": new_model_list,
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"update": True,
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},
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)
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print(f">> Models Merged: {models_to_merge}")
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print(
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f">> New Model Added: {model_merge_info['merged_model_name']}")
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print(f">> New Model Added: {model_merge_info['merged_model_name']}")
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except Exception as e:
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self.handle_exceptions(e)
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@socketio.on('getLoraModels')
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@socketio.on("getLoraModels")
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def get_lora_models():
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try:
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lora_path = global_lora_models_dir()
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lora_folder_ckpt = Path(lora_path).glob("**/*.ckpt")
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lora_folder_safetensors = Path(lora_path).glob("**/*.safetensors")
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ckpt_loras = [x for x in lora_folder_ckpt if x.is_file()]
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safetensors_loras = [x for x in lora_folder_safetensors if x.is_file()]
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loras = ckpt_loras + safetensors_loras
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loras = []
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for root, _, files in os.walk(lora_path):
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models = [
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Path(root, x)
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for x in files
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if Path(x).suffix in [".ckpt", ".pt", ".safetensors"]
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]
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loras = loras + models
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found_loras = []
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for lora in loras:
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location = str(lora.resolve()).replace("\\", "/")
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found_loras.append({"name": lora.stem, "location": location})
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socketio.emit('foundLoras', found_loras)
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socketio.emit("foundLoras", found_loras)
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except Exception as e:
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self.handle_exceptions(e)
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@@ -520,7 +556,8 @@ class InvokeAIWebServer:
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os.remove(thumbnail_path)
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except Exception as e:
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socketio.emit(
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"error", {"message": f"Unable to delete {f}: {str(e)}"})
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"error", {"message": f"Unable to delete {f}: {str(e)}"}
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)
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pass
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socketio.emit("tempFolderEmptied")
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@@ -531,8 +568,7 @@ class InvokeAIWebServer:
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def save_temp_image_to_gallery(url):
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try:
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image_path = self.get_image_path_from_url(url)
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new_path = os.path.join(
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self.result_path, os.path.basename(image_path))
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new_path = os.path.join(self.result_path, os.path.basename(image_path))
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shutil.copy2(image_path, new_path)
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if os.path.splitext(new_path)[1] == ".png":
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@@ -545,8 +581,7 @@ class InvokeAIWebServer:
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(width, height) = pil_image.size
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thumbnail_path = save_thumbnail(
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pil_image, os.path.basename(
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new_path), self.thumbnail_image_path
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pil_image, os.path.basename(new_path), self.thumbnail_image_path
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)
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image_array = [
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@@ -605,8 +640,7 @@ class InvokeAIWebServer:
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(width, height) = pil_image.size
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thumbnail_path = save_thumbnail(
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pil_image, os.path.basename(
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path), self.thumbnail_image_path
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pil_image, os.path.basename(path), self.thumbnail_image_path
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)
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image_array.append(
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@@ -625,7 +659,8 @@ class InvokeAIWebServer:
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)
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except Exception as e:
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socketio.emit(
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"error", {"message": f"Unable to load {path}: {str(e)}"})
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"error", {"message": f"Unable to load {path}: {str(e)}"}
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)
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pass
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socketio.emit(
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@@ -675,8 +710,7 @@ class InvokeAIWebServer:
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(width, height) = pil_image.size
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thumbnail_path = save_thumbnail(
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pil_image, os.path.basename(
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path), self.thumbnail_image_path
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pil_image, os.path.basename(path), self.thumbnail_image_path
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)
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image_array.append(
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@@ -696,7 +730,8 @@ class InvokeAIWebServer:
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except Exception as e:
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print(f">> Unable to load {path}")
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socketio.emit(
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"error", {"message": f"Unable to load {path}: {str(e)}"})
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"error", {"message": f"Unable to load {path}: {str(e)}"}
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)
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pass
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socketio.emit(
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@@ -730,10 +765,9 @@ class InvokeAIWebServer:
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printable_parameters["init_mask"][:64] + "..."
