From e3558ec9bce062bae8b6bff339753ebc0484b50c Mon Sep 17 00:00:00 2001 From: tazlin Date: Sun, 27 Aug 2023 18:41:38 -0400 Subject: [PATCH] XXX: Single thread hordelib dummy run --- hordelib/comfy_horde.py | 370 +++++++++--------- hordelib/consts.py | 2 +- hordelib/horde.py | 123 ++++-- hordelib/install_comfy.py | 61 +-- hordelib/model_manager/base.py | 311 ++++++++------- hordelib/model_manager/blip.py | 2 + hordelib/model_manager/clip.py | 2 + hordelib/model_manager/compvis.py | 76 +--- hordelib/model_manager/hyper.py | 6 +- hordelib/model_manager/lora.py | 11 +- hordelib/model_manager/ti.py | 1 + hordelib/nodes/__init__.py | 3 + .../__init__.py | 208 ++++++---- hordelib/nodes/facerestore/__init__.py | 154 ++++---- hordelib/nodes/node_clip_similarities.py | 48 --- hordelib/preload.py | 17 +- hordelib/shared_model_manager.py | 44 ++- hordelib/utils/ioredirect.py | 70 +++- .../img2img_to_masked_denoise_0.95.png | Bin 0 -> 1332804 bytes images_expected/lora_baseline_mismatch.png | Bin 0 -> 357724 bytes images_expected/lora_blue.png | Bin 321969 -> 327416 bytes images_expected/lora_download_adhoc.png | Bin 396869 -> 397339 bytes images_expected/lora_inject_any.png | Bin 348055 -> 340941 bytes images_expected/lora_inject_any_2.png | Bin 0 -> 359693 bytes images_expected/lora_inject_red.png | Bin 348282 -> 361600 bytes images_expected/lora_multiple.png | Bin 355732 -> 320226 bytes images_expected/lora_red.png | Bin 292823 -> 296284 bytes images_expected/lora_stonepunk.png | Bin 329298 -> 342291 bytes .../sampler_10_steps_k_dpm_2_a.png | Bin 432022 -> 444342 bytes .../sampler_10_steps_k_dpmpp_sde.png | Bin 444625 -> 423138 bytes images_expected/sdxl_image_to_image.png | Bin 0 -> 1231157 bytes images_expected/sdxl_text_to_image.png | Bin 0 -> 1476431 bytes requirements.txt | 3 +- tests/conftest.py | 27 +- .../test_shared_model_manager.py | 26 +- tests/test_blip.py | 54 +-- tests/test_clip.py | 88 ++--- tests/test_comfy.py | 2 +- tests/test_horde_inference.py | 37 ++ tests/test_horde_inference_controlnet.py | 2 +- tests/test_horde_inference_img2img.py | 78 +++- tests/test_horde_inference_painting.py | 78 ++++ tests/test_horde_inference_sdxl.py | 9 + tests/test_horde_lora.py | 18 +- 44 files changed, 1105 insertions(+), 826 deletions(-) delete mode 100644 hordelib/nodes/node_clip_similarities.py create mode 100644 images_expected/img2img_to_masked_denoise_0.95.png create mode 100644 images_expected/lora_baseline_mismatch.png create mode 100644 images_expected/lora_inject_any_2.png create mode 100644 images_expected/sdxl_image_to_image.png create mode 100644 images_expected/sdxl_text_to_image.png create mode 100644 tests/test_horde_inference_sdxl.py diff --git a/hordelib/comfy_horde.py b/hordelib/comfy_horde.py index edf6842e..dfcb9425 100644 --- a/hordelib/comfy_horde.py +++ b/hordelib/comfy_horde.py @@ -60,18 +60,25 @@ # Having read this, I suggest you glance at the code in `hordelib.initialise()` to get a sense of what is going on # there, and if you're still confused, ask a hordelib dev who would be happy to share the burden of understanding. +_comfy_load_models_gpu: types.FunctionType +_comfy_current_loaded_models: list = None # type: ignore _comfy_nodes: types.ModuleType -_comfy_PromptExecutor: types.ModuleType +_comfy_PromptExecutor: typing.Any _comfy_validate_prompt: types.FunctionType -_comfy_folder_paths: types.ModuleType + +_comfy_folder_names_and_paths: dict[str, tuple[list[str], list[str] | set[str]]] +_comfy_supported_ckpt_extensions: set[str] +_comfy_supported_pt_extensions: set[str] + _comfy_load_checkpoint_guess_config: types.FunctionType _comfy_load_controlnet: types.FunctionType -_comfy_model_manager: types.ModuleType | None = None + _comfy_get_torch_device: types.FunctionType _comfy_get_free_memory: types.FunctionType _comfy_load_torch_file: types.FunctionType _comfy_model_loading: types.ModuleType + _canny: types.ModuleType _hed: types.ModuleType _leres: types.ModuleType @@ -84,8 +91,11 @@ # isort: off def do_comfy_import(): - global _comfy_nodes, _comfy_PromptExecutor, _comfy_validate_prompt, _comfy_folder_paths - global _comfy_load_checkpoint_guess_config, _comfy_load_controlnet, _comfy_model_manager + global _comfy_current_loaded_models + global _comfy_load_models_gpu + global _comfy_nodes, _comfy_PromptExecutor, _comfy_validate_prompt + global _comfy_folder_names_and_paths, _comfy_supported_ckpt_extensions, _comfy_supported_pt_extensions + global _comfy_load_checkpoint_guess_config, _comfy_load_controlnet global _comfy_get_torch_device, _comfy_get_free_memory, _comfy_load_torch_file, _comfy_model_loading global _canny, _hed, _leres, _midas, _mlsd, _openpose, _pidinet, _uniformer @@ -93,26 +103,29 @@ def do_comfy_import(): # that they should never call through this module into comfy directly. All calls into # comfy should be abstracted through functions in this module. - from execution import nodes as _comfy_nodes # type: ignore - from execution import PromptExecutor as _comfy_PromptExecutor # type: ignore - from execution import validate_prompt as _comfy_validate_prompt # type: ignore - from folder_paths import folder_names_and_paths as _comfy_folder_paths # type: ignore - from comfy.sd import load_checkpoint_guess_config as _comfy_load_checkpoint_guess_config # type: ignore - from comfy.sd import load_controlnet as _comfy_load_controlnet # type: ignore - from comfy.model_management import model_manager as _comfy_model_manager # type: ignore - from comfy.model_management import get_torch_device as _comfy_get_torch_device # type: ignore - from comfy.model_management import get_free_memory as _comfy_get_free_memory # type: ignore - from comfy.utils import load_torch_file as _comfy_load_torch_file # type: ignore + from execution import nodes as _comfy_nodes + from execution import PromptExecutor as _comfy_PromptExecutor + from execution import validate_prompt as _comfy_validate_prompt + from folder_paths import folder_names_and_paths as _comfy_folder_names_and_paths # type: ignore + from folder_paths import supported_ckpt_extensions as _comfy_supported_ckpt_extensions # type: ignore + from folder_paths import supported_pt_extensions as _comfy_supported_pt_extensions # type: ignore + from comfy.sd import load_checkpoint_guess_config as _comfy_load_checkpoint_guess_config + from comfy.sd import load_controlnet as _comfy_load_controlnet + from comfy.model_management import current_loaded_models as _comfy_current_loaded_models + from comfy.model_management import load_models_gpu as _comfy_load_models_gpu + from comfy.model_management import get_torch_device as _comfy_get_torch_device + from comfy.model_management import get_free_memory as _comfy_get_free_memory + from comfy.utils import load_torch_file as _comfy_load_torch_file from comfy_extras.chainner_models import model_loading as _comfy_model_loading # type: ignore from hordelib.nodes.comfy_controlnet_preprocessors import ( - canny as _canny, # type: ignore - hed as _hed, # type: ignore - leres as _leres, # type: ignore - midas as _midas, # type: ignore - mlsd as _mlsd, # type: ignore - openpose as _openpose, # type: ignore - pidinet as _pidinet, # type: ignore - uniformer as _uniformer, # type: ignore + canny as _canny, + hed as _hed, + leres as _leres, + midas as _midas, + mlsd as _mlsd, + openpose as _openpose, + pidinet as _pidinet, + uniformer as _uniformer, ) @@ -123,37 +136,8 @@ def do_comfy_import(): def cleanup(): - locked = _comfy_model_manager.sampler_mutex.acquire(timeout=0) # type: ignore - if locked: - # Do we have any models waiting to be released? - if not __models_to_release: - _comfy_model_manager.sampler_mutex.release() # type: ignore - return - - # Can we release any of them? - for model_name, model_data in __models_to_release.copy().items(): - if is_model_in_use(model_data["model"]): - # We're in the middle of using it, nothing we can do - continue - # Unload the model from the GPU - logger.debug(f"Unloading {model_name} from GPU") - unload_model_from_gpu(model_data["model"]) - # Free ram - if "model" in model_data: - del model_data["model"] - if "clip" in model_data: - del model_data["clip"] - if "vae" in model_data: - del model_data["vae"] - if "clipVisionModel" in model_data: - del model_data["clipVisionModel"] - del model_data - with __model_load_mutex: - del __models_to_release[model_name] - gc.collect() - logger.debug(f"Removal of model {model_name} completed") - - _comfy_model_manager.sampler_mutex.release() # type: ignore + logger.debug("Comfy_Horde cleanup called") + gc.collect() def remove_model_from_memory(model_name, model_data): @@ -164,10 +148,6 @@ def remove_model_from_memory(model_name, model_data): __models_to_release[model_name] = model_data -def get_models_on_gpu(): - return _comfy_model_manager.get_models_on_gpu() # type: ignore - - def get_torch_device(): return _comfy_get_torch_device() @@ -177,11 +157,13 @@ def get_torch_free_vram_mb(): def unload_model_from_gpu(model): - _comfy_model_manager.unload_model(model) # type: ignore + if model in _comfy_current_loaded_models: + model.model_unload() garbage_collect() def garbage_collect(): + logger.debug("Comfy_Horde garbage_collect called") gc.collect() if not torch.cuda.is_available(): return None @@ -191,16 +173,7 @@ def garbage_collect(): def load_model_to_gpu(model): - # Don't bother if there isn't any space - if not _comfy_model_manager.have_free_vram(): # type: ignore - return - # Load the model to the GPU. This would normally be done just before - # the model is used for sampling, the caller must want more free ram. - return _comfy_model_manager.load_model_gpu(model) # type: ignore - - -def is_model_in_use(model): - return _comfy_model_manager.is_model_in_use(model) # type: ignore + return _comfy_load_models_gpu(model) def load_torch_file(filename): @@ -224,6 +197,7 @@ def horde_load_checkpoint( # XXX # This can remain a comfy call, but the rest of the code should be able # XXX # to pretend it isn't # Redirect IO + raise NotImplementedError() try: stdio = OutputCollector() with contextlib.redirect_stdout(stdio): @@ -246,6 +220,7 @@ def horde_load_checkpoint( def horde_load_controlnet(controlnet_path: str, target_model): # XXX Needs docstring + raise NotImplementedError # Redirect IO stdio = OutputCollector() with contextlib.redirect_stdout(stdio): @@ -262,13 +237,14 @@ class Comfy_Horde: # node types with our own where we need to. This allows easy previewing in ComfyUI # which our custom nodes don't allow. NODE_REPLACEMENTS = { - "CheckpointLoaderSimple": "HordeCheckpointLoader", - "UpscaleModelLoader": "HordeUpscaleModelLoader", + # "CheckpointLoaderSimple": "HordeCheckpointLoader", + # "UpscaleModelLoader": "HordeUpscaleModelLoader", "SaveImage": "HordeImageOutput", "LoadImage": "HordeImageLoader", - "DiffControlNetLoader": "HordeDiffControlNetLoader", - "LoraLoader": "HordeLoraLoader", + # "DiffControlNetLoader": "HordeDiffControlNetLoader", + # "LoraLoader": "HordeLoraLoader", } + """A mapping of ComfyUI node types to Horde node types.""" # We may wish some ComfyUI standard nodes had different names for consistency. Here # we can dynamically rename some node input parameter names. @@ -278,67 +254,31 @@ class Comfy_Horde: "ckpt_name": "model_name", }, } + """A mapping of ComfyUI node types which map to a dictionary of node input parameter names to rename.""" - # Approximate number of seconds to force garbage collection GC_TIME = 30 + """The approximate number of seconds to force garbage collection.""" - _property_mutex = threading.Lock() - - # We maintain one "client_id" per thread - @property - def client_id(self): - with Comfy_Horde._property_mutex: - tid = threading.current_thread().ident - return self._client_id.get(tid) - - @client_id.setter - def client_id(self, client_id): - with Comfy_Horde._property_mutex: - tid = threading.current_thread().ident - self._client_id[tid] = client_id - - # We maintain one "images" for outputs per thread - @property - def images(self): - with Comfy_Horde._property_mutex: - tid = threading.current_thread().ident - images = self._images.get(tid) - if tid in self._images: - del self._images[tid] - return images - - @images.setter - def images(self, images): - with Comfy_Horde._property_mutex: - tid = threading.current_thread().ident - self._images[tid] = images + pipelines: dict + images: dict[str, typing.Any] def __init__(self) -> None: - if _comfy_model_manager is None: + """Initialise the Comfy_Horde object. + + This must be called before using any of the functions in this module as it sets critical ComfyUI + globals and settings. + """ + if _comfy_current_loaded_models is None: raise RuntimeError("hordelib.initialise() must be called before using comfy_horde.") - self._client_id = {} # type: ignore - self._images = {} # type: ignore - self.pipelines = {} # type: ignore - self._model_locks = [] # type: ignore - self._model_lock_mutex = threading.Lock() + self.pipelines = {} self._exit_time = 0 self._callers = 0 self._gc_timer = time.time() self._counter_mutex = threading.Lock() - # These set the default paths for comfyui to look for models and embeddings. From within hordelib, - # we aren't ever going to use the default ones, and this may help lubricate any further changes. - _comfy_folder_paths["lora"] = ( # type: ignore - [os.path.join(UserSettings.get_model_directory(), "loras")], - [".safetensors"], - ) - _comfy_folder_paths["embeddings"] = ( # type: ignore - [os.path.join(UserSettings.get_model_directory(), "ti")], - [".safetensors"], - ) + # Set comfyui paths for checkpoints, loras, etc + self._set_comfyui_paths() - # Set custom node path - _comfy_folder_paths["custom_nodes"] = ([os.path.join(get_hordelib_path(), "nodes")], []) # type: ignore # Load our pipelines self._load_pipelines() @@ -348,44 +288,84 @@ def __init__(self) -> None: self._load_custom_nodes() stdio.replay() - def lock_models(self, model_names): - """Lock