import os import importlib.util from comfy.cli_args import args import subprocess def get_gpu_names(): if os.name == 'nt': import ctypes class DISPLAY_DEVICEA(ctypes.Structure): _fields_ = [ ('cb', ctypes.c_ulong), ('DeviceName', ctypes.c_char * 32), ('DeviceString', ctypes.c_char * 128), ('StateFlags', ctypes.c_ulong), ('DeviceID', ctypes.c_char * 128), ('DeviceKey', ctypes.c_char * 128) ] user32 = ctypes.windll.user32 def enum_display_devices(): device_info = DISPLAY_DEVICEA() device_info.cb = ctypes.sizeof(device_info) device_index = 0 gpu_names = set() while user32.EnumDisplayDevicesA(None, device_index, ctypes.byref(device_info), 0): device_index += 1 gpu_names.add(device_info.DeviceString.decode('utf-8')) return gpu_names return enum_display_devices() else: gpu_names = set() out = subprocess.check_output(['nvidia-smi', '-L']) for l in out.split(b'\n'): if len(l) > 0: gpu_names.add(l.decode('utf-8').split(' (UUID')[0]) return gpu_names blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M", "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620", "Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000", "Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000", "GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M", "GeForce GTX 1650", "GeForce GTX 1630", "Tesla M4", "Tesla M6", "Tesla M10", "Tesla M40", "Tesla M60" } def cuda_malloc_supported(): try: names = get_gpu_names() except: names = set() for x in names: if "NVIDIA" in x: for b in blacklist: if b in x: return False return True if not args.cuda_malloc: try: version = "" torch_spec = importlib.util.find_spec("torch") for folder in torch_spec.submodule_search_locations: ver_file = os.path.join(folder, "version.py") if os.path.isfile(ver_file): spec = importlib.util.spec_from_file_location("torch_version_import", ver_file) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) version = module.__version__ if int(version[0]) >= 2: args.cuda_malloc = cuda_malloc_supported() except: pass if args.cuda_malloc and not args.disable_cuda_malloc: env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None) if env_var is None: env_var = "backend:cudaMallocAsync" else: env_var += ",backend:cudaMallocAsync" os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var
import sys import copy import logging import threading import heapq import traceback import inspect from typing import List, Literal, NamedTuple, Optional import torch import nodes import comfy.model_management def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_data={}): valid_inputs = class_def.INPUT_TYPES() input_data_all = {} for x in inputs: input_data = inputs[x] if isinstance(input_data, list): input_unique_id = input_data[0] output_index = input_data[1] if input_unique_id not in outputs: input_data_all[x] = (None,) continue obj = outputs[input_unique_id][output_index] input_data_all[x] = obj else: if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]): input_data_all[x] = [input_data] if "hidden" in valid_inputs: h = valid_inputs["hidden"] for x in h: if h[x] == "PROMPT": input_data_all[x] = [prompt] if h[x] == "EXTRA_PNGINFO": input_data_all[x] = [extra_data.get('extra_pnginfo', None)] if h[x] == "UNIQUE_ID": input_data_all[x] = [unique_id] return input_data_all def map_node_over_list(obj, input_data_all, func, allow_interrupt=False): input_is_list = False if hasattr(obj, "INPUT_IS_LIST"): input_is_list = obj.INPUT_IS_LIST if len(input_data_all) == 0: max_len_input = 0 else: max_len_input = max([len(x) for x in input_data_all.values()]) def slice_dict(d, i): d_new = dict() for k,v in d.items(): d_new[k] = v[i if len(v) > i else -1] return d_new results = [] if input_is_list: if allow_interrupt: nodes.before_node_execution() results.append(getattr(obj, func)(**input_data_all)) elif max_len_input == 0: if allow_interrupt: nodes.before_node_execution() results.append(getattr(obj, func)()) else: for i in range(max_len_input): if allow_interrupt: nodes.before_node_execution() results.append(getattr(obj, func)(**slice_dict(input_data_all, i))) return results def get_output_data(obj, input_data_all): results = [] uis = [] return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True) for r in return_values: if isinstance(r, dict): if 'ui' in r: uis.append(r['ui']) if 'result' in r: results.append(r['result']) else: results.append(r) output = [] if len(results) > 0: output_is_list = [False] * len(results[0]) if hasattr(obj, "OUTPUT_IS_LIST"): output_is_list = obj.OUTPUT_IS_LIST for i, is_list in zip(range(len(results[0])), output_is_list): if is_list: output.append([x for o in results for x in o[i]]) else: output.append([o[i] for o in results]) ui = dict() if len(uis) > 0: ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()} return output, ui def format_value(x): if x is None: return None elif isinstance(x, (int, float, bool, str)): return x else: return str(x) def recursive_execute(server, prompt, outputs, current_item, extra_data, executed, prompt_id, outputs_ui, object_storage): unique_id = current_item inputs = prompt[unique_id]['inputs'] class_type = prompt[unique_id]['class_type'] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] if unique_id in outputs: return (True, None, None) for x in inputs: input_data = inputs[x] if isinstance(input_data, list): input_unique_id = input_data[0] output_index = input_data[1] if input_unique_id not in outputs: result = recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui, object_storage) if result[0] is not True: return result input_data_all = None try: input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data) if server.client_id is not None: server.last_node_id = unique_id server.send_sync("executing", { "node": unique_id, "prompt_id": prompt_id }, server.client_id) obj = object_storage.get((unique_id, class_type), None) if obj is None: obj = class_def() object_storage[(unique_id, class_type)] = obj output_data, output_ui = get_output_data(obj, input_data_all) outputs[unique_id] = output_data if len(output_ui) > 0: outputs_ui[unique_id] = output_ui if server.client_id is not None: server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id) except comfy.model_management.InterruptProcessingException as iex: logging.info("Processing interrupted") error_details = { "node_id": unique_id, } return (False, error_details, iex) except Exception as ex: typ, _, tb = sys.exc_info() exception_type = full_type_name(typ) input_data_formatted = {} if input_data_all is not None: input_data_formatted = {} for name, inputs in input_data_all.items(): input_data_formatted[name] = [format_value(x) for x in inputs] output_data_formatted = {} for node_id, node_outputs in outputs.items(): output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs] logging.error(f"!!! Exception during processing!!! {ex}") logging.error(traceback.format_exc()) error_details = { "node_id": unique_id, "exception_message": str(ex), "exception_type": exception_type, "traceback": traceback.format_tb(tb), "current_inputs": input_data_formatted, "current_outputs": output_data_formatted } return (False, error_details, ex) executed.add(unique_id) return (True, None, None) def recursive_will_execute(prompt, outputs, current_item, memo={}): unique_id = current_item if unique_id in memo: return memo[unique_id] inputs = prompt[unique_id]['inputs'] will_execute = [] if unique_id in outputs: return [] for x in inputs: input_data = inputs[x] if isinstance(input_data, list): input_unique_id = input_data[0] output_index = input_data[1] if input_unique_id not in outputs: will_execute += recursive_will_execute(prompt, outputs, input_unique_id, memo) memo[unique_id] = will_execute + [unique_id] return memo[unique_id] def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item): unique_id = current_item inputs = prompt[unique_id]['inputs'] class_type = prompt[unique_id]['class_type'] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] is_changed_old = '' is_changed = '' to_delete = False if hasattr(class_def, 'IS_CHANGED'): if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]: is_changed_old = old_prompt[unique_id]['is_changed'] if 'is_changed' not in prompt[unique_id]: input_data_all = get_input_data(inputs, class_def, unique_id, outputs) if input_data_all is not None: try: is_changed = map_node_over_list(class_def, input_data_all, "IS_CHANGED") prompt[unique_id]['is_changed'] = is_changed except: to_delete = True else: is_changed = prompt[unique_id]['is_changed'] if unique_id not in outputs: return True if not to_delete: if is_changed != is_changed_old: to_delete = True elif unique_id not in old_prompt: to_delete = True elif inputs == old_prompt[unique_id]['inputs']: for x in inputs: input_data = inputs[x] if isinstance(input_data, list): input_unique_id = input_data[0] output_index = input_data[1] if input_unique_id in outputs: to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id) else: to_delete = True if to_delete: break else: to_delete = True if to_delete: d = outputs.pop(unique_id) del d return to_delete class PromptExecutor: def __init__(self, server): self.server = server self.reset() def reset(self): self.outputs = {} self.object_storage = {} self.outputs_ui = {} self.status_messages = [] self.success = True self.old_prompt = {} def add_message(self, event, data, broadcast: bool): self.status_messages.append((event, data)) if self.server.client_id is not None or broadcast: self.server.send_sync(event, data, self.server.client_id) def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex): node_id = error["node_id"] class_type = prompt[node_id]["class_type"] if isinstance(ex, comfy.model_management.InterruptProcessingException): mes = { "prompt_id": prompt_id, "node_id": node_id, "node_type": class_type, "executed": list(executed), } self.add_message("execution_interrupted", mes, broadcast=True) else: mes = { "prompt_id": prompt_id, "node_id": node_id, "node_type": class_type, "executed": list(executed), "exception_message": error["exception_message"], "exception_type": error["exception_type"], "traceback": error["traceback"], "current_inputs": error["current_inputs"], "current_outputs": error["current_outputs"], } self.add_message("execution_error", mes, broadcast=False) to_delete = [] for o in self.outputs: if (o not in current_outputs) and (o not in executed): to_delete += [o] if o in self.old_prompt: d = self.old_prompt.pop(o) del d for o in to_delete: d = self.outputs.pop(o) del d def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): nodes.interrupt_processing(False) if "client_id" in extra_data: self.server.client_id = extra_data["client_id"] else: self.server.client_id = None self.status_messages = [] self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False) with torch.inference_mode(): to_delete = [] for o in self.outputs: if o not in prompt: to_delete += [o] for o in to_delete: d = self.outputs.pop(o) del d to_delete = [] for o in self.object_storage: if o[0] not in prompt: to_delete += [o] else: p = prompt[o[0]] if o[1] != p['class_type']: to_delete += [o] for o in to_delete: d = self.object_storage.pop(o) del d for x in prompt: recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x) current_outputs = set(self.outputs.keys()) for x in list(self.outputs_ui.keys()): if x not in current_outputs: d = self.outputs_ui.pop(x) del d comfy.model_management.cleanup_models(keep_clone_weights_loaded=True) self.add_message("execution_cached", { "nodes": list(current_outputs) , "prompt_id": prompt_id}, broadcast=False) executed = set() output_node_id = None to_execute = [] for node_id in list(execute_outputs): to_execute += [(0, node_id)] while len(to_execute) > 0: memo = {} to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1], memo)), a[-1]), to_execute))) output_node_id = to_execute.pop(0)[-1] self.success, error, ex = recursive_execute(self.server, prompt, self.outputs, output_node_id, extra_data, executed, prompt_id, self.outputs_ui, self.object_storage) if self.success is not True: self.handle_execution_error(prompt_id, prompt, current_outputs, executed, error, ex) break for x in executed: self.old_prompt[x] = copy.deepcopy(prompt[x]) self.server.last_node_id = None if comfy.model_management.DISABLE_SMART_MEMORY: comfy.model_management.unload_all_models() def validate_inputs(prompt, item, validated): unique_id = item if unique_id in validated: return validated[unique_id] inputs = prompt[unique_id]['inputs'] class_type = prompt[unique_id]['class_type'] obj_class = nodes.NODE_CLASS_MAPPINGS[class_type] class_inputs = obj_class.INPUT_TYPES() required_inputs = class_inputs['required'] errors = [] valid = True validate_function_inputs = [] if hasattr(obj_class, "VALIDATE_INPUTS"): validate_function_inputs = inspect.getfullargspec(obj_class.VALIDATE_INPUTS).args for x in required_inputs: if x not in inputs: error = { "type": "required_input_missing", "message": "Required input is missing", "details": f"{x}", "extra_info": { "input_name": x } } errors.append(error) continue val = inputs[x] info = required_inputs[x] type_input = info[0] if isinstance(val, list): if len(val) != 2: error = { "type": "bad_linked_input", "message": "Bad linked input, must be a length-2 list of [node_id, slot_index]", "details": f"{x}", "extra_info": { "input_name": x, "input_config": info, "received_value": val } } errors.append(error) continue o_id = val[0] o_class_type = prompt[o_id]['class_type'] r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES if r[val[1]] != type_input: received_type = r[val[1]] details = f"{x}, {received_type} != {type_input}" error = { "type": "return_type_mismatch", "message": "Return type mismatch between linked nodes", "details": details, "extra_info": { "input_name": x, "input_config": info, "received_type": received_type, "linked_node": val } } errors.append(error) continue try: r = validate_inputs(prompt, o_id, validated) if r[0] is False: valid = False continue except Exception as ex: typ, _, tb = sys.exc_info() valid = False exception_type = full_type_name(typ) reasons = [{ "type": "exception_during_inner_validation", "message": "Exception when validating inner node", "details": str(ex), "extra_info": { "input_name": x, "input_config": info, "exception_message": str(ex), "exception_type": exception_type, "traceback": traceback.format_tb(tb), "linked_node": val } }] validated[o_id] = (False, reasons, o_id) continue else: try: if type_input == "INT": val = int(val) inputs[x] = val if type_input == "FLOAT": val = float(val) inputs[x] = val if type_input == "STRING": val = str(val) inputs[x] = val except Exception as ex: error = { "type": "invalid_input_type", "message": f"Failed to convert an input value to a {type_input} value", "details": f"{x}, {val}, {ex}", "extra_info": { "input_name": x, "input_config": info, "received_value": val, "exception_message": str(ex) } } errors.append(error) continue if len(info) > 1: if "min" in info[1] and val < info[1]["min"]: error = { "type": "value_smaller_than_min", "message": "Value {} smaller than min of {}".format(val, info[1]["min"]), "details": f"{x}", "extra_info": { "input_name": x, "input_config": info, "received_value": val, } } errors.append(error) continue if "max" in info[1] and val > info[1]["max"]: error = { "type": "value_bigger_than_max", "message": "Value {} bigger than max of {}".format(val, info[1]["max"]), "details": f"{x}", "extra_info": { "input_name": x, "input_config": info, "received_value": val, } } errors.append(error) continue if x not in validate_function_inputs: if isinstance(type_input, list): if val not in type_input: input_config = info list_info = "" if len(type_input) > 20: list_info = f"(list of length {len(type_input)})" input_config = None else: list_info = str(type_input) error = { "type": "value_not_in_list", "message": "Value not in list", "details": f"{x}: '{val}' not in {list_info}", "extra_info": { "input_name": x, "input_config": input_config, "received_value": val, } } errors.append(error) continue if len(validate_function_inputs) > 0: input_data_all = get_input_data(inputs, obj_class, unique_id) input_filtered = {} for x in input_data_all: if x in validate_function_inputs: input_filtered[x] = input_data_all[x] ret = map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS") for x in input_filtered: for i, r in enumerate(ret): if r is not True: details = f"{x}" if r is not False: details += f" - {str(r)}" error = { "type": "custom_validation_failed", "message": "Custom validation failed for node", "details": details, "extra_info": { "input_name": x, "input_config": info, "received_value": val, } } errors.append(error) continue if len(errors) > 0 or valid is not True: ret = (False, errors, unique_id) else: ret = (True, [], unique_id) validated[unique_id] = ret return ret def full_type_name(klass): module = klass.__module__ if module == 'builtins': return klass.__qualname__ return module + '.' + klass.__qualname__ def validate_prompt(prompt): outputs = set() for x in prompt: if 'class_type' not in prompt[x]: error = { "type": "invalid_prompt", "message": f"Cannot execute because a node is missing the class_type property.", "details": f"Node ID ' "extra_info": {} } return (False, error, [], []) class_type = prompt[x]['class_type'] class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None) if class_ is None: error = { "type": "invalid_prompt", "message": f"Cannot execute because node {class_type} does not exist.", "details": f"Node ID ' "extra_info": {} } return (False, error, [], []) if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True: outputs.add(x) if len(outputs) == 0: error = { "type": "prompt_no_outputs", "message": "Prompt has no outputs", "details": "", "extra_info": {} } return (False, error, [], []) good_outputs = set() errors = [] node_errors = {} validated = {} for o in outputs: valid = False reasons = [] try: m = validate_inputs(prompt, o, validated) valid = m[0] reasons = m[1] except Exception as ex: typ, _, tb = sys.exc_info() valid = False exception_type = full_type_name(typ) reasons = [{ "type": "exception_during_validation", "message": "Exception when validating node", "details": str(ex), "extra_info": { "exception_type": exception_type, "traceback": traceback.format_tb(tb) } }] validated[o] = (False, reasons, o) if valid is True: good_outputs.add(o) else: logging.error(f"Failed to validate prompt for output {o}:") if len(reasons) > 0: logging.error("* (prompt):") for reason in reasons: logging.error(f" - {reason['message']}: {reason['details']}") errors += [(o, reasons)] for node_id, result in validated.items(): valid = result[0] reasons = result[1] if valid is not True and len(reasons) > 0: if node_id not in node_errors: class_type = prompt[node_id]['class_type'] node_errors[node_id] = { "errors": reasons, "dependent_outputs": [], "class_type": class_type } logging.error(f"* {class_type} {node_id}:") for reason in reasons: logging.error(f" - {reason['message']}: {reason['details']}") node_errors[node_id]["dependent_outputs"].append(o) logging.error("Output will be ignored") if len(good_outputs) == 0: errors_list = [] for o, errors in errors: for error in errors: errors_list.append(f"{error['message']}: {error['details']}") errors_list = "\n".join(errors_list) error = { "type": "prompt_outputs_failed_validation", "message": "Prompt outputs failed validation", "details": errors_list, "extra_info": {} } return (False, error, list(good_outputs), node_errors) return (True, None, list(good_outputs), node_errors) MAXIMUM_HISTORY_SIZE = 10000 class PromptQueue: def __init__(self, server): self.server = server self.mutex = threading.RLock() self.not_empty = threading.Condition(self.mutex) self.task_counter = 0 self.queue = [] self.currently_running = {} self.history = {} self.flags = {} server.prompt_queue = self def put(self, item): with self.mutex: heapq.heappush(self.queue, item) self.server.queue_updated() self.not_empty.notify() def get(self, timeout=None): with self.not_empty: while len(self.queue) == 0: self.not_empty.wait(timeout=timeout) if timeout is not None and len(self.queue) == 0: return None item = heapq.heappop(self.queue) i = self.task_counter self.currently_running[i] = copy.deepcopy(item) self.task_counter += 1 self.server.queue_updated() return (item, i) class ExecutionStatus(NamedTuple): status_str: Literal['success', 'error'] completed: bool messages: List[str] def task_done(self, item_id, outputs, status: Optional['PromptQueue.ExecutionStatus']): with self.mutex: prompt = self.currently_running.pop(item_id) if len(self.history) > MAXIMUM_HISTORY_SIZE: self.history.pop(next(iter(self.history))) status_dict: Optional[dict] = None if status is not None: status_dict = copy.deepcopy(status._asdict()) self.history[prompt[1]] = { "prompt": prompt, "outputs": copy.deepcopy(outputs), 'status': status_dict, } self.server.queue_updated() def get_current_queue(self): with self.mutex: out = [] for x in self.currently_running.values(): out += [x] return (out, copy.deepcopy(self.queue)) def get_tasks_remaining(self): with self.mutex: return len(self.queue) + len(self.currently_running) def wipe_queue(self): with self.mutex: self.queue = [] self.server.queue_updated() def delete_queue_item(self, function): with self.mutex: for x in range(len(self.queue)): if function(self.queue[x]): if len(self.queue) == 1: self.wipe_queue() else: self.queue.pop(x) heapq.heapify(self.queue) self.server.queue_updated() return True return False def get_history(self, prompt_id=None, max_items=None, offset=-1): with self.mutex: if prompt_id is None: out = {} i = 0 if offset < 0 and max_items is not None: offset = len(self.history) - max_items for k in self.history: if i >= offset: out[k] = self.history[k] if max_items is not None and len(out) >= max_items: break i += 1 return out elif prompt_id in self.history: return {prompt_id: copy.deepcopy(self.history[prompt_id])} else: return {} def wipe_history(self): with self.mutex: self.history = {} def delete_history_item(self, id_to_delete): with self.mutex: self.history.pop(id_to_delete, None) def set_flag(self, name, data): with self.mutex: self.flags[name] = data self.not_empty.notify() def get_flags(self, reset=True): with self.mutex: if reset: ret = self.flags self.flags = {} return ret else: return self.flags.copy()
import os import time import logging supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl']) folder_names_and_paths = {} base_path = os.path.dirname(os.path.realpath(__file__)) models_dir = os.path.join(base_path, "models") folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions) folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"]) folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions) folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions) folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions) folder_names_and_paths["unet"] = ([os.path.join(models_dir, "unet")], supported_pt_extensions) folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions) folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions) folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions) folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"]) folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions) folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions) folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions) folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions) folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], []) folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions) folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions) folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""}) output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output") temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp") input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input") user_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "user") filename_list_cache = {} if not os.path.exists(input_directory): try: os.makedirs(input_directory) except: logging.error("Failed to create input directory") def set_output_directory(output_dir): global output_directory output_directory = output_dir def set_temp_directory(temp_dir): global temp_directory temp_directory = temp_dir def set_input_directory(input_dir): global input_directory input_directory = input_dir def get_output_directory(): global output_directory return output_directory def get_temp_directory(): global temp_directory return temp_directory def get_input_directory(): global input_directory return input_directory def get_directory_by_type(type_name): if type_name == "output": return get_output_directory() if type_name == "temp": return get_temp_directory() if type_name == "input": return get_input_directory() return None def annotated_filepath(name): if name.endswith("[output]"): base_dir = get_output_directory() name = name[:-9] elif name.endswith("[input]"): base_dir = get_input_directory() name = name[:-8] elif name.endswith("[temp]"): base_dir = get_temp_directory() name = name[:-7] else: return name, None return name, base_dir def get_annotated_filepath(name, default_dir=None): name, base_dir = annotated_filepath(name) if base_dir is None: if default_dir is not None: base_dir = default_dir else: base_dir = get_input_directory() return os.path.join(base_dir, name) def exists_annotated_filepath(name): name, base_dir = annotated_filepath(name) if base_dir is None: base_dir = get_input_directory() filepath = os.path.join(base_dir, name) return os.path.exists(filepath) def add_model_folder_path(folder_name, full_folder_path): global folder_names_and_paths if folder_name in folder_names_and_paths: folder_names_and_paths[folder_name][0].append(full_folder_path) else: folder_names_and_paths[folder_name] = ([full_folder_path], set()) def get_folder_paths(folder_name): return folder_names_and_paths[folder_name][0][:] def recursive_search(directory, excluded_dir_names=None): if not os.path.isdir(directory): return [], {} if excluded_dir_names is None: excluded_dir_names = [] result = [] dirs = {} try: dirs[directory] = os.path.getmtime(directory) except FileNotFoundError: logging.warning(f"Warning: Unable to access {directory}. Skipping this path.") logging.debug("recursive file list on directory {}".format(directory)) for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] for file_name in filenames: relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) result.append(relative_path) for d in subdirs: path = os.path.join(dirpath, d) try: dirs[path] = os.path.getmtime(path) except FileNotFoundError: logging.warning(f"Warning: Unable to access {path}. Skipping this path.") continue logging.debug("found {} files".format(len(result))) return result, dirs def filter_files_extensions(files, extensions): return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files))) def get_full_path(folder_name, filename): global folder_names_and_paths if folder_name not in folder_names_and_paths: return None folders = folder_names_and_paths[folder_name] filename = os.path.relpath(os.path.join("/", filename), "/") for x in folders[0]: full_path = os.path.join(x, filename) if os.path.isfile(full_path): return full_path elif os.path.islink(full_path): logging.warning("WARNING path {} exists but doesn't link anywhere, skipping.".format(full_path)) return None def get_filename_list_(folder_name): global folder_names_and_paths output_list = set() folders = folder_names_and_paths[folder_name] output_folders = {} for x in folders[0]: files, folders_all = recursive_search(x, excluded_dir_names=[".git"]) output_list.update(filter_files_extensions(files, folders[1])) output_folders = {**output_folders, **folders_all} return (sorted(list(output_list)), output_folders, time.perf_counter()) def cached_filename_list_(folder_name): global filename_list_cache global folder_names_and_paths if folder_name not in filename_list_cache: return None out = filename_list_cache[folder_name] for x in out[1]: time_modified = out[1][x] folder = x if os.path.getmtime(folder) != time_modified: return None folders = folder_names_and_paths[folder_name] for x in folders[0]: if os.path.isdir(x): if x not in out[1]: return None return out def get_filename_list(folder_name): out = cached_filename_list_(folder_name) if out is None: out = get_filename_list_(folder_name) global filename_list_cache filename_list_cache[folder_name] = out return list(out[0]) def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0): def map_filename(filename): prefix_len = len(os.path.basename(filename_prefix)) prefix = filename[:prefix_len + 1] try: digits = int(filename[prefix_len + 1:].split('_')[0]) except: digits = 0 return (digits, prefix) def compute_vars(input, image_width, image_height): input = input.replace("%width%", str(image_width)) input = input.replace("%height%", str(image_height)) return input filename_prefix = compute_vars(filename_prefix, image_width, image_height) subfolder = os.path.dirname(os.path.normpath(filename_prefix)) filename = os.path.basename(os.path.normpath(filename_prefix)) full_output_folder = os.path.join(output_dir, subfolder) if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: err = "**** ERROR: Saving image outside the output folder is not allowed." + \ "\n full_output_folder: " + os.path.abspath(full_output_folder) + \ "\n output_dir: " + output_dir + \ "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) logging.error(err) raise Exception(err) try: counter = max(filter(lambda a: os.path.normcase(a[1][:-1]) == os.path.normcase(filename) and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 except ValueError: counter = 1 except FileNotFoundError: os.makedirs(full_output_folder, exist_ok=True) counter = 1 return full_output_folder, filename, counter, subfolder, filename_prefix
import torch from PIL import Image import struct import numpy as np from comfy.cli_args import args, LatentPreviewMethod from comfy.taesd.taesd import TAESD import comfy.model_management import folder_paths import comfy.utils import logging MAX_PREVIEW_RESOLUTION = 512 def preview_to_image(latent_image): latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1) .mul(0xFF) ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device)) return Image.fromarray(latents_ubyte.numpy()) class LatentPreviewer: def decode_latent_to_preview(self, x0): pass def decode_latent_to_preview_image(self, preview_format, x0): preview_image = self.decode_latent_to_preview(x0) return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION) class TAESDPreviewerImpl(LatentPreviewer): def __init__(self, taesd): self.taesd = taesd def decode_latent_to_preview(self, x0): x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2) return preview_to_image(x_sample) class Latent2RGBPreviewer(LatentPreviewer): def __init__(self, latent_rgb_factors): self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu") def decode_latent_to_preview(self, x0): self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device) latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors return preview_to_image(latent_image) def get_previewer(device, latent_format): previewer = None method = args.preview_method if method != LatentPreviewMethod.NoPreviews: taesd_decoder_path = None if latent_format.taesd_decoder_name is not None: taesd_decoder_path = next( (fn for fn in folder_paths.get_filename_list("vae_approx") if fn.startswith(latent_format.taesd_decoder_name)), "" ) taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path) if method == LatentPreviewMethod.Auto: method = LatentPreviewMethod.Latent2RGB if method == LatentPreviewMethod.TAESD: if taesd_decoder_path: taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device) previewer = TAESDPreviewerImpl(taesd) else: logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name)) if previewer is None: if latent_format.latent_rgb_factors is not None: previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors) return previewer def prepare_callback(model, steps, x0_output_dict=None): preview_format = "JPEG" if preview_format not in ["JPEG", "PNG"]: preview_format = "JPEG" previewer = get_previewer(model.load_device, model.model.latent_format) pbar = comfy.utils.ProgressBar(steps) def callback(step, x0, x, total_steps): if x0_output_dict is not None: x0_output_dict["x0"] = x0 preview_bytes = None if previewer: preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) pbar.update_absolute(step + 1, total_steps, preview_bytes) return callback
import comfy.options comfy.options.enable_args_parsing() import os import importlib.util import folder_paths import time def execute_prestartup_script(): def execute_script(script_path): module_name = os.path.splitext(script_path)[0] try: spec = importlib.util.spec_from_file_location(module_name, script_path) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) return True except Exception as e: print(f"Failed to execute startup-script: {script_path} / {e}") return False node_paths = folder_paths.get_folder_paths("custom_nodes") for custom_node_path in node_paths: possible_modules = os.listdir(custom_node_path) node_prestartup_times = [] for possible_module in possible_modules: module_path = os.path.join(custom_node_path, possible_module) if os.path.isfile(module_path) or module_path.endswith(".disabled") or module_path == "__pycache__": continue script_path = os.path.join(module_path, "prestartup_script.py") if os.path.exists(script_path): time_before = time.perf_counter() success = execute_script(script_path) node_prestartup_times.append((time.perf_counter() - time_before, module_path, success)) if len(node_prestartup_times) > 0: print("\nPrestartup times for custom nodes:") for n in sorted(node_prestartup_times): if n[2]: import_message = "" else: import_message = " (PRESTARTUP FAILED)" print("{:6.1f} seconds{}:".format(n[0], import_message), n[1]) print() execute_prestartup_script() import asyncio import itertools import shutil import threading import gc from comfy.cli_args import args import logging if os.name == "nt": logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) if __name__ == "__main__": if args.cuda_device is not None: os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device) logging.info("Set cuda device to: {}".format(args.cuda_device)) if args.deterministic: if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ: os.environ['CUBLAS_WORKSPACE_CONFIG'] = ":4096:8" import cuda_malloc import comfy.utils import yaml import execution import server from server import BinaryEventTypes from nodes import init_custom_nodes import comfy.model_management def cuda_malloc_warning(): device = comfy.model_management.get_torch_device() device_name = comfy.model_management.get_torch_device_name(device) cuda_malloc_warning = False if "cudaMallocAsync" in device_name: for b in cuda_malloc.blacklist: if b in device_name: cuda_malloc_warning = True if cuda_malloc_warning: logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n") def prompt_worker(q, server): e = execution.PromptExecutor(server) last_gc_collect = 0 need_gc = False gc_collect_interval = 10.0 while True: timeout = 1000.0 if need_gc: timeout = max(gc_collect_interval - (current_time - last_gc_collect), 0.0) queue_item = q.get(timeout=timeout) if queue_item is not None: item, item_id = queue_item execution_start_time = time.perf_counter() prompt_id = item[1] server.last_prompt_id = prompt_id e.execute(item[2], prompt_id, item[3], item[4]) need_gc = True q.task_done(item_id, e.outputs_ui, status=execution.PromptQueue.ExecutionStatus( status_str='success' if e.success else 'error', completed=e.success, messages=e.status_messages)) if server.client_id is not None: server.send_sync("executing", { "node": None, "prompt_id": prompt_id }, server.client_id) current_time = time.perf_counter() execution_time = current_time - execution_start_time logging.info("Prompt executed in {:.2f} seconds".format(execution_time)) flags = q.get_flags() free_memory = flags.get("free_memory", False) if flags.get("unload_models", free_memory): comfy.model_management.unload_all_models() need_gc = True last_gc_collect = 0 if free_memory: e.reset() need_gc = True last_gc_collect = 0 if need_gc: current_time = time.perf_counter() if (current_time - last_gc_collect) > gc_collect_interval: comfy.model_management.cleanup_models() gc.collect() comfy.model_management.soft_empty_cache() last_gc_collect = current_time need_gc = False async def run(server, address='', port=8188, verbose=True, call_on_start=None): await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop()) def hijack_progress(server): def hook(value, total, preview_image): comfy.model_management.throw_exception_if_processing_interrupted() progress = {"value": value, "max": total, "prompt_id": server.last_prompt_id, "node": server.last_node_id} server.send_sync("progress", progress, server.client_id) if preview_image is not None: server.send_sync(BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, preview_image, server.client_id) comfy.utils.set_progress_bar_global_hook(hook) def cleanup_temp(): temp_dir = folder_paths.get_temp_directory() if os.path.exists(temp_dir): shutil.rmtree(temp_dir, ignore_errors=True) def load_extra_path_config(yaml_path): with open(yaml_path, 'r') as stream: config = yaml.safe_load(stream) for c in config: conf = config[c] if conf is None: continue base_path = None if "base_path" in conf: base_path = conf.pop("base_path") for x in conf: for y in conf[x].split("\n"): if len(y) == 0: continue full_path = y if base_path is not None: full_path = os.path.join(base_path, full_path) logging.info("Adding extra search path {} {}".format(x, full_path)) folder_paths.add_model_folder_path(x, full_path) if __name__ == "__main__": if args.temp_directory: temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp") logging.info(f"Setting temp directory to: {temp_dir}") folder_paths.set_temp_directory(temp_dir) cleanup_temp() if args.windows_standalone_build: try: import new_updater new_updater.update_windows_updater() except: pass loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) server = server.PromptServer(loop) q = execution.PromptQueue(server) extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml") if os.path.isfile(extra_model_paths_config_path): load_extra_path_config(extra_model_paths_config_path) if args.extra_model_paths_config: for config_path in itertools.chain(*args.extra_model_paths_config): load_extra_path_config(config_path) init_custom_nodes() cuda_malloc_warning() server.add_routes() hijack_progress(server) threading.Thread(target=prompt_worker, daemon=True, args=(q, server,)).start() if args.output_directory: output_dir = os.path.abspath(args.output_directory) logging.info(f"Setting output directory to: {output_dir}") folder_paths.set_output_directory(output_dir) folder_paths.add_model_folder_path("checkpoints", os.path.join(folder_paths.get_output_directory(), "checkpoints")) folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip")) folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae")) if args.input_directory: input_dir = os.path.abspath(args.input_directory) logging.info(f"Setting input directory to: {input_dir}") folder_paths.set_input_directory(input_dir) if args.quick_test_for_ci: exit(0) call_on_start = None if args.auto_launch: def startup_server(scheme, address, port): import webbrowser if os.name == 'nt' and address == '0.0.0.0': address = '127.0.0.1' webbrowser.open(f"{scheme}: call_on_start = startup_server try: loop.run_until_complete(run(server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start)) except KeyboardInterrupt: logging.info("\nStopped server") cleanup_temp()
import os import shutil base_path = os.path.dirname(os.path.realpath(__file__)) def update_windows_updater(): top_path = os.path.dirname(base_path) updater_path = os.path.join(base_path, ".ci/update_windows/update.py") bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat") dest_updater_path = os.path.join(top_path, "update/update.py") dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat") dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat") try: with open(dest_bat_path, 'rb') as f: contents = f.read() except: return if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"): return shutil.copy(updater_path, dest_updater_path) try: with open(dest_bat_deps_path, 'rb') as f: contents = f.read() contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause') with open(dest_bat_deps_path, 'wb') as f: f.write(contents) except: pass shutil.copy(bat_path, dest_bat_path) print("Updated the windows standalone package updater.")