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)
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print(
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f'\n>> Image Generation Parameters:\n\n{printable_parameters}\n')
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print(f'>> ESRGAN Parameters: {esrgan_parameters}')
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print(f'>> Facetool Parameters: {facetool_parameters}')
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print(f"\n>> Image Generation Parameters:\n\n{printable_parameters}\n")
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print(f">> ESRGAN Parameters: {esrgan_parameters}")
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print(f">> Facetool Parameters: {facetool_parameters}")
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self.generate_images(
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generation_parameters,
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@@ -770,11 +804,9 @@ class InvokeAIWebServer:
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if postprocessing_parameters["type"] == "esrgan":
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progress.set_current_status("common.statusUpscalingESRGAN")
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elif postprocessing_parameters["type"] == "gfpgan":
|
||||
progress.set_current_status(
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"common.statusRestoringFacesGFPGAN")
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||||
progress.set_current_status("common.statusRestoringFacesGFPGAN")
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elif postprocessing_parameters["type"] == "codeformer":
|
||||
progress.set_current_status(
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||||
"common.statusRestoringFacesCodeFormer")
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||||
progress.set_current_status("common.statusRestoringFacesCodeFormer")
|
||||
|
||||
socketio.emit("progressUpdate", progress.to_formatted_dict())
|
||||
eventlet.sleep(0)
|
||||
@@ -939,8 +971,7 @@ class InvokeAIWebServer:
|
||||
|
||||
init_img_url = generation_parameters["init_img"]
|
||||
|
||||
original_bounding_box = generation_parameters["bounding_box"].copy(
|
||||
)
|
||||
original_bounding_box = generation_parameters["bounding_box"].copy()
|
||||
|
||||
initial_image = dataURL_to_image(
|
||||
generation_parameters["init_img"]
|
||||
@@ -1017,8 +1048,9 @@ class InvokeAIWebServer:
|
||||
elif generation_parameters["generation_mode"] == "img2img":
|
||||
init_img_url = generation_parameters["init_img"]
|
||||
init_img_path = self.get_image_path_from_url(init_img_url)
|
||||
generation_parameters["init_img"] = Image.open(
|
||||
init_img_path).convert('RGB')
|
||||
generation_parameters["init_img"] = Image.open(init_img_path).convert(
|
||||
"RGB"
|
||||
)
|
||||
|
||||
def image_progress(sample, step):
|
||||
if self.canceled.is_set():
|
||||
@@ -1078,8 +1110,7 @@ class InvokeAIWebServer:
|
||||
)
|
||||
|
||||
if generation_parameters["progress_latents"]:
|
||||
image = self.generate.sample_to_lowres_estimated_image(
|
||||
sample)
|
||||
image = self.generate.sample_to_lowres_estimated_image(sample)
|
||||
(width, height) = image.size
|
||||
width *= 8
|
||||
height *= 8
|
||||
@@ -1098,8 +1129,7 @@ class InvokeAIWebServer:
|
||||
},
|
||||
)
|
||||
|
||||