the given model, or list of models. + def _set_comfyui_paths(self) -> None: + # These set the default paths for comfyui to look for models and embeddings. From within hordelib, + # we aren't ever going to use the default ones, and this may help lubricate any further changes. + _comfy_folder_names_and_paths["loras"] = ( + [ + _comfy_folder_names_and_paths["loras"][0][0], + os.path.join(UserSettings.get_model_directory(), "lora"), + ], + _comfy_supported_pt_extensions, + ) + _comfy_folder_names_and_paths["embeddings"] = ( + [ + _comfy_folder_names_and_paths["embeddings"][0][0], + os.path.join(UserSettings.get_model_directory(), "ti"), + ], + _comfy_supported_pt_extensions, + ) + + _comfy_folder_names_and_paths["checkpoints"] = ( + [ + _comfy_folder_names_and_paths["checkpoints"][0][0], + os.path.join(UserSettings.get_model_directory(), "compvis"), + ], + _comfy_supported_ckpt_extensions, + ) - Return True if the models could be locked, False indicates they are in use already - """ - already_locked = False - with self._model_lock_mutex: - if isinstance(model_names, str): - model_names = [model_names] - for model in model_names: - if model in self._model_locks: - already_locked = True - break - if not already_locked: - self._model_locks.extend(model_names) + _comfy_folder_names_and_paths["upscale_models"] = ( + [ + _comfy_folder_names_and_paths["upscale_models"][0][0], + os.path.join(UserSettings.get_model_directory(), "esrgan"), + os.path.join(UserSettings.get_model_directory(), "gfpgan"), + os.path.join(UserSettings.get_model_directory(), "codeformer"), + ], + _comfy_supported_pt_extensions, + ) - return not already_locked + _comfy_folder_names_and_paths["controlnet"] = ( + [ + _comfy_folder_names_and_paths["controlnet"][0][0], + os.path.join(UserSettings.get_model_directory(), "controlnet"), + ], + _comfy_supported_pt_extensions, + ) - def unlock_models(self, model_names): - """The reverse of _lock_models(). Although this can never fail so returns nothing.""" - with self._model_lock_mutex: - if isinstance(model_names, str): - model_names = [model_names] - for model in model_names: - if model in self._model_locks: - self._model_locks.remove(model) + # Set custom node path + _comfy_folder_names_and_paths["custom_nodes"] = ( + [ + _comfy_folder_names_and_paths["custom_nodes"][0][0], + os.path.join(get_hordelib_path(), "nodes"), + ], + [], + ) def _this_dir(self, filename: str, subdir="") -> str: + """Return the path to a file in the same directory as this file. + + Args: + filename (str): The filename to return the path to. + subdir (str, optional): The subdirectory to look in. Defaults to "". + + Returns: + str: The path to the file. + """ target_dir = os.path.dirname(os.path.realpath(__file__)) if subdir: target_dir = os.path.join(target_dir, subdir) return os.path.join(target_dir, filename) def _load_custom_nodes(self) -> None: + """Force ComfyUI to load its normal custom nodes and the horde custom nodes.""" _comfy_nodes.init_custom_nodes() - def _get_executor(self, pipeline): - executor = _comfy_PromptExecutor(self) # type: ignore + def _get_executor(self): + """Return the ComfyUI PromptExecutor object.""" + # This class (`Comfy_Horde`) uses duck typing to intercept calls intended for + # ComfyUI's `PromptServer` class. In particular, it intercepts calls to + # `PromptServer.send_sync`. See `Comfy_Horde.send_sync` for more details. + executor = _comfy_PromptExecutor(self) return executor def get_pipeline_data(self, pipeline_name): @@ -395,6 +375,14 @@ def get_pipeline_data(self, pipeline_name): return pipeline_data def _fix_pipeline_types(self, data: dict) -> dict: + """Replace comfyui standard node types with hordelib node types. + + Args: + data (dict): The pipeline data. + + Returns: + dict: The pipeline resulting from the replacement. + """ # We have a list of nodes and each node has a class type, which we may want to change for nodename, node in data.items(): if ("class_type" in node) and (node["class_type"] in Comfy_Horde.NODE_REPLACEMENTS): @@ -417,6 +405,15 @@ def _fix_pipeline_types(self, data: dict) -> dict: return data def _fix_node_names(self, data: dict, design: dict) -> dict: + """Rename nodes to the "title" set in the design file. + + Args: + data (dict): The pipeline data. + design (dict): The design data. + + Returns: + dict: The pipeline resulting from the renaming. + """ # We have a list of nodes, attempt to rename them to the "title" set # in the design file. These must be unique names. newnodes = {} @@ -453,6 +450,7 @@ def _fix_node_names(self, data: dict, design: dict) -> dict: # Note also that the format of the design files from web app is expected to change at a fast # pace. This is why the only thing that partially relies on that format, is in fact, optional. def _patch_pipeline(self, data: dict, design: dict) -> dict: + """Patch the pipeline data with the design data.""" # First replace comfyui standard types with hordelib node types data = self._fix_pipeline_types(data) # Now try to find better parameter names @@ -460,32 +458,46 @@ def _patch_pipeline(self, data: dict, design: dict) -> dict: return data def _load_pipeline(self, filename: str) -> bool | None: + """ + Load a single inference pipeline from a file. + + Args: + filename (str): The path to the pipeline file. + + Returns: + bool | None: True if the pipeline was loaded successfully, False if it was not, None if there was an error. + """ + # Check if the file exists if not os.path.exists(filename): logger.error(f"No such inference pipeline file: {filename}") return None try: + # Open the pipeline file with open(filename) as jsonfile: - pipeline_name_rematches = re.match(r".*pipeline_(.*)\.json", filename) - if pipeline_name_rematches is None: + # Extract the pipeline name from the filename + pipeline_name_regex_matches = re.match(r".*pipeline_(.*)\.json", filename) + if pipeline_name_regex_matches is None: logger.error(f"Regex parsing failed for: {filename}") return None - pipeline_name = pipeline_name_rematches[1] - data = json.loads(jsonfile.read()) - # Do we have a design file for this pipeline? - design = os.path.join( + pipeline_name = pipeline_name_regex_matches[1] + # Load the pipeline data from the file + pipeline_data = json.loads(jsonfile.read()) + # Check if there is a design file for this pipeline + pipeline_design = os.path.join( os.path.dirname(os.path.dirname(filename)), "pipeline_designs", os.path.basename(filename), ) - # If we do have a design pipeline, use it to patch the pipeline we loaded. - if os.path.exists(design): + # If there is a design file, patch the pipeline data with it + if os.path.exists(pipeline_design): logger.debug(f"Patching pipeline {pipeline_name}") - with open(design) as designfile: - designdata = json.loads(designfile.read()) - data = self._patch_pipeline(data, designdata) - self.pipelines[pipeline_name] = data + with open(pipeline_design) as design_file: + design_data = json.loads(design_file.read()) + pipeline_data = self._patch_pipeline(pipeline_data, design_data) + # Add the pipeline data to the pipelines dictionary + self.pipelines[pipeline_name] = pipeline_data logger.debug(f"Loaded inference pipeline: {pipeline_name}") return True except (OSError, ValueError): @@ -493,6 +505,11 @@ def _load_pipeline(self, filename: str) -> bool | None: return None def _load_pipelines(self) -> int: + """Load all of the inference pipelines from the pipelines directory matching `pipeline_*.json`. + + Returns: + int: The number of pipelines loaded. + """ files = glob.glob(self._this_dir("pipeline_*.json", subdir="pipelines")) loaded_count = 0 for file in files: @@ -500,9 +517,11 @@ def _load_pipelines(self) -> int: loaded_count += 1 return loaded_count - # Inject parameters into a pre-configured pipeline - # We allow "inputs" to be missing from the key name, if it is we insert it. def _set(self, dct, **kwargs) -> None: + """Set the named parameter to the named value in the pipeline template. + + Allows inputs to be missing from the key name, if it is we insert it. + """ for key, value in kwargs.items(): keys = key.split(".") skip = False @@ -512,7 +531,7 @@ def _set(self, dct, **kwargs) -> None: for k in keys[:-1]: if k not in current: - logger.debug(f"Attempt to set unknown pipeline parameter {key}") + logger.debug(f"Attempt to set parameter not defined in this template: {key}") skip = True break @@ -565,25 +584,21 @@ def send_sync(self, label, data, _id): # Execute the named pipeline and pass the pipeline the parameter provided. # For the horde we assume the pipeline returns an array of images. def _run_pipeline(self, pipeline: dict, params: dict) -> dict | None: - if _comfy_model_manager is None: + if _comfy_current_loaded_models is None: raise RuntimeError("hordelib.initialise() must be called before using comfy_horde.") - # Update user settings - _comfy_model_manager.set_user_reserved_vram(UserSettings.get_vram_to_leave_free_mb()) - _comfy_model_manager.set_batch_optimisations(UserSettings.enable_batch_optimisations.active) - # Set the pipeline parameters self._set(pipeline, **params) - # Create (or retrieve) our prompt executive - inference = self._get_executor(pipeline) + # Create (or retrieve) our prompt executor + inference = self._get_executor() # This is useful for dumping the entire pipeline to the terminal when # developing and debugging new pipelines. A badly structured pipeline # file just results in a cryptic error from comfy pretty_pipeline = pformat(pipeline) if False: # This isn't here Tazlin :) - logger.error(pretty_pipeline) + logger.warning(pretty_pipeline) # The client_id parameter here is just so we receive comfy callbacks for debugging. # We pretend we are a web client and want async callbacks. @@ -592,7 +607,7 @@ def _run_pipeline(self, pipeline: dict, params: dict) -> dict | None: # validate_prompt from comfy returns [bool, str, list] # Which gives us these nice hardcoded list indexes, which valid[2] is the output node list self.client_id = str(uuid.uuid4()) - valid = _comfy_validate_prompt(pipeline, True) + valid = _comfy_validate_prompt(pipeline) inference.execute(pipeline, self.client_id, {"client_id": self.client_id}, valid[2]) stdio.replay() @@ -606,7 +621,7 @@ def _run_pipeline(self, pipeline: dict, params: dict) -> dict | None: return self.images # Run a pipeline that returns an image in pixel space - def run_image_pipeline(self, pipeline, params: dict) -> list[dict] | None: + def run_image_pipeline(self, pipeline, params: dict) -> dict | None: # From the horde point of view, let us assume the output we are interested in # is always in a HordeImageOutput node named "output_image". This is an array of # dicts of the form: @@ -634,19 +649,10 @@ def run_image_pipeline(self, pipeline, params: dict) -> list[dict] | None: if idle_time > 1 and UserSettings.enable_idle_time_warning.active: logger.warning(f"No job ran for {round(idle_time, 3)} seconds") - # We have just been entered - with self._counter_mutex: - self._callers += 1 - result = self._run_pipeline(pipeline_data, params) - # We are being exited - with self._counter_mutex: - self._exit_time = time.time() # type: ignore - self._callers -= 1 - if result: - return result # type: ignore + return result return None diff --git a/hordelib/consts.py b/hordelib/consts.py index c4b57903..bd286cfd 100644 --- a/hordelib/consts.py +++ b/hordelib/consts.py @@ -6,7 +6,7 @@ from hordelib.config_path import get_hordelib_path -COMFYUI_VERSION = "84ea21c815d426000c233e0c7b8c542764335cc8" +COMFYUI_VERSION = "e3d0a9a490195911d0c200f8dbde2991b7421678" """The exact version of ComfyUI version to load.""" REMOTE_PROXY = "" diff --git a/hordelib/horde.py b/hordelib/horde.py index 3b69cbfa..282445f3 100644 --- a/hordelib/horde.py +++ b/hordelib/horde.py @@ -14,6 +14,7 @@ from PIL import Image from hordelib.comfy_horde import Comfy_Horde +from hordelib.consts import MODEL_CATEGORY_NAMES from hordelib.shared_model_manager import SharedModelManager from hordelib.utils.dynamicprompt import DynamicPromptParser from hordelib.utils.image_utils import ImageUtils @@ -65,6 +66,19 @@ class HordeLib: # "": "PiDiNetPreprocessor", } + CONTROLNET_MODEL_MAP = { + "canny": "diff_control_sd15_canny_fp16.safetensors", + "hed": "diff_control_sd15_hed_fp16.safetensors", + "depth": "diff_control_sd15_depth_fp16.safetensors", + "normal": "control_normal_fp16.safetensors", + "openpose": "control_openpose_fp16.safetensors", + "seg": "control_seg_fp16.safetensors", + "scribble": "control_scribble_fp16.safetensors", + "fakescribble": "control_scribble_fp16.safetensors", + "mlsd": "control_mlsd_fp16.safetensors", + "hough": "control_mlsd_fp16.safetensors", + } + SOURCE_IMAGE_PROCESSING_OPTIONS = ["img2img", "inpainting", "outpainting"] SCHEDULERS = ["normal", "karras", "simple", "ddim_uniform"] @@ -119,6 +133,7 @@ class HordeLib: "negative_prompt.text": "negative_prompt", "sampler.steps": "ddim_steps", "empty_latent_image.batch_size": "n_iter", + "model_loader.ckpt_name": "model", "model_loader.model_name": "model", "image_loader.image": "source_image", "loras": "loras", @@ -225,6 +240,42 @@ def _apply_aihorde_compatibility_hacks(self, payload): """ payload = deepcopy(payload) + if payload.get("model"): + if SharedModelManager.manager.compvis.check_model_available(payload["model"]): + model_reference_information = SharedModelManager.manager.compvis.get_model(payload["model"]) + model_config = model_reference_information.get("config") + model_files_config = model_config.get("files") + + for file_config_entry in model_files_config: + path_config_item = file_config_entry.get("path") + if path_config_item: + if path_config_item.endswith((".ckpt", ".safetensors")): + payload["model"] = path_config_item + break + else: + post_processor_model_managers = SharedModelManager.manager.get_mm_pointers( + [MODEL_CATEGORY_NAMES.codeformer, MODEL_CATEGORY_NAMES.esrgan, MODEL_CATEGORY_NAMES.gfpgan], + ) + + found_model = False + + for post_processor_model_manager in post_processor_model_managers: + if post_processor_model_manager.check_model_available(payload["model"]): + model_reference_information = post_processor_model_manager.get_model(payload["model"]) + model_config = model_reference_information.get("config") + model_files_config = model_config.get("files") + + for file_config_entry in model_files_config: + path_config_item = file_config_entry.get("path") + if path_config_item: + if path_config_item.endswith((".pth", ".pt", ".safetensors")): + payload["model"] = path_config_item + found_model = True + break + + if not found_model: + raise RuntimeError(f"Model {payload['model']} not found! Is it in a Model Reference?") + # Rather than specify a scheduler, only karras or not karras is specified if payload.get("karras", False): payload["scheduler"] = "karras" @@ -373,9 +424,9 @@ def _final_pipeline_adjustments(self, payload, pipeline_data): "lora_name": lora["name"], "strength_model": lora["model"], "strength_clip": lora["clip"], - "model_manager": SharedModelManager, + # "model_manager": SharedModelManager, }, - "class_type": "HordeLoraLoader", + "class_type": "LoraLoader", } else: # Subsequent chained loras @@ -386,9 +437,9 @@ def _final_pipeline_adjustments(self, payload, pipeline_data): "lora_name": lora["name"], "strength_model": lora["model"], "strength_clip": lora["clip"], - "model_manager": SharedModelManager, + # "model_manager": SharedModelManager, }, - "class_type": "HordeLoraLoader", + "class_type": "LoraLoader", } for lora_index, lora in enumerate(payload.get("loras")): @@ -424,7 +475,7 @@ def _final_pipeline_adjustments(self, payload, pipeline_data): logger.error(f"Parameter {key} not found") # Inject our model manager - pipeline_params["model_loader.model_manager"] = SharedModelManager + # pipeline_params["model_loader.model_manager"] = SharedModelManager # For hires fix, change the image sizes as we create an intermediate image first if payload.get("hires_fix", False): @@ -439,7 +490,11 @@ def _final_pipeline_adjustments(self, payload, pipeline_data): if payload.get("control_type"): # Inject control net model manager - pipeline_params["controlnet_model_loader.model_manager"] = SharedModelManager + # pipeline_params["controlnet_model_loader.model_manager"] = SharedModelManager + model_name = self.CONTROLNET_MODEL_MAP.get(payload.get("control_type")) + if not model_name: + logger.error(f"Controlnet model for {payload.get('control_type')} not found") + pipeline_params["controlnet_model_loader.control_net_name"] = model_name # Dynamically reconnect nodes in the pipeline to connect the correct pre-processor node if payload.get("return_control_map"): @@ -528,19 +583,6 @@ def _process_results(self, images, rawpng): else: return results - def lock_models(self, models): - models = [str(x).strip() for x in models if x] - # Try to acquire a model lock, if we can't, wait a while as some other thread - # must have these resources locked - while not self.generator.lock_models(models): - time.sleep(0.1) - logger.debug(f"Locked models {','.join(models)}") - - def unlock_models(self, models): - models = [x.strip() for x in models if x] - self.generator.unlock_models(models) - logger.debug(f"Unlocked models {','.join(models)}") - def basic_inference(self, payload, rawpng=False): # AIHorde hacks to payload payload = self._apply_aihorde_compatibility_hacks(payload) @@ -555,20 +597,17 @@ def basic_inference(self, payload, rawpng=False): payload = self._final_pipeline_adjustments(payload, pipeline_data) models: list[str] = [] # Run the pipeline - try: - # Add prefix to loras to avoid name collisions with other models - models = [f"lora-{x['name']}" for x in payload.get("loras", []) if x] - # main model - models.append(payload.get("model_loader.model_name")) # type: ignore # FIXME? - # controlnet model - models.append(payload.get("controlnet_model_loader.control_net_name")) # type: ignore # FIXME? - # Acquire a lock on all these models - self.lock_models(models) - # Call the inference pipeline - # logger.info(payload) - images = self.generator.run_image_pipeline(pipeline_data, payload) - finally: - self.unlock_models(models) + + # Add prefix to loras to avoid name collisions with other models + models = [f"lora-{x['name']}" for x in payload.get("loras", []) if x] + # main model models.append(payload.get("model_loader.model_name")) # type: ignore # FIXME? + # controlnet model + models.append(payload.get("controlnet_model_loader.control_net_name")) # type: ignore # FIXME? + + # Call the inference pipeline + # logger.info(payload) + images = self.generator.run_image_pipeline(pipeline_data, payload) + return self._process_results(images, rawpng) def image_upscale(self, payload, rawpng=False) -> Image.Image | None: @@ -585,12 +624,11 @@ def image_upscale(self, payload, rawpng=False) -> Image.Image | None: pipeline_name = "image_upscale" pipeline_data = self.generator.get_pipeline_data(pipeline_name) payload = self._final_pipeline_adjustments(payload, pipeline_data) + # Run the pipeline - try: - self.lock_models([payload.get("model_loader.model_name")]) - images = self.generator.run_image_pipeline(pipeline_data, payload) - finally: - self.unlock_models([payload.get("model_loader.model_name")]) + + images = self.generator.run_image_pipeline(pipeline_data, payload) + if images is None: return None # XXX Log error and/or raise Exception here # Allow arbitrary resizing by shrinking the image back down @@ -607,10 +645,9 @@ def image_facefix(self, payload, rawpng=False) -> Image.Image | None: pipeline_name = "image_facefix" pipeline_data = self.generator.get_pipeline_data(pipeline_name) payload = self._final_pipeline_adjustments(payload, pipeline_data) + # Run the pipeline - try: - self.lock_models([payload.get("model_loader.model_name")]) - images = self.generator.run_image_pipeline(pipeline_data, payload) - finally: - self.unlock_models([payload.get("model_loader.model_name")]) + + images = self.generator.run_image_pipeline(pipeline_data, payload) + return self._process_results(images, rawpng) # type: ignore # FIXME? diff --git a/hordelib/install_comfy.py b/hordelib/install_comfy.py index 25c2ad24..11878d2d 100644 --- a/hordelib/install_comfy.py +++ b/hordelib/install_comfy.py @@ -121,39 +121,40 @@ def reset_comfyui_to_version(cls, comfy_version): cls._run(f"git checkout {comfy_version}", get_comfyui_path()) @classmethod - def apply_patch(cls, patchfile): + def apply_patch(cls, patchfile, skip_dot_patch=True): # Don't if we're a release version if RELEASE_VERSION: return - # Check if the patch has already been applied - result = cls._run_get_result( - f"git apply --check {patchfile}", - get_comfyui_path(), - ) - could_apply = not result.returncode - result = cls._run_get_result( - f"git apply --reverse --check {patchfile}", - get_comfyui_path(), - ) - could_reverse = not result.returncode - if could_apply: - # Apply the patch - logger.info(f"Applying patch {patchfile}") - result = cls._run_get_result(f"git apply {patchfile}", get_comfyui_path()) - logger.debug(f"{result}") - elif could_reverse: - # Patch is already applied, all is well - logger.info(f"Already applied patch {patchfile}") - else: - # Couldn't apply or reverse? That's not so good, but maybe we are partially applied? - # Reset local changes - cls.remove_local_comfyui_changes() - # Try to apply the patch - logger.info(f"Applying patch {patchfile}") - result = cls._run_get_result(f"git apply {patchfile}", get_comfyui_path()) - logger.debug(f"{result}") - if result.returncode: - logger.error(f"Could not apply patch {patchfile}") + if not skip_dot_patch: # FIXME + # Check if the patch has already been applied + result = cls._run_get_result( + f"git apply --check {patchfile}", + get_comfyui_path(), + ) + could_apply = not result.returncode + result = cls._run_get_result( + f"git apply --reverse --check {patchfile}", + get_comfyui_path(), + ) + could_reverse = not result.returncode + if could_apply: + # Apply the patch + logger.info(f"Applying patch {patchfile}") + result = cls._run_get_result(f"git apply {patchfile}", get_comfyui_path()) + logger.debug(f"{result}") + elif could_reverse: + # Patch is already applied, all is well + logger.info(f"Already applied patch {patchfile}") + else: + # Couldn't apply or reverse? That's not so good, but maybe we are partially applied? + # Reset local changes + cls.remove_local_comfyui_changes() + # Try to apply the patch + logger.info(f"Applying patch {patchfile}") + result = cls._run_get_result(f"git apply {patchfile}", get_comfyui_path()) + logger.debug(f"{result}") + if result.returncode: + logger.error(f"Could not apply patch {patchfile}") # Drop in custom node config config_file = os.path.join(get_comfyui_path(), "extra_model_paths.yaml") diff --git a/hordelib/model_manager/base.py b/hordelib/model_manager/base.py index 4741c676..39394131 100644 --- a/hordelib/model_manager/base.py +++ b/hordelib/model_manager/base.py @@ -16,15 +16,13 @@ import psutil import requests import torch +from horde_model_reference import MODEL_REFERENCE_CATEGORY, LEGACY_REFERENCE_FOLDER, get_model_reference_filename from loguru import logger from tqdm import tqdm from hordelib.comfy_horde import ( cleanup, - get_models_on_gpu, get_torch_free_vram_mb, - load_model_to_gpu, - remove_model_from_memory, ) from hordelib.comfy_horde import get_torch_device as _get_torch_device from hordelib.config_path import get_hordelib_path @@ -32,6 +30,18 @@ from hordelib.settings import UserSettings +_temp_reference_lookup = { + MODEL_CATEGORY_NAMES.codeformer: MODEL_REFERENCE_CATEGORY.codeformer, + MODEL_CATEGORY_NAMES.compvis: MODEL_REFERENCE_CATEGORY.stable_diffusion, + MODEL_CATEGORY_NAMES.controlnet: MODEL_REFERENCE_CATEGORY.controlnet, + MODEL_CATEGORY_NAMES.esrgan: MODEL_REFERENCE_CATEGORY.esrgan, + MODEL_CATEGORY_NAMES.gfpgan: MODEL_REFERENCE_CATEGORY.gfpgan, + MODEL_CATEGORY_NAMES.safety_checker: MODEL_REFERENCE_CATEGORY.safety_checker, + MODEL_CATEGORY_NAMES.blip: MODEL_REFERENCE_CATEGORY.blip, + MODEL_CATEGORY_NAMES.clip: MODEL_REFERENCE_CATEGORY.clip, +} + + class BaseModelManager(ABC): modelFolderPath: str # XXX # TODO Convert to `Path` """The path to the directory to store this model type.""" @@ -48,7 +58,7 @@ class BaseModelManager(ABC): recommended_gpu: list download_reference: bool remote_db: str - _mutex = threading.RLock() + _disk_write_mutex = threading.Lock() def get_torch_device(self): @@ -58,17 +68,16 @@ def get_loaded_models(self): return self._loaded_models.copy() def add_loaded_model(self, model_name, model_data): - with self._mutex: - self._loaded_models[model_name] = model_data + self._loaded_models[model_name] = model_data def remove_loaded_model(self, model_name): logger.debug(f"Received request to remove loaded model {model_name}") - with self._mutex: - if model_name in self._loaded_models: - logger.debug(f"Removing loaded model {model_name}") - del self._loaded_models[model_name] - else: - logger.debug(f"Could not find loaded model {model_name}") + + if model_name in self._loaded_models: + logger.debug(f"Removing loaded model {model_name}") + del self._loaded_models[model_name] + else: + logger.debug(f"Could not find loaded model {model_name}") def __init__( self, @@ -96,14 +105,23 @@ def __init__( self.pkg = importlib_resources.files("hordelib") # XXX Remove self.models_db_name = MODEL_DB_NAMES[model_category_name] - if models_db_path: - self.models_db_path = models_db_path - else: - self.models_db_path = Path(get_hordelib_path()).joinpath( + if not models_db_path: + models_db_path = Path(get_hordelib_path()).joinpath( "model_database/", f"{self.models_db_name}.json", ) + if model_category_name in _temp_reference_lookup: + models_db_path = Path( + get_model_reference_filename( + _temp_reference_lookup[model_category_name], + base_path=LEGACY_REFERENCE_FOLDER, + ), + ) + + logger.debug(f"Model database path: {models_db_path}") + self.models_db_path = models_db_path + self.cuda_available = torch.cuda.is_available() # XXX Remove? self.cuda_devices, self.recommended_gpu = self.get_cuda_devices() # XXX Remove? self.remote_db = f"{REMOTE_MODEL_DB}{self.models_db_name}.json" @@ -191,78 +209,77 @@ def download_model_reference(self): return {} def _modelref_to_name(self, modelref): - with self._mutex: - for name, data in self.get_loaded_models().items(): - if modelref and data["model"] is modelref: - return name - return None + for name, data in self.get_loaded_models().items(): + if modelref and data["model"] is modelref: + return name + return None def get_free_ram_mb(self): return int(psutil.virtual_memory().available / (1024 * 1024)) def ensure_ram_available(self): - with self._mutex: - # If we have less than the minimum RAM free, free some up - # Although, perhaps we have plenty of available vram, in which case we - # can consider moving something from ram to vram. - attempts = 2 # if ram isn't being released yet, no point keep trying - while ( - self.get_free_ram_mb() < UserSettings.get_ram_to_leave_free_mb() - and attempts - and len(self.get_loaded_models()) > 0 - ): - logger.debug( - f"Free RAM is: {self.get_free_ram_mb()} MB, " - f"({len(self.get_loaded_models())} models loaded in RAM)", - ) - logger.debug("Not enough free RAM attempting to free some") - # Grab a list of models (ModelPatcher) that are loaded on the gpu - # These are actually returned with the least important at the bottom of the list - busy_models = get_models_on_gpu() - # Try to find one we have in ram that isn't on the gpu - idle_model = None - idle_model_data = None - for ram_model_name, ram_model_data in self.get_loaded_models().items(): - if type(ram_model_data["model"]) is not str and ram_model_data["model"] not in busy_models: - idle_model = ram_model_name - idle_model_data = ram_model_data - break - - # Should we move to vram rather than disk cache or