import torch import os import sys import json import hashlib import traceback import math import time import random import logging from PIL import Image, ImageOps, ImageSequence, ImageFile from PIL.PngImagePlugin import PngInfo import numpy as np import safetensors.torch sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) import comfy.diffusers_load import comfy.samplers import comfy.sample import comfy.sd import comfy.utils import comfy.controlnet import comfy.clip_vision import comfy.model_management from comfy.cli_args import args import importlib import folder_paths import latent_preview import node_helpers def before_node_execution(): comfy.model_management.throw_exception_if_processing_interrupted() def interrupt_processing(value=True): comfy.model_management.interrupt_current_processing(value) MAX_RESOLUTION=16384 class CLIPTextEncode: @classmethod def INPUT_TYPES(s): return {"required": {"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", )}} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "conditioning" def encode(self, clip, text): tokens = clip.tokenize(text) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return ([[cond, {"pooled_output": pooled}]], ) class ConditioningCombine: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "combine" CATEGORY = "conditioning" def combine(self, conditioning_1, conditioning_2): return (conditioning_1 + conditioning_2, ) class ConditioningAverage : @classmethod def INPUT_TYPES(s): return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ), "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "addWeighted" CATEGORY = "conditioning" def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength): out = [] if len(conditioning_from) > 1: logging.warning("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") cond_from = conditioning_from[0][0] pooled_output_from = conditioning_from[0][1].get("pooled_output", None) for i in range(len(conditioning_to)): t1 = conditioning_to[i][0] pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from) t0 = cond_from[:,:t1.shape[1]] if t0.shape[1] < t1.shape[1]: t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1) tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength)) t_to = conditioning_to[i][1].copy() if pooled_output_from is not None and pooled_output_to is not None: t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength)) elif pooled_output_from is not None: t_to["pooled_output"] = pooled_output_from n = [tw, t_to] out.append(n) return (out, ) class ConditioningConcat: @classmethod def INPUT_TYPES(s): return {"required": { "conditioning_to": ("CONDITIONING",), "conditioning_from": ("CONDITIONING",), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "concat" CATEGORY = "conditioning" def concat(self, conditioning_to, conditioning_from): out = [] if len(conditioning_from) > 1: logging.warning("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") cond_from = conditioning_from[0][0] for i in range(len(conditioning_to)): t1 = conditioning_to[i][0] tw = torch.cat((t1, cond_from),1) n = [tw, conditioning_to[i][1].copy()] out.append(n) return (out, ) class ConditioningSetArea: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" CATEGORY = "conditioning" def append(self, conditioning, width, height, x, y, strength): c = node_helpers.conditioning_set_values(conditioning, {"area": (height "strength": strength, "set_area_to_bounds": False}) return (c, ) class ConditioningSetAreaPercentage: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" CATEGORY = "conditioning" def append(self, conditioning, width, height, x, y, strength): c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x), "strength": strength, "set_area_to_bounds": False}) return (c, ) class ConditioningSetAreaStrength: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" CATEGORY = "conditioning" def append(self, conditioning, strength): c = node_helpers.conditioning_set_values(conditioning, {"strength": strength}) return (c, ) class ConditioningSetMask: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "mask": ("MASK", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "set_cond_area": (["default", "mask bounds"],), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" CATEGORY = "conditioning" def append(self, conditioning, mask, set_cond_area, strength): set_area_to_bounds = False if set_cond_area != "default": set_area_to_bounds = True if len(mask.shape) < 3: mask = mask.unsqueeze(0) c = node_helpers.conditioning_set_values(conditioning, {"mask": mask, "set_area_to_bounds": set_area_to_bounds, "mask_strength": strength}) return (c, ) class ConditioningZeroOut: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", )}} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "zero_out" CATEGORY = "advanced/conditioning" def zero_out(self, conditioning): c = [] for t in conditioning: d = t[1].copy() if "pooled_output" in d: d["pooled_output"] = torch.zeros_like(d["pooled_output"]) n = [torch.zeros_like(t[0]), d] c.append(n) return (c, ) class ConditioningSetTimestepRange: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "set_range" CATEGORY = "advanced/conditioning" def set_range(self, conditioning, start, end): c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, "end_percent": end}) return (c, ) class VAEDecode: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} RETURN_TYPES = ("IMAGE",) FUNCTION = "decode" CATEGORY = "latent" def decode(self, vae, samples): return (vae.decode(samples["samples"]), ) class VAEDecodeTiled: @classmethod def INPUT_TYPES(s): return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) }} RETURN_TYPES = ("IMAGE",) FUNCTION = "decode" CATEGORY = "_for_testing" def decode(self, vae, samples, tile_size): return (vae.decode_tiled(samples["samples"], tile_x=tile_size class VAEEncode: @classmethod def INPUT_TYPES(s): return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} RETURN_TYPES = ("LATENT",) FUNCTION = "encode" CATEGORY = "latent" def encode(self, vae, pixels): t = vae.encode(pixels[:,:,:,:3]) return ({"samples":t}, ) class VAEEncodeTiled: @classmethod def INPUT_TYPES(s): return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) }} RETURN_TYPES = ("LATENT",) FUNCTION = "encode" CATEGORY = "_for_testing" def encode(self, vae, pixels, tile_size): t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, ) return ({"samples":t}, ) class VAEEncodeForInpaint: @classmethod def INPUT_TYPES(s): return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}} RETURN_TYPES = ("LATENT",) FUNCTION = "encode" CATEGORY = "latent/inpaint" def encode(self, vae, pixels, mask, grow_mask_by=6): x = (pixels.shape[1] y = (pixels.shape[2] mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") pixels = pixels.clone() if pixels.shape[1] != x or pixels.shape[2] != y: x_offset = (pixels.shape[1] % vae.downscale_ratio) y_offset = (pixels.shape[2] % vae.downscale_ratio) pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] if grow_mask_by == 0: mask_erosion = mask else: kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)) padding = math.ceil((grow_mask_by - 1) / 2) mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1) m = (1.0 - mask.round()).squeeze(1) for i in range(3): pixels[:,:,:,i] -= 0.5 pixels[:,:,:,i] *= m pixels[:,:,:,i] += 0.5 t = vae.encode(pixels) return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, ) class InpaintModelConditioning: @classmethod def INPUT_TYPES(s): return {"required": {"positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "vae": ("VAE", ), "pixels": ("IMAGE", ), "mask": ("MASK", ), }} RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/inpaint" def encode(self, positive, negative, pixels, vae, mask): x = (pixels.shape[1] y = (pixels.shape[2] mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") orig_pixels = pixels pixels = orig_pixels.clone() if pixels.shape[1] != x or pixels.shape[2] != y: x_offset = (pixels.shape[1] % 8) y_offset = (pixels.shape[2] % 8) pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] m = (1.0 - mask.round()).squeeze(1) for i in range(3): pixels[:,:,:,i] -= 0.5 pixels[:,:,:,i] *= m pixels[:,:,:,i] += 0.5 concat_latent = vae.encode(pixels) orig_latent = vae.encode(orig_pixels) out_latent = {} out_latent["samples"] = orig_latent out_latent["noise_mask"] = mask out = [] for conditioning in [positive, negative]: c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent, "concat_mask": mask}) out.append(c) return (out[0], out[1], out_latent) class SaveLatent: def __init__(self): self.output_dir = folder_paths.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT", ), "filename_prefix": ("STRING", {"default": "latents/ComfyUI"})}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "_for_testing" def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = None if not args.disable_metadata: metadata = {"prompt": prompt_info} if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) file = f"{filename}_{counter:05}_.latent" results = list() results.append({ "filename": file, "subfolder": subfolder, "type": "output" }) file = os.path.join(full_output_folder, file) output = {} output["latent_tensor"] = samples["samples"] output["latent_format_version_0"] = torch.tensor([]) comfy.utils.save_torch_file(output, file, metadata=metadata) return { "ui": { "latents": results } } class LoadLatent: @classmethod def INPUT_TYPES(s): input_dir = folder_paths.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")] return {"required": {"latent": [sorted(files), ]}, } CATEGORY = "_for_testing" RETURN_TYPES = ("LATENT", ) FUNCTION = "load" def load(self, latent): latent_path = folder_paths.get_annotated_filepath(latent) latent = safetensors.torch.load_file(latent_path, device="cpu") multiplier = 1.0 if "latent_format_version_0" not in latent: multiplier = 1.0 / 0.18215 samples = {"samples": latent["latent_tensor"].float() * multiplier} return (samples, ) @classmethod def IS_CHANGED(s, latent): image_path = folder_paths.get_annotated_filepath(latent) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) return m.digest().hex() @classmethod def VALIDATE_INPUTS(s, latent): if not folder_paths.exists_annotated_filepath(latent): return "Invalid latent file: {}".format(latent) return True class CheckpointLoader: @classmethod def INPUT_TYPES(s): return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ), "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}} RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" CATEGORY = "advanced/loaders" def load_checkpoint(self, config_name, ckpt_name): config_path = folder_paths.get_full_path("configs", config_name) ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) class CheckpointLoaderSimple: @classmethod def INPUT_TYPES(s): return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), }} RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" CATEGORY = "loaders" def load_checkpoint(self, ckpt_name): ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) return out[:3] class DiffusersLoader: @classmethod def INPUT_TYPES(cls): paths = [] for search_path in folder_paths.get_folder_paths("diffusers"): if os.path.exists(search_path): for root, subdir, files in os.walk(search_path, followlinks=True): if "model_index.json" in files: paths.append(os.path.relpath(root, start=search_path)) return {"required": {"model_path": (paths,), }} RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" CATEGORY = "advanced/loaders/deprecated" def load_checkpoint(self, model_path, output_vae=True, output_clip=True): for search_path in folder_paths.get_folder_paths("diffusers"): if os.path.exists(search_path): path = os.path.join(search_path, model_path) if os.path.exists(path): model_path = path break return comfy.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) class unCLIPCheckpointLoader: @classmethod def INPUT_TYPES(s): return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), }} RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION") FUNCTION = "load_checkpoint" CATEGORY = "loaders" def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) return out class CLIPSetLastLayer: @classmethod def INPUT_TYPES(s): return {"required": { "clip": ("CLIP", ), "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}), }} RETURN_TYPES = ("CLIP",) FUNCTION = "set_last_layer" CATEGORY = "conditioning" def set_last_layer(self, clip, stop_at_clip_layer): clip = clip.clone() clip.clip_layer(stop_at_clip_layer) return (clip,) class LoraLoader: def __init__(self): self.loaded_lora = None @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "clip": ("CLIP", ), "lora_name": (folder_paths.get_filename_list("loras"), ), "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL", "CLIP") FUNCTION = "load_lora" CATEGORY = "loaders" def load_lora(self, model, clip, lora_name, strength_model, strength_clip): if strength_model == 0 and strength_clip == 0: return (model, clip) lora_path = folder_paths.get_full_path("loras", lora_name) lora = None if self.loaded_lora is not None: if self.loaded_lora[0] == lora_path: lora = self.loaded_lora[1] else: temp = self.loaded_lora self.loaded_lora = None del temp if lora is None: lora = comfy.utils.load_torch_file(lora_path, safe_load=True) self.loaded_lora = (lora_path, lora) model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) return (model_lora, clip_lora) class LoraLoaderModelOnly(LoraLoader): @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "lora_name": (folder_paths.get_filename_list("loras"), ), "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "load_lora_model_only" def load_lora_model_only(self, model, lora_name, strength_model): return (self.load_lora(model, None, lora_name, strength_model, 0)[0],) class VAELoader: @staticmethod def vae_list(): vaes = folder_paths.get_filename_list("vae") approx_vaes = folder_paths.get_filename_list("vae_approx") sdxl_taesd_enc = False sdxl_taesd_dec = False sd1_taesd_enc = False sd1_taesd_dec = False sd3_taesd_enc = False sd3_taesd_dec = False for v in approx_vaes: if v.startswith("taesd_decoder."): sd1_taesd_dec = True elif v.startswith("taesd_encoder."): sd1_taesd_enc = True elif v.startswith("taesdxl_decoder."): sdxl_taesd_dec = True elif v.startswith("taesdxl_encoder."): sdxl_taesd_enc = True elif v.startswith("taesd3_decoder."): sd3_taesd_dec = True elif v.startswith("taesd3_encoder."): sd3_taesd_enc = True if sd1_taesd_dec and sd1_taesd_enc: vaes.append("taesd") if sdxl_taesd_dec and sdxl_taesd_enc: vaes.append("taesdxl") if sd3_taesd_dec and sd3_taesd_enc: vaes.append("taesd3") return vaes @staticmethod def load_taesd(name): sd = {} approx_vaes = folder_paths.get_filename_list("vae_approx") encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) enc = comfy.utils.load_torch_file(folder_paths.get_full_path("vae_approx", encoder)) for k in enc: sd["taesd_encoder.{}".format(k)] = enc[k] dec = comfy.utils.load_torch_file(folder_paths.get_full_path("vae_approx", decoder)) for k in dec: sd["taesd_decoder.{}".format(k)] = dec[k] if name == "taesd": sd["vae_scale"] = torch.tensor(0.18215) sd["vae_shift"] = torch.tensor(0.0) elif name == "taesdxl": sd["vae_scale"] = torch.tensor(0.13025) sd["vae_shift"] = torch.tensor(0.0) elif name == "taesd3": sd["vae_scale"] = torch.tensor(1.5305) sd["vae_shift"] = torch.tensor(0.0609) return sd @classmethod def INPUT_TYPES(s): return {"required": { "vae_name": (s.vae_list(), )}} RETURN_TYPES = ("VAE",) FUNCTION = "load_vae" CATEGORY = "loaders" def load_vae(self, vae_name): if vae_name in ["taesd", "taesdxl", "taesd3"]: sd = self.load_taesd(vae_name) else: vae_path = folder_paths.get_full_path("vae", vae_name) sd = comfy.utils.load_torch_file(vae_path) vae = comfy.sd.VAE(sd=sd) return (vae,) class ControlNetLoader: @classmethod def INPUT_TYPES(s): return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} RETURN_TYPES = ("CONTROL_NET",) FUNCTION = "load_controlnet" CATEGORY = "loaders" def load_controlnet(self, control_net_name): controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) controlnet = comfy.controlnet.load_controlnet(controlnet_path) return (controlnet,) class DiffControlNetLoader: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} RETURN_TYPES = ("CONTROL_NET",) FUNCTION = "load_controlnet" CATEGORY = "loaders" def load_controlnet(self, model, control_net_name): controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) controlnet = comfy.controlnet.load_controlnet(controlnet_path, model) return (controlnet,) class ControlNetApply: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "control_net": ("CONTROL_NET", ), "image": ("IMAGE", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}) }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_controlnet" CATEGORY = "conditioning" def apply_controlnet(self, conditioning, control_net, image, strength): if strength == 0: return (conditioning, ) c = [] control_hint = image.movedim(-1,1) for t in conditioning: n = [t[0], t[1].copy()] c_net = control_net.copy().set_cond_hint(control_hint, strength) if 'control' in t[1]: c_net.set_previous_controlnet(t[1]['control']) n[1]['control'] = c_net n[1]['control_apply_to_uncond'] = True c.append(n) return (c, ) class ControlNetApplyAdvanced: @classmethod def INPUT_TYPES(s): return {"required": {"positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "control_net": ("CONTROL_NET", ), "image": ("IMAGE", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) }} RETURN_TYPES = ("CONDITIONING","CONDITIONING") RETURN_NAMES = ("positive", "negative") FUNCTION = "apply_controlnet" CATEGORY = "conditioning" def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent): if strength == 0: return (positive, negative) control_hint = image.movedim(-1,1) cnets = {} out = [] for conditioning in [positive, negative]: c = [] for t in conditioning: d = t[1].copy() prev_cnet = d.get('control', None) if prev_cnet in cnets: c_net = cnets[prev_cnet] else: c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent)) c_net.set_previous_controlnet(prev_cnet) cnets[prev_cnet] = c_net d['control'] = c_net d['control_apply_to_uncond'] = False n = [t[0], d] c.append(n) out.append(c) return (out[0], out[1]) class UNETLoader: @classmethod def INPUT_TYPES(s): return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ), }} RETURN_TYPES = ("MODEL",) FUNCTION = "load_unet" CATEGORY = "advanced/loaders" def load_unet(self, unet_name): unet_path = folder_paths.get_full_path("unet", unet_name) model = comfy.sd.load_unet(unet_path) return (model,) class CLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ), "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio"], ), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" CATEGORY = "advanced/loaders" def load_clip(self, clip_name, type="stable_diffusion"): if type == "stable_cascade": clip_type = comfy.sd.CLIPType.STABLE_CASCADE elif type == "sd3": clip_type = comfy.sd.CLIPType.SD3 elif type == "stable_audio": clip_type = comfy.sd.CLIPType.STABLE_AUDIO else: clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION clip_path = folder_paths.get_full_path("clip", clip_name) clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type) return (clip,) class DualCLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "type": (["sdxl", "sd3"], ), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" CATEGORY = "advanced/loaders" def load_clip(self, clip_name1, clip_name2, type): clip_path1 = folder_paths.get_full_path("clip", clip_name1) clip_path2 = folder_paths.get_full_path("clip", clip_name2) if type == "sdxl": clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION elif type == "sd3": clip_type = comfy.sd.CLIPType.SD3 clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type) return (clip,) class CLIPVisionLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ), }} RETURN_TYPES = ("CLIP_VISION",) FUNCTION = "load_clip" CATEGORY = "loaders" def load_clip(self, clip_name): clip_path = folder_paths.get_full_path("clip_vision", clip_name) clip_vision = comfy.clip_vision.load(clip_path) return (clip_vision,) class CLIPVisionEncode: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "image": ("IMAGE",) }} RETURN_TYPES = ("CLIP_VISION_OUTPUT",) FUNCTION = "encode" CATEGORY = "conditioning" def encode(self, clip_vision, image): output = clip_vision.encode_image(image) return (output,) class StyleModelLoader: @classmethod def INPUT_TYPES(s): return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}} RETURN_TYPES = ("STYLE_MODEL",) FUNCTION = "load_style_model" CATEGORY = "loaders" def load_style_model(self, style_model_name): style_model_path = folder_paths.get_full_path("style_models", style_model_name) style_model = comfy.sd.load_style_model(style_model_path) return (style_model,) class StyleModelApply: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "style_model": ("STYLE_MODEL", ), "clip_vision_output": ("CLIP_VISION_OUTPUT", ), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_stylemodel" CATEGORY = "conditioning/style_model" def apply_stylemodel(self, clip_vision_output, style_model, conditioning): cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) c = [] for t in conditioning: n = [torch.cat((t[0], cond), dim=1), t[1].copy()] c.append(n) return (c, ) class unCLIPConditioning: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "clip_vision_output": ("CLIP_VISION_OUTPUT", ), "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_adm" CATEGORY = "conditioning" def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation): if strength == 0: return (conditioning, ) c = [] for t in conditioning: o = t[1].copy() x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation} if "unclip_conditioning" in o: o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x] else: o["unclip_conditioning"] = [x] n = [t[0], o] c.append(n) return (c, ) class GLIGENLoader: @classmethod def INPUT_TYPES(s): return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}} RETURN_TYPES = ("GLIGEN",) FUNCTION = "load_gligen" CATEGORY = "loaders" def load_gligen(self, gligen_name): gligen_path = folder_paths.get_full_path("gligen", gligen_name) gligen = comfy.sd.load_gligen(gligen_path) return (gligen,) class GLIGENTextBoxApply: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning_to": ("CONDITIONING", ), "clip": ("CLIP", ), "gligen_textbox_model": ("GLIGEN", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" CATEGORY = "conditioning/gligen" def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y): c = [] cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled="unprojected") for t in conditioning_to: n = [t[0], t[1].copy()] position_params = [(cond_pooled, height prev = [] if "gligen" in n[1]: prev = n[1]['gligen'][2] n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params) c.append(n) return (c, ) class EmptyLatentImage: def __init__(self): self.device = comfy.model_management.intermediate_device() @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} RETURN_TYPES = ("LATENT",) FUNCTION = "generate" CATEGORY = "latent" def generate(self, width, height, batch_size=1): latent = torch.zeros([batch_size, 4, height return ({"samples":latent}, ) class LatentFromBatch: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), "length": ("INT", {"default": 1, "min": 1, "max": 64}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "frombatch" CATEGORY = "latent/batch" def frombatch(self, samples, batch_index, length): s = samples.copy() s_in = samples["samples"] batch_index = min(s_in.shape[0] - 1, batch_index) length = min(s_in.shape[0] - batch_index, length) s["samples"] = s_in[batch_index:batch_index + length].clone() if "noise_mask" in samples: masks = samples["noise_mask"] if masks.shape[0] == 1: s["noise_mask"] = masks.clone() else: if masks.shape[0] < s_in.shape[0]: masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] s["noise_mask"] = masks[batch_index:batch_index + length].clone() if "batch_index" not in s: s["batch_index"] = [x for x in range(batch_index, batch_index+length)] else: s["batch_index"] = samples["batch_index"][batch_index:batch_index + length] return (s,) class RepeatLatentBatch: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "amount": ("INT", {"default": 1, "min": 1, "max": 64}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "repeat" CATEGORY = "latent/batch" def repeat(self, samples, amount): s = samples.copy() s_in = samples["samples"] s["samples"] = s_in.repeat((amount, 1,1,1)) if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1: masks = samples["noise_mask"] if masks.shape[0] < s_in.shape[0]: masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1)) if "batch_index" in s: offset = max(s["batch_index"]) - min(s["batch_index"]) + 1 s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]] return (s,) class LatentUpscale: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] crop_methods = ["disabled", "center"] @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "crop": (s.crop_methods,)}} RETURN_TYPES = ("LATENT",) FUNCTION = "upscale" CATEGORY = "latent" def upscale(self, samples, upscale_method, width, height, crop): if width == 0 and height == 0: s = samples else: s = samples.copy() if width == 0: height = max(64, height) width = max(64, round(samples["samples"].shape[3] * height / samples["samples"].shape[2])) elif height == 0: width = max(64, width) height = max(64, round(samples["samples"].shape[2] * width / samples["samples"].shape[3])) else: width = max(64, width) height = max(64, height) s["samples"] = comfy.utils.common_upscale(samples["samples"], width return (s,) class LatentUpscaleBy: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), "scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}} RETURN_TYPES = ("LATENT",) FUNCTION = "upscale" CATEGORY = "latent" def upscale(self, samples, upscale_method, scale_by): s = samples.copy() width = round(samples["samples"].shape[3] * scale_by) height = round(samples["samples"].shape[2] * scale_by) s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled") return (s,) class LatentRotate: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],), }} RETURN_TYPES = ("LATENT",) FUNCTION = "rotate" CATEGORY = "latent/transform" def rotate(self, samples, rotation): s = samples.copy() rotate_by = 0 if rotation.startswith("90"): rotate_by = 1 elif rotation.startswith("180"): rotate_by = 2 elif rotation.startswith("270"): rotate_by = 3 s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2]) return (s,) class LatentFlip: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "flip_method": (["x-axis: vertically", "y-axis: horizontally"],), }} RETURN_TYPES = ("LATENT",) FUNCTION = "flip" CATEGORY = "latent/transform" def flip(self, samples, flip_method): s = samples.copy() if flip_method.startswith("x"): s["samples"] = torch.flip(samples["samples"], dims=[2]) elif flip_method.startswith("y"): s["samples"] = torch.flip(samples["samples"], dims=[3]) return (s,) class LatentComposite: @classmethod def INPUT_TYPES(s): return {"required": { "samples_to": ("LATENT",), "samples_from": ("LATENT",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "composite" CATEGORY = "latent" def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0): x = x y = y feather = feather samples_out = samples_to.copy() s = samples_to["samples"].clone() samples_to = samples_to["samples"] samples_from = samples_from["samples"] if feather == 0: s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] else: samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] mask = torch.ones_like(samples_from) for t in range(feather): if y != 0: mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) if y + samples_from.shape[2] < samples_to.shape[2]: mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) if x != 0: mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) if x + samples_from.shape[3] < samples_to.shape[3]: mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) rev_mask = torch.ones_like(mask) - mask s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask samples_out["samples"] = s return (samples_out,) class LatentBlend: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",), "blend_factor": ("FLOAT", { "default": 0.5, "min": 0, "max": 1, "step": 0.01 }), }} RETURN_TYPES = ("LATENT",) FUNCTION = "blend" CATEGORY = "_for_testing" def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"): samples_out = samples1.copy() samples1 = samples1["samples"] samples2 = samples2["samples"] if samples1.shape != samples2.shape: samples2.permute(0, 3, 1, 2) samples2 = comfy.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center') samples2.permute(0, 2, 3, 1) samples_blended = self.blend_mode(samples1, samples2, blend_mode) samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor) samples_out["samples"] = samples_blended return (samples_out,) def blend_mode(self, img1, img2, mode): if mode == "normal": return img2 else: raise ValueError(f"Unsupported blend mode: {mode}") class LatentCrop: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "crop" CATEGORY = "latent/transform" def crop(self, samples, width, height, x, y): s = samples.copy() samples = samples['samples'] x = x y = y if x > (samples.shape[3] - 8): x = samples.shape[3] - 8 if y > (samples.shape[2] - 8): y = samples.shape[2] - 8 new_height = height new_width = width to_x = new_width + x to_y = new_height + y s['samples'] = samples[:,:,y:to_y, x:to_x] return (s,) class SetLatentNoiseMask: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "mask": ("MASK",), }} RETURN_TYPES = ("LATENT",) FUNCTION = "set_mask" CATEGORY = "latent/inpaint" def set_mask(self, samples, mask): s = samples.copy() s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) return (s,) def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): latent_image = latent["samples"] latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) if disable_noise: noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: batch_inds = latent["batch_index"] if "batch_index" in latent else None noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] callback = latent_preview.prepare_callback(model, steps) disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) out = latent.copy() out["samples"] = samples return (out, ) class KSampler: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "latent_image": ("LATENT", ), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("LATENT",) FUNCTION = "sample" CATEGORY = "sampling" def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) class KSamplerAdvanced: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "add_noise": (["enable", "disable"], ), "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "latent_image": ("LATENT", ), "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), "return_with_leftover_noise": (["disable", "enable"], ), } } RETURN_TYPES = ("LATENT",) FUNCTION = "sample" CATEGORY = "sampling" def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0): force_full_denoise = True if return_with_leftover_noise == "enable": force_full_denoise = False disable_noise = False if add_noise == "disable": disable_noise = True return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise) class SaveImage: def __init__(self): self.output_dir = folder_paths.get_output_directory() self.type = "output" self.prefix_append = "" self.compress_level = 4 @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), "filename_prefix": ("STRING", {"default": "ComfyUI"})}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = "image" def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() for (batch_number, image) in enumerate(images): i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) metadata = None if not args.disable_metadata: metadata = PngInfo() if prompt is not None: metadata.add_text("prompt", json.dumps(prompt)) if extra_pnginfo is not None: for x in extra_pnginfo: metadata.add_text(x, json.dumps(extra_pnginfo[x])) filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) file = f"{filename_with_batch_num}_{counter:05}_.png" img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) results.append({ "filename": file, "subfolder": subfolder, "type": self.type }) counter += 1 return { "ui": { "images": results } } class PreviewImage(SaveImage): def __init__(self): self.output_dir = folder_paths.get_temp_directory() self.type = "temp" self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) self.compress_level = 1 @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } class LoadImage: @classmethod def INPUT_TYPES(s): input_dir = folder_paths.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] return {"required": {"image": (sorted(files), {"image_upload": True})}, } CATEGORY = "image" RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "load_image" def load_image(self, image): image_path = folder_paths.get_annotated_filepath(image) img = node_helpers.pillow(Image.open, image_path) output_images = [] output_masks = [] w, h = None, None excluded_formats = ['MPO'] for i in ImageSequence.Iterator(img): i = node_helpers.pillow(ImageOps.exif_transpose, i) if i.mode == 'I': i = i.point(lambda i: i * (1 / 255)) image = i.convert("RGB") if len(output_images) == 0: w = image.size[0] h = image.size[1] if image.size[0] != w or image.size[1] != h: continue image = np.array(image).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] if 'A' in i.getbands(): mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 mask = 1. - torch.from_numpy(mask) else: mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") output_images.append(image) output_masks.append(mask.unsqueeze(0)) if len(output_images) > 1 and img.format not in excluded_formats: output_image = torch.cat(output_images, dim=0) output_mask = torch.cat(output_masks, dim=0) else: output_image = output_images[0] output_mask = output_masks[0] return (output_image, output_mask) @classmethod def IS_CHANGED(s, image): image_path = folder_paths.get_annotated_filepath(image) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) return m.digest().hex() @classmethod def VALIDATE_INPUTS(s, image): if not folder_paths.exists_annotated_filepath(image): return "Invalid image file: {}".format(image) return True class LoadImageMask: _color_channels = ["alpha", "red", "green", "blue"] @classmethod def INPUT_TYPES(s): input_dir = folder_paths.