self.socketio.emit(
|
||||
"progressUpdate", progress.to_formatted_dict())
|
||||
self.socketio.emit("progressUpdate", progress.to_formatted_dict())
|
||||
eventlet.sleep(0)
|
||||
|
||||
def image_done(image, seed, first_seed, attention_maps_image=None):
|
||||
@@ -1126,8 +1156,7 @@ class InvokeAIWebServer:
|
||||
|
||||
progress.set_current_status("common.statusGenerationComplete")
|
||||
|
||||
self.socketio.emit(
|
||||
"progressUpdate", progress.to_formatted_dict())
|
||||
self.socketio.emit("progressUpdate", progress.to_formatted_dict())
|
||||
eventlet.sleep(0)
|
||||
|
||||
all_parameters = generation_parameters
|
||||
@@ -1138,8 +1167,7 @@ class InvokeAIWebServer:
|
||||
and all_parameters["variation_amount"] > 0
|
||||
):
|
||||
first_seed = first_seed or seed
|
||||
this_variation = [
|
||||
[seed, all_parameters["variation_amount"]]]
|
||||
this_variation = [[seed, all_parameters["variation_amount"]]]
|
||||
all_parameters["with_variations"] = (
|
||||
prior_variations + this_variation
|
||||
)
|
||||
@@ -1155,14 +1183,13 @@ class InvokeAIWebServer:
|
||||
if esrgan_parameters:
|
||||
progress.set_current_status("common.statusUpscaling")
|
||||
progress.set_current_status_has_steps(False)
|
||||
self.socketio.emit(
|
||||
"progressUpdate", progress.to_formatted_dict())
|
||||
self.socketio.emit("progressUpdate", progress.to_formatted_dict())
|
||||
eventlet.sleep(0)
|
||||
|
||||
image = self.esrgan.process(
|
||||
image=image,
|
||||
upsampler_scale=esrgan_parameters["level"],
|
||||
denoise_str=esrgan_parameters['denoise_str'],
|
||||
denoise_str=esrgan_parameters["denoise_str"],
|
||||
strength=esrgan_parameters["strength"],
|
||||
seed=seed,
|
||||
)
|
||||
@@ -1170,7 +1197,7 @@ class InvokeAIWebServer:
|
||||
postprocessing = True
|
||||
all_parameters["upscale"] = [
|
||||
esrgan_parameters["level"],
|
||||
esrgan_parameters['denoise_str'],
|
||||
esrgan_parameters["denoise_str"],
|
||||
esrgan_parameters["strength"],
|
||||
]
|
||||
|
||||
@@ -1179,15 +1206,14 @@ class InvokeAIWebServer:
|
||||
|
||||
if facetool_parameters:
|
||||
if facetool_parameters["type"] == "gfpgan":
|
||||
progress.set_current_status(
|
||||
"common.statusRestoringFacesGFPGAN")
|
||||
progress.set_current_status("common.statusRestoringFacesGFPGAN")
|
||||
elif facetool_parameters["type"] == "codeformer":
|
||||
progress.set_current_status(
|
||||
"common.statusRestoringFacesCodeFormer")
|
||||
"common.statusRestoringFacesCodeFormer"
|
||||
)
|
||||
|
||||
progress.set_current_status_has_steps(False)
|
||||
self.socketio.emit(
|
||||
"progressUpdate", progress.to_formatted_dict())
|
||||
self.socketio.emit("progressUpdate", progress.to_formatted_dict())
|
||||
eventlet.sleep(0)
|
||||
|
||||
if facetool_parameters["type"] == "gfpgan":
|
||||
@@ -1217,8 +1243,7 @@ class InvokeAIWebServer:
|
||||
all_parameters["facetool_type"] = facetool_parameters["type"]
|
||||
|
||||
progress.set_current_status("common.statusSavingImage")
|
||||
self.socketio.emit(
|
||||
"progressUpdate", progress.to_formatted_dict())
|
||||
self.socketio.emit("progressUpdate", progress.to_formatted_dict())
|
||||
eventlet.sleep(0)
|
||||
|
||||
# restore the stashed URLS and discard the paths, we are about to send the result to client
|
||||
@@ -1235,8 +1260,7 @@ class InvokeAIWebServer:
|
||||
if generation_parameters["generation_mode"] == "unifiedCanvas":
|
||||