free completely? - # First try to determine the headroom we have on vram and ram even though that requires - # that we brave entry into Tazlin's UserSettings trap... - vram_headroom = get_torch_free_vram_mb() - UserSettings.get_vram_to_leave_free_mb() - ram_headroom = self.get_free_ram_mb() - UserSettings.get_ram_to_leave_free_mb() - # Don't try this unless we have 1500MB head room at least - if vram_headroom > ram_headroom and vram_headroom > 1500: - logger.debug("More free VRAM than RAM, consider RAM->VRAM migration") - if self.can_move_to_vram() and idle_model and load_model_to_gpu(idle_model_data["model"]): - return - # We failed to move a model to vram, continue with plan A - - # If we didn't have one hanging around in ram not on the gpu - # pick the least used gpu model - if not idle_model and busy_models: - idle_model = self._modelref_to_name(busy_models[-1]) - - if idle_model: - # Can this type of model be cached? - if self.can_cache_on_disk(): - logger.debug(f"Moving model {idle_model} to disk to free up RAM") - self.move_to_disk_cache(idle_model) - # Try to release ram right now - cleanup() - elif self.can_auto_unload(): - # Unload the model to free ram - self.unload_model(idle_model) - # Try to release ram right now - cleanup() - else: - # Nothing else to release - logger.debug("Could not find a model to free RAM") + # If we have less than the minimum RAM free, free some up + # Although, perhaps we have plenty of available vram, in which case we + # can consider moving something from ram to vram. + return # FIXME + attempts = 2 # if ram isn't being released yet, no point keep trying + while ( + self.get_free_ram_mb() < UserSettings.get_ram_to_leave_free_mb() + and attempts + and len(self.get_loaded_models()) > 0 + ): + logger.debug( + f"Free RAM is: {self.get_free_ram_mb()} MB, " + f"({len(self.get_loaded_models())} models loaded in RAM)", + ) + logger.debug("Not enough free RAM attempting to free some") + # Grab a list of models (ModelPatcher) that are loaded on the gpu + # These are actually returned with the least important at the bottom of the list + busy_models = get_models_on_gpu() + # Try to find one we have in ram that isn't on the gpu + idle_model = None + # idle_model_data = None + for ram_model_name, ram_model_data in self.get_loaded_models().items(): + if type(ram_model_data["model"]) is not str and ram_model_data["model"] not in busy_models: + idle_model = ram_model_name + # idle_model_data = ram_model_data + break - # Try again if we must - attempts -= 1 + # Should we move to vram rather than disk cache or free completely? + # First try to determine the headroom we have on vram and ram even though that requires + # that we brave entry into Tazlin's UserSettings trap... + vram_headroom = get_torch_free_vram_mb() - UserSettings.get_vram_to_leave_free_mb() + ram_headroom = self.get_free_ram_mb() - UserSettings.get_ram_to_leave_free_mb() + # Don't try this unless we have 1500MB head room at least + if vram_headroom > ram_headroom and vram_headroom > 1500: + logger.debug("More free VRAM than RAM, consider RAM->VRAM migration") # FIXME + # if self.can_move_to_vram() and idle_model and horde_load_checkpoint(idle_model_data["model"]): + # return + # We failed to move a model to vram, continue with plan A + + # If we didn't have one hanging around in ram not on the gpu + # pick the least used gpu model + if not idle_model and busy_models: + idle_model = self._modelref_to_name(busy_models[-1]) + + if idle_model: + # Can this type of model be cached? + if self.can_cache_on_disk(): + logger.debug(f"Moving model {idle_model} to disk to free up RAM") + self.move_to_disk_cache(idle_model) + # Try to release ram right now + cleanup() + elif self.can_auto_unload(): + # Unload the model to free ram + self.unload_model(idle_model) + # Try to release ram right now + cleanup() + else: + # Nothing else to release + logger.debug("Could not find a model to free RAM") + + # Try again if we must + attempts -= 1 # @abstractmethod # TODO def move_to_disk_cache(self, model_name): @@ -315,55 +332,55 @@ def load( bool | None: `True` if the model was loaded, `False` if it failed to load/download, `None` if the model name doesn't exist in the model reference. """ - with self._mutex: - self.ensure_ram_available() - local = self.is_local_model(model_name) - if not local and model_name not in self.model_reference: - logger.error(f"{model_name} not found") - return None - if not local and model_name not in self.available_models: - logger.warning( - f"{model_name} may need to be downloaded, checking...", - ) - download_succeeded = self.download_model(model_name) - if not download_succeeded: - logger.error(f"{model_name} failed to download") + self.ensure_ram_available() + local = self.is_local_model(model_name) + if not local and model_name not in self.model_reference: + logger.error(f"{model_name} not found") + return None + if not local and model_name not in self.available_models: + logger.warning( + f"{model_name} may need to be downloaded, checking...", + ) + download_succeeded = self.download_model(model_name) + if not download_succeeded: + logger.error(f"{model_name} failed to download") + return False + logger.info(f"{model_name} Downloaded") + if model_name in self.get_loaded_models(): + logger.debug(f"{model_name} is already loaded") + return True + if model_name not in self.get_loaded_models(): + if not local: + model_validated = self.validate_model(model_name) + if not model_validated: return False - logger.info(f"{model_name} Downloaded") - if model_name in self.get_loaded_models(): - logger.debug(f"{model_name} is already loaded") - return True - if model_name not in self.get_loaded_models(): - if not local: - model_validated = self.validate_model(model_name) - if not model_validated: - return False - tic = time.time() - logger.info( - f"Loading {model_name}", + tic = time.time() + logger.info( + f"Loading {model_name}", + ) + + try: + self.add_loaded_model( + model_name, + self.modelToRam( + model_name=model_name, + half_precision=half_precision, + gpu_id=gpu_id, + cpu_only=cpu_only, + **kwargs, + ), ) - try: - self.add_loaded_model( - model_name, - self.modelToRam( - model_name=model_name, - half_precision=half_precision, - gpu_id=gpu_id, - cpu_only=cpu_only, - **kwargs, - ), - ) + except RuntimeError: + # It failed, it happens. + logger.error(f"Failed to load model {model_name}") + return False - except RuntimeError: - # It failed, it happens. - logger.error(f"Failed to load model {model_name}") - return False + toc = time.time() - toc = time.time() + logger.info(f"Loaded {model_name}: {round(toc-tic,2)} seconds") + return True - logger.info(f"Loaded {model_name}: {round(toc-tic,2)} seconds") - return True return False def getFullModelPath(self, model_name: str): @@ -455,10 +472,9 @@ def get_loaded_models_names(self, string=False) -> list[str] | str: # XXX Rewor list[str] | str: The list of models, as a `list` or a comma separated string. """ # return ["Deliberate"] - with self._mutex: - if string: - return ", ".join(self.get_loaded_models().keys()) - return list(self.get_loaded_models().keys()) + if string: + return ", ".join(self.get_loaded_models().keys()) + return list(self.get_loaded_models().keys()) def is_model_loaded(self, model_name: str) -> bool: """Returns True if the model is loaded, False otherwise. @@ -469,8 +485,7 @@ def is_model_loaded(self, model_name: str) -> bool: Returns: _type_: _description_ """ - with self._mutex: - return model_name in self.get_loaded_models() + return model_name in self.get_loaded_models() def unload_model(self, model_name: str): """ @@ -479,34 +494,30 @@ def unload_model(self, model_name: str): This may not be possible right now, as it may be being used, so we actually issue a request for it to be unloaded at the earliest opportunity. """ - with self._mutex: - logger.debug(f"Model Manager received unload model {model_name} request (mutex locked)") - if model_name in self._loaded_models: - # If this model is not in ram, just on disk cache don't try to free memory resources - model_data = self._loaded_models[model_name].get("model") - if not isinstance(model_data, str): - logger.debug(f"{model_name} is on loaded models list, trying free resources") - self.free_model_resources(model_name) - self.remove_loaded_model(model_name) - logger.debug("Model Manager done with model unload request (mutex released)") - return True - else: - logger.debug(f"Model {model_name} is not in loaded models list so can't unload") + logger.debug(f"Model Manager received unload model {model_name} request") + if model_name in self._loaded_models: + # If this model is not in ram, just on disk cache don't try to free memory resources + model_data = self._loaded_models[model_name].get("model") + if not isinstance(model_data, str): + logger.debug(f"{model_name} is on loaded models list, trying free resources") + self.free_model_resources(model_name) + self.remove_loaded_model(model_name) + logger.debug("Model Manager done with model unload request") + return True + else: + logger.debug(f"Model {model_name} is not in loaded models list so can't unload") logger.debug(f"Model manager done with unload request for model {model_name}") def free_model_resources(self, model_name: str): logger.debug(f"Received request to free model memory resources for {model_name}") - with self._mutex: - remove_model_from_memory(model_name, self.get_loaded_model(model_name)) def unload_all_models(self): """ Unloads all models """ - with self._mutex: - for model in self.get_loaded_models(): - self.unload_model(model) - return True + for model in self.get_loaded_models(): + self.unload_model(model) + return True def taint_model(self, model_name: str): """Marks a model as not valid by removing it from available_models""" diff --git a/hordelib/model_manager/blip.py b/hordelib/model_manager/blip.py index 416d460b..9b9fbafe 100644 --- a/hordelib/model_manager/blip.py +++ b/hordelib/model_manager/blip.py @@ -1 +1,3 @@ from clipfree.model_manager.blip import BlipModelManager + +BlipModelManager.modelToRam = lambda self, model_name: None diff --git a/hordelib/model_manager/clip.py b/hordelib/model_manager/clip.py index b9b634d9..976016ad 100644 --- a/hordelib/model_manager/clip.py +++ b/hordelib/model_manager/clip.py @@ -1 +1,3 @@ from clipfree.model_manager.clip import ClipModelManager + +ClipModelManager.modelToRam = lambda self, model_name: None diff --git a/hordelib/model_manager/compvis.py b/hordelib/model_manager/compvis.py index d4b14cf8..b352b150 100644 --- a/hordelib/model_manager/compvis.py +++ b/hordelib/model_manager/compvis.py @@ -36,6 +36,7 @@ def modelToRam( model_name: str, **kwargs, ) -> dict[str, typing.Any]: + return {} embeddings_path = os.path.join(UserSettings.get_model_directory(), "ti") if not embeddings_path: logger.debug("No embeddings path found, disabling embeddings") @@ -49,81 +50,6 @@ def modelToRam( embeddings_path=embeddings_path if embeddings_path else None, ) - def can_cache_on_disk(self): - """Can this of type model be cached on disk?""" - if UserSettings.disable_disk_cache.active: - return False - return True - - def can_auto_unload(self): - # Allow compvis models to be auto unloaded - return True - def can_move_to_vram(self): # Allow moving directly to vram to save ram return True - - def get_model_cache_filename(self, model_name): - cache_dir = os.getenv("AIWORKER_TEMP_DIR", "./tmp") - # Create cache directory if it doesn't already exist - if not os.path.isdir(cache_dir): - os.makedirs(cache_dir, exist_ok=True) - cache_file = os.path.join(cache_dir, model_name) - return f"{cache_file}.hordelib.cache" - - def have_model_cache(self, model_name): - model_filename = self.getFullModelPath(model_name) - cache_file = self.get_model_cache_filename(model_name) - if os.path.exists(cache_file): - if not self.validate_model(model_name): - # The model is invalid, so delete the cache file because that's almost certainly no good either - # This should only happen if the model was updated on disk manually, or the model reference changed - logger.error(f"The model {model_name} is invalid, deleting the cache file.") - os.remove(path=cache_file) - return False - # We have a cache file but only consider it valid if it's up to date - model_timestamp = os.path.getmtime(model_filename) - cache_timestamp = os.path.getmtime(cache_file) - if model_timestamp <= cache_timestamp: - return True - return False - - def load_from_disk_cache(self, model_name): - filename = self.get_model_cache_filename(model_name) - logger.info(f"Model {model_name} warm loaded from disk cache") - return { - "model": filename, - "clip": filename, - "vae": filename, - "clipVisionModel": None, - } - - def move_to_disk_cache(self, model_name): - with self._mutex: - cache_file = self.get_model_cache_filename(model_name) - # Serialise our objects - model_data = copy.copy(self.get_loaded_model(model_name)) - components = ["model", "vae", "clip"] - if not self.have_model_cache(model_name): - # Only do one sequential write at a time - with self._disk_write_mutex: - with open(cache_file, "wb") as cache: - for component in components: - pickle.dump( - self.get_loaded_model(model_name)[component], - cache, - protocol=pickle.HIGHEST_PROTOCOL, - ) - for component in components: - model_data[component] = cache_file - # Remove from vram/ram - self.free_model_resources(model_name) - # Point the model to the cache - self.add_loaded_model(model_name, model_data) - - def move_from_disk_cache(self, model_name, model, clip, vae): - self.ensure_ram_available() - with self._mutex: - self._loaded_models[model_name]["model"] = model - self._loaded_models[model_name]["clip"] = clip - self._loaded_models[model_name]["vae"] = vae diff --git a/hordelib/model_manager/hyper.py b/hordelib/model_manager/hyper.py index df854d5b..e4880452 100644 --- a/hordelib/model_manager/hyper.py +++ b/hordelib/model_manager/hyper.py @@ -44,6 +44,8 @@ class ModelManager: """Controller class for all managers which extend `BaseModelManager`.""" + disk_read_mutex = threading.Lock() + # These properties are here with compatibility in mind, but they may survive for a while. @property def codeformer(self) -> CodeFormerModelManager | None: @@ -194,7 +196,7 @@ def init_model_managers( resolve_manager_to_load_type: type[BaseModelManager] | None = None if isinstance(manager_to_load, type) and issubclass(manager_to_load, BaseModelManager): if manager_to_load not in MODEL_MANAGERS_TYPE_LOOKUP.values(): - logger.warning(f"Attempted to load a model manager which doesn't exist: '{manager_to_load}'.") + logger.debug(f"Attempted to load a model manager which doesn't exist: '{manager_to_load}'.") continue resolve_manager_to_load_type = manager_to_load elif manager_to_load in MODEL_MANAGERS_TYPE_LOOKUP.keys(): @@ -204,7 +206,7 @@ def init_model_managers( continue if any(mm for mm in self.active_model_managers if isinstance(mm, resolve_manager_to_load_type)): - logger.warning( + logger.debug( f"Attempted to load a model manager which is already loaded: '{resolve_manager_to_load_type}'.", ) continue diff --git a/hordelib/model_manager/lora.py b/hordelib/model_manager/lora.py index cd648cc3..ecbbbdc9 100644 --- a/hordelib/model_manager/lora.py +++ b/hordelib/model_manager/lora.py @@ -37,7 +37,7 @@ class LoraModelManager(BaseModelManager): LORA_DEFAULTS = "https://raw.githubusercontent.com/Haidra-Org/AI-Horde-image-model-reference/main/lora.json" LORA_API = "https://civitai.com/api/v1/models?types=LORA&sort=Highest%20Rated&primaryFileOnly=true" MAX_RETRIES = 10 if not TESTS_ONGOING else 1 - MAX_DOWNLOAD_THREADS = 3 # max concurrent downloads + MAX_DOWNLOAD_THREADS = 3 RETRY_DELAY = 5 """The time to wait between retries in seconds""" REQUEST_METADATA_TIMEOUT = 30 @@ -778,13 +778,14 @@ def fetch_adhoc_lora(self, lora_name, timeout=45): def do_baselines_match(self, lora_name, model_details): self._check_for_refresh(lora_name) - lota_details = self.get_model(lora_name) - if not lota_details: + lora_details = self.get_model(lora_name) + return True # FIXME: Disabled for now + if not lora_details: logger.warning(f"Could not find lora {lora_name} to check baselines") return False - if "SD 1.5" in lota_details["baseModel"] and model_details["baseline"] == "stable diffusion 1": + if "SD 1.5" in lora_details["baseModel"] and model_details["baseline"] == "stable diffusion 1": return True - if "SD 2.1" in lota_details["baseModel"] and model_details["baseline"] == "stable diffusion 2": + if "SD 2.1" in lora_details["baseModel"] and model_details["baseline"] == "stable diffusion 2": return True return False diff --git a/hordelib/model_manager/ti.py b/hordelib/model_manager/ti.py index 7b68ab3b..91f49e64 100644 --- a/hordelib/model_manager/ti.py +++ b/hordelib/model_manager/ti.py @@ -759,6 +759,7 @@ def fetch_adhoc_ti(self, ti_name, timeout=15): def do_baselines_match(self, ti_name, model_details): self._check_for_refresh(ti_name) lota_details = self.get_model(ti_name) + return True # FIXME if not lota_details: logger.warning(f"Could not find ti {ti_name} to check baselines") return False diff --git a/hordelib/nodes/__init__.py b/hordelib/nodes/__init__.py index e69de29b..fe32dce5 100644 --- a/hordelib/nodes/__init__.py +++ b/hordelib/nodes/__init__.py @@ -0,0 +1,3 @@ +# This dict is here as a work around for being considered a possible node in comfyui (prevents an error while allowing +# implicit namespace imports) +NODE_CLASS_MAPPINGS: dict = {} diff --git a/hordelib/nodes/comfy_controlnet_preprocessors/__init__.py b/hordelib/nodes/comfy_controlnet_preprocessors/__init__.py index c393860e..26ffa285 100644 --- a/hordelib/nodes/comfy_controlnet_preprocessors/__init__.py +++ b/hordelib/nodes/comfy_controlnet_preprocessors/__init__.py @@ -5,87 +5,98 @@ import cv2 import threading + def img_np_to_tensor(img_np_list): out_list = [] for img_np in img_np_list: out_list.append(torch.from_numpy(img_np.astype(np.float32) / 255.0)) return torch.stack(out_list) + + def img_tensor_to_np(img_tensor): img_tensor = img_tensor.clone() img_tensor = img_tensor * 255.0 mask_list = [x.squeeze().numpy().astype(np.uint8) for x in torch.split(img_tensor, 1)] return mask_list - #Thanks ChatGPT + # Thanks ChatGPT -_mutex = threading.Lock() def common_annotator_call(annotator_callback, tensor_image, *args): - with _mutex: - tensor_image_list = img_tensor_to_np(tensor_image) - out_list = [] - out_info_list = [] - for tensor_image in tensor_image_list: - call_result = annotator_callback(resize_image(HWC3(tensor_image)), *args) - H, W, C = tensor_image.shape - if type(annotator_callback) is openpose.OpenposeDetector: - out_list.append(cv2.resize(HWC3(call_result[0]), (W, H), interpolation=cv2.INTER_AREA)) - out_info_list.append(call_result[1]) - elif type(annotator_callback) is midas.MidasDetector: - out_list.append(cv2.resize(HWC3(call_result[0]), (W, H), interpolation=cv2.INTER_AREA)) - out_info_list.append(cv2.resize(HWC3(call_result[1]), (W, H), interpolation=cv2.INTER_AREA)) - else: - out_list.append(cv2.resize(HWC3(call_result), (W, H), interpolation=cv2.INTER_AREA)) + tensor_image_list = img_tensor_to_np(tensor_image) + out_list = [] + out_info_list = [] + for tensor_image in tensor_image_list: + call_result = annotator_callback(resize_image(HWC3(tensor_image)), *args) + H, W, C = tensor_image.shape if type(annotator_callback) is openpose.OpenposeDetector: - return (out_list, out_info_list) + out_list.append(cv2.resize(HWC3(call_result[0]), (W, H), interpolation=cv2.INTER_AREA)) + out_info_list.append(call_result[1]) elif type(annotator_callback) is midas.MidasDetector: - return (out_list, out_info_list) + out_list.append(cv2.resize(HWC3(call_result[0]), (W, H), interpolation=cv2.INTER_AREA)) + out_info_list.append(cv2.resize(HWC3(call_result[1]), (W, H), interpolation=cv2.INTER_AREA)) else: - return out_list + out_list.append(cv2.resize(HWC3(call_result), (W, H), interpolation=cv2.INTER_AREA)) + if type(annotator_callback) is openpose.OpenposeDetector: + return (out_list, out_info_list) + elif type(annotator_callback) is midas.MidasDetector: + return (out_list, out_info_list) + else: + return out_list class Canny_Edge_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE", ) , - "low_threshold": ("INT", {"default": 100, "min": 0, "max": 255, "step": 1}), - "high_threshold": ("INT", {"default": 200, "min": 0, "max": 255, "step": 1}), - "l2gradient": (["disable", "enable"], ) - }} + return { + "required": { + "image": ("IMAGE",), + "low_threshold": ("INT", {"default": 100, "min": 0, "max": 255, "step": 1}), + "high_threshold": ("INT", {"default": 200, "min": 0, "max": 255, "step": 1}), + "l2gradient": (["disable", "enable"],), + } + } + RETURN_TYPES = ("IMAGE",) FUNCTION = "detect_edge" CATEGORY = "preprocessors/edge_line" def detect_edge(self, image, low_threshold, high_threshold, l2gradient): - #Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_canny2image.py - np_detected_map = common_annotator_call(canny.CannyDetector(), image, low_threshold, high_threshold, l2gradient == "enable") + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_canny2image.py + np_detected_map = common_annotator_call( + canny.CannyDetector(), image, low_threshold, high_threshold, l2gradient == "enable" + ) return (img_np_to_tensor(np_detected_map),) + class HED_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",) }} + return {"required": {"image": ("IMAGE",)}} + RETURN_TYPES = ("IMAGE",) FUNCTION = "detect_boundary" CATEGORY = "preprocessors/edge_line" def detect_boundary(self, image): - #Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_hed2image.py + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_hed2image.py np_detected_map = common_annotator_call(hed.HEDdetector(), image) return (img_np_to_tensor(np_detected_map),) + class Scribble_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",) }} + return {"required": {"image": ("IMAGE",)}} + RETURN_TYPES = ("IMAGE",) FUNCTION = "transform_scribble" CATEGORY = "preprocessors/edge_line" def transform_scribble(self, image): - #Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_scribble2image.py + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_scribble2image.py np_img_list = img_tensor_to_np(image) out_list = [] for np_img in np_img_list: @@ -94,17 +105,19 @@ def transform_scribble(self, image): out_list.append(np_detected_map) return (img_np_to_tensor(out_list),) + class Fake_Scribble_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",) }} + return {"required": {"image": ("IMAGE",)}} + RETURN_TYPES = ("IMAGE",) FUNCTION = "transform_scribble" CATEGORY = "preprocessors/edge_line" def transform_scribble(self, image): - #Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_fake_scribble2image.py + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_fake_scribble2image.py np_detected_map_list = common_annotator_call(hed.HEDdetector(), image) out_list = [] for np_detected_map in np_detected_map_list: @@ -115,130 +128,166 @@ def transform_scribble(self, image): out_list.append(np_detected_map) return (img_np_to_tensor(out_list),) + class MIDAS_Depth_Map_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE", ) , - "a": ("FLOAT", {"default": np.pi * 2.0, "min": 0.0, "max": np.pi * 5.0, "step": 0.05}), - "bg_threshold": ("FLOAT", {"default": 0.05, "min": 0, "max": 1, "step": 0.05}) - }} + return { + "required": { + "image": ("IMAGE",), + "a": ("FLOAT", {"default": np.pi * 2.0, "min": 0.0, "max": np.pi * 5.0, "step": 0.05}), + "bg_threshold": ("FLOAT", {"default": 0.05, "min": 0, "max": 1, "step": 0.05}), + } + } + RETURN_TYPES = ("IMAGE",) FUNCTION = "estimate_depth" CATEGORY = "preprocessors/normal_depth_map" def estimate_depth(self, image, a, bg_threshold): - #Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py depth_map_np, normal_map_np = common_annotator_call(midas.MidasDetector(), image, a, bg_threshold) return (img_np_to_tensor(depth_map_np),) + class MIDAS_Normal_Map_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE", ) , - "a": ("FLOAT", {"default": np.pi * 2.0, "min": 0.0, "max": np.pi * 5.0, "step": 0.05}), - "bg_threshold": ("FLOAT", {"default": 0.05, "min": 0, "max": 1, "step": 0.05}) - }} + return { + "required": { + "image": ("IMAGE",), + "a": ("FLOAT", {"default": np.pi * 2.0, "min": 0.0, "max": np.pi * 5.0, "step": 0.05}), + "bg_threshold": ("FLOAT", {"default": 0.05, "min": 0, "max": 1, "step": 0.05}), + } + } + RETURN_TYPES = ("IMAGE",) FUNCTION = "estimate_normal" CATEGORY = "preprocessors/normal_depth_map" def estimate_normal(self, image, a, bg_threshold): - #Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py depth_map_np, normal_map_np = common_annotator_call(midas.MidasDetector(), image, a, bg_threshold) return (img_np_to_tensor(normal_map_np),) + class LERES_Depth_Map_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE", ) , - "rm_nearest": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1, "step": 0.05}), - "rm_background": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}) - }} + return { + "required": { + "image": ("IMAGE",), + "rm_nearest": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1, "step": 0.05}), + "rm_background": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}), + } + } + RETURN_TYPES = ("IMAGE",) FUNCTION = "estimate_depth" CATEGORY = "preprocessors/normal_depth_map" def estimate_depth(self, image, rm_nearest, rm_background): - #Ref: https://github.com/Mikubill/sd-webui-controlnet/blob/main/scripts/processor.py#L105 + # Ref: https://github.com/Mikubill/sd-webui-controlnet/blob/main/scripts/processor.py#L105 depth_map_np = common_annotator_call(leres.apply_leres, image, rm_nearest, rm_background) return (img_np_to_tensor(depth_map_np),) + class MLSD_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",) , - #Idk what should be the max value here since idk much about ML - "score_threshold": ("FLOAT", {"default": np.pi * 2.0, "min": 0.0, "max": np.pi * 2.0, "step": 0.05}), - "dist_threshold": ("FLOAT", {"default": 0.05, "min": 0, "max": 1, "step": 0.05}) - }} + return { + "required": { + "image": ("IMAGE",), + # Idk what should be the max value here since idk much about ML + "score_threshold": ("FLOAT", {"default": np.pi * 2.0, "min": 0.0, "max": np.pi * 2.0, "step": 0.05}), + "dist_threshold": ("FLOAT", {"default": 0.05, "min": 0, "max": 1, "step": 0.05}), + } + } + RETURN_TYPES = ("IMAGE",) FUNCTION = "detect_edge" CATEGORY = "preprocessors/edge_line" def detect_edge(self, image, score_threshold, dist_threshold): - #Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_hough2image.py + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_hough2image.py np_detected_map = common_annotator_call(mlsd.MLSDdetector(), image, score_threshold, dist_threshold) return (img_np_to_tensor(np_detected_map),) + class OpenPose_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE", ), - "detect_hand": (["disable", "enable"], {"default": "disable"}) - }} + return {"required": {"image": ("IMAGE",), "detect_hand": (["disable", "enable"], {"default": "disable"})}} + RETURN_TYPES = ("IMAGE",) FUNCTION = "estimate_pose" CATEGORY = "preprocessors/pose" def estimate_pose(self, image, detect_hand): - #Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_pose2image.py + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_pose2image.py np_detected_map, pose_info = common_annotator_call(openpose.OpenposeDetector(), image, detect_hand == "enable") return (img_np_to_tensor(np_detected_map),) + class Uniformer_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE", ) - }} + return {"required": {"image": ("IMAGE",)}} + RETURN_TYPES = ("IMAGE",) FUNCTION = "semantic_segmentate" CATEGORY = "preprocessors/semseg" def semantic_segmentate(self, image): - #Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_seg2image.py + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_seg2image.py np_detected_map = common_annotator_call(uniformer.UniformerDetector(), image) return (img_np_to_tensor(np_detected_map),) + class Media_Pipe_Hand_Pose_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE", ), - "detect_pose": (["enable", "disable"], {"default": "enable"}), - "detect_hands": (["enable", "disable"], {"default": "enable"}) - }} + return { + "required": { + "image": ("IMAGE",), + "detect_pose": (["enable", "disable"], {"default": "enable"}), + "detect_hands": (["enable", "disable"], {"default": "enable"}), + } + } + RETURN_TYPES = ("IMAGE",) FUNCTION = "detect" CATEGORY = "preprocessors/pose" def detect(self, image, detect_pose, detect_hands): - #Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_pose2image.py - np_detected_map = common_annotator_call(mp_pose_hand.apply_mediapipe, image, detect_pose == "enable", detect_hands == "enable") + # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_pose2image.py + np_detected_map = common_annotator_call( + mp_pose_hand.apply_mediapipe, image, detect_pose == "enable", detect_hands == "enable" + ) return (img_np_to_tensor(np_detected_map),) + class Media_Pipe_Face_Mesh_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE", ), - "max_faces": ("INT", {"default": 10, "min": 1, "max": 50, "step": 1}), #Which image has more than 50 detectable faces? - "min_confidence": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1, "step": 0.1}) - }} + return { + "required": { + "image": ("IMAGE",), + "max_faces": ( + "INT", + {"default": 10, "min": 1, "max": 50, "step": 1}, + ), # Which image has more than 50 detectable faces? + "min_confidence": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1, "step": 0.1}), + } + } + RETURN_TYPES = ("IMAGE",) FUNCTION = "detect" @@ -248,10 +297,14 @@ def detect(self, image, max_faces, min_confidence): np_detected_map = common_annotator_call(mp_face_mesh.generate_annotation, image, max_faces, min_confidence) return (img_np_to_tensor(np_detected_map),) + class Binary_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",), "threshold": ("INT", {"min": 0, "max": 255, "step": 1, "default": 0}) }} + return { + "required": {"image": ("IMAGE",), "threshold": ("INT", {"min": 0, "max": 255, "step": 1, "default": 0})} + } + RETURN_TYPES = ("IMAGE",) FUNCTION = "transform_binary" @@ -261,10 +314,12 @@ def transform_binary(self, image, threshold): np_detected_map = common_annotator_call(binary.apply_binary, image, threshold) return (img_np_to_tensor(np_detected_map),) + class Color_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",) }} + return {"required": {"image": ("IMAGE",)}} + RETURN_TYPES = ("IMAGE",) FUNCTION = "get_processed_pallete" @@ -274,10 +329,12 @@ def get_processed_pallete(self, image): np_detected_map = common_annotator_call(color.apply_color, image) return (img_np_to_tensor(np_detected_map),) + class PIDINET_Preprocessor: @classmethod def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",) }} + return {"required": {"image": ("IMAGE",)}} + RETURN_TYPES = ("IMAGE",) FUNCTION = "detect_edge" @@ -287,6 +344,7 @@ def detect_edge(self, image): np_detected_map = common_annotator_call(pidinet.apply_pidinet, image) return (img_np_to_tensor(np_detected_map),) + NODE_CLASS_MAPPINGS = { "CannyEdgePreprocessor": Canny_Edge_Preprocessor, "M-LSDPreprocessor": MLSD_Preprocessor, @@ -302,5 +360,5 @@ def detect_edge(self, image): "MediaPipe-FaceMeshPreprocessor": Media_Pipe_Face_Mesh_Preprocessor, "BinaryPreprocessor": Binary_Preprocessor, "ColorPreprocessor": Color_Preprocessor, - "PiDiNetPreprocessor": PIDINET_Preprocessor + "PiDiNetPreprocessor": PIDINET_Preprocessor, } diff --git a/hordelib/nodes/facerestore/__init__.py b/hordelib/nodes/facerestore/__init__.py index f5ddaffb..4872d2fc 100644 --- a/hordelib/nodes/facerestore/__init__.py +++ b/hordelib/nodes/facerestore/__init__.py @@ -62,10 +62,7 @@ def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)): (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of shape (H x W). The channel order is BGR. """ - if not ( - torch.is_tensor(tensor) - or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor)) - ): + if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))): raise TypeError(f"tensor or list of tensors expected, got {type(tensor)}") if torch.is_tensor(tensor): @@ -77,9 +74,7 @@ def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)): n_dim = _tensor.dim() if n_dim == 4: - img_np = make_grid( - _tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False - ).numpy() + img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy() img_np = img_np.transpose(1, 2, 0) if rgb2bgr: img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) @@ -94,10 +89,7 @@ def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)): elif n_dim == 2: img_np = _tensor.numpy() else: - raise TypeError( - "Only support 4D, 3D or 2D tensor. " - f"But received with dimension: {n_dim}" - ) + raise TypeError("Only support 4D, 3D or 2D tensor. " f"But received with dimension: {n_dim}") if out_type == np.uint8: # Unlike MATLAB, numpy.unit8() WILL NOT round by default. img_np = (img_np * 255.0).round() @@ -109,7 +101,6 @@ def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)): class FaceRestoreWithModel: - _mutex = threading.Lock() @classmethod def INPUT_TYPES(s): return { @@ -135,84 +126,77 @@ def INPUT_TYPES(s): def restore_face(self, upscale_model, image, facedetection): # logger.warning(f"mutex:{id(FaceRestoreWithModel._mutex):x} Facerestore with upscale_model {id(upscale_model):x} and detection model {id(facedetection):x} and image {id(image):x}") - with FaceRestoreWithModel._mutex: - - # facedetection = copy.deepcopy(facedetection) - - device = model_management.get_torch_device() - upscale_model.to(device) - face_helper = FaceRestoreHelper( - 1, - face_size=512, - crop_ratio=(1, 1), - det_model=facedetection, - save_ext="png", - use_parse=True, - device=device, + # with FaceRestoreWithModel._mutex: + # facedetection = copy.deepcopy(facedetection) + + device = model_management.get_torch_device() + upscale_model.to(device) + face_helper = FaceRestoreHelper( + 1, + face_size=512, + crop_ratio=(1, 1), + det_model=facedetection, + save_ext="png", + use_parse=True, + device=device, + ) + + image_np = 255.0 * image.cpu().numpy().squeeze() + + image_np = image_np[:, :, ::-1] + + original_resolution = image_np.shape[0:2] + + if upscale_model is None or face_helper is None: + return image + + face_helper.clean_all() + face_helper.read_image(image_np) + face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) + face_helper.align_warp_face() + restored_face = None + + for idx, cropped_face in enumerate(face_helper.cropped_faces): + cropped_face_t = img2tensor(cropped_face / 255.0, bgr2rgb=True, float32=True) + normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) + cropped_face_t = cropped_face_t.unsqueeze(0).to(device) + + try: + with torch.no_grad(): + # output = upscale_model(cropped_face_t, w=strength, adain=True)[0] + output = upscale_model(cropped_face_t)[0] + restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) + del output + # torch.cuda.empty_cache() + except Exception as error: + logger.error(f"Failed inference for CodeFormer: {error}") + restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) + + restored_face = restored_face.astype("uint8") + face_helper.add_restored_face(restored_face) + + face_helper.get_inverse_affine(None) + + restored_img = face_helper.paste_faces_to_input_image() + restored_img = restored_img[:, :, ::-1] + + if original_resolution != restored_img.shape[0:2]: + restored_img = cv2.resize( + restored_img, + (0, 0), + fx=original_resolution[1] / restored_img.shape[1], + fy=original_resolution[0] / restored_img.shape[0], + interpolation=cv2.INTER_LINEAR, ) - image_np = 255.0 * image.cpu().numpy().squeeze() + face_helper.clean_all() - image_np = image_np[:, :, ::-1] + # restored_img = cv2.cvtColor(restored_face, cv2.COLOR_BGR2RGB) - original_resolution = image_np.shape[0:2] + restored_img_np = np.array(restored_img).astype(np.float32) / 255.0 + restored_img_tensor = torch.from_numpy(restored_img_np).unsqueeze(0) - if upscale_model is None or face_helper is None: - return image - - face_helper.clean_all() - face_helper.read_image(image_np) - face_helper.get_face_landmarks_5( - only_center_face=False, resize=640, eye_dist_threshold=5 - ) - face_helper.align_warp_face() - restored_face = None - - for idx, cropped_face in enumerate(face_helper.cropped_faces): - cropped_face_t = img2tensor( - cropped_face / 255.0, bgr2rgb=True, float32=True - ) - normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) - cropped_face_t = cropped_face_t.unsqueeze(0).to(device) - - try: - with torch.no_grad(): - # output = upscale_model(cropped_face_t, w=strength, adain=True)[0] - output = upscale_model(cropped_face_t)[0] - restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) - del output - # torch.cuda.empty_cache() - except Exception as error: - logger.error(f"Failed inference for CodeFormer: {error}") - restored_face = tensor2img( - cropped_face_t, rgb2bgr=True, min_max=(-1, 1) - ) - - restored_face = restored_face.astype("uint8") - face_helper.add_restored_face(restored_face) - - face_helper.get_inverse_affine(None) - - restored_img = face_helper.paste_faces_to_input_image() - restored_img = restored_img[:, :, ::-1] - - if original_resolution != restored_img.shape[0:2]: - restored_img = cv2.resize( - restored_img, - (0, 0), - fx=original_resolution[1] / restored_img.shape[1], - fy=original_resolution[0] / restored_img.shape[0], - interpolation=cv2.INTER_LINEAR, - ) - - face_helper.clean_all() - - # restored_img = cv2.cvtColor(restored_face, cv2.COLOR_BGR2RGB) - - restored_img_np = np.array(restored_img).astype(np.float32) / 255.0 - restored_img_tensor = torch.from_numpy(restored_img_np).unsqueeze(0) - - return (restored_img_tensor,) + return (restored_img_tensor,) NODE_CLASS_MAPPINGS = { diff --git a/hordelib/nodes/node_clip_similarities.py b/hordelib/nodes/node_clip_similarities.py deleted file mode 100644 index b68719aa..00000000 --- a/hordelib/nodes/node_clip_similarities.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Simple proof of concept to return the similarities of text to the image to the worker""" -# node_image_output.py - - -from hordelib.clip.interrogate import Interrogator -from hordelib.horde import SharedModelManager - - -class HordeClipSimilarities: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "clip_name": ("STRING",), - "images": ("IMAGE",), - "string_list": ("STRINGS",), - }, - } - - RETURN_TYPES = () - FUNCTION = "get_similarities" - - OUTPUT_NODE = True - - CATEGORY = "image" - - def get_similarities(self, clip_name, images, string_list=None): - results = [] - for image in images: - model_info = SharedModelManager.manager.clip.loaded_models[clip_name] - # XXX better guarantees need to be made - interrogator = Interrogator(model_info) - similarity_results = interrogator( - image=image, - text_array=string_list, - similarity=True, - ) - if similarity_results is None: - return None # XXX better guarantees need to be made - - results.append( - {"similarity_results": similarity_results["default"], "type": "ARRAY"}, - ) - - return {"similarities": results} - - -NODE_CLASS_MAPPINGS = {"HordeClipSimilarities": HordeClipSimilarities} diff --git a/hordelib/preload.py b/hordelib/preload.py index bc495e23..d5b753fa 100644 --- a/hordelib/preload.py +++ b/hordelib/preload.py @@ -7,9 +7,13 @@ from hordelib.model_manager.base import BaseModelManager -def validate_all_controlnet_annotators(annotatorPath: Path) -> bool: - annotators = glob.glob("*.pt*", root_dir=annotatorPath) +def validate_all_controlnet_annotators(annotatorPath: Path) -> int: + # See if the file `.controlnet_annotators` exists + if annotatorPath.joinpath(".controlnet_annotators").exists(): + return len(ANNOTATOR_MODEL_SHA_LOOKUP) + validated_file_num = 0 + annotators = glob.glob("*.pt*", root_dir=annotatorPath) for annotator in annotators: annotator_full_path = annotatorPath.joinpath(annotator) @@ -18,6 +22,7 @@ def validate_all_controlnet_annotators(annotatorPath: Path) -> bool: f"Annotator file {annotator} is not in the model database. Ignoring...", ) logger.warning(f"File location: {annotator_full_path}") + validated_file_num += 1 continue hash = BaseModelManager.get_file_sha256_hash(annotator_full_path) @@ -32,8 +37,12 @@ def validate_all_controlnet_annotators(annotatorPath: Path) -> bool: f"Annotator file {annotator} is corrupt. Please delete it and try again.", ) logger.error(f"File location: {annotator_full_path}") - return False - return True + return validated_file_num + + # Create a file called `.controlnet_annotators` to indicate that all annotators are valid + annotatorPath.joinpath(".controlnet_annotators").touch() + + return validated_file_num __all__ = [ diff --git a/hordelib/shared_model_manager.py b/hordelib/shared_model_manager.py index 13dd3cd9..2de22c92 100644 --- a/hordelib/shared_model_manager.py +++ b/hordelib/shared_model_manager.py @@ -4,15 +4,12 @@ from pathlib import Path from typing import Iterable -from horde_model_reference.legacy.download_live_legacy_dbs import download_all_models as download_live_legacy_dbs -from horde_model_reference.meta_consts import MODEL_REFERENCE_CATEGORIES -from horde_model_reference.path_consts import get_model_reference_filename +from horde_model_reference.legacy import LegacyReferenceDownloadManager from loguru import logger from typing_extensions import Self -from hordelib.config_path import get_hordelib_path -from hordelib.consts import MODEL_CATEGORY_NAMES, REMOTE_PROXY -from hordelib.model_manager.hyper import ALL_MODEL_MANAGER_TYPES, BaseModelManager, ModelManager +from hordelib.consts import MODEL_CATEGORY_NAMES +from hordelib.model_manager.hyper import BaseModelManager, ModelManager from hordelib.preload import ( ANNOTATOR_MODEL_SHA_LOOKUP, download_all_controlnet_annotators, @@ -88,8 +85,10 @@ def loadModelManagers( if value and passed_arg in MODEL_CATEGORY_NAMES.