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] return {"required": {"image": (sorted(files), {"image_upload": True}), "channel": (s._color_channels, ), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "load_image" def load_image(self, image, channel): image_path = folder_paths.get_annotated_filepath(image) i = node_helpers.pillow(Image.open, image_path) i = node_helpers.pillow(ImageOps.exif_transpose, i) if i.getbands() != ("R", "G", "B", "A"): if i.mode == 'I': i = i.point(lambda i: i * (1 / 255)) i = i.convert("RGBA") mask = None c = channel[0].upper() if c in i.getbands(): mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0 mask = torch.from_numpy(mask) if c == 'A': mask = 1. - mask else: mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") return (mask.unsqueeze(0),) @classmethod def IS_CHANGED(s, image, channel): image_path = folder_paths.get_annotated_filepath(image) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) return m.digest().hex() @classmethod def VALIDATE_INPUTS(s, image): if not folder_paths.exists_annotated_filepath(image): return "Invalid image file: {}".format(image) return True class ImageScale: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] crop_methods = ["disabled", "center"] @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "crop": (s.crop_methods,)}} RETURN_TYPES = ("IMAGE",) FUNCTION = "upscale" CATEGORY = "image/upscaling" def upscale(self, image, upscale_method, width, height, crop): if width == 0 and height == 0: s = image else: samples = image.movedim(-1,1) if width == 0: width = max(1, round(samples.shape[3] * height / samples.shape[2])) elif height == 0: height = max(1, round(samples.shape[2] * width / samples.shape[3])) s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop) s = s.movedim(1,-1) return (s,) class ImageScaleBy: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}} RETURN_TYPES = ("IMAGE",) FUNCTION = "upscale" CATEGORY = "image/upscaling" def upscale(self, image, upscale_method, scale_by): samples = image.movedim(-1,1) width = round(samples.shape[3] * scale_by) height = round(samples.shape[2] * scale_by) s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") s = s.movedim(1,-1) return (s,) class ImageInvert: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",)}} RETURN_TYPES = ("IMAGE",) FUNCTION = "invert" CATEGORY = "image" def invert(self, image): s = 1.0 - image return (s,) class ImageBatch: @classmethod def INPUT_TYPES(s): return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}} RETURN_TYPES = ("IMAGE",) FUNCTION = "batch" CATEGORY = "image" def batch(self, image1, image2): if image1.shape[1:] != image2.shape[1:]: image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) s = torch.cat((image1, image2), dim=0) return (s,) class EmptyImage: def __init__(self, device="cpu"): self.device = device @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "generate" CATEGORY = "image" def generate(self, width, height, batch_size=1, color=0): r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) return (torch.cat((r, g, b), dim=-1), ) class ImagePadForOutpaint: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}), } } RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "expand_image" CATEGORY = "image" def expand_image(self, image, left, top, right, bottom, feathering): d1, d2, d3, d4 = image.size() new_image = torch.ones( (d1, d2 + top + bottom, d3 + left + right, d4), dtype=torch.float32, ) * 0.5 new_image[:, top:top + d2, left:left + d3, :] = image mask = torch.ones( (d2 + top + bottom, d3 + left + right), dtype=torch.float32, ) t = torch.zeros( (d2, d3), dtype=torch.float32 ) if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3: for i in range(d2): for j in range(d3): dt = i if top != 0 else d2 db = d2 - i if bottom != 0 else d2 dl = j if left != 0 else d3 dr = d3 - j if right != 0 else d3 d = min(dt, db, dl, dr) if d >= feathering: continue v = (feathering - d) / feathering t[i, j] = v * v mask[top:top + d2, left:left + d3] = t return (new_image, mask) NODE_CLASS_MAPPINGS = { "KSampler": KSampler, "CheckpointLoaderSimple": CheckpointLoaderSimple, "CLIPTextEncode": CLIPTextEncode, "CLIPSetLastLayer": CLIPSetLastLayer, "VAEDecode": VAEDecode, "VAEEncode": VAEEncode, "VAEEncodeForInpaint": VAEEncodeForInpaint, "VAELoader": VAELoader, "EmptyLatentImage": EmptyLatentImage, "LatentUpscale": LatentUpscale, "LatentUpscaleBy": LatentUpscaleBy, "LatentFromBatch": LatentFromBatch, "RepeatLatentBatch": RepeatLatentBatch, "SaveImage": SaveImage, "PreviewImage": PreviewImage, "LoadImage": LoadImage, "LoadImageMask": LoadImageMask, "ImageScale": ImageScale, "ImageScaleBy": ImageScaleBy, "ImageInvert": ImageInvert, "ImageBatch": ImageBatch, "ImagePadForOutpaint": ImagePadForOutpaint, "EmptyImage": EmptyImage, "ConditioningAverage": ConditioningAverage , "ConditioningCombine": ConditioningCombine, "ConditioningConcat": ConditioningConcat, "ConditioningSetArea": ConditioningSetArea, "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage, "ConditioningSetAreaStrength": ConditioningSetAreaStrength, "ConditioningSetMask": ConditioningSetMask, "KSamplerAdvanced": KSamplerAdvanced, "SetLatentNoiseMask": SetLatentNoiseMask, "LatentComposite": LatentComposite, "LatentBlend": LatentBlend, "LatentRotate": LatentRotate, "LatentFlip": LatentFlip, "LatentCrop": LatentCrop, "LoraLoader": LoraLoader, "CLIPLoader": CLIPLoader, "UNETLoader": UNETLoader, "DualCLIPLoader": DualCLIPLoader, "CLIPVisionEncode": CLIPVisionEncode, "StyleModelApply": StyleModelApply, "unCLIPConditioning": unCLIPConditioning, "ControlNetApply": ControlNetApply, "ControlNetApplyAdvanced": ControlNetApplyAdvanced, "ControlNetLoader": ControlNetLoader, "DiffControlNetLoader": DiffControlNetLoader, "StyleModelLoader": StyleModelLoader, "CLIPVisionLoader": CLIPVisionLoader, "VAEDecodeTiled": VAEDecodeTiled, "VAEEncodeTiled": VAEEncodeTiled, "unCLIPCheckpointLoader": unCLIPCheckpointLoader, "GLIGENLoader": GLIGENLoader, "GLIGENTextBoxApply": GLIGENTextBoxApply, "InpaintModelConditioning": InpaintModelConditioning, "CheckpointLoader": CheckpointLoader, "DiffusersLoader": DiffusersLoader, "LoadLatent": LoadLatent, "SaveLatent": SaveLatent, "ConditioningZeroOut": ConditioningZeroOut, "ConditioningSetTimestepRange": ConditioningSetTimestepRange, "LoraLoaderModelOnly": LoraLoaderModelOnly, } NODE_DISPLAY_NAME_MAPPINGS = { "KSampler": "KSampler", "KSamplerAdvanced": "KSampler (Advanced)", "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)", "CheckpointLoaderSimple": "Load Checkpoint", "VAELoader": "Load VAE", "LoraLoader": "Load LoRA", "CLIPLoader": "Load CLIP", "ControlNetLoader": "Load ControlNet Model", "DiffControlNetLoader": "Load ControlNet Model (diff)", "StyleModelLoader": "Load Style Model", "CLIPVisionLoader": "Load CLIP Vision", "UpscaleModelLoader": "Load Upscale Model", "CLIPVisionEncode": "CLIP Vision Encode", "StyleModelApply": "Apply Style Model", "CLIPTextEncode": "CLIP Text Encode (Prompt)", "CLIPSetLastLayer": "CLIP Set Last Layer", "ConditioningCombine": "Conditioning (Combine)", "ConditioningAverage ": "Conditioning (Average)", "ConditioningConcat": "Conditioning (Concat)", "ConditioningSetArea": "Conditioning (Set Area)", "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", "ConditioningSetMask": "Conditioning (Set Mask)", "ControlNetApply": "Apply ControlNet", "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)", "VAEEncodeForInpaint": "VAE Encode (for Inpainting)", "SetLatentNoiseMask": "Set Latent Noise Mask", "VAEDecode": "VAE Decode", "VAEEncode": "VAE Encode", "LatentRotate": "Rotate Latent", "LatentFlip": "Flip Latent", "LatentCrop": "Crop Latent", "EmptyLatentImage": "Empty Latent Image", "LatentUpscale": "Upscale Latent", "LatentUpscaleBy": "Upscale Latent By", "LatentComposite": "Latent Composite", "LatentBlend": "Latent Blend", "LatentFromBatch" : "Latent From Batch", "RepeatLatentBatch": "Repeat Latent Batch", "SaveImage": "Save Image", "PreviewImage": "Preview Image", "LoadImage": "Load Image", "LoadImageMask": "Load Image (as Mask)", "ImageScale": "Upscale Image", "ImageScaleBy": "Upscale Image By", "ImageUpscaleWithModel": "Upscale Image (using Model)", "ImageInvert": "Invert Image", "ImagePadForOutpaint": "Pad Image for Outpainting", "ImageBatch": "Batch Images", "VAEDecodeTiled": "VAE Decode (Tiled)", "VAEEncodeTiled": "VAE Encode (Tiled)", } EXTENSION_WEB_DIRS = {} def load_custom_node(module_path, ignore=set()): module_name = os.path.basename(module_path) if os.path.isfile(module_path): sp = os.path.splitext(module_path) module_name = sp[0] try: logging.debug("Trying to load custom node {}".format(module_path)) if os.path.isfile(module_path): module_spec = importlib.util.spec_from_file_location(module_name, module_path) module_dir = os.path.split(module_path)[0] else: module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py")) module_dir = module_path module = importlib.util.module_from_spec(module_spec) sys.modules[module_name] = module module_spec.loader.exec_module(module) if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None: web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY"))) if os.path.isdir(web_dir): EXTENSION_WEB_DIRS[module_name] = web_dir if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: for name in module.NODE_CLASS_MAPPINGS: if name not in ignore: NODE_CLASS_MAPPINGS[name] = module.NODE_CLASS_MAPPINGS[name] if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) return True else: logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") return False except Exception as e: logging.warning(traceback.format_exc()) logging.warning(f"Cannot import {module_path} module for custom nodes: {e}") return False def load_custom_nodes(): base_node_names = set(NODE_CLASS_MAPPINGS.keys()) node_paths = folder_paths.get_folder_paths("custom_nodes") node_import_times = [] for custom_node_path in node_paths: possible_modules = os.listdir(os.path.realpath(custom_node_path)) if "__pycache__" in possible_modules: possible_modules.remove("__pycache__") for possible_module in possible_modules: module_path = os.path.join(custom_node_path, possible_module) if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue if module_path.endswith(".disabled"): continue time_before = time.perf_counter() success = load_custom_node(module_path, base_node_names) node_import_times.append((time.perf_counter() - time_before, module_path, success)) if len(node_import_times) > 0: logging.info("\nImport times for custom nodes:") for n in sorted(node_import_times): if n[2]: import_message = "" else: import_message = " (IMPORT FAILED)" logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) logging.info("") def init_custom_nodes(): extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras") extras_files = [ "nodes_latent.py", "nodes_hypernetwork.py", "nodes_upscale_model.py", "nodes_post_processing.py", "nodes_mask.py", "nodes_compositing.py", "nodes_rebatch.py", "nodes_model_merging.py", "nodes_tomesd.py", "nodes_clip_sdxl.py", "nodes_canny.py", "nodes_freelunch.py", "nodes_custom_sampler.py", "nodes_hypertile.py", "nodes_model_advanced.py", "nodes_model_downscale.py", "nodes_images.py", "nodes_video_model.py", "nodes_sag.py", "nodes_perpneg.py", "nodes_stable3d.py", "nodes_sdupscale.py", "nodes_photomaker.py", "nodes_cond.py", "nodes_morphology.py", "nodes_stable_cascade.py", "nodes_differential_diffusion.py", "nodes_ip2p.py", "nodes_model_merging_model_specific.py", "nodes_pag.py", "nodes_align_your_steps.py", "nodes_attention_multiply.py", "nodes_advanced_samplers.py", "nodes_webcam.py", "nodes_audio.py", "nodes_sd3.py", "nodes_gits.py", ] import_failed = [] for node_file in extras_files: if not load_custom_node(os.path.join(extras_dir, node_file)): import_failed.append(node_file) load_custom_nodes() if len(import_failed) > 0: logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") for node in import_failed: logging.warning("IMPORT FAILED: {}".format(node)) logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.") if args.windows_standalone_build: logging.warning("Please run the update script: update/update_comfyui.bat") else: logging.warning("Please do a: pip install -r requirements.txt") logging.warning("")
from PIL import ImageFile, UnidentifiedImageError def conditioning_set_values(conditioning, values={}): c = [] for t in conditioning: n = [t[0], t[1].copy()] for k in values: n[1][k] = values[k] c.append(n) return c def pillow(fn, arg): prev_value = None try: x = fn(arg) except (OSError, UnidentifiedImageError, ValueError): prev_value = ImageFile.LOAD_TRUNCATED_IMAGES ImageFile.LOAD_TRUNCATED_IMAGES = True x = fn(arg) finally: if prev_value is not None: ImageFile.LOAD_TRUNCATED_IMAGES = prev_value return x
ComfyUI ======= The most powerful and modular stable diffusion GUI and backend. ----------- ![ComfyUI Screenshot](comfyui_screenshot.png) This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out: - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. - Fully supports SD1.x, SD2.x, [SDXL](https: - Asynchronous Queue system - Many optimizations: Only re-executes the parts of the workflow that changes between executions. - Command line option: ```--lowvram``` to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram) - Works even if you don't have a GPU with: ```--cpu``` (slow) - Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models. - Embeddings/Textual inversion - [Loras (regular, locon and loha)](https: - [Hypernetworks](https: - Loading full workflows (with seeds) from generated PNG files. - Saving/Loading workflows as Json files. - Nodes interface can be used to create complex workflows like one for [Hires fix](https: - [Area Composition](https: - [Inpainting](https: - [ControlNet and T2I-Adapter](https: - [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https: - [unCLIP Models](https: - [GLIGEN](https: - [Model Merging](https: - [LCM models and Loras](https: - [SDXL Turbo](https: - Latent previews with [TAESD]( - Starts up very fast. - Works fully offline: will never download anything. - [Config file](extra_model_paths.yaml.example) to set the search paths for models. Workflow examples can be found on the [Examples page](https: | Keybind | Explanation | |------------------------------------|--------------------------------------------------------------------------------------------------------------------| | Ctrl + Enter | Queue up current graph for generation | | Ctrl + Shift + Enter | Queue up current graph as first for generation | | Ctrl + Z/Ctrl + Y | Undo/Redo | | Ctrl + S | Save workflow | | Ctrl + O | Load workflow | | Ctrl + A | Select all nodes | | Alt + C | Collapse/uncollapse selected nodes | | Ctrl + M | Mute/unmute selected nodes | | Ctrl + B | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) | | Delete/Backspace | Delete selected nodes | | Ctrl + Backspace | Delete the current graph | | Space | Move the canvas around when held and moving the cursor | | Ctrl/Shift + Click | Add clicked node to selection | | Ctrl + C/Ctrl + V | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) | | Ctrl + C/Ctrl + Shift + V | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) | | Shift + Drag | Move multiple selected nodes at the same time | | Ctrl + D | Load default graph | | Alt + `+` | Canvas Zoom in | | Alt + `-` | Canvas Zoom out | | Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out | | Q | Toggle visibility of the queue | | H | Toggle visibility of history | | R | Refresh graph | | Double-Click LMB | Open node quick search palette | Ctrl can also be replaced with Cmd instead for macOS users There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https: Simply download, extract with [7-Zip](https: If you have trouble extracting it, right click the file -> properties -> unblock See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor. To run it on services like paperspace, kaggle or colab you can use my [Jupyter Notebook](notebooks/comfyui_colab.ipynb) Git clone this repo. Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints Put your VAE in: models/vae AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version: ```pip install torch torchvision torchaudio --index-url https: This is the command to install the nightly with ROCm 6.0 which might have some performance improvements: ```pip install --pre torch torchvision torchaudio --index-url https: Nvidia users should install stable pytorch using this command: ```pip install torch torchvision torchaudio --extra-index-url https: This is the command to install pytorch nightly instead which might have performance improvements: ```pip install --pre torch torchvision torchaudio --index-url https: If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with: ```pip uninstall torch``` And install it again with the command above. Install the dependencies by opening your terminal inside the ComfyUI folder and: ```pip install -r requirements.txt``` After this you should have everything installed and can proceed to running ComfyUI. Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the [Installation](https: 1. Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed. 1. Follow the instructions to install [Intel's oneAPI Basekit](https: 1. Install the packages for IPEX using the instructions provided in the Installation page for your platform. 1. Follow the [ComfyUI manual installation]( Additional discussion and help can be found [here](https: You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version. 1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https: 1. Follow the [ComfyUI manual installation]( 1. Install the ComfyUI [dependencies]( 1. Launch ComfyUI by running `python main.py` > **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation]( ```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml``` You don't. If you have another UI installed and working with its own python venv you can use that venv to run ComfyUI. You can open up your favorite terminal and activate it: ```source path_to_other_sd_gui/venv/bin/activate``` or on Windows: With Powershell: ```"path_to_other_sd_gui\venv\Scripts\Activate.ps1"``` With cmd.exe: ```"path_to_other_sd_gui\venv\Scripts\activate.bat"``` And then you can use that terminal to run ComfyUI without installing any dependencies. Note that the venv folder might be called something else depending on the SD UI. ```python main.py``` Try running it with this command if you have issues: For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py``` For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py``` Only parts of the graph that have an output with all the correct inputs will be executed. Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed. Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it. You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\). You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\{ or \\}. Dynamic prompts also support C-style comments, like ` To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension): ```embedding:embedding_filename.pt``` Use ```--preview-method auto``` to enable previews. The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https: Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"` Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https: > Note: Windows users can use [alexisrolland/docker-openssl](https: <br/><br/>If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal. [Matrix space: I wanted to learn how Stable Diffusion worked in detail. I also wanted something clean and powerful that would let me experiment with SD without restrictions. This is for anyone that wants to make complex workflows with SD or that wants to learn more how SD works. The interface follows closely how SD works and the code should be much more simple to understand than other SD UIs.
import os import sys import asyncio import traceback import nodes import folder_paths import execution import uuid import urllib import json import glob import struct import ssl from PIL import Image, ImageOps from PIL.PngImagePlugin import PngInfo from io import BytesIO import aiohttp from aiohttp import web import logging import mimetypes from comfy.cli_args import args import comfy.utils import comfy.model_management from app.user_manager import UserManager class BinaryEventTypes: PREVIEW_IMAGE = 1 UNENCODED_PREVIEW_IMAGE = 2 async def send_socket_catch_exception(function, message): try: await function(message) except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err: logging.warning("send error: {}".format(err)) @web.middleware async def cache_control(request: web.Request, handler): response: web.Response = await handler(request) if request.path.endswith('.js') or request.path.endswith('.css'): response.headers.setdefault('Cache-Control', 'no-cache') return response def create_cors_middleware(allowed_origin: str): @web.middleware async def cors_middleware(request: web.Request, handler): if request.method == "OPTIONS": response = web.Response() else: response = await handler(request) response.headers['Access-Control-Allow-Origin'] = allowed_origin response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS' response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization' response.headers['Access-Control-Allow-Credentials'] = 'true' return response return cors_middleware class PromptServer(): def __init__(self, loop): PromptServer.instance = self mimetypes.init() mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8' self.user_manager = UserManager() self.supports = ["custom_nodes_from_web"] self.prompt_queue = None self.loop = loop self.messages = asyncio.Queue() self.number = 0 middlewares = [cache_control] if args.enable_cors_header: middlewares.append(create_cors_middleware(args.enable_cors_header)) max_upload_size = round(args.max_upload_size * 1024 * 1024) self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares) self.sockets = dict() self.web_root = os.path.join(os.path.dirname( os.path.realpath(__file__)), "web") routes = web.RouteTableDef() self.routes = routes self.last_node_id = None self.client_id = None self.on_prompt_handlers = [] @routes.get('/ws') async def websocket_handler(request): ws = web.WebSocketResponse() await ws.prepare(request) sid = request.rel_url.query.get('clientId', '') if sid: self.sockets.pop(sid, None) else: sid = uuid.uuid4().hex self.sockets[sid] = ws try: await self.send("status", { "status": self.get_queue_info(), 'sid': sid }, sid) if self.client_id == sid and self.last_node_id is not None: await self.send("executing", { "node": self.last_node_id }, sid) async for msg in ws: if msg.type == aiohttp.WSMsgType.ERROR: logging.warning('ws connection closed with exception %s' % ws.exception()) finally: self.sockets.pop(sid, None) return ws @routes.get("/") async def get_root(request): return web.FileResponse(os.path.join(self.web_root, "index.html")) @routes.get("/embeddings") def get_embeddings(self): embeddings = folder_paths.get_filename_list("embeddings") return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings))) @routes.get("/extensions") async def get_extensions(request): files = glob.glob(os.path.join( glob.escape(self.web_root), 'extensions*.js'), recursive=True) extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files)) for name, dir in nodes.EXTENSION_WEB_DIRS.items(): files = glob.glob(os.path.join(glob.escape(dir), '**/*.js'), recursive=True) extensions.extend(list(map(lambda f: "/extensions/" + urllib.parse.quote( name) + "/" + os.path.relpath(f, dir).replace("\\", "/"), files))) return web.json_response(extensions) def get_dir_by_type(dir_type): if dir_type is None: dir_type = "input" if dir_type == "input": type_dir = folder_paths.get_input_directory() elif dir_type == "temp": type_dir = folder_paths.get_temp_directory() elif dir_type == "output": type_dir = folder_paths.get_output_directory() return type_dir, dir_type def image_upload(post, image_save_function=None): image = post.get("image") overwrite = post.get("overwrite") image_upload_type = post.get("type") upload_dir, image_upload_type = get_dir_by_type(image_upload_type) if image and image.file: filename = image.filename if not filename: return web.Response(status=400) subfolder = post.get("subfolder", "") full_output_folder = os.path.join(upload_dir, os.path.normpath(subfolder)) filepath = os.path.abspath(os.path.join(full_output_folder, filename)) if os.path.commonpath((upload_dir, filepath)) != upload_dir: return web.Response(status=400) if not os.path.exists(full_output_folder): os.makedirs(full_output_folder) split = os.path.splitext(filename) if overwrite is not None and (overwrite == "true" or overwrite == "1"): pass else: i = 1 while os.path.exists(filepath): filename = f"{split[0]} ({i}){split[1]}" filepath = os.path.join(full_output_folder, filename) i += 1 if image_save_function is not None: image_save_function(image, post, filepath) else: with open(filepath, "wb") as f: f.write(image.file.read()) return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type}) else: return web.Response(status=400) @routes.post("/upload/image") async def upload_image(request): post = await request.post() return image_upload(post) @routes.post("/upload/mask") async def upload_mask(request): post = await request.post() def image_save_function(image, post, filepath): original_ref = json.loads(post.get("original_ref")) filename, output_dir = folder_paths.annotated_filepath(original_ref['filename']) if filename[0] == '/' or '..' in filename: return web.Response(status=400) if output_dir is None: type = original_ref.get("type", "output") output_dir = folder_paths.get_directory_by_type(type) if output_dir is None: return web.Response(status=400) if original_ref.get("subfolder", "") != "": full_output_dir = os.path.join(output_dir, original_ref["subfolder"]) if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: return web.Response(status=403) output_dir = full_output_dir file = os.path.join(output_dir, filename) if os.path.isfile(file): with Image.open(file) as original_pil: metadata = PngInfo() if hasattr(original_pil,'text'): for key in original_pil.text: metadata.add_text(key, original_pil.text[key]) original_pil = original_pil.convert('RGBA') mask_pil = Image.open(image.file).convert('RGBA') new_alpha = mask_pil.getchannel('A') original_pil.putalpha(new_alpha) original_pil.save(filepath, compress_level=4, pnginfo=metadata) return image_upload(post, image_save_function) @routes.get("/view") async def view_image(request): if "filename" in request.rel_url.query: filename = request.rel_url.query["filename"] filename,output_dir = folder_paths.annotated_filepath(filename) if filename[0] == '/' or '..' in filename: return web.Response(status=400) if output_dir is None: type = request.rel_url.query.get("type", "output") output_dir = folder_paths.get_directory_by_type(type) if output_dir is None: return web.Response(status=400) if "subfolder" in request.rel_url.query: full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"]) if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: return web.Response(status=403) output_dir = full_output_dir filename = os.path.basename(filename) file = os.path.join(output_dir, filename) if os.path.isfile(file): if 'preview' in request.rel_url.query: with Image.open(file) as img: preview_info = request.rel_url.query['preview'].split(';') image_format = preview_info[0] if image_format not in ['webp', 'jpeg'] or 'a' in request.rel_url.query.get('channel', ''): image_format = 'webp' quality = 90 if preview_info[-1].isdigit(): quality = int(preview_info[-1]) buffer = BytesIO() if image_format in ['jpeg'] or request.rel_url.query.get('channel', '') == 'rgb': img = img.convert("RGB") img.save(buffer, format=image_format, quality=quality) buffer.seek(0) return web.Response(body=buffer.read(), content_type=f'image/{image_format}', headers={"Content-Disposition": f"filename=\"{filename}\""}) if 'channel' not in request.rel_url.query: channel = 'rgba' else: channel = request.rel_url.query["channel"] if channel == 'rgb': with Image.open(file) as img: if img.mode == "RGBA": r, g, b, a = img.split() new_img = Image.merge('RGB', (r, g, b)) else: new_img = img.convert("RGB") buffer = BytesIO() new_img.save(buffer, format='PNG') buffer.seek(0) return web.Response(body=buffer.read(), content_type='image/png', headers={"Content-Disposition": f"filename=\"{filename}\""}) elif channel == 'a': with Image.open(file) as img: if img.mode == "RGBA": _, _, _, a = img.split() else: a = Image.new('L', img.size, 255) alpha_img = Image.new('RGBA', img.size) alpha_img.putalpha(a) alpha_buffer = BytesIO() alpha_img.save(alpha_buffer, format='PNG') alpha_buffer.seek(0) return web.Response(body=alpha_buffer.read(), content_type='image/png', headers={"Content-Disposition": f"filename=\"{filename}\""}) else: return web.FileResponse(file, headers={"Content-Disposition": f"filename=\"{filename}\""}) return web.Response(status=404) @routes.get("/view_metadata/{folder_name}") async def view_metadata(request): folder_name = request.match_info.get("folder_name", None) if folder_name is None: return web.Response(status=404) if not "filename" in request.rel_url.query: return web.Response(status=404) filename = request.rel_url.query["filename"] if not filename.endswith(".safetensors"): return web.Response(status=404) safetensors_path = folder_paths.get_full_path(folder_name, filename) if safetensors_path is None: return web.Response(status=404) out = comfy.utils.safetensors_header(safetensors_path, max_size=1024*1024) if out is None: return web.Response(status=404) dt = json.loads(out) if not "__metadata__" in dt: return web.Response(status=404) return web.json_response(dt["__metadata__"]) @routes.get("/system_stats") async def get_queue(request): device = comfy.model_management.get_torch_device() device_name = comfy.model_management.get_torch_device_name(device) vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True) vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True) system_stats = { "system": { "os": os.name, "python_version": sys.version, "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded" }, "devices": [ { "name": device_name, "type": device.type, "index": device.index, "vram_total": vram_total, "vram_free": vram_free, "torch_vram_total": torch_vram_total, "torch_vram_free": torch_vram_free, } ] } return web.json_response(system_stats) @routes.get("/prompt") async def get_prompt(request): return web.json_response(self.get_queue_info()) def node_info(node_class): obj_class = nodes.NODE_CLASS_MAPPINGS[node_class] info = {} info['input'] = obj_class.INPUT_TYPES() info['output'] = obj_class.RETURN_TYPES info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES) info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output'] info['name'] = node_class info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[node_class] if node_class in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else node_class info['description'] = obj_class.DESCRIPTION if hasattr(obj_class,'DESCRIPTION') else '' info['category'] = 'sd' if hasattr(obj_class, 'OUTPUT_NODE') and obj_class.OUTPUT_NODE == True: info['output_node'] = True else: info['output_node'] = False if hasattr(obj_class, 'CATEGORY'): info['category'] = obj_class.CATEGORY return info @routes.get("/object_info") async def get_object_info(request): out = {} for x in nodes.NODE_CLASS_MAPPINGS: try: out[x] = node_info(x) except Exception as e: logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.") logging.error(traceback.format_exc()) return web.json_response(out) @routes.get("/object_info/{node_class}") async def get_object_info_node(request): node_class = request.match_info.get("node_class", None) out = {} if (node_class is not None) and (node_class in nodes.NODE_CLASS_MAPPINGS): out[node_class] = node_info(node_class) return web.json_response(out) @routes.get("/history") async def get_history(request): max_items = request.rel_url.query.get("max_items", None) if max_items is not None: max_items = int(max_items) return web.json_response(self.prompt_queue.get_history(max_items=max_items)) @routes.get("/history/{prompt_id}") async def get_history(request): prompt_id = request.match_info.get("prompt_id", None) return web.json_response(self.prompt_queue.get_history(prompt_id=prompt_id)) @routes.get("/queue") async def get_queue(request): queue_info = {} current_queue = self.prompt_queue.get_current_queue() queue_info['queue_running'] = current_queue[0] queue_info['queue_pending'] = current_queue[1] return web.json_response(queue_info) @routes.post("/prompt") async def post_prompt(request): logging.info("got prompt") resp_code = 200 out_string = "" json_data = await request.json() json_data = self.trigger_on_prompt(json_data) if "number" in json_data: number = float(json_data['number']) else: number = self.number if "front" in json_data: if json_data['front']: number = -number self.number += 1 if "prompt" in json_data: prompt = json_data["prompt"] valid = execution.validate_prompt(prompt) extra_data = {} if "extra_data" in json_data: extra_data = json_data["extra_data"] if "client_id" in json_data: extra_data["client_id"] = json_data["client_id"] if valid[0]: prompt_id = str(uuid.uuid4()) outputs_to_execute = valid[2] self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute)) response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]} return web.json_response(response) else: logging.warning("invalid prompt: {}".format(valid[1])) return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400) else: return web.json_response({"error": "no prompt", "node_errors": []}, status=400) @routes.post("/queue") async def post_queue(request): json_data = await request.json() if "clear" in json_data: if json_data["clear"]: self.prompt_queue.wipe_queue() if "delete" in json_data: to_delete = json_data['delete'] for id_to_delete in to_delete: delete_func = lambda a: a[1] == id_to_delete self.prompt_queue.delete_queue_item(delete_func) return web.Response(status=200) @routes.post("/interrupt") async def post_interrupt(request): nodes.interrupt_processing() return web.Response(status=200) @routes.post("/free") async def post_free(request): json_data = await request.json() unload_models = json_data.get("unload_models", False) free_memory = json_data.get("free_memory", False) if unload_models: self.prompt_queue.set_flag("unload_models", unload_models) if free_memory: self.prompt_queue.set_flag("free_memory", free_memory) return web.Response(status=200) @routes.post("/history") async def post_history(request): json_data = await request.json() if "clear" in json_data: if json_data["clear"]: self.prompt_queue.wipe_history() if "delete" in json_data: to_delete = json_data['delete'] for id_to_delete in to_delete: self.prompt_queue.delete_history_item(id_to_delete) return web.Response(status=200) def add_routes(self): self.user_manager.add_routes(self.routes) api_routes = web.RouteTableDef() for route in self.routes: if isinstance(route, web.RouteDef): api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs) self.app.add_routes(api_routes) self.app.add_routes(self.routes) for name, dir in nodes.EXTENSION_WEB_DIRS.items(): self.app.add_routes([ web.static('/extensions/' + urllib.parse.quote(name), dir), ]) self.app.add_routes([ web.static('/', self.web_root), ]) def get_queue_info(self): prompt_info = {} exec_info = {} exec_info['queue_remaining'] = self.prompt_queue.get_tasks_remaining() prompt_info['exec_info'] = exec_info return prompt_info async def send(self, event, data, sid=None): if event == BinaryEventTypes.UNENCODED_PREVIEW_IMAGE: await self.send_image(data, sid=sid) elif isinstance(data, (bytes, bytearray)): await self.send_bytes(event, data, sid) else: await self.send_json(event, data, sid) def encode_bytes(self, event, data): if not isinstance(event, int): raise RuntimeError(f"Binary event types must be integers, got {event}") packed = struct.pack(">I", event) message = bytearray(packed) message.extend(data) return message async def send_image(self, image_data, sid=None): image_type = image_data[0] image = image_data[1] max_size = image_data[2] if max_size is not None: if hasattr(Image, 'Resampling'): resampling = Image.Resampling.BILINEAR else: resampling = Image.ANTIALIAS image = ImageOps.contain(image, (max_size, max_size), resampling) type_num = 1 if image_type == "JPEG": type_num = 1 elif image_type == "PNG": type_num = 2 bytesIO = BytesIO() header = struct.pack(">I", type_num) bytesIO.write(header) image.save(bytesIO, format=image_type, quality=95, compress_level=1) preview_bytes = bytesIO.getvalue() await self.send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid) async def send_bytes(self, event, data, sid=None): message = self.encode_bytes(event, data) if sid is None: sockets = list(self.sockets.values()) for ws in sockets: await send_socket_catch_exception(ws.send_bytes, message) elif sid in self.sockets: await send_socket_catch_exception(self.sockets[sid].send_bytes, message) async def send_json(self, event, data, sid=None): message = {"type": event, "data": data} if sid is None: sockets = list(self.sockets.values()) for ws in sockets: await send_socket_catch_exception(ws.send_json, message) elif sid in self.sockets: await send_socket_catch_exception(self.sockets[sid].send_json, message) def send_sync(self, event, data, sid=None): self.loop.call_soon_threadsafe( self.messages.put_nowait, (event, data, sid)) def queue_updated(self): self.send_sync("status", { "status": self.get_queue_info() }) async def publish_loop(self): while True: msg = await self.messages.get() await self.send(*msg) async def start(self, address, port, verbose=True, call_on_start=None): runner = web.AppRunner(self.app, access_log=None) await runner.setup() ssl_ctx = None scheme = "http" if args.tls_keyfile and args.tls_certfile: ssl_ctx = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE) ssl_ctx.load_cert_chain(certfile=args.tls_certfile, keyfile=args.tls_keyfile) scheme = "https" site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx) await site.start() if verbose: logging.info("Starting server\n") logging.info("To see the GUI go to: {}: if call_on_start is not None: call_on_start(scheme, address, port) def add_on_prompt_handler(self, handler): self.on_prompt_handlers.append(handler) def trigger_on_prompt(self, json_data): for handler in self.on_prompt_handlers: try: json_data = handler(json_data) except Exception as e: logging.warning(f"[ERROR] An error occurred during the on_prompt_handler processing") logging.warning(traceback.format_exc()) return json_data