all_parameters["bounding_box"] = original_bounding_box
|
||||
|
||||
metadata = self.parameters_to_generated_image_metadata(
|
||||
all_parameters)
|
||||
metadata = self.parameters_to_generated_image_metadata(all_parameters)
|
||||
|
||||
command = parameters_to_command(all_parameters)
|
||||
|
||||
@@ -1266,22 +1290,27 @@ class InvokeAIWebServer:
|
||||
|
||||
if progress.total_iterations > progress.current_iteration:
|
||||
progress.set_current_step(1)
|
||||
progress.set_current_status(
|
||||
"common.statusIterationComplete")
|
||||
progress.set_current_status("common.statusIterationComplete")
|
||||
progress.set_current_status_has_steps(False)
|
||||
else:
|
||||
progress.mark_complete()
|
||||
|
||||
self.socketio.emit(
|
||||
"progressUpdate", progress.to_formatted_dict())
|
||||
self.socketio.emit("progressUpdate", progress.to_formatted_dict())
|
||||
eventlet.sleep(0)
|
||||
|
||||
parsed_prompt, _ = get_prompt_structure(
|
||||
generation_parameters["prompt"])
|
||||
tokens = None if type(parsed_prompt) is Blend else \
|
||||
get_tokens_for_prompt_object(get_tokenizer(self.generate.model), parsed_prompt)
|
||||
attention_maps_image_base64_url = None if attention_maps_image is None \
|
||||
parsed_prompt, _ = get_prompt_structure(generation_parameters["prompt"])
|
||||
tokens = (
|
||||
None
|
||||
if type(parsed_prompt) is Blend
|
||||
else get_tokens_for_prompt_object(
|
||||
get_tokenizer(self.generate.model), parsed_prompt
|
||||
)
|
||||
)
|
||||
attention_maps_image_base64_url = (
|
||||
None
|
||||
if attention_maps_image is None
|
||||
else image_to_dataURL(attention_maps_image)
|
||||
)
|
||||
|
||||
self.socketio.emit(
|
||||
"generationResult",
|
||||
@@ -1313,7 +1342,7 @@ class InvokeAIWebServer:
|
||||
self.generate.prompt2image(
|
||||
**generation_parameters,
|
||||
step_callback=diffusers_step_callback_adapter,
|
||||
image_callback=image_done
|
||||
image_callback=image_done,
|
||||
)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
@@ -1436,8 +1465,7 @@ class InvokeAIWebServer:
|
||||
self, parameters, original_image_path
|
||||
):
|
||||
try:
|
||||
current_metadata = retrieve_metadata(
|
||||
original_image_path)["sd-metadata"]
|
||||
current_metadata = retrieve_metadata(original_image_path)["sd-metadata"]
|
||||
postprocessing_metadata = {}
|
||||
|
||||
"""
|
||||
@@ -1477,8 +1505,7 @@ class InvokeAIWebServer:
|
||||
postprocessing_metadata
|
||||
)
|
||||
else:
|
||||
current_metadata["image"]["postprocessing"] = [
|
||||
postprocessing_metadata]
|
||||
current_metadata["image"]["postprocessing"] = [postprocessing_metadata]
|
||||
|
||||
return current_metadata
|
||||
|
||||
@@ -1574,8 +1601,7 @@ class InvokeAIWebServer:
|
||||
)
|
||||
elif "thumbnails" in url:
|
||||
return os.path.abspath(
|
||||
os.path.join(self.thumbnail_image_path,
|
||||
os.path.basename(url))
|
||||
os.path.join(self.thumbnail_image_path, os.path.basename(url))
|
||||
)
|
||||
else:
|
||||
return os.path.abspath(
|
||||
@@ -1621,7 +1647,7 @@ class InvokeAIWebServer:
|
||||
except Exception as e:
|
||||
self.handle_exceptions(e)
|
||||
|
||||
def handle_exceptions(self, exception, emit_key: str = 'error'):
|
||||
def handle_exceptions(self, exception, emit_key: str = "error"):
|
||||
self.socketio.emit(emit_key, {"message": (str(exception))})
|
||||
print("\n")
|
||||
traceback.print_exc()
|
||||
|
||||
Reference in New Issue
Block a user