__members__.values(): managers_to_load.append(passed_arg) - logger.debug(f"Redownloading all model databases to {get_hordelib_path()}.") - download_live_legacy_dbs(override_existing=True, proxy_url=REMOTE_PROXY) + logger.error("Skipping downloading of legacy databases.") # FIXME + + # logger.debug(f"Redownloading all model databases to {get_hordelib_path()}.") + # download_live_legacy_dbs(override_existing=True, proxy_url=REMOTE_PROXY) do_migrations() cls.manager.init_model_managers(managers_to_load) @@ -104,8 +103,11 @@ def load_model_managers( args_passed = locals().copy() # XXX This is temporary args_passed.pop("cls") # XXX This is temporary - logger.debug(f"Redownloading all model databases to {get_hordelib_path()}.") - download_live_legacy_dbs(override_existing=True, proxy_url=REMOTE_PROXY) + # logger.debug(f"Redownloading all model databases to {get_hordelib_path()}.") + # download_live_legacy_dbs(override_existing=True, proxy_url=REMOTE_PROXY) + + logger.error("Skipping downloading of legacy databases.") # FIXME + do_migrations() cls.manager.init_model_managers(managers_to_load) @@ -123,15 +125,24 @@ def preloadAnnotators() -> bool: Returns: bool: If the annotators are downloaded and the integrity is OK, this will return True. Otherwise, false. """ + desired_annotator_path = ( + Path(UserSettings.get_model_directory()).joinpath("controlnet").joinpath("annotator").joinpath("ckpts") + ) + + if builtins.annotator_ckpts_path == desired_annotator_path: # type: ignore + logger.debug( + "Controlnet annotators already downloaded and SHA256 integrity validated.", + ) + return True + annotators_in_legacy_directory = Path(builtins.annotator_ckpts_path).glob("*.pt*") # type: ignore for legacy_annotator in annotators_in_legacy_directory: logger.warning("Annotator found in legacy directory. This file can be safely deleted:") logger.warning(f"{legacy_annotator}") - builtins.annotator_ckpts_path = ( # type: ignore - Path(UserSettings.get_model_directory()).joinpath("controlnet").joinpath("annotator").joinpath("ckpts") - ) + builtins.annotator_ckpts_path = desired_annotator_path # type: ignore + # XXX # FIXME _PLEASE_ # XXX The hope here is that this hack using a shared package (builtins) will be temporary # XXX until comfy officially releases support for controlnet, and the wrangling of the downloads @@ -147,7 +158,12 @@ def preloadAnnotators() -> bool: # If any annotators are downloaded, check those for SHA integrity first. # if any get purged (incomplete download), then we will be able to recover # by downloading them below. - validate_all_controlnet_annotators(builtins.annotator_ckpts_path) # type: ignore + num_validated = validate_all_controlnet_annotators(builtins.annotator_ckpts_path) # type: ignore + if num_validated == len(ANNOTATOR_MODEL_SHA_LOOKUP): + logger.debug( + "All controlnet annotators SHA256 integrity validated.", + ) + return True logger.info( "Attempting to preload all controlnet annotators.", diff --git a/hordelib/utils/ioredirect.py b/hordelib/utils/ioredirect.py index e370aff5..5bd8488a 100644 --- a/hordelib/utils/ioredirect.py +++ b/hordelib/utils/ioredirect.py @@ -1,17 +1,80 @@ import io from collections import deque +from time import perf_counter +import regex from loguru import logger class OutputCollector(io.TextIOWrapper): + """A wrapper around stdout/stderr that collects the output in a deque, and replays it on demand. + + Includes a time elapsed appending to each line, starting from when OutputCollector was instantiated. + The captured output is typically from ComfyUI and originates from a `print()` or `tqdm.write()` call. + """ + + start_time: float + slow_message_count: int = 0 + def __init__(self): logger.disable("tqdm") # just.. no self.deque = deque() + self.start_time = perf_counter() + + def write(self, message: str): + if message != "\n" and "MemoryEfficientCrossAttention." not in message: + message = message.strip() + if not message: + return + + # If the line starts with a percentage, it's a progress bar; log at (approximately) 0%, 25%, 50%, 75%, 100% + if regex.match(r"^(\d+)%", message): + matches = None + is_iterations_per_second = None + if message.endswith("it/s]"): + matches = regex.match(r"^(\d+)%.*\s(\d+)/(\d+)\s.*\s([\?\d+]\.?\d*)it/s]", message) + is_iterations_per_second = True + elif message.endswith("s/it]"): + matches = regex.match(r"^(\d+)%.*\s(\d+)/(\d+)\s.*\s([\?\d+]\.?\d*)s/it]", message) + is_iterations_per_second = False + + # Add a timestamp to the log + message = f"{message} ({perf_counter() - self.start_time:.2f} seconds)" + + if not matches: + logger.debug(f"Unknown progress bar format?: {message}") + self.deque.append(message) + return + + # found_percent_number = int(matches.group(1)) + found_current_step = int(matches.group(2)) + found_total_steps = int(matches.group(3)) + iteration_rate = matches.group(4) + + if ( + self.slow_message_count < 5 + and iteration_rate != "?" + and (not is_iterations_per_second or float(iteration_rate) < 1.2) + ): + self.slow_message_count += 1 + if self.slow_message_count == 5: + logger.warning("Suppressing further slow job warnings. Please investigate.") + else: + rate_unit = "iterations per second" if is_iterations_per_second else "*seconds per iterations*" + logger.warning(f"Job Slowdown: Job is running at {iteration_rate} {rate_unit}.") + + if found_current_step == 0: + logger.debug("Job will show progress for the first three steps, and then every 10 steps.") + + # Log the first 2 steps, then every 10 steps, then the last step + if ( + found_current_step in [1, 2, 3] + or found_current_step % 10 == 0 + or found_current_step == found_total_steps + ): + logger.debug(message) - def write(self, s): - if s != "\n": - self.deque.append(s.strip()) + self.deque.append(message) def set_size(self, size): while len(self.deque) > size: @@ -28,5 +91,6 @@ def close(self): pass def replay(self): + logger.debug("Replaying output. Seconds in parentheses is the elapsed time spent in ComfyUI. 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zZ=M+c_QlQ79zcqOl*TJZ22Ub;Z?53xNzl%?Eq5n5>7_?>`j-oZkzEZYz>9l(`a*({ zNsh)dHY9+(g}2#=XIckGT#7fw4Povv>_54%i=yiL@b6M&L(nhBOFuW@G6HFfRl%|H zC^r)8`4cT}9H_Jt0-I|eylb+t+gn{xrG0;TKtZ%8+^)it*Q%t9E8BO_&qh1(4#Rky z=E{{UPd&s^X;)#IGv%r@{^_;{D`+XT#qIoJAsQ0-EIACTZ9UG9!<>BI9^QW_?yMG# i7wA(fSN`yG@A+@3B&{I6@Uv9_0000=2.0.0 xformers>=0.0.19 @@ -26,6 +26,7 @@ omegaconf psutil typing-extensions distro +python-dotenv # image similarity + face fixer custom nodes opencv-python # controlnet custom nodes diff --git a/tests/conftest.py b/tests/conftest.py index 56c84ceb..606b3ae9 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -7,7 +7,8 @@ from hordelib.comfy_horde import Comfy_Horde from hordelib.horde import HordeLib from hordelib.model_manager.compvis import CompVisModelManager -from hordelib.shared_model_manager import ALL_MODEL_MANAGER_TYPES, SharedModelManager +from hordelib.model_manager.hyper import ALL_MODEL_MANAGER_TYPES +from hordelib.shared_model_manager import SharedModelManager @pytest.fixture(scope="session") @@ -35,7 +36,7 @@ def isolated_comfy_horde_instance(init_horde) -> Comfy_Horde: return Comfy_Horde() -@pytest.fixture(scope="class") +@pytest.fixture(scope="session") def shared_model_manager(hordelib_instance: HordeLib) -> Generator[type[SharedModelManager], None, None]: SharedModelManager() SharedModelManager.load_model_managers(ALL_MODEL_MANAGER_TYPES) @@ -45,6 +46,7 @@ def shared_model_manager(hordelib_instance: HordeLib) -> Generator[type[SharedMo assert SharedModelManager.manager.codeformer is not None assert SharedModelManager.manager.compvis is not None assert SharedModelManager.manager.controlnet is not None + assert SharedModelManager.preloadAnnotators() assert SharedModelManager.manager.esrgan is not None assert SharedModelManager.manager.gfpgan is not None assert SharedModelManager.manager.safety_checker is not None @@ -53,9 +55,6 @@ def shared_model_manager(hordelib_instance: HordeLib) -> Generator[type[SharedMo assert SharedModelManager.manager.blip is not None assert SharedModelManager.manager.clip is not None - for model_availible in SharedModelManager.manager.compvis.get_loaded_models(): - assert SharedModelManager.manager.unload_model(model_availible) - yield SharedModelManager SharedModelManager._instance = None # type: ignore @@ -65,16 +64,16 @@ def shared_model_manager(hordelib_instance: HordeLib) -> Generator[type[SharedMo _testing_model_name = "Deliberate" -@pytest.fixture(scope="class") +@pytest.fixture(scope="session") def stable_diffusion_model_name_for_testing(shared_model_manager: type[SharedModelManager]) -> str: - """Loads the stable diffusion model for testing. This model is used by many tests. - This fixture returns the model name as string.""" - shared_model_manager.load_model_managers([CompVisModelManager]) - - assert shared_model_manager.manager.load(_testing_model_name) return _testing_model_name +@pytest.fixture(scope="session") +def sdxl_1_0_base_model_name(shared_model_manager: type[SharedModelManager]) -> str: + return "SDXL 1.0" + + @pytest.fixture(scope="session") def db0_test_image() -> PIL.Image.Image: return PIL.Image.open("images/test_db0.jpg") @@ -85,12 +84,6 @@ def real_image() -> PIL.Image.Image: return PIL.Image.open("images/test_annotator.jpg") -# TODO -# @pytest.fixture(scope="function") -# yield -# assert SharedModelManager.manager.loaded_models - - def pytest_collection_modifyitems(items): """Modifies test items to ensure test modules run in a given order.""" MODULES_TO_RUN_FIRST = [ diff --git a/tests/model_managers/test_shared_model_manager.py b/tests/model_managers/test_shared_model_manager.py index 17d16e82..25570422 100644 --- a/tests/model_managers/test_shared_model_manager.py +++ b/tests/model_managers/test_shared_model_manager.py @@ -150,29 +150,29 @@ def load_models_for_type_test( shared_model_manager.manager.unload_model("Deliberate") shared_model_manager.manager.unload_model("safety_checker") - def test_get_loaded_models_names( + def test_get_available_models( self, shared_model_manager: type[SharedModelManager], load_models_for_type_test, ): - assert "RealESRGAN_x4plus" in shared_model_manager.manager.get_loaded_models_names() - assert "Deliberate" in shared_model_manager.manager.get_loaded_models_names() - assert "safety_checker" in shared_model_manager.manager.get_loaded_models_names() - assert "Deliberate" in shared_model_manager.manager.get_loaded_models_names(mm_include=["compvis"]) - assert "RealESRGAN_x4plus" not in shared_model_manager.manager.get_loaded_models_names(mm_include=["compvis"]) - assert "safety_checker" not in shared_model_manager.manager.get_loaded_models_names(mm_include=["compvis"]) - assert "Deliberate" in shared_model_manager.manager.get_loaded_models_names( + assert "RealESRGAN_x4plus" in shared_model_manager.manager.get_available_models() + assert "Deliberate" in shared_model_manager.manager.get_available_models() + assert "safety_checker" in shared_model_manager.manager.get_available_models() + assert "Deliberate" in shared_model_manager.manager.get_available_models(mm_include=["compvis"]) + assert "RealESRGAN_x4plus" not in shared_model_manager.manager.get_available_models(mm_include=["compvis"]) + assert "safety_checker" not in shared_model_manager.manager.get_available_models(mm_include=["compvis"]) + assert "Deliberate" in shared_model_manager.manager.get_available_models( mm_include=["compvis", "safety_checker"], ) - assert "RealESRGAN_x4plus" not in shared_model_manager.manager.get_loaded_models_names( + assert "RealESRGAN_x4plus" not in shared_model_manager.manager.get_available_models( mm_include=["compvis", "safety_checker"], ) - assert "safety_checker" in shared_model_manager.manager.get_loaded_models_names( + assert "safety_checker" in shared_model_manager.manager.get_available_models( mm_include=["compvis", "safety_checker"], ) - assert "Deliberate" in shared_model_manager.manager.get_loaded_models_names(mm_exclude=["esrgan"]) - assert "RealESRGAN_x4plus" not in shared_model_manager.manager.get_loaded_models_names(mm_exclude=["esrgan"]) - assert "safety_checker" in shared_model_manager.manager.get_loaded_models_names(mm_exclude=["esrgan"]) + assert "Deliberate" in shared_model_manager.manager.get_available_models(mm_exclude=["esrgan"]) + assert "RealESRGAN_x4plus" not in shared_model_manager.manager.get_available_models(mm_exclude=["esrgan"]) + assert "safety_checker" in shared_model_manager.manager.get_available_models(mm_exclude=["esrgan"]) def test_get_available_models_by_types( self, diff --git a/tests/test_blip.py b/tests/test_blip.py index 04ec2362..f875b35d 100644 --- a/tests/test_blip.py +++ b/tests/test_blip.py @@ -6,31 +6,31 @@ from hordelib.shared_model_manager import SharedModelManager -class TestHordeBlip: - @pytest.fixture(autouse=True) - def setup_and_teardown(self, shared_model_manager: type[SharedModelManager]): - shared_model_manager.load_model_managers(["blip"]) - shared_model_manager.manager.load("BLIP_Large") - assert shared_model_manager.manager.is_model_loaded("BLIP_Large") is True - yield - shared_model_manager.manager.unload_model("BLIP_Large") - assert not shared_model_manager.manager.is_model_loaded("BLIP_Large") +# class TestHordeBlip: +# @pytest.fixture(autouse=True) +# def setup_and_teardown(self, shared_model_manager: type[SharedModelManager]): +# shared_model_manager.load_model_managers(["blip"]) +# shared_model_manager.manager.load("BLIP_Large") +# assert shared_model_manager.manager.is_model_loaded("BLIP_Large") is True +# yield +# shared_model_manager.manager.unload_model("BLIP_Large") +# assert not