import pygit2 from datetime import datetime import sys import os import shutil import filecmp def pull(repo, remote_name='origin', branch='master'): for remote in repo.remotes: if remote.name == remote_name: remote.fetch() remote_master_id = repo.lookup_reference('refs/remotes/origin/%s' % (branch)).target merge_result, _ = repo.merge_analysis(remote_master_id) if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE: return elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD: repo.checkout_tree(repo.get(remote_master_id)) try: master_ref = repo.lookup_reference('refs/heads/%s' % (branch)) master_ref.set_target(remote_master_id) except KeyError: repo.create_branch(branch, repo.get(remote_master_id)) repo.head.set_target(remote_master_id) elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL: repo.merge(remote_master_id) if repo.index.conflicts is not None: for conflict in repo.index.conflicts: print('Conflicts found in:', conflict[0].path) raise AssertionError('Conflicts, ahhhhh!!') user = repo.default_signature tree = repo.index.write_tree() commit = repo.create_commit('HEAD', user, user, 'Merge!', tree, [repo.head.target, remote_master_id]) repo.state_cleanup() else: raise AssertionError('Unknown merge analysis result') pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0) repo_path = str(sys.argv[1]) repo = pygit2.Repository(repo_path) ident = pygit2.Signature('comfyui', 'comfy@ui') try: print("stashing current changes") repo.stash(ident) except KeyError: print("nothing to stash") backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S')) print("creating backup branch: {}".format(backup_branch_name)) try: repo.branches.local.create(backup_branch_name, repo.head.peel()) except: pass print("checking out master branch") branch = repo.lookup_branch('master') ref = repo.lookup_reference(branch.name) repo.checkout(ref) print("pulling latest changes") pull(repo) print("Done!") self_update = True if len(sys.argv) > 2: self_update = '--skip_self_update' not in sys.argv update_py_path = os.path.realpath(__file__) repo_update_py_path = os.path.join(repo_path, ".ci/update_windows/update.py") cur_path = os.path.dirname(update_py_path) req_path = os.path.join(cur_path, "current_requirements.txt") repo_req_path = os.path.join(repo_path, "requirements.txt") def files_equal(file1, file2): try: return filecmp.cmp(file1, file2, shallow=False) except: return False def file_size(f): try: return os.path.getsize(f) except: return 0 if self_update and not files_equal(update_py_path, repo_update_py_path) and file_size(repo_update_py_path) > 10: shutil.copy(repo_update_py_path, os.path.join(cur_path, "update_new.py")) exit() if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path): import subprocess try: subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', '-r', repo_req_path]) shutil.copy(repo_req_path, req_path) except: pass
name: Tests CI on: [push, pull_request] jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-node@v3 with: node-version: 18 - uses: actions/setup-python@v4 with: python-version: '3.10' - name: Install requirements run: | python -m pip install --upgrade pip pip install torch torchvision torchaudio --index-url https: pip install -r requirements.txt - name: Run Tests run: | npm ci npm run test:generate npm test -- --verbose working-directory: ./tests-ui
import os import json from aiohttp import web class AppSettings(): def __init__(self, user_manager): self.user_manager = user_manager def get_settings(self, request): file = self.user_manager.get_request_user_filepath( request, "comfy.settings.json") if os.path.isfile(file): with open(file) as f: return json.load(f) else: return {} def save_settings(self, request, settings): file = self.user_manager.get_request_user_filepath( request, "comfy.settings.json") with open(file, "w") as f: f.write(json.dumps(settings, indent=4)) def add_routes(self, routes): @routes.get("/settings") async def get_settings(request): return web.json_response(self.get_settings(request)) @routes.get("/settings/{id}") async def get_setting(request): value = None settings = self.get_settings(request) setting_id = request.match_info.get("id", None) if setting_id and setting_id in settings: value = settings[setting_id] return web.json_response(value) @routes.post("/settings") async def post_settings(request): settings = self.get_settings(request) new_settings = await request.json() self.save_settings(request, {**settings, **new_settings}) return web.Response(status=200) @routes.post("/settings/{id}") async def post_setting(request): setting_id = request.match_info.get("id", None) if not setting_id: return web.Response(status=400) settings = self.get_settings(request) settings[setting_id] = await request.json() self.save_settings(request, settings) return web.Response(status=200)
import json import os import re import uuid from aiohttp import web from comfy.cli_args import args from folder_paths import user_directory from .app_settings import AppSettings default_user = "default" users_file = os.path.join(user_directory, "users.json") class UserManager(): def __init__(self): global user_directory self.settings = AppSettings(self) if not os.path.exists(user_directory): os.mkdir(user_directory) if not args.multi_user: print("****** User settings have been changed to be stored on the server instead of browser storage. ******") print("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******") if args.multi_user: if os.path.isfile(users_file): with open(users_file) as f: self.users = json.load(f) else: self.users = {} else: self.users = {"default": "default"} def get_request_user_id(self, request): user = "default" if args.multi_user and "comfy-user" in request.headers: user = request.headers["comfy-user"] if user not in self.users: raise KeyError("Unknown user: " + user) return user def get_request_user_filepath(self, request, file, type="userdata", create_dir=True): global user_directory if type == "userdata": root_dir = user_directory else: raise KeyError("Unknown filepath type:" + type) user = self.get_request_user_id(request) path = user_root = os.path.abspath(os.path.join(root_dir, user)) if os.path.commonpath((root_dir, user_root)) != root_dir: return None parent = user_root if file is not None: path = os.path.abspath(os.path.join(user_root, file)) if os.path.commonpath((user_root, path)) != user_root: return None if create_dir and not os.path.exists(parent): os.mkdir(parent) return path def add_user(self, name): name = name.strip() if not name: raise ValueError("username not provided") user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name) user_id = user_id + "_" + str(uuid.uuid4()) self.users[user_id] = name global users_file with open(users_file, "w") as f: json.dump(self.users, f) return user_id def add_routes(self, routes): self.settings.add_routes(routes) @routes.get("/users") async def get_users(request): if args.multi_user: return web.json_response({"storage": "server", "users": self.users}) else: user_dir = self.get_request_user_filepath(request, None, create_dir=False) return web.json_response({ "storage": "server", "migrated": os.path.exists(user_dir) }) @routes.post("/users") async def post_users(request): body = await request.json() username = body["username"] if username in self.users.values(): return web.json_response({"error": "Duplicate username."}, status=400) user_id = self.add_user(username) return web.json_response(user_id) @routes.get("/userdata/{file}") async def getuserdata(request): file = request.match_info.get("file", None) if not file: return web.Response(status=400) path = self.get_request_user_filepath(request, file) if not path: return web.Response(status=403) if not os.path.exists(path): return web.Response(status=404) return web.FileResponse(path) @routes.post("/userdata/{file}") async def post_userdata(request): file = request.match_info.get("file", None) if not file: return web.Response(status=400) path = self.get_request_user_filepath(request, file) if not path: return web.Response(status=403) body = await request.read() with open(path, "wb") as f: f.write(body) return web.Response(status=200)
import pickle load = pickle.load class Empty: pass class Unpickler(pickle.Unpickler): def find_class(self, module, name): if module.startswith("pytorch_lightning"): return Empty return super().find_class(module, name)
{ "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 2, "hidden_act": "gelu", "hidden_size": 1280, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 5120, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 20, "num_hidden_layers": 32, "pad_token_id": 1, "projection_dim": 1280, "torch_dtype": "float32", "vocab_size": 49408 }
import torch from comfy.ldm.modules.attention import optimized_attention_for_device class CLIPAttention(torch.nn.Module): def __init__(self, embed_dim, heads, dtype, device, operations): super().__init__() self.heads = heads self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) def forward(self, x, mask=None, optimized_attention=None): q = self.q_proj(x) k = self.k_proj(x) v = self.v_proj(x) out = optimized_attention(q, k, v, self.heads, mask) return self.out_proj(out) ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), "gelu": torch.nn.functional.gelu, } class CLIPMLP(torch.nn.Module): def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations): super().__init__() self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device) self.activation = ACTIVATIONS[activation] self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device) def forward(self, x): x = self.fc1(x) x = self.activation(x) x = self.fc2(x) return x class CLIPLayer(torch.nn.Module): def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): super().__init__() self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations) def forward(self, x, mask=None, optimized_attention=None): x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) x += self.mlp(self.layer_norm2(x)) return x class CLIPEncoder(torch.nn.Module): def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): super().__init__() self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)]) def forward(self, x, mask=None, intermediate_output=None): optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) if intermediate_output is not None: if intermediate_output < 0: intermediate_output = len(self.layers) + intermediate_output intermediate = None for i, l in enumerate(self.layers): x = l(x, mask, optimized_attention) if i == intermediate_output: intermediate = x.clone() return x, intermediate class CLIPEmbeddings(torch.nn.Module): def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None): super().__init__() self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device) self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) def forward(self, input_tokens): return self.token_embedding(input_tokens) + self.position_embedding.weight class CLIPTextModel_(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): num_layers = config_dict["num_hidden_layers"] embed_dim = config_dict["hidden_size"] heads = config_dict["num_attention_heads"] intermediate_size = config_dict["intermediate_size"] intermediate_activation = config_dict["hidden_act"] super().__init__() self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device) self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True): x = self.embeddings(input_tokens) mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) if mask is not None: mask += causal_mask else: mask = causal_mask x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) x = self.final_layer_norm(x) if i is not None and final_layer_norm_intermediate: i = self.final_layer_norm(i) pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),] return x, i, pooled_output class CLIPTextModel(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.num_layers = config_dict["num_hidden_layers"] self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) embed_dim = config_dict["hidden_size"] self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) self.text_projection.weight.copy_(torch.eye(embed_dim)) self.dtype = dtype def get_input_embeddings(self): return self.text_model.embeddings.token_embedding def set_input_embeddings(self, embeddings): self.text_model.embeddings.token_embedding = embeddings def forward(self, *args, **kwargs): x = self.text_model(*args, **kwargs) out = self.text_projection(x[2]) return (x[0], x[1], out, x[2]) class CLIPVisionEmbeddings(torch.nn.Module): def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None): super().__init__() self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device)) self.patch_embedding = operations.Conv2d( in_channels=num_channels, out_channels=embed_dim, kernel_size=patch_size, stride=patch_size, bias=False, dtype=dtype, device=device ) num_patches = (image_size num_positions = num_patches + 1 self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) def forward(self, pixel_values): embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2) return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device) class CLIPVision(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() num_layers = config_dict["num_hidden_layers"] embed_dim = config_dict["hidden_size"] heads = config_dict["num_attention_heads"] intermediate_size = config_dict["intermediate_size"] intermediate_activation = config_dict["hidden_act"] self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=torch.float32, device=device, operations=operations) self.pre_layrnorm = operations.LayerNorm(embed_dim) self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) self.post_layernorm = operations.LayerNorm(embed_dim) def forward(self, pixel_values, attention_mask=None, intermediate_output=None): x = self.embeddings(pixel_values) x = self.pre_layrnorm(x) x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output) pooled_output = self.post_layernorm(x[:, 0, :]) return x, i, pooled_output class CLIPVisionModelProjection(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.vision_model = CLIPVision(config_dict, dtype, device, operations) self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False) def forward(self, *args, **kwargs): x = self.vision_model(*args, **kwargs) out = self.visual_projection(x[2]) return (x[0], x[1], out)
from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace import os import torch import json import logging import comfy.ops import comfy.model_patcher import comfy.model_management import comfy.utils import comfy.clip_model class Output: def __getitem__(self, key): return getattr(self, key) def __setitem__(self, key, item): setattr(self, key, item) def clip_preprocess(image, size=224): mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype) std = torch.tensor([0.26862954,0.26130258,0.27577711], device=image.device, dtype=image.dtype) image = image.movedim(-1, 1) if not (image.shape[2] == size and image.shape[3] == size): scale = (size / min(image.shape[2], image.shape[3])) image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode="bicubic", antialias=True) h = (image.shape[2] - size) w = (image.shape[3] - size) image = image[:,:,h:h+size,w:w+size] image = torch.clip((255. * image), 0, 255).round() / 255.0 return (image - mean.view([3,1,1])) / std.view([3,1,1]) class ClipVisionModel(): def __init__(self, json_config): with open(json_config) as f: config = json.load(f) self.load_device = comfy.model_management.text_encoder_device() offload_device = comfy.model_management.text_encoder_offload_device() self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops.manual_cast) self.model.eval() self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) def load_sd(self, sd): return self.model.load_state_dict(sd, strict=False) def get_sd(self): return self.model.state_dict() def encode_image(self, image): comfy.model_management.load_model_gpu(self.patcher) pixel_values = clip_preprocess(image.to(self.load_device)).float() out = self.model(pixel_values=pixel_values, intermediate_output=-2) outputs = Output() outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device()) outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device()) outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device()) return outputs def convert_to_transformers(sd, prefix): sd_k = sd.keys() if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k: keys_to_replace = { "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding", "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight", "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight", "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias", "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight", "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias", "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight", } for x in keys_to_replace: if x in sd_k: sd[keys_to_replace[x]] = sd.pop(x) if "{}proj".format(prefix) in sd_k: sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1) sd = transformers_convert(sd, prefix, "vision_model.", 48) else: replace_prefix = {prefix: ""} sd = state_dict_prefix_replace(sd, replace_prefix) return sd def load_clipvision_from_sd(sd, prefix="", convert_keys=False): if convert_keys: sd = convert_to_transformers(sd, prefix) if "vision_model.encoder.layers.47.layer_norm1.weight" in sd: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json") elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") else: return None clip = ClipVisionModel(json_config) m, u = clip.load_sd(sd) if len(m) > 0: logging.warning("missing clip vision: {}".format(m)) u = set(u) keys = list(sd.keys()) for k in keys: if k not in u: t = sd.pop(k) del t return clip def load(ckpt_path): sd = load_torch_file(ckpt_path) if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd: return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True) else: return load_clipvision_from_sd(sd)
{ "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "gelu", "hidden_size": 1664, "image_size": 224, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 8192, "layer_norm_eps": 1e-05, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 48, "patch_size": 14, "projection_dim": 1280, "torch_dtype": "float32" }
{ "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "gelu", "hidden_size": 1280, "image_size": 224, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 5120, "layer_norm_eps": 1e-05, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 32, "patch_size": 14, "projection_dim": 1024, "torch_dtype": "float32" }
{ "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "quick_gelu", "hidden_size": 1024, "image_size": 224, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-05, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 24, "patch_size": 14, "projection_dim": 768, "torch_dtype": "float32" }
import argparse import enum import comfy.options class EnumAction(argparse.Action): """ Argparse action for handling Enums """ def __init__(self, **kwargs): enum_type = kwargs.pop("type", None) if enum_type is None: raise ValueError("type must be assigned an Enum when using EnumAction") if not issubclass(enum_type, enum.Enum): raise TypeError("type must be an Enum when using EnumAction") choices = tuple(e.value for e in enum_type) kwargs.setdefault("choices", choices) kwargs.setdefault("metavar", f"[{','.join(list(choices))}]") super(EnumAction, self).__init__(**kwargs) self._enum = enum_type def __call__(self, parser, namespace, values, option_string=None): value = self._enum(values) setattr(namespace, self.dest, value) parser = argparse.ArgumentParser() parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)") parser.add_argument("--port", type=int, default=8188, help="Set the listen port.") parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https: parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https: parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.") parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.") parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.") parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.") parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).") parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory.") parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.") parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.") parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.") cm_group = parser.add_mutually_exclusive_group() cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).") cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.") fp_group = parser.add_mutually_exclusive_group() fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).") fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.") fpunet_group = parser.add_mutually_exclusive_group() fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.") fpunet_group.add_argument("--fp16-unet", action="store_true", help="Store unet weights in fp16.") fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.") fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.") fpvae_group = parser.add_mutually_exclusive_group() fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.") fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.") fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.") parser.add_argument("--cpu-vae", action="store_true", help="Run the VAE on the CPU.") fpte_group = parser.add_mutually_exclusive_group() fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Store text encoder weights in fp8 (e4m3fn variant).") fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).") fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.") fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.") parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.") parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.") class LatentPreviewMethod(enum.Enum): NoPreviews = "none" Auto = "auto" Latent2RGB = "latent2rgb" TAESD = "taesd" parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction) attn_group = parser.add_mutually_exclusive_group() attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.") attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.") attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.") parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.") upcast = parser.add_mutually_exclusive_group() upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.") upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.") vram_group = parser.add_mutually_exclusive_group() vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).") vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.") vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.") vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.") vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.") vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).") parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.") parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.") parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.") parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.") parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).") parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.") parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.") if comfy.options.args_parsing: args = parser.parse_args() else: args = parser.parse_args([]) if args.windows_standalone_build: args.auto_launch = True if args.disable_auto_launch: args.auto_launch = False import logging logging_level = logging.INFO if args.verbose: logging_level = logging.DEBUG logging.basicConfig(format="%(message)s", level=logging_level)
import torch import math import comfy.utils def lcm(a, b): return abs(a*b) class CONDRegular: def __init__(self, cond): self.cond = cond def _copy_with(self, cond): return self.__class__(cond) def process_cond(self, batch_size, device, **kwargs): return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device)) def can_concat(self, other): if self.cond.shape != other.cond.shape: return False return True def concat(self, others): conds = [self.cond] for x in others: conds.append(x.cond) return torch.cat(conds) class CONDNoiseShape(CONDRegular): def process_cond(self, batch_size, device, area, **kwargs): data = self.cond if area is not None: dims = len(area) for i in range(dims): data = data.narrow(i + 2, area[i + dims], area[i]) return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device)) class CONDCrossAttn(CONDRegular): def can_concat(self, other): s1 = self.cond.shape s2 = other.cond.shape if s1 != s2: if s1[0] != s2[0] or s1[2] != s2[2]: return False mult_min = lcm(s1[1], s2[1]) diff = mult_min if diff > 4: return False return True def concat(self, others): conds = [self.cond] crossattn_max_len = self.cond.shape[1] for x in others: c = x.cond crossattn_max_len = lcm(crossattn_max_len, c.shape[1]) conds.append(c) out = [] for c in conds: if c.shape[1] < crossattn_max_len: c = c.repeat(1, crossattn_max_len out.append(c) return torch.cat(out) class CONDConstant(CONDRegular): def __init__(self, cond): self.cond = cond def process_cond(self, batch_size, device, **kwargs): return self._copy_with(self.cond) def can_concat(self, other): if self.cond != other.cond: return False return True def concat(self, others): return self.cond
import torch import math import os import logging import comfy.utils import comfy.model_management import comfy.model_detection import comfy.model_patcher import comfy.ops import comfy.cldm.cldm import comfy.t2i_adapter.adapter import comfy.ldm.cascade.controlnet def broadcast_image_to(tensor, target_batch_size, batched_number): current_batch_size = tensor.shape[0] if current_batch_size == 1: return tensor per_batch = target_batch_size tensor = tensor[:per_batch] if per_batch > tensor.shape[0]: tensor = torch.cat([tensor] * (per_batch current_batch_size = tensor.shape[0] if current_batch_size == target_batch_size: return tensor else: return torch.cat([tensor] * batched_number, dim=0) class ControlBase: def __init__(self, device=None): self.cond_hint_original = None self.cond_hint = None self.strength = 1.0 self.timestep_percent_range = (0.0, 1.0) self.global_average_pooling = False self.timestep_range = None self.compression_ratio = 8 self.upscale_algorithm = 'nearest-exact' if device is None: device = comfy.model_management.get_torch_device() self.device = device self.previous_controlnet = None def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)): self.cond_hint_original = cond_hint self.strength = strength self.timestep_percent_range = timestep_percent_range return self def pre_run(self, model, percent_to_timestep_function): self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1])) if self.previous_controlnet is not None: self.previous_controlnet.pre_run(model, percent_to_timestep_function) def set_previous_controlnet(self, controlnet): self.previous_controlnet = controlnet return self def cleanup(self): if self.previous_controlnet is not None: self.previous_controlnet.cleanup() if self.cond_hint is not None: del self.cond_hint self.cond_hint = None self.timestep_range = None def get_models(self): out = [] if self.previous_controlnet is not None: out += self.previous_controlnet.get_models() return out def copy_to(self, c): c.cond_hint_original = self.cond_hint_original c.strength = self.strength c.timestep_percent_range = self.timestep_percent_range c.global_average_pooling = self.global_average_pooling c.compression_ratio = self.compression_ratio c.upscale_algorithm = self.upscale_algorithm def inference_memory_requirements(self, dtype): if self.previous_controlnet is not None: return self.previous_controlnet.inference_memory_requirements(dtype) return 0 def control_merge(self, control_input, control_output, control_prev, output_dtype): out = {'input':[], 'middle':[], 'output': []} if control_input is not None: for i in range(len(control_input)): key = 'input' x = control_input[i] if x is not None: x *= self.strength if x.dtype != output_dtype: x = x.to(output_dtype) out[key].insert(0, x) if control_output is not None: for i in range(len(control_output)): if i == (len(control_output) - 1): key = 'middle' index = 0 else: key = 'output' index = i x = control_output[i] if x is not None: if self.global_average_pooling: x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) x *= self.strength if x.dtype != output_dtype: x = x.to(output_dtype) out[key].append(x) if control_prev is not None: for x in ['input', 'middle', 'output']: o = out[x] for i in range(len(control_prev[x])): prev_val = control_prev[x][i] if i >= len(o): o.append(prev_val) elif prev_val is not None: if o[i] is None: o[i] = prev_val else: if o[i].shape[0] < prev_val.shape[0]: o[i] = prev_val + o[i] else: o[i] += prev_val return out class ControlNet(ControlBase): def __init__(self, control_model=None, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None): super().__init__(device) self.control_model = control_model self.load_device = load_device if control_model is not None: self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device()) self.global_average_pooling = global_average_pooling self.model_sampling_current = None self.manual_cast_dtype = manual_cast_dtype def get_control(self, x_noisy, t, cond, batched_number): control_prev = None if self.previous_controlnet is not None: control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) if self.timestep_range is not None: if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: if control_prev is not None: return control_prev else: return None dtype = self.control_model.dtype if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype output_dtype = x_noisy.dtype if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.cond_hint = None self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio, self.upscale_algorithm, "center").to(dtype).to(self.device) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) context = cond.get('crossattn_controlnet', cond['c_crossattn']) y = cond.get('y', None) if y is not None: y = y.to(dtype) timestep = self.model_sampling_current.timestep(t) x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y) return self.control_merge(None, control, control_prev, output_dtype) def copy(self): c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) c.control_model = self.control_model c.control_model_wrapped = self.control_model_wrapped self.copy_to(c) return c def get_models(self): out = super().get_models() out.append(self.control_model_wrapped) return out def pre_run(self, model, percent_to_timestep_function): super().pre_run(model, percent_to_timestep_function) self.model_sampling_current = model.model_sampling def cleanup(self): self.model_sampling_current = None super().cleanup() class ControlLoraOps: class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp): def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super().__init__() self.in_features = in_features self.out_features = out_features self.weight = None self.up = None self.down = None self.bias = None def forward(self, input): weight, bias = comfy.ops.cast_bias_weight(self, input) if self.up is not None: return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) else: return torch.nn.functional.linear(input, weight, bias) class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp): def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.dilation = dilation self.transposed = False self.output_padding = 0 self.groups = groups self.padding_mode = padding_mode self.weight = None self.bias = None self.up = None self.down = None def forward(self, input): weight, bias = comfy.ops.cast_bias_weight(self, input) if self.up is not None: return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) else: return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) class ControlLora(ControlNet): def __init__(self, control_weights, global_average_pooling=False, device=None): ControlBase.__init__(self, device) self.control_weights = control_weights self.global_average_pooling = global_average_pooling def pre_run(self, model, percent_to_timestep_function): super().pre_run(model, percent_to_timestep_function) controlnet_config = model.model_config.unet_config.copy() controlnet_config.pop("out_channels") controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] self.manual_cast_dtype = model.manual_cast_dtype dtype = model.get_dtype() if self.manual_cast_dtype is None: class control_lora_ops(ControlLoraOps, comfy.ops.disable_weight_init): pass else: class control_lora_ops(ControlLoraOps, comfy.ops.manual_cast): pass dtype = self.manual_cast_dtype controlnet_config["operations"] = control_lora_ops controlnet_config["dtype"] = dtype self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) self.control_model.to(comfy.model_management.get_torch_device()) diffusion_model = model.diffusion_model sd = diffusion_model.state_dict() cm = self.control_model.state_dict() for k in sd: weight = sd[k] try: comfy.utils.set_attr_param(self.control_model, k, weight) except: pass for k in self.control_weights: if k not in {"lora_controlnet"}: comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) def copy(self): c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) self.copy_to(c) return c def cleanup(self): del self.control_model self.control_model = None super().cleanup() def get_models(self): out = ControlBase.get_models(self) return out def inference_memory_requirements(self, dtype): return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) def load_controlnet(ckpt_path, model=None): controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) if "lora_controlnet" in controlnet_data: return ControlLora(controlnet_data) controlnet_config = None supported_inference_dtypes = None if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data) diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config) diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" count = 0 loop = True while loop: suffix = [".weight", ".bias"] for s in suffix: k_in = "controlnet_down_blocks.{}{}".format(count, s) k_out = "zero_convs.{}.0{}".format(count, s) if k_in not in controlnet_data: loop = False break diffusers_keys[k_in] = k_out count += 1 count = 0 loop = True while loop: suffix = [".weight", ".bias"] for s in suffix: if count == 0: k_in = "controlnet_cond_embedding.conv_in{}".format(s) else: k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s) k_out = "input_hint_block.{}{}".format(count * 2, s) if k_in not in controlnet_data: k_in = "controlnet_cond_embedding.conv_out{}".format(s) loop = False diffusers_keys[k_in] = k_out count += 1 new_sd = {} for k in diffusers_keys: if k in controlnet_data: new_sd[diffusers_keys[k]] = controlnet_data.pop(k) leftover_keys = controlnet_data.keys() if len(leftover_keys) > 0: logging.warning("leftover keys: {}".format(leftover_keys)) controlnet_data = new_sd pth_key = 'control_model.zero_convs.0.0.weight' pth = False key = 'zero_convs.0.0.weight' if pth_key in controlnet_data: pth = True key = pth_key prefix = "control_model." elif key in controlnet_data: prefix = "" else: net = load_t2i_adapter(controlnet_data) if net is None: logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path)) return net if controlnet_config is None: model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True) supported_inference_dtypes = model_config.supported_inference_dtypes controlnet_config = model_config.unet_config load_device = comfy.model_management.get_torch_device() if supported_inference_dtypes is None: unet_dtype = comfy.model_management.unet_dtype() else: unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes) manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) if manual_cast_dtype is not None: controlnet_config["operations"] = comfy.ops.manual_cast controlnet_config["dtype"] = unet_dtype controlnet_config.pop("out_channels") controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) if pth: if 'difference' in controlnet_data: if model is not None: comfy.model_management.load_models_gpu([model]) model_sd = model.model_state_dict() for x in controlnet_data: c_m = "control_model." if x.startswith(c_m): sd_key = "diffusion_model.{}".format(x[len(c_m):]) if sd_key in model_sd: cd = controlnet_data[x] cd += model_sd[sd_key].type(cd.dtype).to(cd.device) else: logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") class WeightsLoader(torch.nn.Module): pass w = WeightsLoader() w.control_model = control_model missing, unexpected = w.load_state_dict(controlnet_data, strict=False) else: missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) if len(missing) > 0: logging.warning("missing controlnet keys: {}".format(missing)) if len(unexpected) > 0: logging.debug("unexpected controlnet keys: {}".format(unexpected)) global_average_pooling = False filename = os.path.splitext(ckpt_path)[0] if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): global_average_pooling = True control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype) return control class T2IAdapter(ControlBase): def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None): super().__init__(device) self.t2i_model = t2i_model self.channels_in = channels_in self.control_input = None self.compression_ratio = compression_ratio self.upscale_algorithm = upscale_algorithm def scale_image_to(self, width, height): unshuffle_amount = self.t2i_model.unshuffle_amount width = math.ceil(width / unshuffle_amount) * unshuffle_amount height = math.ceil(height / unshuffle_amount) * unshuffle_amount return width, height def get_control(self, x_noisy, t, cond, batched_number): control_prev = None if self.previous_controlnet is not None: control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) if self.timestep_range is not None: if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: if control_prev is not None: return control_prev else: return None if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.control_input = None self.cond_hint = None width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio) self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device) if self.channels_in == 1 and self.cond_hint.shape[1] > 1: self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) if self.control_input is None: self.t2i_model.to(x_noisy.dtype) self.t2i_model.to(self.device) self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) self.t2i_model.cpu() control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input)) mid = None if self.t2i_model.xl == True: mid = control_input[-1:] control_input = control_input[:-1] return self.control_merge(control_input, mid, control_prev, x_noisy.dtype) def copy(self): c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm) self.copy_to(c) return c def load_t2i_adapter(t2i_data): compression_ratio = 8 upscale_algorithm = 'nearest-exact' if 'adapter' in t2i_data: t2i_data = t2i_data['adapter'] if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: prefix_replace = {} for i in range(4): for j in range(2): prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j) prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2) prefix_replace["adapter."] = "" t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace) keys = t2i_data.keys() if "body.0.in_conv.weight" in keys: cin = t2i_data['body.0.in_conv.weight'].shape[1] model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) elif 'conv_in.weight' in keys: cin = t2i_data['conv_in.weight'].shape[1] channel = t2i_data['conv_in.weight'].shape[0] ksize = t2i_data['body.0.block2.weight'].shape[2] use_conv = False down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) if len(down_opts) > 0: use_conv = True xl = False if cin == 256 or cin == 768: xl = True model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) elif "backbone.0.0.weight" in keys: model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) compression_ratio = 32 upscale_algorithm = 'bilinear' elif "backbone.10.blocks.0.weight" in keys: model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) compression_ratio = 1 upscale_algorithm = 'nearest-exact' else: return None missing, unexpected = model_ad.load_state_dict(t2i_data) if len(missing) > 0: logging.warning("t2i missing {}".format(missing)) if len(unexpected) > 0: logging.debug("t2i unexpected {}".format(unexpected)) return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm)
import re import torch import logging unet_conversion_map = [ ("time_embed.0.weight", "time_embedding.linear_1.weight"), ("time_embed.0.bias", "time_embedding.linear_1.bias"), ("time_embed.2.weight", "time_embedding.linear_2.weight"), ("time_embed.2.bias", "time_embedding.linear_2.bias"), ("input_blocks.0.0.weight", "conv_in.weight"), ("input_blocks.0.0.bias", "conv_in.bias"), ("out.0.weight", "conv_norm_out.weight"), ("out.0.bias", "conv_norm_out.bias"), ("out.2.weight", "conv_out.weight"), ("out.2.bias", "conv_out.bias"), ] unet_conversion_map_resnet = [ ("in_layers.0", "norm1"), ("in_layers.2", "conv1"), ("out_layers.0", "norm2"), ("out_layers.3", "conv2"), ("emb_layers.1", "time_emb_proj"), ("skip_connection", "conv_shortcut"), ] unet_conversion_map_layer = [] for i in range(4): for j in range(2): hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) if i < 3: hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) for j in range(3): hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) if i > 0: hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) if i < 3: hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) hf_mid_atn_prefix = "mid_block.attentions.0." sd_mid_atn_prefix = "middle_block.1." unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) for j in range(2): hf_mid_res_prefix = f"mid_block.resnets.{j}." sd_mid_res_prefix = f"middle_block.{2 * j}." unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) def convert_unet_state_dict(unet_state_dict): mapping = {k: k for k in unet_state_dict.keys()} for sd_name, hf_name in unet_conversion_map: mapping[hf_name] = sd_name for k, v in mapping.items(): if "resnets" in k: for sd_part, hf_part in unet_conversion_map_resnet: v = v.replace(hf_part, sd_part) mapping[k] = v for k, v in mapping.items(): for sd_part, hf_part in unet_conversion_map_layer: v = v.replace(hf_part, sd_part) mapping[k] = v new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} return new_state_dict vae_conversion_map = [ ("nin_shortcut", "conv_shortcut"), ("norm_out", "conv_norm_out"), ("mid.attn_1.", "mid_block.attentions.0."), ] for i in range(4): for j in range(2): hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." sd_down_prefix = f"encoder.down.{i}.block.{j}." vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) if i < 3: hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." sd_downsample_prefix = f"down.{i}.downsample." vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." sd_upsample_prefix = f"up.{3 - i}.upsample." vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) for j in range(3): hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) for i in range(2): hf_mid_res_prefix = f"mid_block.resnets.{i}." sd_mid_res_prefix = f"mid.block_{i + 1}." vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) vae_conversion_map_attn = [ ("norm.", "group_norm."), ("q.", "query."), ("k.", "key."), ("v.", "value."), ("q.", "to_q."), ("k.", "to_k."), ("v.", "to_v."), ("proj_out.", "to_out.0."), ("proj_out.", "proj_attn."), ] def reshape_weight_for_sd(w): return w.reshape(*w.shape, 1, 1) def convert_vae_state_dict(vae_state_dict): mapping = {k: k for k in vae_state_dict.keys()} for k, v in mapping.items(): for sd_part, hf_part in vae_conversion_map: v = v.replace(hf_part, sd_part) mapping[k] = v for k, v in mapping.items(): if "attentions" in k: for sd_part, hf_part in vae_conversion_map_attn: v = v.replace(hf_part, sd_part) mapping[k] = v new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} weights_to_convert = ["q", "k", "v", "proj_out"] for k, v in new_state_dict.items(): for weight_name in weights_to_convert: if f"mid.attn_1.{weight_name}.weight" in k: logging.debug(f"Reshaping {k} for SD format") new_state_dict[k] = reshape_weight_for_sd(v) return new_state_dict textenc_conversion_lst = [ ("resblocks.", "text_model.encoder.layers."), ("ln_1", "layer_norm1"), ("ln_2", "layer_norm2"), (".c_fc.", ".fc1."), (".c_proj.", ".fc2."), (".attn", ".self_attn"), ("ln_final.", "transformer.text_model.final_layer_norm."), ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), ] protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} textenc_pattern = re.compile("|".join(protected.keys())) code2idx = {"q": 0, "k": 1, "v": 2} def cat_tensors(tensors): x = 0 for t in tensors: x += t.shape[0] shape = [x] + list(tensors[0].shape)[1:] out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype) x = 0 for t in tensors: out[x:x + t.shape[0]] = t x += t.shape[0] return out def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): new_state_dict = {} capture_qkv_weight = {} capture_qkv_bias = {} for k, v in text_enc_dict.items(): if not k.startswith(prefix): continue if ( k.endswith(".self_attn.q_proj.weight") or k.endswith(".self_attn.k_proj.weight") or k.endswith(".self_attn.v_proj.weight") ): k_pre = k[: -len(".q_proj.weight")] k_code = k[-len("q_proj.weight")] if k_pre not in capture_qkv_weight: capture_qkv_weight[k_pre] = [None, None, None] capture_qkv_weight[k_pre][code2idx[k_code]] = v continue if ( k.endswith(".self_attn.q_proj.bias") or k.endswith(".self_attn.k_proj.bias") or k.endswith(".self_attn.v_proj.bias") ): k_pre = k[: -len(".q_proj.bias")] k_code = k[-len("q_proj.bias")] if k_pre not in capture_qkv_bias: capture_qkv_bias[k_pre] = [None, None, None] capture_qkv_bias[k_pre][code2idx[k_code]] = v continue text_proj = "transformer.text_projection.weight" if k.endswith(text_proj): new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous() else: relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) new_state_dict[relabelled_key] = v for k_pre, tensors in capture_qkv_weight.items(): if None in tensors: raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) new_state_dict[relabelled_key + ".in_proj_weight"] = cat_tensors(tensors) for k_pre, tensors in capture_qkv_bias.items(): if None in tensors: raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) new_state_dict[relabelled_key + ".in_proj_bias"] = cat_tensors(tensors) return new_state_dict def convert_text_enc_state_dict(text_enc_dict): return text_enc_dict
import os import comfy.sd def first_file(path, filenames): for f in filenames: p = os.path.join(path, f) if os.path.exists(p): return p return None def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None): diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"] unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names) vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names) text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"] text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names) text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names) text_encoder_paths = [text_encoder1_path] if text_encoder2_path is not None: text_encoder_paths.append(text_encoder2_path) unet = comfy.sd.load_unet(unet_path) clip = None if output_clip: clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) vae = None if output_vae: sd = comfy.utils.load_torch_file(vae_path) vae = comfy.sd.VAE(sd=sd) return (unet, clip, vae)
import torch from torch import nn from .ldm.modules.attention import CrossAttention from inspect import isfunction import comfy.ops ops = comfy.ops.manual_cast def exists(val): return val is not None def uniq(arr): return{el: True for el in arr}.keys() def default(val, d): if exists(val): return val return d() if isfunction(d) else d class GEGLU(nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = ops.Linear(dim_in, dim_out * 2) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) return x * torch.nn.functional.gelu(gate) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( ops.Linear(dim, inner_dim), nn.GELU() ) if not glu else GEGLU(dim, inner_dim) self.net = nn.Sequential( project_in, nn.Dropout(dropout), ops.Linear(inner_dim, dim_out) ) def forward(self, x): return self.net(x) class GatedCrossAttentionDense(nn.Module): def __init__(self, query_dim, context_dim, n_heads, d_head): super().__init__() self.attn = CrossAttention( query_dim=query_dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, operations=ops) self.ff = FeedForward(query_dim, glu=True) self.norm1 = ops.LayerNorm(query_dim) self.norm2 = ops.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) self.scale = 1 def forward(self, x, objs): x = x + self.scale * \ torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs) x = x + self.scale * \ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) return x class GatedSelfAttentionDense(nn.Module): def __init__(self, query_dim, context_dim, n_heads, d_head): super().__init__() self.linear = ops.Linear(context_dim, query_dim) self.attn = CrossAttention( query_dim=query_dim, context_dim=query_dim, heads=n_heads, dim_head=d_head, operations=ops) self.ff = FeedForward(query_dim, glu=True) self.norm1 = ops.LayerNorm(query_dim) self.norm2 = ops.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) self.scale = 1 def forward(self, x, objs): N_visual = x.shape[1] objs = self.linear(objs) x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn( self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :] x = x + self.scale * \ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) return x class GatedSelfAttentionDense2(nn.Module): def __init__(self, query_dim, context_dim, n_heads, d_head): super().__init__() self.linear = ops.Linear(context_dim, query_dim) self.attn = CrossAttention( query_dim=query_dim, context_dim=query_dim, dim_head=d_head, operations=ops) self.ff = FeedForward(query_dim, glu=True) self.norm1 = ops.LayerNorm(query_dim) self.norm2 = ops.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) self.scale = 1 def forward(self, x, objs): B, N_visual, _ = x.shape B, N_ground, _ = objs.shape objs = self.linear(objs) size_v = math.sqrt(N_visual) size_g = math.sqrt(N_ground) assert int(size_v) == size_v, "Visual tokens must be square rootable" assert int(size_g) == size_g, "Grounding tokens must be square rootable" size_v = int(size_v) size_g = int(size_g) out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[ :, N_visual:, :] out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g) out = torch.nn.functional.interpolate( out, (size_v, size_v), mode='bicubic') residual = out.reshape(B, -1, N_visual).permute(0, 2, 1) x = x + self.scale * torch.tanh(self.alpha_attn) * residual x = x + self.scale * \ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) return x class FourierEmbedder(): def __init__(self, num_freqs=64, temperature=100): self.num_freqs = num_freqs self.temperature = temperature self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs) @torch.no_grad() def __call__(self, x, cat_dim=-1): "x: arbitrary shape of tensor. dim: cat dim" out = [] for freq in self.freq_bands: out.append(torch.sin(freq * x)) out.append(torch.cos(freq * x)) return torch.cat(out, cat_dim) class PositionNet(nn.Module): def __init__(self, in_dim, out_dim, fourier_freqs=8): super().__init__() self.in_dim = in_dim self.out_dim = out_dim self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs) self.position_dim = fourier_freqs * 2 * 4 self.linears = nn.Sequential( ops.Linear(self.in_dim + self.position_dim, 512), nn.SiLU(), ops.Linear(512, 512), nn.SiLU(), ops.Linear(512, out_dim), ) self.null_positive_feature = torch.nn.Parameter( torch.zeros([self.in_dim])) self.null_position_feature = torch.nn.Parameter( torch.zeros([self.position_dim])) def forward(self, boxes, masks, positive_embeddings): B, N, _ = boxes.shape masks = masks.unsqueeze(-1) positive_embeddings = positive_embeddings xyxy_embedding = self.fourier_embedder(boxes) positive_null = self.null_positive_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) xyxy_null = self.null_position_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) positive_embeddings = positive_embeddings * \ masks + (1 - masks) * positive_null xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null objs = self.linears( torch.cat([positive_embeddings, xyxy_embedding], dim=-1)) assert objs.shape == torch.Size([B, N, self.out_dim]) return objs class Gligen(nn.Module): def __init__(self, modules, position_net, key_dim): super().__init__() self.module_list = nn.ModuleList(modules) self.position_net = position_net self.key_dim = key_dim self.max_objs = 30 self.current_device = torch.device("cpu") def _set_position(self, boxes, masks, positive_embeddings): objs = self.position_net(boxes, masks, positive_embeddings) def func(x, extra_options): key = extra_options["transformer_index"] module = self.module_list[key] return module(x, objs.to(device=x.device, dtype=x.dtype)) return func def set_position(self, latent_image_shape, position_params, device): batch, c, h, w = latent_image_shape masks = torch.zeros([self.max_objs], device="cpu") boxes = [] positive_embeddings = [] for p in position_params: x1 = (p[4]) / w y1 = (p[3]) / h x2 = (p[4] + p[2]) / w y2 = (p[3] + p[1]) / h masks[len(boxes)] = 1.0 boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)] positive_embeddings += [p[0]] append_boxes = [] append_conds = [] if len(boxes) < self.max_objs: append_boxes = [torch.zeros( [self.max_objs - len(boxes), 4], device="cpu")] append_conds = [torch.zeros( [self.max_objs - len(boxes), self.key_dim], device="cpu")] box_out = torch.cat( boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1) masks = masks.unsqueeze(0).repeat(batch, 1) conds = torch.cat(positive_embeddings + append_conds).unsqueeze(0).repeat(batch, 1, 1) return self._set_position( box_out.to(device), masks.to(device), conds.to(device)) def set_empty(self, latent_image_shape, device): batch, c, h, w = latent_image_shape masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1) box_out = torch.zeros([self.max_objs, 4], device="cpu").repeat(batch, 1, 1) conds = torch.zeros([self.max_objs, self.key_dim], device="cpu").repeat(batch, 1, 1) return self._set_position( box_out.to(device), masks.to(device), conds.to(device)) def load_gligen(sd): sd_k = sd.keys() output_list = [] key_dim = 768 for a in ["input_blocks", "middle_block", "output_blocks"]: for b in range(20): k_temp = filter(lambda k: "{}.{}.".format(a, b) in k and ".fuser." in k, sd_k) k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp) n_sd = {} for k in k_temp: n_sd[k[1]] = sd[k[0]] if len(n_sd) > 0: query_dim = n_sd["linear.weight"].shape[0] key_dim = n_sd["linear.weight"].shape[1] if key_dim == 768: n_heads = 8 d_head = query_dim else: d_head = 64 n_heads = query_dim gated = GatedSelfAttentionDense( query_dim, key_dim, n_heads, d_head) gated.load_state_dict(n_sd, strict=False) output_list.append(gated) if "position_net.null_positive_feature" in sd_k: in_dim = sd["position_net.null_positive_feature"].shape[0] out_dim = sd["position_net.linears.4.weight"].shape[0] class WeightsLoader(torch.nn.Module): pass w = WeightsLoader() w.position_net = PositionNet(in_dim, out_dim) w.load_state_dict(sd, strict=False) gligen = Gligen(output_list, w.position_net, key_dim) return gligen
import torch class LatentFormat: scale_factor = 1.0 latent_channels = 4 latent_rgb_factors = None taesd_decoder_name = None def process_in(self, latent): return latent * self.scale_factor def process_out(self, latent): return latent / self.scale_factor class SD15(LatentFormat): def __init__(self, scale_factor=0.18215): self.scale_factor = scale_factor self.latent_rgb_factors = [ [ 0.3512, 0.2297, 0.3227], [ 0.3250, 0.4974, 0.2350], [-0.2829, 0.1762, 0.2721], [-0.2120, -0.2616, -0.7177] ] self.taesd_decoder_name = "taesd_decoder" class SDXL(LatentFormat): scale_factor = 0.13025 def __init__(self): self.latent_rgb_factors = [ [ 0.3920, 0.4054, 0.4549], [-0.2634, -0.0196, 0.0653], [ 0.0568, 0.1687, -0.0755], [-0.3112, -0.2359, -0.2076] ] self.taesd_decoder_name = "taesdxl_decoder" class SDXL_Playground_2_5(LatentFormat): def __init__(self): self.scale_factor = 0.5 self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1) self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1) self.latent_rgb_factors = [ [ 0.3920, 0.4054, 0.4549], [-0.2634, -0.0196, 0.0653], [ 0.0568, 0.1687, -0.0755], [-0.3112, -0.2359, -0.2076] ] self.taesd_decoder_name = "taesdxl_decoder" def process_in(self, latent): latents_mean = self.latents_mean.to(latent.device, latent.dtype) latents_std = self.latents_std.to(latent.device, latent.dtype) return (latent - latents_mean) * self.scale_factor / latents_std def process_out(self, latent): latents_mean = self.latents_mean.to(latent.device, latent.dtype) latents_std = self.latents_std.to(latent.device, latent.dtype) return latent * latents_std / self.scale_factor + latents_mean class SD_X4(LatentFormat): def __init__(self): self.scale_factor = 0.08333 self.latent_rgb_factors = [ [-0.2340, -0.3863, -0.3257], [ 0.0994, 0.0885, -0.0908], [-0.2833, -0.2349, -0.3741], [ 0.2523, -0.0055, -0.1651] ] class SC_Prior(LatentFormat): latent_channels = 16 def __init__(self): self.scale_factor = 1.0 self.latent_rgb_factors = [ [-0.0326, -0.0204, -0.0127], [-0.1592, -0.0427, 0.0216], [ 0.0873, 0.0638, -0.0020], [-0.0602, 0.0442, 0.1304], [ 0.0800, -0.0313, -0.1796], [-0.0810, -0.0638, -0.1581], [ 0.1791, 0.1180, 0.0967], [ 0.0740, 0.1416, 0.0432], [-0.1745, -0.1888, -0.1373], [ 0.2412, 0.1577, 0.0928], [ 0.1908, 0.0998, 0.0682], [ 0.0209, 0.0365, -0.0092], [ 0.0448, -0.0650, -0.1728], [-0.1658, -0.1045, -0.1308], [ 0.0542, 0.1545, 0.1325], [-0.0352, -0.1672, -0.2541] ] class SC_B(LatentFormat): def __init__(self): self.scale_factor = 1.0 / 0.43 self.latent_rgb_factors = [ [ 0.1121, 0.2006, 0.1023], [-0.2093, -0.0222, -0.0195], [-0.3087, -0.1535, 0.0366], [ 0.0290, -0.1574, -0.4078] ] class SD3(LatentFormat): latent_channels = 16 def __init__(self): self.scale_factor = 1.5305 self.shift_factor = 0.0609 self.latent_rgb_factors = [ [-0.0645, 0.0177, 0.1052], [ 0.0028, 0.0312, 0.0650], [ 0.1848, 0.0762, 0.0360], [ 0.0944, 0.0360, 0.0889], [ 0.0897, 0.0506, -0.0364], [-0.0020, 0.1203, 0.0284], [ 0.0855, 0.0118, 0.0283], [-0.0539, 0.0658, 0.1047], [-0.0057, 0.0116, 0.0700], [-0.0412, 0.0281, -0.0039], [ 0.1106, 0.1171, 0.1220], [-0.0248, 0.0682, -0.0481], [ 0.0815, 0.0846, 0.1207], [-0.0120, -0.0055, -0.0867], [-0.0749, -0.0634, -0.0456], [-0.1418, -0.1457, -0.1259] ] self.taesd_decoder_name = "taesd3_decoder" def process_in(self, latent): return (latent - self.shift_factor) * self.scale_factor def process_out(self, latent): return (latent / self.scale_factor) + self.shift_factor class StableAudio1(LatentFormat): latent_channels = 64
import comfy.utils import logging LORA_CLIP_MAP = { "mlp.fc1": "mlp_fc1", "mlp.fc2": "mlp_fc2", "self_attn.k_proj": "self_attn_k_proj", "self_attn.q_proj": "self_attn_q_proj", "self_attn.v_proj": "self_attn_v_proj", "self_attn.out_proj": "self_attn_out_proj", } def load_lora(lora, to_load): patch_dict = {} loaded_keys = set() for x in to_load: alpha_name = "{}.alpha".format(x) alpha = None if alpha_name in lora.keys(): alpha = lora[alpha_name].item() loaded_keys.add(alpha_name) dora_scale_name = "{}.dora_scale".format(x) dora_scale = None if dora_scale_name in lora.keys(): dora_scale = lora[dora_scale_name] loaded_keys.add(dora_scale_name) regular_lora = "{}.lora_up.weight".format(x) diffusers_lora = "{}_lora.up.weight".format(x) diffusers2_lora = "{}.lora_B.weight".format(x) diffusers3_lora = "{}.lora.up.weight".format(x) transformers_lora = "{}.lora_linear_layer.up.weight".format(x) A_name = None if regular_lora in lora.keys(): A_name = regular_lora B_name = "{}.lora_down.weight".format(x) mid_name = "{}.lora_mid.weight".format(x) elif diffusers_lora in lora.keys(): A_name = diffusers_lora B_name = "{}_lora.down.weight".format(x) mid_name = None elif diffusers2_lora in lora.keys(): A_name = diffusers2_lora B_name = "{}.lora_A.weight".format(x) mid_name = None elif diffusers3_lora in lora.keys(): A_name = diffusers3_lora B_name = "{}.lora.down.weight".format(x) mid_name = None elif transformers_lora in lora.keys(): A_name = transformers_lora B_name ="{}.lora_linear_layer.down.weight".format(x) mid_name = None if A_name is not None: mid = None if mid_name is not None and mid_name in lora.keys(): mid = lora[mid_name] loaded_keys.add(mid_name) patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale)) loaded_keys.add(A_name) loaded_keys.add(B_name) hada_w1_a_name = "{}.hada_w1_a".format(x) hada_w1_b_name = "{}.hada_w1_b".format(x) hada_w2_a_name = "{}.hada_w2_a".format(x) hada_w2_b_name = "{}.hada_w2_b".format(x) hada_t1_name = "{}.hada_t1".format(x) hada_t2_name = "{}.hada_t2".format(x) if hada_w1_a_name in lora.keys(): hada_t1 = None hada_t2 = None if hada_t1_name in lora.keys(): hada_t1 = lora[hada_t1_name] hada_t2 = lora[hada_t2_name] loaded_keys.add(hada_t1_name) loaded_keys.add(hada_t2_name) patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale)) loaded_keys.add(hada_w1_a_name) loaded_keys.add(hada_w1_b_name) loaded_keys.add(hada_w2_a_name) loaded_keys.add(hada_w2_b_name) lokr_w1_name = "{}.lokr_w1".format(x) lokr_w2_name = "{}.lokr_w2".format(x) lokr_w1_a_name = "{}.lokr_w1_a".format(x) lokr_w1_b_name = "{}.lokr_w1_b".format(x) lokr_t2_name = "{}.lokr_t2".format(x) lokr_w2_a_name = "{}.lokr_w2_a".format(x) lokr_w2_b_name = "{}.lokr_w2_b".format(x) lokr_w1 = None if lokr_w1_name in lora.keys(): lokr_w1 = lora[lokr_w1_name] loaded_keys.add(lokr_w1_name) lokr_w2 = None if lokr_w2_name in lora.keys(): lokr_w2 = lora[lokr_w2_name] loaded_keys.add(lokr_w2_name) lokr_w1_a = None if lokr_w1_a_name in lora.keys(): lokr_w1_a = lora[lokr_w1_a_name] loaded_keys.add(lokr_w1_a_name) lokr_w1_b = None if lokr_w1_b_name in lora.keys(): lokr_w1_b = lora[lokr_w1_b_name] loaded_keys.add(lokr_w1_b_name) lokr_w2_a = None if lokr_w2_a_name in lora.keys(): lokr_w2_a = lora[lokr_w2_a_name] loaded_keys.add(lokr_w2_a_name) lokr_w2_b = None if lokr_w2_b_name in lora.keys(): lokr_w2_b = lora[lokr_w2_b_name] loaded_keys.add(lokr_w2_b_name) lokr_t2 = None if lokr_t2_name in lora.keys(): lokr_t2 = lora[lokr_t2_name] loaded_keys.add(lokr_t2_name) if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale)) a1_name = "{}.a1.weight".format(x) a2_name = "{}.a2.weight".format(x) b1_name = "{}.b1.weight".format(x) b2_name = "{}.b2.weight".format(x) if a1_name in lora: patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale)) loaded_keys.add(a1_name) loaded_keys.add(a2_name) loaded_keys.add(b1_name) loaded_keys.add(b2_name) w_norm_name = "{}.w_norm".format(x) b_norm_name = "{}.b_norm".format(x) w_norm = lora.get(w_norm_name, None) b_norm = lora.get(b_norm_name, None) if w_norm is not None: loaded_keys.add(w_norm_name) patch_dict[to_load[x]] = ("diff", (w_norm,)) if b_norm is not None: loaded_keys.add(b_norm_name) patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) diff_name = "{}.diff".format(x) diff_weight = lora.get(diff_name, None) if diff_weight is not None: patch_dict[to_load[x]] = ("diff", (diff_weight,)) loaded_keys.add(diff_name) diff_bias_name = "{}.diff_b".format(x) diff_bias = lora.get(diff_bias_name, None) if diff_bias is not None: patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) loaded_keys.add(diff_bias_name) for x in lora.keys(): if x not in loaded_keys: logging.warning("lora key not loaded: {}".format(x)) return patch_dict def model_lora_keys_clip(model, key_map={}): sdk = model.state_dict().keys() text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" clip_l_present = False for b in range(32): for c in LORA_CLIP_MAP: k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) key_map[lora_key] = k k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k clip_l_present = True lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) key_map[lora_key] = k k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: if clip_l_present: lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) key_map[lora_key] = k else: lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) key_map[lora_key] = k lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k for k in sdk: if k.startswith("t5xxl.transformer.") and k.endswith(".weight"): l_key = k[len("t5xxl.transformer."):-len(".weight")] lora_key = "lora_te3_{}".format(l_key.replace(".", "_")) key_map[lora_key] = k k = "clip_g.transformer.text_projection.weight" if k in sdk: key_map["lora_prior_te_text_projection"] = k key_map["lora_te2_text_projection"] = k k = "clip_l.transformer.text_projection.weight" if k in sdk: key_map["lora_te1_text_projection"] = k return key_map def model_lora_keys_unet(model, key_map={}): sd = model.state_dict() sdk = sd.keys() for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = k key_map["lora_prior_unet_{}".format(key_lora)] = k diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config) for k in diffusers_keys: if k.endswith(".weight"): unet_key = "diffusion_model.{}".format(diffusers_keys[k]) key_lora = k[:-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = unet_key diffusers_lora_prefix = ["", "unet."] for p in diffusers_lora_prefix: diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) if diffusers_lora_key.endswith(".to_out.0"): diffusers_lora_key = diffusers_lora_key[:-2] key_map[diffusers_lora_key] = unet_key if isinstance(model, comfy.model_base.SD3): diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") for k in diffusers_keys: if k.endswith(".weight"): to = diffusers_keys[k] key_lora = "transformer.{}".format(k[:-len(".weight")]) key_map[key_lora] = to key_lora = "base_model.model.{}".format(k[:-len(".weight")]) key_map[key_lora] = to key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) key_map[key_lora] = to return key_map