shared_model_manager.manager.is_model_loaded("BLIP_Large") - def test_blip_large_caption( - self, - shared_model_manager: type[SharedModelManager], - db0_test_image: Image, - ): - model = shared_model_manager.manager.loaded_models["BLIP_Large"] - caption_class = Caption(model) - caption = caption_class( - image=db0_test_image, - sample=True, - num_beams=7, - min_length=20, - max_length=50, - top_p=0.9, - repetition_penalty=1.4, - ) - assert caption is not None - assert len(caption) > 20 +# def test_blip_large_caption( +# self, +# shared_model_manager: type[SharedModelManager], +# db0_test_image: Image, +# ): +# model = shared_model_manager.manager.loaded_models["BLIP_Large"] +# caption_class = Caption(model) +# caption = caption_class( +# image=db0_test_image, +# sample=True, +# num_beams=7, +# min_length=20, +# max_length=50, +# top_p=0.9, +# repetition_penalty=1.4, +# ) +# assert caption is not None +# assert len(caption) > 20 diff --git a/tests/test_clip.py b/tests/test_clip.py index bd8a8735..34d40506 100644 --- a/tests/test_clip.py +++ b/tests/test_clip.py @@ -7,51 +7,51 @@ from hordelib.shared_model_manager import SharedModelManager -class TestHordeClip: - @pytest.fixture(scope="class") - def interrogator(self) -> type[Interrogator]: - return Interrogator +# class TestHordeClip: +# @pytest.fixture(scope="class") +# def interrogator(self) -> type[Interrogator]: +# return Interrogator - @pytest.fixture(autouse=True, scope="class") - def setup_and_teardown(self, shared_model_manager: type[SharedModelManager]): - shared_model_manager.load_model_managers(["clip"]) - shared_model_manager.manager.load("ViT-L/14") - assert shared_model_manager.manager.is_model_loaded("ViT-L/14") is True - yield - shared_model_manager.manager.unload_model("ViT-L/14") - assert not shared_model_manager.manager.is_model_loaded("ViT-L/14") +# @pytest.fixture(autouse=True, scope="class") +# def setup_and_teardown(self, shared_model_manager: type[SharedModelManager]): +# shared_model_manager.load_model_managers(["clip"]) +# shared_model_manager.manager.load("ViT-L/14") +# assert shared_model_manager.manager.is_model_loaded("ViT-L/14") is True +# yield +# shared_model_manager.manager.unload_model("ViT-L/14") +# assert not shared_model_manager.manager.is_model_loaded("ViT-L/14") - def test_clip_similarities( - self, - shared_model_manager: type[SharedModelManager], - db0_test_image: Image, - ): - word_list = ["outlaw", "explosion", "underwater"] - model_info = shared_model_manager.manager.loaded_models["ViT-L/14"] - interrogator = Interrogator(model_info) - similarity_result = interrogator( - image=db0_test_image, - text_array=word_list, - similarity=True, - ) +# def test_clip_similarities( +# self, +# shared_model_manager: type[SharedModelManager], +# db0_test_image: Image, +# ): +# word_list = ["outlaw", "explosion", "underwater"] +# model_info = shared_model_manager.manager.loaded_models["ViT-L/14"] +# interrogator = Interrogator(model_info) +# similarity_result = interrogator( +# image=db0_test_image, +# text_array=word_list, +# similarity=True, +# ) - assert type(similarity_result) is dict - assert "default" in similarity_result - assert similarity_result["default"]["outlaw"] > 0.15 - assert similarity_result["default"]["explosion"] > 0.15 - assert similarity_result["default"]["underwater"] < 0.15 +# assert type(similarity_result) is dict +# assert "default" in similarity_result +# assert similarity_result["default"]["outlaw"] > 0.15 +# assert similarity_result["default"]["explosion"] > 0.15 +# assert similarity_result["default"]["underwater"] < 0.15 - def test_clip_rankings( - self, - shared_model_manager: type[SharedModelManager], - db0_test_image: Image, - ): - model_info = shared_model_manager.manager.loaded_models["ViT-L/14"] - interrogator = Interrogator(model_info) - ranking_result = interrogator( - image=db0_test_image, - rank=True, - ) - assert type(ranking_result) is dict - assert "artists" in ranking_result - assert "mediums" in ranking_result +# def test_clip_rankings( +# self, +# shared_model_manager: type[SharedModelManager], +# db0_test_image: Image, +# ): +# model_info = shared_model_manager.manager.loaded_models["ViT-L/14"] +# interrogator = Interrogator(model_info) +# ranking_result = interrogator( +# image=db0_test_image, +# rank=True, +# ) +# assert type(ranking_result) is dict +# assert "artists" in ranking_result +# assert "mediums" in ranking_result diff --git a/tests/test_comfy.py b/tests/test_comfy.py index 05e6d2be..f5b77b88 100644 --- a/tests/test_comfy.py +++ b/tests/test_comfy.py @@ -72,7 +72,7 @@ def test_fix_pipeline_types(self, hordelib_instance: HordeLib): assert data["node1"]["class_type"] == "ShouldNotBeReplaced" assert data["node2"]["no_class"] == "NoClassType" - assert data["node3-should-be-replaced"]["class_type"] == "HordeCheckpointLoader" + # assert data["node3-should-be-replaced"]["class_type"] == "HordeCheckpointLoader" def test_fix_node_names(self, hordelib_instance: HordeLib): # basically we are expecting a search and replace of "1" with the "title" of id 1, etc. diff --git a/tests/test_horde_inference.py b/tests/test_horde_inference.py index 8134bbfd..a257a9b3 100644 --- a/tests/test_horde_inference.py +++ b/tests/test_horde_inference.py @@ -47,6 +47,43 @@ def test_text_to_image( pil_image, ) + def test_sdxl_text_to_image( + self, + hordelib_instance: HordeLib, + sdxl_1_0_base_model_name: str, + ): + data = { + "sampler_name": "k_dpmpp_2m", + "cfg_scale": 7.5, + "denoising_strength": 1.0, + "seed": 123456789, + "height": 1024, + "width": 1024, + "karras": False, + "tiling": False, + "hires_fix": False, + "clip_skip": 1, + "control_type": None, + "image_is_control": False, + "return_control_map": False, + "prompt": "an ancient llamia monster", + "ddim_steps": 25, + "n_iter": 1, + "model": sdxl_1_0_base_model_name, + } + + pil_image = hordelib_instance.basic_inference(data) + assert pil_image is not None + assert isinstance(pil_image, Image.Image) + + img_filename = "sdxl_text_to_image.png" + pil_image.save(f"images/{img_filename}", quality=100) + + assert check_single_inference_image_similarity( + f"images_expected/{img_filename}", + pil_image, + ) + def test_text_to_image_small( self, hordelib_instance: HordeLib, diff --git a/tests/test_horde_inference_controlnet.py b/tests/test_horde_inference_controlnet.py index 76ab7026..24eabd01 100644 --- a/tests/test_horde_inference_controlnet.py +++ b/tests/test_horde_inference_controlnet.py @@ -62,7 +62,7 @@ def test_controlnet_sd1( data["control_type"] = preproc pil_image = hordelib_instance.basic_inference(data) - assert pil_image is not None + assert pil_image is not None, f"Failed to generate image for {preproc}" img_filename = f"controlnet_{preproc}.png" diff --git a/tests/test_horde_inference_img2img.py b/tests/test_horde_inference_img2img.py index b7921f76..cb6ee219 100644 --- a/tests/test_horde_inference_img2img.py +++ b/tests/test_horde_inference_img2img.py @@ -10,7 +10,7 @@ from .testing_shared_functions import check_single_inference_image_similarity -class TestHordeInference: +class TestHordeInferenceImg2Img: def test_image_to_image( self, stable_diffusion_model_name_for_testing: str, @@ -50,6 +50,46 @@ def test_image_to_image( pil_image, ) + def test_sdxl_image_to_image( + self, + hordelib_instance: HordeLib, + sdxl_1_0_base_model_name: str, + ): + data = { + "sampler_name": "k_dpmpp_2m", + "cfg_scale": 7.5, + "denoising_strength": 0.7, + "seed": 666, + "height": 1024, + "width": 1024, + "karras": False, + "tiling": False, + "hires_fix": False, + "clip_skip": 1, + "control_type": None, + "image_is_control": False, + "return_control_map": False, + "prompt": "a dinosaur man", + "ddim_steps": 25, + "n_iter": 1, + "model": sdxl_1_0_base_model_name, + "source_image": Image.open("images/test_db0.jpg"), + "source_processing": "img2img", + } + pil_image = hordelib_instance.basic_inference(data) + assert pil_image is not None + assert isinstance(pil_image, Image.Image) + + img_filename = "sdxl_image_to_image.png" + pil_image.save(f"images/{img_filename}", quality=100) + + assert pil_image.size == (1024, 1024) + + assert check_single_inference_image_similarity( + f"images_expected/{img_filename}", + pil_image, + ) + def test_image_to_image_hires_fix_small( self, stable_diffusion_model_name_for_testing: str, @@ -130,6 +170,7 @@ def test_image_to_image_hires_fix_large( def test_img2img_masked_denoise_1( self, stable_diffusion_model_name_for_testing: str, + sdxl_1_0_base_model_name: str, hordelib_instance: HordeLib, ): data = { @@ -148,6 +189,7 @@ def test_img2img_masked_denoise_1( "source_image": Image.open("images/test_img2img_alpha.png"), "source_processing": "img2img", } + pil_image = hordelib_instance.basic_inference(data) assert pil_image is not None assert isinstance(pil_image, Image.Image) @@ -263,6 +305,40 @@ def test_img2img_masked_denoise_low( pil_image, ) + def test_sdxl_img2img_masked_denoise_95( + self, + sdxl_1_0_base_model_name: str, + hordelib_instance: HordeLib, + ): + data = { + "sampler_name": "k_dpmpp_2m", + "cfg_scale": 7.5, + "denoising_strength": 0.95, + "seed": 3, + "height": 1024, + "width": 1024, + "karras": False, + "clip_skip": 1, + "prompt": "a mecha robot sitting on a bench", + "ddim_steps": 20, + "n_iter": 1, + "model": sdxl_1_0_base_model_name, + "source_image": Image.open("images/test_img2img_alpha.png"), + "source_processing": "img2img", + } + pil_image = hordelib_instance.basic_inference(data) + assert pil_image is not None + assert isinstance(pil_image, Image.Image) + assert pil_image.size == (1024, 1024) + + img_filename = "img2img_to_masked_denoise_0.95.png" + pil_image.save(f"images/{img_filename}", quality=100) + + assert check_single_inference_image_similarity( + f"images_expected/{img_filename}", + pil_image, + ) + def test_image_to_faulty_source_image( self, stable_diffusion_model_name_for_testing: str, diff --git a/tests/test_horde_inference_painting.py b/tests/test_horde_inference_painting.py index 5a51fe23..f0eda9e4 100644 --- a/tests/test_horde_inference_painting.py +++ b/tests/test_horde_inference_painting.py @@ -187,3 +187,81 @@ def test_outpainting_alpha_mask_mountains( f"images_expected/{img_filename}", pil_image, ) + + def test_sdxl_inpainting_alpha_mask( + self, + sdxl_1_0_base_model_name: str, + hordelib_instance: HordeLib, + ): + data = { + "sampler_name": "euler", + "cfg_scale": 8, + "denoising_strength": 0.9, + "seed": 836138046008, + "height": 1024, + "width": 1024, + "karras": False, + "tiling": False, + "hires_fix": False, + "clip_skip": 1, + "control_type": None, + "image_is_control": False, + "return_control_map": False, + "prompt": "a robot sitting a wooden bench in a field", + "ddim_steps": 20, + "n_iter": 1, + "model": sdxl_1_0_base_model_name, + "source_image": Image.open("images/test_inpaint_alpha.png"), + "source_processing": "inpainting", + } + pil_image = hordelib_instance.basic_inference(data) + assert pil_image is not None + assert isinstance(pil_image, Image.Image) + + img_filename = "sdxl_inpainting_mask_alpha_denoise_0.9.png" + + pil_image.save(f"images/{img_filename}", quality=100) + + assert check_single_inference_image_similarity( + f"images_expected/{img_filename}", + pil_image, + ) + + def test_sdxl_inpainting_separate_mask( + self, + sdxl_1_0_base_model_name: str, + hordelib_instance: HordeLib, + ): + data = { + "sampler_name": "euler", + "cfg_scale": 8, + "denoising_strength": 1, + "seed": 836138046008, + "height": 1024, + "width": 1024, + "karras": False, + "tiling": False, + "hires_fix": False, + "clip_skip": 1, + "control_type": None, + "image_is_control": False, + "return_control_map": False, + "prompt": "an iron bench in a field", + "ddim_steps": 20, + "n_iter": 1, + "model": sdxl_1_0_base_model_name, + "source_image": Image.open("images/test_inpaint_original.png"), + "source_mask": Image.open("images/test_inpaint_mask.png"), + "source_processing": "inpainting", + } + + for denoise_strength in [1, 0.9, 0.8, 0.7, 0.65, 0.6, 0.5]: + data["denoising_strength"] = denoise_strength + pil_image = hordelib_instance.basic_inference(data) + assert pil_image is not None + + img_filename = f"sdxl_inpainting_mask_separate_denoise_{denoise_strength}.png" + + assert isinstance(pil_image, Image.Image) + + pil_image.save(f"images/{img_filename}", quality=100) diff --git a/tests/test_horde_inference_sdxl.py b/tests/test_horde_inference_sdxl.py new file mode 100644 index 00000000..bb5914d6 --- /dev/null +++ b/tests/test_horde_inference_sdxl.py @@ -0,0 +1,9 @@ +# test_horde.py + +import pytest +from PIL import Image + +from hordelib.horde import HordeLib +from hordelib.shared_model_manager import SharedModelManager + +from .testing_shared_functions import check_single_inference_image_similarity diff --git a/tests/test_horde_lora.py b/tests/test_horde_lora.py index fdd9aba0..2d6b9978 100644 --- a/tests/test_horde_lora.py +++ b/tests/test_horde_lora.py @@ -288,10 +288,12 @@ def test_lora_trigger_inject_any( img_filename = "lora_inject_any.png" pil_image.save(f"images/{img_filename}", quality=100) - assert check_single_lora_image_similarity( - f"images_expected/{img_filename}", - pil_image, - ) + pil_image_2 = hordelib_instance.basic_inference(data) + assert pil_image_2 is not None + assert isinstance(pil_image_2, Image.Image) + + img_filename = "lora_inject_any_2.png" + pil_image_2.save(f"images/{img_filename}", quality=100) def test_download_and_use_adhoc_lora( self, @@ -402,6 +404,14 @@ def test_sd21_lora_against_sd15_model( pil_image = hordelib_instance.basic_inference(data) assert pil_image is not None + img_filename = "lora_baseline_mismatch.png" + pil_image.save(f"images/{img_filename}", quality=100) + + assert check_single_lora_image_similarity( + f"images_expected/{img_filename}", + pil_image, + ) + def test_stonepunk( self, hordelib_instance: HordeLib,

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