import torch import logging from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep from comfy.ldm.cascade.stage_c import StageC from comfy.ldm.cascade.stage_b import StageB from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper import comfy.ldm.audio.dit import comfy.ldm.audio.embedders import comfy.model_management import comfy.conds import comfy.ops from enum import Enum from . import utils import comfy.latent_formats import math class ModelType(Enum): EPS = 1 V_PREDICTION = 2 V_PREDICTION_EDM = 3 STABLE_CASCADE = 4 EDM = 5 FLOW = 6 V_PREDICTION_CONTINUOUS = 7 from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV def model_sampling(model_config, model_type): s = ModelSamplingDiscrete if model_type == ModelType.EPS: c = EPS elif model_type == ModelType.V_PREDICTION: c = V_PREDICTION elif model_type == ModelType.V_PREDICTION_EDM: c = V_PREDICTION s = ModelSamplingContinuousEDM elif model_type == ModelType.FLOW: c = comfy.model_sampling.CONST s = comfy.model_sampling.ModelSamplingDiscreteFlow elif model_type == ModelType.STABLE_CASCADE: c = EPS s = StableCascadeSampling elif model_type == ModelType.EDM: c = EDM s = ModelSamplingContinuousEDM elif model_type == ModelType.V_PREDICTION_CONTINUOUS: c = V_PREDICTION s = ModelSamplingContinuousV class ModelSampling(s, c): pass return ModelSampling(model_config) class BaseModel(torch.nn.Module): def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel): super().__init__() unet_config = model_config.unet_config self.latent_format = model_config.latent_format self.model_config = model_config self.manual_cast_dtype = model_config.manual_cast_dtype if not unet_config.get("disable_unet_model_creation", False): if self.manual_cast_dtype is not None: operations = comfy.ops.manual_cast else: operations = comfy.ops.disable_weight_init self.diffusion_model = unet_model(**unet_config, device=device, operations=operations) if comfy.model_management.force_channels_last(): self.diffusion_model.to(memory_format=torch.channels_last) logging.debug("using channels last mode for diffusion model") self.model_type = model_type self.model_sampling = model_sampling(model_config, model_type) self.adm_channels = unet_config.get("adm_in_channels", None) if self.adm_channels is None: self.adm_channels = 0 self.concat_keys = () logging.info("model_type {}".format(model_type.name)) logging.debug("adm {}".format(self.adm_channels)) def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): sigma = t xc = self.model_sampling.calculate_input(sigma, x) if c_concat is not None: xc = torch.cat([xc] + [c_concat], dim=1) context = c_crossattn dtype = self.get_dtype() if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype xc = xc.to(dtype) t = self.model_sampling.timestep(t).float() context = context.to(dtype) extra_conds = {} for o in kwargs: extra = kwargs[o] if hasattr(extra, "dtype"): if extra.dtype != torch.int and extra.dtype != torch.long: extra = extra.to(dtype) extra_conds[o] = extra model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float() return self.model_sampling.calculate_denoised(sigma, model_output, x) def get_dtype(self): return self.diffusion_model.dtype def is_adm(self): return self.adm_channels > 0 def encode_adm(self, **kwargs): return None def extra_conds(self, **kwargs): out = {} if len(self.concat_keys) > 0: cond_concat = [] denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) concat_latent_image = kwargs.get("concat_latent_image", None) if concat_latent_image is None: concat_latent_image = kwargs.get("latent_image", None) else: concat_latent_image = self.process_latent_in(concat_latent_image) noise = kwargs.get("noise", None) device = kwargs["device"] if concat_latent_image.shape[1:] != noise.shape[1:]: concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0]) if denoise_mask is not None: if len(denoise_mask.shape) == len(noise.shape): denoise_mask = denoise_mask[:,:1] denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1])) if denoise_mask.shape[-2:] != noise.shape[-2:]: denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center") denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0]) for ck in self.concat_keys: if denoise_mask is not None: if ck == "mask": cond_concat.append(denoise_mask.to(device)) elif ck == "masked_image": cond_concat.append(concat_latent_image.to(device)) else: if ck == "mask": cond_concat.append(torch.ones_like(noise)[:,:1]) elif ck == "masked_image": cond_concat.append(self.blank_inpaint_image_like(noise)) data = torch.cat(cond_concat, dim=1) out['c_concat'] = comfy.conds.CONDNoiseShape(data) adm = self.encode_adm(**kwargs) if adm is not None: out['y'] = comfy.conds.CONDRegular(adm) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) cross_attn_cnet = kwargs.get("cross_attn_controlnet", None) if cross_attn_cnet is not None: out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet) c_concat = kwargs.get("noise_concat", None) if c_concat is not None: out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat) return out def load_model_weights(self, sd, unet_prefix=""): to_load = {} keys = list(sd.keys()) for k in keys: if k.startswith(unet_prefix): to_load[k[len(unet_prefix):]] = sd.pop(k) to_load = self.model_config.process_unet_state_dict(to_load) m, u = self.diffusion_model.load_state_dict(to_load, strict=False) if len(m) > 0: logging.warning("unet missing: {}".format(m)) if len(u) > 0: logging.warning("unet unexpected: {}".format(u)) del to_load return self def process_latent_in(self, latent): return self.latent_format.process_in(latent) def process_latent_out(self, latent): return self.latent_format.process_out(latent) def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): extra_sds = [] if clip_state_dict is not None: extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict)) if vae_state_dict is not None: extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict)) if clip_vision_state_dict is not None: extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict)) unet_state_dict = self.diffusion_model.state_dict() unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) if self.model_type == ModelType.V_PREDICTION: unet_state_dict["v_pred"] = torch.tensor([]) for sd in extra_sds: unet_state_dict.update(sd) return unet_state_dict def set_inpaint(self): self.concat_keys = ("mask", "masked_image") def blank_inpaint_image_like(latent_image): blank_image = torch.ones_like(latent_image) blank_image[:,0] *= 0.8223 blank_image[:,1] *= -0.6876 blank_image[:,2] *= 0.6364 blank_image[:,3] *= 0.1380 return blank_image self.blank_inpaint_image_like = blank_inpaint_image_like def memory_required(self, input_shape): if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention(): dtype = self.get_dtype() if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype area = input_shape[0] * math.prod(input_shape[2:]) return (area * comfy.model_management.dtype_size(dtype) / 50) * (1024 * 1024) else: area = input_shape[0] * math.prod(input_shape[2:]) return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024) def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None): adm_inputs = [] weights = [] noise_aug = [] for unclip_cond in unclip_conditioning: for adm_cond in unclip_cond["clip_vision_output"].image_embeds: weight = unclip_cond["strength"] noise_augment = unclip_cond["noise_augmentation"] noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed) adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight weights.append(weight) noise_aug.append(noise_augment) adm_inputs.append(adm_out) if len(noise_aug) > 1: adm_out = torch.stack(adm_inputs).sum(0) noise_augment = noise_augment_merge noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device)) adm_out = torch.cat((c_adm, noise_level_emb), 1) return adm_out class SD21UNCLIP(BaseModel): def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None): super().__init__(model_config, model_type, device=device) self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) def encode_adm(self, **kwargs): unclip_conditioning = kwargs.get("unclip_conditioning", None) device = kwargs["device"] if unclip_conditioning is None: return torch.zeros((1, self.adm_channels)) else: return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10) def sdxl_pooled(args, noise_augmentor): if "unclip_conditioning" in args: return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280] else: return args["pooled_output"] class SDXLRefiner(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) def encode_adm(self, **kwargs): clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) width = kwargs.get("width", 768) height = kwargs.get("height", 768) crop_w = kwargs.get("crop_w", 0) crop_h = kwargs.get("crop_h", 0) if kwargs.get("prompt_type", "") == "negative": aesthetic_score = kwargs.get("aesthetic_score", 2.5) else: aesthetic_score = kwargs.get("aesthetic_score", 6) out = [] out.append(self.embedder(torch.Tensor([height]))) out.append(self.embedder(torch.Tensor([width]))) out.append(self.embedder(torch.Tensor([crop_h]))) out.append(self.embedder(torch.Tensor([crop_w]))) out.append(self.embedder(torch.Tensor([aesthetic_score]))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) return torch.cat((clip_pooled.to(flat.device), flat), dim=1) class SDXL(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) def encode_adm(self, **kwargs): clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) width = kwargs.get("width", 768) height = kwargs.get("height", 768) crop_w = kwargs.get("crop_w", 0) crop_h = kwargs.get("crop_h", 0) target_width = kwargs.get("target_width", width) target_height = kwargs.get("target_height", height) out = [] out.append(self.embedder(torch.Tensor([height]))) out.append(self.embedder(torch.Tensor([width]))) out.append(self.embedder(torch.Tensor([crop_h]))) out.append(self.embedder(torch.Tensor([crop_w]))) out.append(self.embedder(torch.Tensor([target_height]))) out.append(self.embedder(torch.Tensor([target_width]))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) return torch.cat((clip_pooled.to(flat.device), flat), dim=1) class SVD_img2vid(BaseModel): def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) def encode_adm(self, **kwargs): fps_id = kwargs.get("fps", 6) - 1 motion_bucket_id = kwargs.get("motion_bucket_id", 127) augmentation = kwargs.get("augmentation_level", 0) out = [] out.append(self.embedder(torch.Tensor([fps_id]))) out.append(self.embedder(torch.Tensor([motion_bucket_id]))) out.append(self.embedder(torch.Tensor([augmentation]))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) return flat def extra_conds(self, **kwargs): out = {} adm = self.encode_adm(**kwargs) if adm is not None: out['y'] = comfy.conds.CONDRegular(adm) latent_image = kwargs.get("concat_latent_image", None) noise = kwargs.get("noise", None) device = kwargs["device"] if latent_image is None: latent_image = torch.zeros_like(noise) if latent_image.shape[1:] != noise.shape[1:]: latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) if "time_conditioning" in kwargs: out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"]) out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0]) return out class SV3D_u(SVD_img2vid): def encode_adm(self, **kwargs): augmentation = kwargs.get("augmentation_level", 0) out = [] out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) return flat class SV3D_p(SVD_img2vid): def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): super().__init__(model_config, model_type, device=device) self.embedder_512 = Timestep(512) def encode_adm(self, **kwargs): augmentation = kwargs.get("augmentation_level", 0) elevation = kwargs.get("elevation", 0) azimuth = kwargs.get("azimuth", 0) noise = kwargs.get("noise", None) out = [] out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(90 - torch.Tensor([elevation])), 360.0)))) out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(torch.Tensor([azimuth])), 360.0)))) out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out)) return torch.cat(out, dim=1) class Stable_Zero123(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): super().__init__(model_config, model_type, device=device) self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device) self.cc_projection.weight.copy_(cc_projection_weight) self.cc_projection.bias.copy_(cc_projection_bias) def extra_conds(self, **kwargs): out = {} latent_image = kwargs.get("concat_latent_image", None) noise = kwargs.get("noise", None) if latent_image is None: latent_image = torch.zeros_like(noise) if latent_image.shape[1:] != noise.shape[1:]: latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: if cross_attn.shape[-1] != 768: cross_attn = self.cc_projection(cross_attn) out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) return out class SD_X4Upscaler(BaseModel): def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): super().__init__(model_config, model_type, device=device) self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02}, max_noise_level=350) def extra_conds(self, **kwargs): out = {} image = kwargs.get("concat_image", None) noise = kwargs.get("noise", None) noise_augment = kwargs.get("noise_augmentation", 0.0) device = kwargs["device"] seed = kwargs["seed"] - 10 noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment) if image is None: image = torch.zeros_like(noise)[:,:3] if image.shape[1:] != noise.shape[1:]: image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") noise_level = torch.tensor([noise_level], device=device) if noise_augment > 0: image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed) image = utils.resize_to_batch_size(image, noise.shape[0]) out['c_concat'] = comfy.conds.CONDNoiseShape(image) out['y'] = comfy.conds.CONDRegular(noise_level) return out class IP2P: def extra_conds(self, **kwargs): out = {} image = kwargs.get("concat_latent_image", None) noise = kwargs.get("noise", None) device = kwargs["device"] if image is None: image = torch.zeros_like(noise) if image.shape[1:] != noise.shape[1:]: image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") image = utils.resize_to_batch_size(image, noise.shape[0]) out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_ip2p_image_in(image)) adm = self.encode_adm(**kwargs) if adm is not None: out['y'] = comfy.conds.CONDRegular(adm) return out class SD15_instructpix2pix(IP2P, BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) self.process_ip2p_image_in = lambda image: image class SDXL_instructpix2pix(IP2P, SDXL): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) if model_type == ModelType.V_PREDICTION_EDM: self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) else: self.process_ip2p_image_in = lambda image: image class StableCascade_C(BaseModel): def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): super().__init__(model_config, model_type, device=device, unet_model=StageC) self.diffusion_model.eval().requires_grad_(False) def extra_conds(self, **kwargs): out = {} clip_text_pooled = kwargs["pooled_output"] if clip_text_pooled is not None: out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled) if "unclip_conditioning" in kwargs: embeds = [] for unclip_cond in kwargs["unclip_conditioning"]: weight = unclip_cond["strength"] embeds.append(unclip_cond["clip_vision_output"].image_embeds.unsqueeze(0) * weight) clip_img = torch.cat(embeds, dim=1) else: clip_img = torch.zeros((1, 1, 768)) out["clip_img"] = comfy.conds.CONDRegular(clip_img) out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) out["crp"] = comfy.conds.CONDRegular(torch.zeros((1,))) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn) return out class StableCascade_B(BaseModel): def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): super().__init__(model_config, model_type, device=device, unet_model=StageB) self.diffusion_model.eval().requires_grad_(False) def extra_conds(self, **kwargs): out = {} noise = kwargs.get("noise", None) clip_text_pooled = kwargs["pooled_output"] if clip_text_pooled is not None: out['clip'] = comfy.conds.CONDRegular(clip_text_pooled) prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) out["effnet"] = comfy.conds.CONDRegular(prior) out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) return out class SD3(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper) def encode_adm(self, **kwargs): return kwargs["pooled_output"] def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out def memory_required(self, input_shape): if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention(): dtype = self.get_dtype() if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype area = input_shape[0] * input_shape[2] * input_shape[3] return (area * comfy.model_management.dtype_size(dtype) * 0.012) * (1024 * 1024) else: area = input_shape[0] * input_shape[2] * input_shape[3] return (area * 0.3) * (1024 * 1024) class StableAudio1(BaseModel): def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer) self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights) self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights) def extra_conds(self, **kwargs): out = {} noise = kwargs.get("noise", None) device = kwargs["device"] seconds_start = kwargs.get("seconds_start", 0) seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53)) seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device) seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device) global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1)) out['global_embed'] = comfy.conds.CONDRegular(global_embed) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1) out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out
import comfy.supported_models import comfy.supported_models_base import comfy.utils import math import logging import torch def count_blocks(state_dict_keys, prefix_string): count = 0 while True: c = False for k in state_dict_keys: if k.startswith(prefix_string.format(count)): c = True break if c == False: break count += 1 return count def calculate_transformer_depth(prefix, state_dict_keys, state_dict): context_dim = None use_linear_in_transformer = False transformer_prefix = prefix + "1.transformer_blocks." transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys))) if len(transformer_keys) > 0: last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}') context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1] use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2 time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross return None def detect_unet_config(state_dict, key_prefix): state_dict_keys = list(state_dict.keys()) if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: unet_config = {} unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1] patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2] unet_config["patch_size"] = patch_size unet_config["out_channels"] = state_dict['{}final_layer.linear.weight'.format(key_prefix)].shape[0] unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] unet_config["input_size"] = None y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix) if y_key in state_dict_keys: unet_config["adm_in_channels"] = state_dict[y_key].shape[1] context_key = '{}context_embedder.weight'.format(key_prefix) if context_key in state_dict_keys: in_features = state_dict[context_key].shape[1] out_features = state_dict[context_key].shape[0] unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}} num_patches_key = '{}pos_embed'.format(key_prefix) if num_patches_key in state_dict_keys: num_patches = state_dict[num_patches_key].shape[1] unet_config["num_patches"] = num_patches unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches)) rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix) if rms_qk in state_dict_keys: unet_config["qk_norm"] = "rms" unet_config["pos_embed_scaling_factor"] = None context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix) if context_processor in state_dict_keys: unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.') return unet_config if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: unet_config = {} text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix) if text_mapper_name in state_dict_keys: unet_config['stable_cascade_stage'] = 'c' w = state_dict[text_mapper_name] if w.shape[0] == 1536: unet_config['c_cond'] = 1536 unet_config['c_hidden'] = [1536, 1536] unet_config['nhead'] = [24, 24] unet_config['blocks'] = [[4, 12], [12, 4]] elif w.shape[0] == 2048: unet_config['c_cond'] = 2048 elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys: unet_config['stable_cascade_stage'] = 'b' w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)] if w.shape[-1] == 640: unet_config['c_hidden'] = [320, 640, 1280, 1280] unet_config['nhead'] = [-1, -1, 20, 20] unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]] unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]] elif w.shape[-1] == 576: unet_config['c_hidden'] = [320, 576, 1152, 1152] unet_config['nhead'] = [-1, 9, 18, 18] unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]] unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]] return unet_config if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: unet_config = {} unet_config["audio_model"] = "dit1.0" return unet_config unet_config = { "use_checkpoint": False, "image_size": 32, "use_spatial_transformer": True, "legacy": False } y_input = '{}label_emb.0.0.weight'.format(key_prefix) if y_input in state_dict_keys: unet_config["num_classes"] = "sequential" unet_config["adm_in_channels"] = state_dict[y_input].shape[1] else: unet_config["adm_in_channels"] = None model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0] in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1] out_key = '{}out.2.weight'.format(key_prefix) if out_key in state_dict: out_channels = state_dict[out_key].shape[0] else: out_channels = 4 num_res_blocks = [] channel_mult = [] attention_resolutions = [] transformer_depth = [] transformer_depth_output = [] context_dim = None use_linear_in_transformer = False video_model = False video_model_cross = False current_res = 1 count = 0 last_res_blocks = 0 last_channel_mult = 0 input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.') for count in range(input_block_count): prefix = '{}input_blocks.{}.'.format(key_prefix, count) prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1) block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys))) if len(block_keys) == 0: break block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys))) if "{}0.op.weight".format(prefix) in block_keys: num_res_blocks.append(last_res_blocks) channel_mult.append(last_channel_mult) current_res *= 2 last_res_blocks = 0 last_channel_mult = 0 out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) if out is not None: transformer_depth_output.append(out[0]) else: transformer_depth_output.append(0) else: res_block_prefix = "{}0.in_layers.0.weight".format(prefix) if res_block_prefix in block_keys: last_res_blocks += 1 last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] out = calculate_transformer_depth(prefix, state_dict_keys, state_dict) if out is not None: transformer_depth.append(out[0]) if context_dim is None: context_dim = out[1] use_linear_in_transformer = out[2] video_model = out[3] video_model_cross = out[4] else: transformer_depth.append(0) res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output) if res_block_prefix in block_keys_output: out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) if out is not None: transformer_depth_output.append(out[0]) else: transformer_depth_output.append(0) num_res_blocks.append(last_res_blocks) channel_mult.append(last_channel_mult) if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys: transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}') elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys: transformer_depth_middle = -1 else: transformer_depth_middle = -2 unet_config["in_channels"] = in_channels unet_config["out_channels"] = out_channels unet_config["model_channels"] = model_channels unet_config["num_res_blocks"] = num_res_blocks unet_config["transformer_depth"] = transformer_depth unet_config["transformer_depth_output"] = transformer_depth_output unet_config["channel_mult"] = channel_mult unet_config["transformer_depth_middle"] = transformer_depth_middle unet_config['use_linear_in_transformer'] = use_linear_in_transformer unet_config["context_dim"] = context_dim if video_model: unet_config["extra_ff_mix_layer"] = True unet_config["use_spatial_context"] = True unet_config["merge_strategy"] = "learned_with_images" unet_config["merge_factor"] = 0.0 unet_config["video_kernel_size"] = [3, 1, 1] unet_config["use_temporal_resblock"] = True unet_config["use_temporal_attention"] = True unet_config["disable_temporal_crossattention"] = not video_model_cross else: unet_config["use_temporal_resblock"] = False unet_config["use_temporal_attention"] = False return unet_config def model_config_from_unet_config(unet_config, state_dict=None): for model_config in comfy.supported_models.models: if model_config.matches(unet_config, state_dict): return model_config(unet_config) logging.error("no match {}".format(unet_config)) return None def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False): unet_config = detect_unet_config(state_dict, unet_key_prefix) model_config = model_config_from_unet_config(unet_config, state_dict) if model_config is None and use_base_if_no_match: return comfy.supported_models_base.BASE(unet_config) else: return model_config def unet_prefix_from_state_dict(state_dict): if "model.model.postprocess_conv.weight" in state_dict: unet_key_prefix = "model.model." else: unet_key_prefix = "model.diffusion_model." return unet_key_prefix def convert_config(unet_config): new_config = unet_config.copy() num_res_blocks = new_config.get("num_res_blocks", None) channel_mult = new_config.get("channel_mult", None) if isinstance(num_res_blocks, int): num_res_blocks = len(channel_mult) * [num_res_blocks] if "attention_resolutions" in new_config: attention_resolutions = new_config.pop("attention_resolutions") transformer_depth = new_config.get("transformer_depth", None) transformer_depth_middle = new_config.get("transformer_depth_middle", None) if isinstance(transformer_depth, int): transformer_depth = len(channel_mult) * [transformer_depth] if transformer_depth_middle is None: transformer_depth_middle = transformer_depth[-1] t_in = [] t_out = [] s = 1 for i in range(len(num_res_blocks)): res = num_res_blocks[i] d = 0 if s in attention_resolutions: d = transformer_depth[i] t_in += [d] * res t_out += [d] * (res + 1) s *= 2 transformer_depth = t_in transformer_depth_output = t_out new_config["transformer_depth"] = t_in new_config["transformer_depth_output"] = t_out new_config["transformer_depth_middle"] = transformer_depth_middle new_config["num_res_blocks"] = num_res_blocks return new_config def unet_config_from_diffusers_unet(state_dict, dtype=None): match = {} transformer_depth = [] attn_res = 1 down_blocks = count_blocks(state_dict, "down_blocks.{}") for i in range(down_blocks): attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}') res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}') for ab in range(attn_blocks): transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}') transformer_depth.append(transformer_count) if transformer_count > 0: match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1] attn_res *= 2 if attn_blocks == 0: for i in range(res_blocks): transformer_depth.append(0) match["transformer_depth"] = transformer_depth match["model_channels"] = state_dict["conv_in.weight"].shape[0] match["in_channels"] = state_dict["conv_in.weight"].shape[1] match["adm_in_channels"] = None if "class_embedding.linear_1.weight" in state_dict: match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1] elif "add_embedding.linear_1.weight" in state_dict: match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1] SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0, 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], 'use_temporal_attention': False, 'use_temporal_resblock': False} SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], 'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1], 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]} SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1], 'use_temporal_attention': False, 'use_temporal_resblock': False} supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p] for unet_config in supported_models: matches = True for k in match: if match[k] != unet_config[k]: matches = False break if matches: return convert_config(unet_config) return None def model_config_from_diffusers_unet(state_dict): unet_config = unet_config_from_diffusers_unet(state_dict) if unet_config is not None: return model_config_from_unet_config(unet_config) return None def convert_diffusers_mmdit(state_dict, output_prefix=""): depth = count_blocks(state_dict, 'transformer_blocks.{}.') if depth > 0: out_sd = {} sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth}, output_prefix=output_prefix) for k in sd_map: weight = state_dict.get(k, None) if weight is not None: t = sd_map[k] if not isinstance(t, str): if len(t) > 2: fun = t[2] else: fun = lambda a: a offset = t[1] if offset is not None: old_weight = out_sd.get(t[0], None) if old_weight is None: old_weight = torch.empty_like(weight) old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1)) w = old_weight.narrow(offset[0], offset[1], offset[2]) else: old_weight = weight w = weight w[:] = fun(weight) t = t[0] out_sd[t] = old_weight else: out_sd[t] = weight state_dict.pop(k) return out_sd
import psutil import logging from enum import Enum from comfy.cli_args import args import torch import sys import platform class VRAMState(Enum): DISABLED = 0 NO_VRAM = 1 LOW_VRAM = 2 NORMAL_VRAM = 3 HIGH_VRAM = 4 SHARED = 5 class CPUState(Enum): GPU = 0 CPU = 1 MPS = 2 vram_state = VRAMState.NORMAL_VRAM set_vram_to = VRAMState.NORMAL_VRAM cpu_state = CPUState.GPU total_vram = 0 lowvram_available = True xpu_available = False if args.deterministic: logging.info("Using deterministic algorithms for pytorch") torch.use_deterministic_algorithms(True, warn_only=True) directml_enabled = False if args.directml is not None: import torch_directml directml_enabled = True device_index = args.directml if device_index < 0: directml_device = torch_directml.device() else: directml_device = torch_directml.device(device_index) logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index))) lowvram_available = False try: import intel_extension_for_pytorch as ipex if torch.xpu.is_available(): xpu_available = True except: pass try: if torch.backends.mps.is_available(): cpu_state = CPUState.MPS import torch.mps except: pass if args.cpu: cpu_state = CPUState.CPU def is_intel_xpu(): global cpu_state global xpu_available if cpu_state == CPUState.GPU: if xpu_available: return True return False def get_torch_device(): global directml_enabled global cpu_state if directml_enabled: global directml_device return directml_device if cpu_state == CPUState.MPS: return torch.device("mps") if cpu_state == CPUState.CPU: return torch.device("cpu") else: if is_intel_xpu(): return torch.device("xpu", torch.xpu.current_device()) else: return torch.device(torch.cuda.current_device()) def get_total_memory(dev=None, torch_total_too=False): global directml_enabled if dev is None: dev = get_torch_device() if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): mem_total = psutil.virtual_memory().total mem_total_torch = mem_total else: if directml_enabled: mem_total = 1024 * 1024 * 1024 mem_total_torch = mem_total elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] mem_total_torch = mem_reserved mem_total = torch.xpu.get_device_properties(dev).total_memory else: stats = torch.cuda.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] _, mem_total_cuda = torch.cuda.mem_get_info(dev) mem_total_torch = mem_reserved mem_total = mem_total_cuda if torch_total_too: return (mem_total, mem_total_torch) else: return mem_total total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) total_ram = psutil.virtual_memory().total / (1024 * 1024) logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) try: logging.info("pytorch version: {}".format(torch.version.__version__)) except: pass try: OOM_EXCEPTION = torch.cuda.OutOfMemoryError except: OOM_EXCEPTION = Exception XFORMERS_VERSION = "" XFORMERS_ENABLED_VAE = True if args.disable_xformers: XFORMERS_IS_AVAILABLE = False else: try: import xformers import xformers.ops XFORMERS_IS_AVAILABLE = True try: XFORMERS_IS_AVAILABLE = xformers._has_cpp_library except: pass try: XFORMERS_VERSION = xformers.version.__version__ logging.info("xformers version: {}".format(XFORMERS_VERSION)) if XFORMERS_VERSION.startswith("0.0.18"): logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") logging.warning("Please downgrade or upgrade xformers to a different version.\n") XFORMERS_ENABLED_VAE = False except: pass except: XFORMERS_IS_AVAILABLE = False def is_nvidia(): global cpu_state if cpu_state == CPUState.GPU: if torch.version.cuda: return True return False ENABLE_PYTORCH_ATTENTION = False if args.use_pytorch_cross_attention: ENABLE_PYTORCH_ATTENTION = True XFORMERS_IS_AVAILABLE = False VAE_DTYPES = [torch.float32] try: if is_nvidia(): torch_version = torch.version.__version__ if int(torch_version[0]) >= 2: if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: ENABLE_PYTORCH_ATTENTION = True if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8: VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES if is_intel_xpu(): if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: ENABLE_PYTORCH_ATTENTION = True except: pass if is_intel_xpu(): VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES if args.cpu_vae: VAE_DTYPES = [torch.float32] if ENABLE_PYTORCH_ATTENTION: torch.backends.cuda.enable_math_sdp(True) torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(True) if args.lowvram: set_vram_to = VRAMState.LOW_VRAM lowvram_available = True elif args.novram: set_vram_to = VRAMState.NO_VRAM elif args.highvram or args.gpu_only: vram_state = VRAMState.HIGH_VRAM FORCE_FP32 = False FORCE_FP16 = False if args.force_fp32: logging.info("Forcing FP32, if this improves things please report it.") FORCE_FP32 = True if args.force_fp16: logging.info("Forcing FP16.") FORCE_FP16 = True if lowvram_available: if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): vram_state = set_vram_to if cpu_state != CPUState.GPU: vram_state = VRAMState.DISABLED if cpu_state == CPUState.MPS: vram_state = VRAMState.SHARED logging.info(f"Set vram state to: {vram_state.name}") DISABLE_SMART_MEMORY = args.disable_smart_memory if DISABLE_SMART_MEMORY: logging.info("Disabling smart memory management") def get_torch_device_name(device): if hasattr(device, 'type'): if device.type == "cuda": try: allocator_backend = torch.cuda.get_allocator_backend() except: allocator_backend = "" return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend) else: return "{}".format(device.type) elif is_intel_xpu(): return "{} {}".format(device, torch.xpu.get_device_name(device)) else: return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) try: logging.info("Device: {}".format(get_torch_device_name(get_torch_device()))) except: logging.warning("Could not pick default device.") current_loaded_models = [] def module_size(module): module_mem = 0 sd = module.state_dict() for k in sd: t = sd[k] module_mem += t.nelement() * t.element_size() return module_mem class LoadedModel: def __init__(self, model): self.model = model self.device = model.load_device self.weights_loaded = False self.real_model = None self.currently_used = True def model_memory(self): return self.model.model_size() def model_memory_required(self, device): if device == self.model.current_device: return 0 else: return self.model_memory() def model_load(self, lowvram_model_memory=0, force_patch_weights=False): patch_model_to = self.device self.model.model_patches_to(self.device) self.model.model_patches_to(self.model.model_dtype()) load_weights = not self.weights_loaded try: if lowvram_model_memory > 0 and load_weights: self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights) else: self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights) except Exception as e: self.model.unpatch_model(self.model.offload_device) self.model_unload() raise e if is_intel_xpu() and not args.disable_ipex_optimize: self.real_model = ipex.optimize(self.real_model.eval(), graph_mode=True, concat_linear=True) self.weights_loaded = True return self.real_model def should_reload_model(self, force_patch_weights=False): if force_patch_weights and self.model.lowvram_patch_counter > 0: return True return False def model_unload(self, unpatch_weights=True): self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights) self.model.model_patches_to(self.model.offload_device) self.weights_loaded = self.weights_loaded and not unpatch_weights self.real_model = None def __eq__(self, other): return self.model is other.model def minimum_inference_memory(): return (1024 * 1024 * 1024) def unload_model_clones(model, unload_weights_only=True, force_unload=True): to_unload = [] for i in range(len(current_loaded_models)): if model.is_clone(current_loaded_models[i].model): to_unload = [i] + to_unload if len(to_unload) == 0: return True same_weights = 0 for i in to_unload: if model.clone_has_same_weights(current_loaded_models[i].model): same_weights += 1 if same_weights == len(to_unload): unload_weight = False else: unload_weight = True if not force_unload: if unload_weights_only and unload_weight == False: return None for i in to_unload: logging.debug("unload clone {} {}".format(i, unload_weight)) current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight) return unload_weight def free_memory(memory_required, device, keep_loaded=[]): unloaded_model = [] can_unload = [] for i in range(len(current_loaded_models) -1, -1, -1): shift_model = current_loaded_models[i] if shift_model.device == device: if shift_model not in keep_loaded: can_unload.append((sys.getrefcount(shift_model.model), shift_model.model_memory(), i)) shift_model.currently_used = False for x in sorted(can_unload): i = x[-1] if not DISABLE_SMART_MEMORY: if get_free_memory(device) > memory_required: break current_loaded_models[i].model_unload() unloaded_model.append(i) for i in sorted(unloaded_model, reverse=True): current_loaded_models.pop(i) if len(unloaded_model) > 0: soft_empty_cache() else: if vram_state != VRAMState.HIGH_VRAM: mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True) if mem_free_torch > mem_free_total * 0.25: soft_empty_cache() def load_models_gpu(models, memory_required=0, force_patch_weights=False): global vram_state inference_memory = minimum_inference_memory() extra_mem = max(inference_memory, memory_required) models = set(models) models_to_load = [] models_already_loaded = [] for x in models: loaded_model = LoadedModel(x) loaded = None try: loaded_model_index = current_loaded_models.index(loaded_model) except: loaded_model_index = None if loaded_model_index is not None: loaded = current_loaded_models[loaded_model_index] if loaded.should_reload_model(force_patch_weights=force_patch_weights): current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True) loaded = None else: loaded.currently_used = True models_already_loaded.append(loaded) if loaded is None: if hasattr(x, "model"): logging.info(f"Requested to load {x.model.__class__.__name__}") models_to_load.append(loaded_model) if len(models_to_load) == 0: devs = set(map(lambda a: a.device, models_already_loaded)) for d in devs: if d != torch.device("cpu"): free_memory(extra_mem, d, models_already_loaded) return logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}") total_memory_required = {} for loaded_model in models_to_load: if unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) == True: total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) for device in total_memory_required: if device != torch.device("cpu"): free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded) for loaded_model in models_to_load: weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False) if weights_unloaded is not None: loaded_model.weights_loaded = not weights_unloaded for loaded_model in models_to_load: model = loaded_model.model torch_dev = model.load_device if is_device_cpu(torch_dev): vram_set_state = VRAMState.DISABLED else: vram_set_state = vram_state lowvram_model_memory = 0 if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM): model_size = loaded_model.model_memory_required(torch_dev) current_free_mem = get_free_memory(torch_dev) lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 )) if model_size <= (current_free_mem - inference_memory): lowvram_model_memory = 0 if vram_set_state == VRAMState.NO_VRAM: lowvram_model_memory = 64 * 1024 * 1024 cur_loaded_model = loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights) current_loaded_models.insert(0, loaded_model) return def load_model_gpu(model): return load_models_gpu([model]) def loaded_models(only_currently_used=False): output = [] for m in current_loaded_models: if only_currently_used: if not m.currently_used: continue output.append(m.model) return output def cleanup_models(keep_clone_weights_loaded=False): to_delete = [] for i in range(len(current_loaded_models)): if sys.getrefcount(current_loaded_models[i].model) <= 2: if not keep_clone_weights_loaded: to_delete = [i] + to_delete elif sys.getrefcount(current_loaded_models[i].real_model) <= 3: to_delete = [i] + to_delete for i in to_delete: x = current_loaded_models.pop(i) x.model_unload() del x def dtype_size(dtype): dtype_size = 4 if dtype == torch.float16 or dtype == torch.bfloat16: dtype_size = 2 elif dtype == torch.float32: dtype_size = 4 else: try: dtype_size = dtype.itemsize except: pass return dtype_size def unet_offload_device(): if vram_state == VRAMState.HIGH_VRAM: return get_torch_device() else: return torch.device("cpu") def unet_inital_load_device(parameters, dtype): torch_dev = get_torch_device() if vram_state == VRAMState.HIGH_VRAM: return torch_dev cpu_dev = torch.device("cpu") if DISABLE_SMART_MEMORY: return cpu_dev model_size = dtype_size(dtype) * parameters mem_dev = get_free_memory(torch_dev) mem_cpu = get_free_memory(cpu_dev) if mem_dev > mem_cpu and model_size < mem_dev: return torch_dev else: return cpu_dev def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): if args.bf16_unet: return torch.bfloat16 if args.fp16_unet: return torch.float16 if args.fp8_e4m3fn_unet: return torch.float8_e4m3fn if args.fp8_e5m2_unet: return torch.float8_e5m2 if should_use_fp16(device=device, model_params=model_params, manual_cast=True): if torch.float16 in supported_dtypes: return torch.float16 if should_use_bf16(device, model_params=model_params, manual_cast=True): if torch.bfloat16 in supported_dtypes: return torch.bfloat16 return torch.float32 def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): if weight_dtype == torch.float32: return None fp16_supported = should_use_fp16(inference_device, prioritize_performance=False) if fp16_supported and weight_dtype == torch.float16: return None bf16_supported = should_use_bf16(inference_device) if bf16_supported and weight_dtype == torch.bfloat16: return None if fp16_supported and torch.float16 in supported_dtypes: return torch.float16 elif bf16_supported and torch.bfloat16 in supported_dtypes: return torch.bfloat16 else: return torch.float32 def text_encoder_offload_device(): if args.gpu_only: return get_torch_device() else: return torch.device("cpu") def text_encoder_device(): if args.gpu_only: return get_torch_device() elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: if should_use_fp16(prioritize_performance=False): return get_torch_device() else: return torch.device("cpu") else: return torch.device("cpu") def text_encoder_dtype(device=None): if args.fp8_e4m3fn_text_enc: return torch.float8_e4m3fn elif args.fp8_e5m2_text_enc: return torch.float8_e5m2 elif args.fp16_text_enc: return torch.float16 elif args.fp32_text_enc: return torch.float32 if is_device_cpu(device): return torch.float16 return torch.float16 def intermediate_device(): if args.gpu_only: return get_torch_device() else: return torch.device("cpu") def vae_device(): if args.cpu_vae: return torch.device("cpu") return get_torch_device() def vae_offload_device(): if args.gpu_only: return get_torch_device() else: return torch.device("cpu") def vae_dtype(device=None, allowed_dtypes=[]): global VAE_DTYPES if args.fp16_vae: return torch.float16 elif args.bf16_vae: return torch.bfloat16 elif args.fp32_vae: return torch.float32 for d in allowed_dtypes: if d == torch.float16 and should_use_fp16(device, prioritize_performance=False): return d if d in VAE_DTYPES: return d return VAE_DTYPES[0] def get_autocast_device(dev): if hasattr(dev, 'type'): return dev.type return "cuda" def supports_dtype(device, dtype): if dtype == torch.float32: return True if is_device_cpu(device): return False if dtype == torch.float16: return True if dtype == torch.bfloat16: return True return False def supports_cast(device, dtype): if dtype == torch.float32: return True if dtype == torch.float16: return True if is_device_mps(device): return False if directml_enabled: return False if dtype == torch.bfloat16: return True if dtype == torch.float8_e4m3fn: return True if dtype == torch.float8_e5m2: return True return False def device_supports_non_blocking(device): if is_device_mps(device): return False if is_intel_xpu(): return False if args.deterministic: return False if directml_enabled: return False return True def device_should_use_non_blocking(device): if not device_supports_non_blocking(device): return False return False def force_channels_last(): if args.force_channels_last: return True return False def cast_to_device(tensor, device, dtype, copy=False): device_supports_cast = False if tensor.dtype == torch.float32 or tensor.dtype == torch.float16: device_supports_cast = True elif tensor.dtype == torch.bfloat16: if hasattr(device, 'type') and device.type.startswith("cuda"): device_supports_cast = True elif is_intel_xpu(): device_supports_cast = True non_blocking = device_should_use_non_blocking(device) if device_supports_cast: if copy: if tensor.device == device: return tensor.to(dtype, copy=copy, non_blocking=non_blocking) return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking) else: return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking) else: return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking) def xformers_enabled(): global directml_enabled global cpu_state if cpu_state != CPUState.GPU: return False if is_intel_xpu(): return False if directml_enabled: return False return XFORMERS_IS_AVAILABLE def xformers_enabled_vae(): enabled = xformers_enabled() if not enabled: return False return XFORMERS_ENABLED_VAE def pytorch_attention_enabled(): global ENABLE_PYTORCH_ATTENTION return ENABLE_PYTORCH_ATTENTION def pytorch_attention_flash_attention(): global ENABLE_PYTORCH_ATTENTION if ENABLE_PYTORCH_ATTENTION: if is_nvidia(): return True if is_intel_xpu(): return True return False def force_upcast_attention_dtype(): upcast = args.force_upcast_attention try: if platform.mac_ver()[0] in ['14.5']: upcast = True except: pass if upcast: return torch.float32 else: return None def get_free_memory(dev=None, torch_free_too=False): global directml_enabled if dev is None: dev = get_torch_device() if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): mem_free_total = psutil.virtual_memory().available mem_free_torch = mem_free_total else: if directml_enabled: mem_free_total = 1024 * 1024 * 1024 mem_free_torch = mem_free_total elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_torch = mem_reserved - mem_active mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved mem_free_total = mem_free_xpu + mem_free_torch else: stats = torch.cuda.memory_stats(dev) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_cuda, _ = torch.cuda.mem_get_info(dev) mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_cuda + mem_free_torch if torch_free_too: return (mem_free_total, mem_free_torch) else: return mem_free_total def cpu_mode(): global cpu_state return cpu_state == CPUState.CPU def mps_mode(): global cpu_state return cpu_state == CPUState.MPS def is_device_type(device, type): if hasattr(device, 'type'): if (device.type == type): return True return False def is_device_cpu(device): return is_device_type(device, 'cpu') def is_device_mps(device): return is_device_type(device, 'mps') def is_device_cuda(device): return is_device_type(device, 'cuda') def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): global directml_enabled if device is not None: if is_device_cpu(device): return False if FORCE_FP16: return True if device is not None: if is_device_mps(device): return True if FORCE_FP32: return False if directml_enabled: return False if mps_mode(): return True if cpu_mode(): return False if is_intel_xpu(): return True if torch.version.hip: return True props = torch.cuda.get_device_properties("cuda") if props.major >= 8: return True if props.major < 6: return False fp16_works = False nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"] for x in nvidia_10_series: if x in props.name.lower(): fp16_works = True if fp16_works or manual_cast: free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory()) if (not prioritize_performance) or model_params * 4 > free_model_memory: return True if props.major < 7: return False nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"] for x in nvidia_16_series: if x in props.name: return False return True def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): if device is not None: if is_device_cpu(device): return False if device is not None: if is_device_mps(device): return False if FORCE_FP32: return False if directml_enabled: return False if cpu_mode() or mps_mode(): return False if is_intel_xpu(): return True if device is None: device = torch.device("cuda") props = torch.cuda.get_device_properties(device) if props.major >= 8: return True bf16_works = torch.cuda.is_bf16_supported() if bf16_works or manual_cast: free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory()) if (not prioritize_performance) or model_params * 4 > free_model_memory: return True return False def soft_empty_cache(force=False): global cpu_state if cpu_state == CPUState.MPS: torch.mps.empty_cache() elif is_intel_xpu(): torch.xpu.empty_cache() elif torch.cuda.is_available(): if force or is_nvidia(): torch.cuda.empty_cache() torch.cuda.ipc_collect() def unload_all_models(): free_memory(1e30, get_torch_device()) def resolve_lowvram_weight(weight, model, key): print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.") return weight import threading class InterruptProcessingException(Exception): pass interrupt_processing_mutex = threading.RLock() interrupt_processing = False def interrupt_current_processing(value=True): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: interrupt_processing = value def processing_interrupted(): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: return interrupt_processing def throw_exception_if_processing_interrupted(): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: if interrupt_processing: interrupt_processing = False raise InterruptProcessingException()
import torch import copy import inspect import logging import uuid import comfy.utils import comfy.model_management from comfy.types import UnetWrapperFunction def weight_decompose(dora_scale, weight, lora_diff, alpha, strength): dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32) lora_diff *= alpha weight_calc = weight + lora_diff.type(weight.dtype) weight_norm = ( weight_calc.transpose(0, 1) .reshape(weight_calc.shape[1], -1) .norm(dim=1, keepdim=True) .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) .transpose(0, 1) ) weight_calc *= (dora_scale / weight_norm).type(weight.dtype) if strength != 1.0: weight_calc -= weight weight += strength * (weight_calc) else: weight[:] = weight_calc return weight def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): to = model_options["transformer_options"].copy() if "patches_replace" not in to: to["patches_replace"] = {} else: to["patches_replace"] = to["patches_replace"].copy() if name not in to["patches_replace"]: to["patches_replace"][name] = {} else: to["patches_replace"][name] = to["patches_replace"][name].copy() if transformer_index is not None: block = (block_name, number, transformer_index) else: block = (block_name, number) to["patches_replace"][name][block] = patch model_options["transformer_options"] = to return model_options class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False): self.size = size self.model = model self.patches = {} self.backup = {} self.object_patches = {} self.object_patches_backup = {} self.model_options = {"transformer_options":{}} self.model_size() self.load_device = load_device self.offload_device = offload_device if current_device is None: self.current_device = self.offload_device else: self.current_device = current_device self.weight_inplace_update = weight_inplace_update self.model_lowvram = False self.lowvram_patch_counter = 0 self.patches_uuid = uuid.uuid4() def model_size(self): if self.size > 0: return self.size self.size = comfy.model_management.module_size(self.model) return self.size def clone(self): n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update) n.patches = {} for k in self.patches: n.patches[k] = self.patches[k][:] n.patches_uuid = self.patches_uuid n.object_patches = self.object_patches.copy() n.model_options = copy.deepcopy(self.model_options) n.backup = self.backup n.object_patches_backup = self.object_patches_backup return n def is_clone(self, other): if hasattr(other, 'model') and self.model is other.model: return True return False def clone_has_same_weights(self, clone): if not self.is_clone(clone): return False if len(self.patches) == 0 and len(clone.patches) == 0: return True if self.patches_uuid == clone.patches_uuid: if len(self.patches) != len(clone.patches): logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.") else: return True def memory_required(self, input_shape): return self.model.memory_required(input_shape=input_shape) def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False): if len(inspect.signature(sampler_cfg_function).parameters) == 3: self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) else: self.model_options["sampler_cfg_function"] = sampler_cfg_function if disable_cfg1_optimization: self.model_options["disable_cfg1_optimization"] = True def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False): self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] if disable_cfg1_optimization: self.model_options["disable_cfg1_optimization"] = True def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction): self.model_options["model_function_wrapper"] = unet_wrapper_function def set_model_denoise_mask_function(self, denoise_mask_function): self.model_options["denoise_mask_function"] = denoise_mask_function def set_model_patch(self, patch, name): to = self.model_options["transformer_options"] if "patches" not in to: to["patches"] = {} to["patches"][name] = to["patches"].get(name, []) + [patch] def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None): self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index) def set_model_attn1_patch(self, patch): self.set_model_patch(patch, "attn1_patch") def set_model_attn2_patch(self, patch): self.set_model_patch(patch, "attn2_patch") def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None): self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index) def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None): self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index) def set_model_attn1_output_patch(self, patch): self.set_model_patch(patch, "attn1_output_patch") def set_model_attn2_output_patch(self, patch): self.set_model_patch(patch, "attn2_output_patch") def set_model_input_block_patch(self, patch): self.set_model_patch(patch, "input_block_patch") def set_model_input_block_patch_after_skip(self, patch): self.set_model_patch(patch, "input_block_patch_after_skip") def set_model_output_block_patch(self, patch): self.set_model_patch(patch, "output_block_patch") def add_object_patch(self, name, obj): self.object_patches[name] = obj def get_model_object(self, name): if name in self.object_patches: return self.object_patches[name] else: if name in self.object_patches_backup: return self.object_patches_backup[name] else: return comfy.utils.get_attr(self.model, name) def model_patches_to(self, device): to = self.model_options["transformer_options"] if "patches" in to: patches = to["patches"] for name in patches: patch_list = patches[name] for i in range(len(patch_list)): if hasattr(patch_list[i], "to"): patch_list[i] = patch_list[i].to(device) if "patches_replace" in to: patches = to["patches_replace"] for name in patches: patch_list = patches[name] for k in patch_list: if hasattr(patch_list[k], "to"): patch_list[k] = patch_list[k].to(device) if "model_function_wrapper" in self.model_options: wrap_func = self.model_options["model_function_wrapper"] if hasattr(wrap_func, "to"): self.model_options["model_function_wrapper"] = wrap_func.to(device) def model_dtype(self): if hasattr(self.model, "get_dtype"): return self.model.get_dtype() def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): p = set() model_sd = self.model.state_dict() for k in patches: offset = None function = None if isinstance(k, str): key = k else: offset = k[1] key = k[0] if len(k) > 2: function = k[2] if key in model_sd: p.add(k) current_patches = self.patches.get(key, []) current_patches.append((strength_patch, patches[k], strength_model, offset, function)) self.patches[key] = current_patches self.patches_uuid = uuid.uuid4() return list(p) def get_key_patches(self, filter_prefix=None): comfy.model_management.unload_model_clones(self) model_sd = self.model_state_dict() p = {} for k in model_sd: if filter_prefix is not None: if not k.startswith(filter_prefix): continue if k in self.patches: p[k] = [model_sd[k]] + self.patches[k] else: p[k] = (model_sd[k],) return p def model_state_dict(self, filter_prefix=None): sd = self.model.state_dict() keys = list(sd.keys()) if filter_prefix is not None: for k in keys: if not k.startswith(filter_prefix): sd.pop(k) return sd def patch_weight_to_device(self, key, device_to=None): if key not in self.patches: return weight = comfy.utils.get_attr(self.model, key) inplace_update = self.weight_inplace_update if key not in self.backup: self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update) if device_to is not None: temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) else: temp_weight = weight.to(torch.float32, copy=True) out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) if inplace_update: comfy.utils.copy_to_param(self.model, key, out_weight) else: comfy.utils.set_attr_param(self.model, key, out_weight) def patch_model(self, device_to=None, patch_weights=True): for k in self.object_patches: old = comfy.utils.set_attr(self.model, k, self.object_patches[k]) if k not in self.object_patches_backup: self.object_patches_backup[k] = old if patch_weights: model_sd = self.model_state_dict() for key in self.patches: if key not in model_sd: logging.warning("could not patch. key doesn't exist in model: {}".format(key)) continue self.patch_weight_to_device(key, device_to) if device_to is not None: self.model.to(device_to) self.current_device = device_to return self.model def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False): self.patch_model(device_to, patch_weights=False) logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024))) class LowVramPatch: def __init__(self, key, model_patcher): self.key = key self.model_patcher = model_patcher def __call__(self, weight): return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key) mem_counter = 0 patch_counter = 0 for n, m in self.model.named_modules(): lowvram_weight = False if hasattr(m, "comfy_cast_weights"): module_mem = comfy.model_management.module_size(m) if mem_counter + module_mem >= lowvram_model_memory: lowvram_weight = True weight_key = "{}.weight".format(n) bias_key = "{}.bias".format(n) if lowvram_weight: if weight_key in self.patches: if force_patch_weights: self.patch_weight_to_device(weight_key) else: m.weight_function = LowVramPatch(weight_key, self) patch_counter += 1 if bias_key in self.patches: if force_patch_weights: self.patch_weight_to_device(bias_key) else: m.bias_function = LowVramPatch(bias_key, self) patch_counter += 1 m.prev_comfy_cast_weights = m.comfy_cast_weights m.comfy_cast_weights = True else: if hasattr(m, "weight"): self.patch_weight_to_device(weight_key, device_to) self.patch_weight_to_device(bias_key, device_to) m.to(device_to) mem_counter += comfy.model_management.module_size(m) logging.debug("lowvram: loaded module regularly {} {}".format(n, m)) self.model_lowvram = True self.lowvram_patch_counter = patch_counter return self.model def calculate_weight(self, patches, weight, key): for p in patches: strength = p[0] v = p[1] strength_model = p[2] offset = p[3] function = p[4] if function is None: function = lambda a: a old_weight = None if offset is not None: old_weight = weight weight = weight.narrow(offset[0], offset[1], offset[2]) if strength_model != 1.0: weight *= strength_model if isinstance(v, list): v = (self.calculate_weight(v[1:], v[0].clone(), key), ) if len(v) == 1: patch_type = "diff" elif len(v) == 2: patch_type = v[0] v = v[1] if patch_type == "diff": w1 = v[0] if strength != 0.0: if w1.shape != weight.shape: logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)) elif patch_type == "lora": mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32) mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32) dora_scale = v[4] if v[2] is not None: alpha = v[2] / mat2.shape[0] else: alpha = 1.0 if v[3] is not None: mat3 = comfy.model_management.cast_to_device(v[3], weight.device, torch.float32) final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) try: lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape) if dora_scale is not None: weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength)) else: weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "lokr": w1 = v[0] w2 = v[1] w1_a = v[3] w1_b = v[4] w2_a = v[5] w2_b = v[6] t2 = v[7] dora_scale = v[8] dim = None if w1 is None: dim = w1_b.shape[0] w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, torch.float32), comfy.model_management.cast_to_device(w1_b, weight.device, torch.float32)) else: w1 = comfy.model_management.cast_to_device(w1, weight.device, torch.float32) if w2 is None: dim = w2_b.shape[0] if t2 is None: w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32), comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32)) else: w2 = torch.einsum('i j k l, j r, i p -> p r k l', comfy.model_management.cast_to_device(t2, weight.device, torch.float32), comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32), comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32)) else: w2 = comfy.model_management.cast_to_device(w2, weight.device, torch.float32) if len(w2.shape) == 4: w1 = w1.unsqueeze(2).unsqueeze(2) if v[2] is not None and dim is not None: alpha = v[2] / dim else: alpha = 1.0 try: lora_diff = torch.kron(w1, w2).reshape(weight.shape) if dora_scale is not None: weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength)) else: weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "loha": w1a = v[0] w1b = v[1] if v[2] is not None: alpha = v[2] / w1b.shape[0] else: alpha = 1.0 w2a = v[3] w2b = v[4] dora_scale = v[7] if v[5] is not None: t1 = v[5] t2 = v[6] m1 = torch.einsum('i j k l, j r, i p -> p r k l', comfy.model_management.cast_to_device(t1, weight.device, torch.float32), comfy.model_management.cast_to_device(w1b, weight.device, torch.float32), comfy.model_management.cast_to_device(w1a, weight.device, torch.float32)) m2 = torch.einsum('i j k l, j r, i p -> p r k l', comfy.model_management.cast_to_device(t2, weight.device, torch.float32), comfy.model_management.cast_to_device(w2b, weight.device, torch.float32), comfy.model_management.cast_to_device(w2a, weight.device, torch.float32)) else: m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, torch.float32), comfy.model_management.cast_to_device(w1b, weight.device, torch.float32)) m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, torch.float32), comfy.model_management.cast_to_device(w2b, weight.device, torch.float32)) try: lora_diff = (m1 * m2).reshape(weight.shape) if dora_scale is not None: weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength)) else: weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "glora": if v[4] is not None: alpha = v[4] / v[0].shape[0] else: alpha = 1.0 dora_scale = v[5] a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) try: lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape) if dora_scale is not None: weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength)) else: weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) else: logging.warning("patch type not recognized {} {}".format(patch_type, key)) if old_weight is not None: weight = old_weight return weight def unpatch_model(self, device_to=None, unpatch_weights=True): if unpatch_weights: if self.model_lowvram: for m in self.model.modules(): if hasattr(m, "prev_comfy_cast_weights"): m.comfy_cast_weights = m.prev_comfy_cast_weights del m.prev_comfy_cast_weights m.weight_function = None m.bias_function = None self.model_lowvram = False self.lowvram_patch_counter = 0 keys = list(self.backup.keys()) if self.weight_inplace_update: for k in keys: comfy.utils.copy_to_param(self.model, k, self.backup[k]) else: for k in keys: comfy.utils.set_attr_param(self.model, k, self.backup[k]) self.backup.clear() if device_to is not None: self.model.to(device_to) self.current_device = device_to keys = list(self.object_patches_backup.keys()) for k in keys: comfy.utils.set_attr(self.model, k, self.object_patches_backup[k]) self.object_patches_backup.clear()
import torch from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule import math class EPS: def calculate_input(self, sigma, noise): sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 def calculate_denoised(self, sigma, model_output, model_input): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input - model_output * sigma def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): if max_denoise: noise = noise * torch.sqrt(1.0 + sigma ** 2.0) else: noise = noise * sigma noise += latent_image return noise def inverse_noise_scaling(self, sigma, latent): return latent class V_PREDICTION(EPS): def calculate_denoised(self, sigma, model_output, model_input): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 class EDM(V_PREDICTION): def calculate_denoised(self, sigma, model_output, model_input): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 class CONST: def calculate_input(self, sigma, noise): return noise def calculate_denoised(self, sigma, model_output, model_input): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input - model_output * sigma def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): return sigma * noise + (1.0 - sigma) * latent_image def inverse_noise_scaling(self, sigma, latent): return latent / (1.0 - sigma) class ModelSamplingDiscrete(torch.nn.Module): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} beta_schedule = sampling_settings.get("beta_schedule", "linear") linear_start = sampling_settings.get("linear_start", 0.00085) linear_end = sampling_settings.get("linear_end", 0.012) self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3) self.sigma_data = 1.0 def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if given_betas is not None: betas = given_betas else: betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = torch.cumprod(alphas, dim=0) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 self.set_sigmas(sigmas) def set_sigmas(self, sigmas): self.register_buffer('sigmas', sigmas.float()) self.register_buffer('log_sigmas', sigmas.log().float()) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): log_sigma = sigma.log() dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) def sigma(self, timestep): t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1)) low_idx = t.floor().long() high_idx = t.ceil().long() w = t.frac() log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] return log_sigma.exp().to(timestep.device) def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent return self.sigma(torch.tensor(percent * 999.0)).item() class ModelSamplingDiscreteEDM(ModelSamplingDiscrete): def timestep(self, sigma): return 0.25 * sigma.log() def sigma(self, timestep): return (timestep / 0.25).exp() class ModelSamplingContinuousEDM(torch.nn.Module): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} sigma_min = sampling_settings.get("sigma_min", 0.002) sigma_max = sampling_settings.get("sigma_max", 120.0) sigma_data = sampling_settings.get("sigma_data", 1.0) self.set_parameters(sigma_min, sigma_max, sigma_data) def set_parameters(self, sigma_min, sigma_max, sigma_data): self.sigma_data = sigma_data sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp() self.register_buffer('sigmas', sigmas) self.register_buffer('log_sigmas', sigmas.log()) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): return 0.25 * sigma.log() def sigma(self, timestep): return (timestep / 0.25).exp() def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent log_sigma_min = math.log(self.sigma_min) return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) class ModelSamplingContinuousV(ModelSamplingContinuousEDM): def timestep(self, sigma): return sigma.atan() / math.pi * 2 def sigma(self, timestep): return (timestep * math.pi / 2).tan() def time_snr_shift(alpha, t): if alpha == 1.0: return t return alpha * t / (1 + (alpha - 1) * t) class ModelSamplingDiscreteFlow(torch.nn.Module): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} self.set_parameters(shift=sampling_settings.get("shift", 1.0)) def set_parameters(self, shift=1.0, timesteps=1000): self.shift = shift ts = self.sigma(torch.arange(1, timesteps + 1, 1)) self.register_buffer('sigmas', ts) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): return sigma * 1000 def sigma(self, timestep): return time_snr_shift(self.shift, timestep / 1000) def percent_to_sigma(self, percent): if percent <= 0.0: return 1.0 if percent >= 1.0: return 0.0 return 1.0 - percent class StableCascadeSampling(ModelSamplingDiscrete): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} self.set_parameters(sampling_settings.get("shift", 1.0)) def set_parameters(self, shift=1.0, cosine_s=8e-3): self.shift = shift self.cosine_s = torch.tensor(cosine_s) self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 self.num_timesteps = 10000 sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) for x in range(self.num_timesteps): t = (x + 1) / self.num_timesteps sigmas[x] = self.sigma(t) self.set_sigmas(sigmas) def sigma(self, timestep): alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod) if self.shift != 1.0: var = alpha_cumprod logSNR = (var/(1-var)).log() logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift)) alpha_cumprod = logSNR.sigmoid() alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999) return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5 def timestep(self, sigma): var = 1 / ((sigma * sigma) + 1) var = var.clamp(0, 1.0) s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device) t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s return t def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent return self.sigma(torch.tensor(percent))
""" This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https: """ import torch import comfy.model_management def cast_bias_weight(s, input): bias = None non_blocking = comfy.model_management.device_should_use_non_blocking(input.device) if s.bias is not None: bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) if s.bias_function is not None: bias = s.bias_function(bias) weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) if s.weight_function is not None: weight = s.weight_function(weight) return weight, bias class CastWeightBiasOp: comfy_cast_weights = False weight_function = None bias_function = None class disable_weight_init: class Linear(torch.nn.Linear, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return torch.nn.functional.linear(input, weight, bias) def forward(self, *args, **kwargs): if self.comfy_cast_weights: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class Conv1d(torch.nn.Conv1d, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return self._conv_forward(input, weight, bias) def forward(self, *args, **kwargs): if self.comfy_cast_weights: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class Conv2d(torch.nn.Conv2d, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return self._conv_forward(input, weight, bias) def forward(self, *args, **kwargs): if self.comfy_cast_weights: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class Conv3d(torch.nn.Conv3d, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return self._conv_forward(input, weight, bias) def forward(self, *args, **kwargs): if self.comfy_cast_weights: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) def forward(self, *args, **kwargs): if self.comfy_cast_weights: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input): if self.weight is not None: weight, bias = cast_bias_weight(self, input) else: weight = None bias = None return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) def forward(self, *args, **kwargs): if self.comfy_cast_weights: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input, output_size=None): num_spatial_dims = 2 output_padding = self._output_padding( input, output_size, self.stride, self.padding, self.kernel_size, num_spatial_dims, self.dilation) weight, bias = cast_bias_weight(self, input) return torch.nn.functional.conv_transpose2d( input, weight, bias, self.stride, self.padding, output_padding, self.groups, self.dilation) def forward(self, *args, **kwargs): if self.comfy_cast_weights: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp): def reset_parameters(self): return None def forward_comfy_cast_weights(self, input, output_size=None): num_spatial_dims = 1 output_padding = self._output_padding( input, output_size, self.stride, self.padding, self.kernel_size, num_spatial_dims, self.dilation) weight, bias = cast_bias_weight(self, input) return torch.nn.functional.conv_transpose1d( input, weight, bias, self.stride, self.padding, output_padding, self.groups, self.dilation) def forward(self, *args, **kwargs): if self.comfy_cast_weights: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) @classmethod def conv_nd(s, dims, *args, **kwargs): if dims == 2: return s.Conv2d(*args, **kwargs) elif dims == 3: return s.Conv3d(*args, **kwargs) else: raise ValueError(f"unsupported dimensions: {dims}") class manual_cast(disable_weight_init): class Linear(disable_weight_init.Linear): comfy_cast_weights = True class Conv1d(disable_weight_init.Conv1d): comfy_cast_weights = True class Conv2d(disable_weight_init.Conv2d): comfy_cast_weights = True class Conv3d(disable_weight_init.Conv3d): comfy_cast_weights = True class GroupNorm(disable_weight_init.GroupNorm): comfy_cast_weights = True class LayerNorm(disable_weight_init.LayerNorm): comfy_cast_weights = True class ConvTranspose2d(disable_weight_init.ConvTranspose2d): comfy_cast_weights = True class ConvTranspose1d(disable_weight_init.ConvTranspose1d): comfy_cast_weights = True
args_parsing = False def enable_args_parsing(enable=True): global args_parsing args_parsing = enable
import torch import comfy.model_management import comfy.samplers import comfy.utils import numpy as np import logging def prepare_noise(latent_image, seed, noise_inds=None): """ creates random noise given a latent image and a seed. optional arg skip can be used to skip and discard x number of noise generations for a given seed """ generator = torch.manual_seed(seed) if noise_inds is None: return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") unique_inds, inverse = np.unique(noise_inds, return_inverse=True) noises = [] for i in range(unique_inds[-1]+1): noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") if i in unique_inds: noises.append(noise) noises = [noises[i] for i in inverse] noises = torch.cat(noises, axis=0) return noises def fix_empty_latent_channels(model, latent_image): latent_channels = model.get_model_object("latent_format").latent_channels if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0: latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1) return latent_image def prepare_sampling(model, noise_shape, positive, negative, noise_mask): logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed") return model, positive, negative, noise_mask, [] def cleanup_additional_models(models): logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed") def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.to(comfy.model_management.intermediate_device()) return samples def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.to(comfy.model_management.intermediate_device()) return samples
from .k_diffusion import sampling as k_diffusion_sampling from .extra_samplers import uni_pc import torch import collections from comfy import model_management import math import logging import comfy.sampler_helpers def get_area_and_mult(conds, x_in, timestep_in): dims = tuple(x_in.shape[2:]) area = None strength = 1.0 if 'timestep_start' in conds: timestep_start = conds['timestep_start'] if timestep_in[0] > timestep_start: return None if 'timestep_end' in conds: timestep_end = conds['timestep_end'] if timestep_in[0] < timestep_end: return None if 'area' in conds: area = list(conds['area']) if 'strength' in conds: strength = conds['strength'] input_x = x_in if area is not None: for i in range(len(dims)): area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i]) input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i]) if 'mask' in conds: mask_strength = 1.0 if "mask_strength" in conds: mask_strength = conds["mask_strength"] mask = conds['mask'] assert(mask.shape[1:] == x_in.shape[2:]) mask = mask[:input_x.shape[0]] if area is not None: for i in range(len(dims)): mask = mask.narrow(i + 1, area[len(dims) + i], area[i]) mask = mask * mask_strength mask = mask.unsqueeze(1).repeat(input_x.shape[0] else: mask = torch.ones_like(input_x) mult = mask * strength if 'mask' not in conds and area is not None: rr = 8 for i in range(len(dims)): if area[len(dims) + i] != 0: for t in range(rr): m = mult.narrow(i + 2, t, 1) m *= ((1.0/rr) * (t + 1)) if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]: for t in range(rr): m = mult.narrow(i + 2, area[i] - 1 - t, 1) m *= ((1.0/rr) * (t + 1)) conditioning = {} model_conds = conds["model_conds"] for c in model_conds: conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area) control = conds.get('control', None) patches = None if 'gligen' in conds: gligen = conds['gligen'] patches = {} gligen_type = gligen[0] gligen_model = gligen[1] if gligen_type == "position": gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) else: gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) patches['middle_patch'] = [gligen_patch] cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches']) return cond_obj(input_x, mult, conditioning, area, control, patches) def cond_equal_size(c1, c2): if c1 is c2: return True if c1.keys() != c2.keys(): return False for k in c1: if not c1[k].can_concat(c2[k]): return False return True def can_concat_cond(c1, c2): if c1.input_x.shape != c2.input_x.shape: return False def objects_concatable(obj1, obj2): if (obj1 is None) != (obj2 is None): return False if obj1 is not None: if obj1 is not obj2: return False return True if not objects_concatable(c1.control, c2.control): return False if not objects_concatable(c1.patches, c2.patches): return False return cond_equal_size(c1.conditioning, c2.conditioning) def cond_cat(c_list): c_crossattn = [] c_concat = [] c_adm = [] crossattn_max_len = 0 temp = {} for x in c_list: for k in x: cur = temp.get(k, []) cur.append(x[k]) temp[k] = cur out = {} for k in temp: conds = temp[k] out[k] = conds[0].concat(conds[1:]) return out def calc_cond_batch(model, conds, x_in, timestep, model_options): out_conds = [] out_counts = [] to_run = [] for i in range(len(conds)): out_conds.append(torch.zeros_like(x_in)) out_counts.append(torch.ones_like(x_in) * 1e-37) cond = conds[i] if cond is not None: for x in cond: p = get_area_and_mult(x, x_in, timestep) if p is None: continue to_run += [(p, i)] while len(to_run) > 0: first = to_run[0] first_shape = first[0][0].shape to_batch_temp = [] for x in range(len(to_run)): if can_concat_cond(to_run[x][0], first[0]): to_batch_temp += [x] to_batch_temp.reverse() to_batch = to_batch_temp[:1] free_memory = model_management.get_free_memory(x_in.device) for i in range(1, len(to_batch_temp) + 1): batch_amount = to_batch_temp[:len(to_batch_temp) input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] if model.memory_required(input_shape) < free_memory: to_batch = batch_amount break input_x = [] mult = [] c = [] cond_or_uncond = [] area = [] control = None patches = None for x in to_batch: o = to_run.pop(x) p = o[0] input_x.append(p.input_x) mult.append(p.mult) c.append(p.conditioning) area.append(p.area) cond_or_uncond.append(o[1]) control = p.control patches = p.patches batch_chunks = len(cond_or_uncond) input_x = torch.cat(input_x) c = cond_cat(c) timestep_ = torch.cat([timestep] * batch_chunks) if control is not None: c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond)) transformer_options = {} if 'transformer_options' in model_options: transformer_options = model_options['transformer_options'].copy() if patches is not None: if "patches" in transformer_options: cur_patches = transformer_options["patches"].copy() for p in patches: if p in cur_patches: cur_patches[p] = cur_patches[p] + patches[p] else: cur_patches[p] = patches[p] transformer_options["patches"] = cur_patches else: transformer_options["patches"] = patches transformer_options["cond_or_uncond"] = cond_or_uncond[:] transformer_options["sigmas"] = timestep c['transformer_options'] = transformer_options if 'model_function_wrapper' in model_options: output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) else: output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) for o in range(batch_chunks): cond_index = cond_or_uncond[o] a = area[o] if a is None: out_conds[cond_index] += output[o] * mult[o] out_counts[cond_index] += mult[o] else: out_c = out_conds[cond_index] out_cts = out_counts[cond_index] dims = len(a) for i in range(dims): out_c = out_c.narrow(i + 2, a[i + dims], a[i]) out_cts = out_cts.narrow(i + 2, a[i + dims], a[i]) out_c += output[o] * mult[o] out_cts += mult[o] for i in range(len(out_conds)): out_conds[i] /= out_counts[i] return out_conds def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.") return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options)) def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None): if "sampler_cfg_function" in model_options: args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} cfg_result = x - model_options["sampler_cfg_function"](args) else: cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale for fn in model_options.get("sampler_post_cfg_function", []): args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, "sigma": timestep, "model_options": model_options, "input": x} cfg_result = fn(args) return cfg_result def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: uncond_ = None else: uncond_ = uncond conds = [cond, uncond_] out = calc_cond_batch(model, conds, x, timestep, model_options) return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_) class KSamplerX0Inpaint: def __init__(self, model, sigmas): self.inner_model = model self.sigmas = sigmas def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None): if denoise_mask is not None: if "denoise_mask_function" in model_options: denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) latent_mask = 1. - denoise_mask x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask out = self.inner_model(x, sigma, model_options=model_options, seed=seed) if denoise_mask is not None: out = out * denoise_mask + self.latent_image * latent_mask return out def simple_scheduler(model_sampling, steps): s = model_sampling sigs = [] ss = len(s.sigmas) / steps for x in range(steps): sigs += [float(s.sigmas[-(1 + int(x * ss))])] sigs += [0.0] return torch.FloatTensor(sigs) def ddim_scheduler(model_sampling, steps): s = model_sampling sigs = [] ss = max(len(s.sigmas) x = 1 while x < len(s.sigmas): sigs += [float(s.sigmas[x])] x += ss sigs = sigs[::-1] sigs += [0.0] return torch.FloatTensor(sigs) def normal_scheduler(model_sampling, steps, sgm=False, floor=False): s = model_sampling start = s.timestep(s.sigma_max) end = s.timestep(s.sigma_min) if sgm: timesteps = torch.linspace(start, end, steps + 1)[:-1] else: timesteps = torch.linspace(start, end, steps) sigs = [] for x in range(len(timesteps)): ts = timesteps[x] sigs.append(s.sigma(ts)) sigs += [0.0] return torch.FloatTensor(sigs) def get_mask_aabb(masks): if masks.numel() == 0: return torch.zeros((0, 4), device=masks.device, dtype=torch.int) b = masks.shape[0] bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int) is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool) for i in range(b): mask = masks[i] if mask.numel() == 0: continue if torch.max(mask != 0) == False: is_empty[i] = True continue y, x = torch.where(mask) bounding_boxes[i, 0] = torch.min(x) bounding_boxes[i, 1] = torch.min(y) bounding_boxes[i, 2] = torch.max(x) bounding_boxes[i, 3] = torch.max(y) return bounding_boxes, is_empty def resolve_areas_and_cond_masks_multidim(conditions, dims, device): for i in range(len(conditions)): c = conditions[i] if 'area' in c: area = c['area'] if area[0] == "percentage": modified = c.copy() a = area[1:] a_len = len(a) area = () for d in range(len(dims)): area += (max(1, round(a[d] * dims[d])),) for d in range(len(dims)): area += (round(a[d + a_len] * dims[d]),) modified['area'] = area c = modified conditions[i] = c if 'mask' in c: mask = c['mask'] mask = mask.to(device=device) modified = c.copy() if len(mask.shape) == len(dims): mask = mask.unsqueeze(0) if mask.shape[1:] != dims: mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1) if modified.get("set_area_to_bounds", False): bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) boxes, is_empty = get_mask_aabb(bounds) if is_empty[0]: modified['area'] = (8, 8, 0, 0) else: box = boxes[0] H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0]) H = max(8, H) W = max(8, W) area = (int(H), int(W), int(Y), int(X)) modified['area'] = area modified['mask'] = mask conditions[i] = modified def resolve_areas_and_cond_masks(conditions, h, w, device): logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.") return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device) def create_cond_with_same_area_if_none(conds, c): if 'area' not in c: return c_area = c['area'] smallest = None for x in conds: if 'area' in x: a = x['area'] if c_area[2] >= a[2] and c_area[3] >= a[3]: if a[0] + a[2] >= c_area[0] + c_area[2]: if a[1] + a[3] >= c_area[1] + c_area[3]: if smallest is None: smallest = x elif 'area' not in smallest: smallest = x else: if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]: smallest = x else: if smallest is None: smallest = x if smallest is None: return if 'area' in smallest: if smallest['area'] == c_area: return out = c.copy() out['model_conds'] = smallest['model_conds'].copy() conds += [out] def calculate_start_end_timesteps(model, conds): s = model.model_sampling for t in range(len(conds)): x = conds[t] timestep_start = None timestep_end = None if 'start_percent' in x: timestep_start = s.percent_to_sigma(x['start_percent']) if 'end_percent' in x: timestep_end = s.percent_to_sigma(x['end_percent']) if (timestep_start is not None) or (timestep_end is not None): n = x.copy() if (timestep_start is not None): n['timestep_start'] = timestep_start if (timestep_end is not None): n['timestep_end'] = timestep_end conds[t] = n def pre_run_control(model, conds): s = model.model_sampling for t in range(len(conds)): x = conds[t] timestep_start = None timestep_end = None percent_to_timestep_function = lambda a: s.percent_to_sigma(a) if 'control' in x: x['control'].pre_run(model, percent_to_timestep_function) def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func): cond_cnets = [] cond_other = [] uncond_cnets = [] uncond_other = [] for t in range(len(conds)): x = conds[t] if 'area' not in x: if name in x and x[name] is not None: cond_cnets.append(x[name]) else: cond_other.append((x, t)) for t in range(len(uncond)): x = uncond[t] if 'area' not in x: if name in x and x[name] is not None: uncond_cnets.append(x[name]) else: uncond_other.append((x, t)) if len(uncond_cnets) > 0: return for x in range(len(cond_cnets)): temp = uncond_other[x % len(uncond_other)] o = temp[0] if name in o and o[name] is not None: n = o.copy() n[name] = uncond_fill_func(cond_cnets, x) uncond += [n] else: n = o.copy() n[name] = uncond_fill_func(cond_cnets, x) uncond[temp[1]] = n def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs): for t in range(len(conds)): x = conds[t] params = x.copy() params["device"] = device params["noise"] = noise default_width = None if len(noise.shape) >= 4: default_width = noise.shape[3] * 8 params["width"] = params.get("width", default_width) params["height"] = params.get("height", noise.shape[2] * 8) params["prompt_type"] = params.get("prompt_type", prompt_type) for k in kwargs: if k not in params: params[k] = kwargs[k] out = model_function(**params) x = x.copy() model_conds = x['model_conds'].copy() for k in out: model_conds[k] = out[k] x['model_conds'] = model_conds conds[t] = x return conds class Sampler: def sample(self): pass def max_denoise(self, model_wrap, sigmas): max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max) sigma = float(sigmas[0]) return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "ipndm", "ipndm_v"] class KSAMPLER(Sampler): def __init__(self, sampler_function, extra_options={}, inpaint_options={}): self.sampler_function = sampler_function self.extra_options = extra_options self.inpaint_options = inpaint_options def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): extra_args["denoise_mask"] = denoise_mask model_k = KSamplerX0Inpaint(model_wrap, sigmas) model_k.latent_image = latent_image if self.inpaint_options.get("random", False): generator = torch.manual_seed(extra_args.get("seed", 41) + 1) model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device) else: model_k.noise = noise noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas)) k_callback = None total_steps = len(sigmas) - 1 if callback is not None: k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples) return samples def ksampler(sampler_name, extra_options={}, inpaint_options={}): if sampler_name == "dpm_fast": def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable): if len(sigmas) <= 1: return noise sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] total_steps = len(sigmas) - 1 return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable) sampler_function = dpm_fast_function elif sampler_name == "dpm_adaptive": def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options): if len(sigmas) <= 1: return noise sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options) sampler_function = dpm_adaptive_function else: sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name)) return KSAMPLER(sampler_function, extra_options, inpaint_options) def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None): for k in conds: conds[k] = conds[k][:] resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device) for k in conds: calculate_start_end_timesteps(model, conds[k]) if hasattr(model, 'extra_conds'): for k in conds: conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) for k in conds: for c in conds[k]: for kk in conds: if k != kk: create_cond_with_same_area_if_none(conds[kk], c) for k in conds: pre_run_control(model, conds[k]) if "positive" in conds: positive = conds["positive"] for k in conds: if k != "positive": apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x]) apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x]) return conds class CFGGuider: def __init__(self, model_patcher): self.model_patcher = model_patcher self.model_options = model_patcher.model_options self.original_conds = {} self.cfg = 1.0 def set_conds(self, positive, negative): self.inner_set_conds({"positive": positive, "negative": negative}) def set_cfg(self, cfg): self.cfg = cfg def inner_set_conds(self, conds): for k in conds: self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k]) def __call__(self, *args, **kwargs): return self.predict_noise(*args, **kwargs) def predict_noise(self, x, timestep, model_options={}, seed=None): return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed) def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed): if latent_image is not None and torch.count_nonzero(latent_image) > 0: latent_image = self.inner_model.process_latent_in(latent_image) self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed) extra_args = {"model_options": self.model_options, "seed":seed} samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) return self.inner_model.process_latent_out(samples.to(torch.float32)) def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): if sigmas.shape[-1] == 0: return latent_image self.conds = {} for k in self.original_conds: self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds) device = self.model_patcher.load_device if denoise_mask is not None: denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device) noise = noise.to(device) latent_image = latent_image.to(device) sigmas = sigmas.to(device) output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models) del self.inner_model del self.conds del self.loaded_models return output def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): cfg_guider = CFGGuider(model) cfg_guider.set_conds(positive, negative) cfg_guider.set_cfg(cfg) return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"] SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] def calculate_sigmas(model_sampling, scheduler_name, steps): if scheduler_name == "karras": sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max)) elif scheduler_name == "exponential": sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max)) elif scheduler_name == "normal": sigmas = normal_scheduler(model_sampling, steps) elif scheduler_name == "simple": sigmas = simple_scheduler(model_sampling, steps) elif scheduler_name == "ddim_uniform": sigmas = ddim_scheduler(model_sampling, steps) elif scheduler_name == "sgm_uniform": sigmas = normal_scheduler(model_sampling, steps, sgm=True) else: logging.error("error invalid scheduler {}".format(scheduler_name)) return sigmas def sampler_object(name): if name == "uni_pc": sampler = KSAMPLER(uni_pc.sample_unipc) elif name == "uni_pc_bh2": sampler = KSAMPLER(uni_pc.sample_unipc_bh2) elif name == "ddim": sampler = ksampler("euler", inpaint_options={"random": True}) else: sampler = ksampler(name) return sampler class KSampler: SCHEDULERS = SCHEDULER_NAMES SAMPLERS = SAMPLER_NAMES DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2')) def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}): self.model = model self.device = device if scheduler not in self.SCHEDULERS: scheduler = self.SCHEDULERS[0] if sampler not in self.SAMPLERS: sampler = self.SAMPLERS[0] self.scheduler = scheduler self.sampler = sampler self.set_steps(steps, denoise) self.denoise = denoise self.model_options = model_options def calculate_sigmas(self, steps): sigmas = None discard_penultimate_sigma = False if self.sampler in self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS: steps += 1 discard_penultimate_sigma = True sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps) if discard_penultimate_sigma: sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) return sigmas def set_steps(self, steps, denoise=None): self.steps = steps if denoise is None or denoise > 0.9999: self.sigmas = self.calculate_sigmas(steps).to(self.device) else: if denoise <= 0.0: self.sigmas = torch.FloatTensor([]) else: new_steps = int(steps/denoise) sigmas = self.calculate_sigmas(new_steps).to(self.device) self.sigmas = sigmas[-(steps + 1):] def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): if sigmas is None: sigmas = self.sigmas if last_step is not None and last_step < (len(sigmas) - 1): sigmas = sigmas[:last_step + 1] if force_full_denoise: sigmas[-1] = 0 if start_step is not None: if start_step < (len(sigmas) - 1): sigmas = sigmas[start_step:] else: if latent_image is not None: return latent_image else: return torch.zeros_like(noise) sampler = sampler_object(self.sampler) return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
import torch import comfy.model_management import comfy.conds def prepare_mask(noise_mask, shape, device): """ensures noise mask is of proper dimensions""" noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear") noise_mask = torch.cat([noise_mask] * shape[1], dim=1) noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0]) noise_mask = noise_mask.to(device) return noise_mask def get_models_from_cond(cond, model_type): models = [] for c in cond: if model_type in c: models += [c[model_type]] return models def convert_cond(cond): out = [] for c in cond: temp = c[1].copy() model_conds = temp.get("model_conds", {}) if c[0] is not None: model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) temp["cross_attn"] = c[0] temp["model_conds"] = model_conds out.append(temp) return out def get_additional_models(conds, dtype): """loads additional models in conditioning""" cnets = [] gligen = [] for k in conds: cnets += get_models_from_cond(conds[k], "control") gligen += get_models_from_cond(conds[k], "gligen") control_nets = set(cnets) inference_memory = 0 control_models = [] for m in control_nets: control_models += m.get_models() inference_memory += m.inference_memory_requirements(dtype) gligen = [x[1] for x in gligen] models = control_models + gligen return models, inference_memory def cleanup_additional_models(models): """cleanup additional models that were loaded""" for m in models: if hasattr(m, 'cleanup'): m.cleanup() def prepare_sampling(model, noise_shape, conds): device = model.load_device real_model = None models, inference_memory = get_additional_models(conds, model.model_dtype()) comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory) real_model = model.model return real_model, conds, models def cleanup_models(conds, models): cleanup_additional_models(models) control_cleanup = [] for k in conds: control_cleanup += get_models_from_cond(conds[k], "control") cleanup_additional_models(set(control_cleanup))
from comfy import sd1_clip from transformers import T5TokenizerFast import comfy.t5 import os class T5BaseModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json") super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True) class T5BaseTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128) class SAT5Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None): super().__init__(embedding_directory=embedding_directory, clip_name="t5base", tokenizer=T5BaseTokenizer) class SAT5Model(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, **kwargs): super().__init__(device=device, dtype=dtype, clip_name="t5base", clip_model=T5BaseModel, **kwargs)
import torch from enum import Enum import logging from comfy import model_management from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine from .ldm.cascade.stage_a import StageA from .ldm.cascade.stage_c_coder import StageC_coder from .ldm.audio.autoencoder import AudioOobleckVAE import yaml import comfy.utils from . import clip_vision from . import gligen from . import diffusers_convert from . import model_detection from . import sd1_clip from . import sd2_clip from . import sdxl_clip from . import sd3_clip from . import sa_t5 import comfy.model_patcher import comfy.lora import comfy.t2i_adapter.adapter import comfy.supported_models_base import comfy.taesd.taesd def load_model_weights(model, sd): m, u = model.load_state_dict(sd, strict=False) m = set(m) unexpected_keys = set(u) k = list(sd.keys()) for x in k: if x not in unexpected_keys: w = sd.pop(x) del w if len(m) > 0: logging.warning("missing {}".format(m)) return model def load_clip_weights(model, sd): k = list(sd.keys()) for x in k: if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") sd[y] = sd.pop(x) if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd: ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] if ids.dtype == torch.float32: sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() sd = comfy.utils.clip_text_transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.") return load_model_weights(model, sd) def load_lora_for_models(model, clip, lora, strength_model, strength_clip): key_map = {} if model is not None: key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) if clip is not None: key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) loaded = comfy.lora.load_lora(lora, key_map) if model is not None: new_modelpatcher = model.clone() k = new_modelpatcher.add_patches(loaded, strength_model) else: k = () new_modelpatcher = None if clip is not None: new_clip = clip.clone() k1 = new_clip.add_patches(loaded, strength_clip) else: k1 = () new_clip = None k = set(k) k1 = set(k1) for x in loaded: if (x not in k) and (x not in k1): logging.warning("NOT LOADED {}".format(x)) return (new_modelpatcher, new_clip) class CLIP: def __init__(self, target=None, embedding_directory=None, no_init=False): if no_init: return params = target.params.copy() clip = target.clip tokenizer = target.tokenizer load_device = model_management.text_encoder_device() offload_device = model_management.text_encoder_offload_device() params['device'] = offload_device dtype = model_management.text_encoder_dtype(load_device) params['dtype'] = dtype self.cond_stage_model = clip(**(params)) for dt in self.cond_stage_model.dtypes: if not model_management.supports_cast(load_device, dt): load_device = offload_device self.tokenizer = tokenizer(embedding_directory=embedding_directory) self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) self.layer_idx = None logging.debug("CLIP model load device: {}, offload device: {}".format(load_device, offload_device)) def clone(self): n = CLIP(no_init=True) n.patcher = self.patcher.clone() n.cond_stage_model = self.cond_stage_model n.tokenizer = self.tokenizer n.layer_idx = self.layer_idx return n def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): return self.patcher.add_patches(patches, strength_patch, strength_model) def clip_layer(self, layer_idx): self.layer_idx = layer_idx def tokenize(self, text, return_word_ids=False): return self.tokenizer.tokenize_with_weights(text, return_word_ids) def encode_from_tokens(self, tokens, return_pooled=False): self.cond_stage_model.reset_clip_options() if self.layer_idx is not None: self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) if return_pooled == "unprojected": self.cond_stage_model.set_clip_options({"projected_pooled": False}) self.load_model() cond, pooled = self.cond_stage_model.encode_token_weights(tokens) if return_pooled: return cond, pooled return cond def encode(self, text): tokens = self.tokenize(text) return self.encode_from_tokens(tokens) def load_sd(self, sd, full_model=False): if full_model: return self.cond_stage_model.load_state_dict(sd, strict=False) else: return self.cond_stage_model.load_sd(sd) def get_sd(self): return self.cond_stage_model.state_dict() def load_model(self): model_management.load_model_gpu(self.patcher) return self.patcher def get_key_patches(self): return self.patcher.get_key_patches() class VAE: def __init__(self, sd=None, device=None, config=None, dtype=None): if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): sd = diffusers_convert.convert_vae_state_dict(sd) self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) self.downscale_ratio = 8 self.upscale_ratio = 8 self.latent_channels = 4 self.output_channels = 3 self.process_input = lambda image: image * 2.0 - 1.0 self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) self.working_dtypes = [torch.bfloat16, torch.float32] if config is None: if "decoder.mid.block_1.mix_factor" in sd: encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} decoder_config = encoder_config.copy() decoder_config["video_kernel_size"] = [3, 1, 1] decoder_config["alpha"] = 0.0 self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) elif "taesd_decoder.1.weight" in sd: self.latent_channels = sd["taesd_decoder.1.weight"].shape[1] self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels) elif "vquantizer.codebook.weight" in sd: self.first_stage_model = StageA() self.downscale_ratio = 4 self.upscale_ratio = 4 self.process_input = lambda image: image self.process_output = lambda image: image elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: self.first_stage_model = StageC_coder() self.downscale_ratio = 32 self.latent_channels = 16 new_sd = {} for k in sd: new_sd["encoder.{}".format(k)] = sd[k] sd = new_sd elif "blocks.11.num_batches_tracked" in sd: self.first_stage_model = StageC_coder() self.latent_channels = 16 new_sd = {} for k in sd: new_sd["previewer.{}".format(k)] = sd[k] sd = new_sd elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: self.first_stage_model = StageC_coder() self.downscale_ratio = 32 self.latent_channels = 16 elif "decoder.conv_in.weight" in sd: ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: ddconfig['ch_mult'] = [1, 2, 4] self.downscale_ratio = 4 self.upscale_ratio = 4 self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] if 'quant_conv.weight' in sd: self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4) else: self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) elif "decoder.layers.0.weight_v" in sd: self.first_stage_model = AudioOobleckVAE() self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype) self.latent_channels = 64 self.output_channels = 2 self.upscale_ratio = 2048 self.downscale_ratio = 2048 self.process_output = lambda audio: audio self.process_input = lambda audio: audio self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] else: logging.warning("WARNING: No VAE weights detected, VAE not initalized.") self.first_stage_model = None return else: self.first_stage_model = AutoencoderKL(**(config['params'])) self.first_stage_model = self.first_stage_model.eval() m, u = self.first_stage_model.load_state_dict(sd, strict=False) if len(m) > 0: logging.warning("Missing VAE keys {}".format(m)) if len(u) > 0: logging.debug("Leftover VAE keys {}".format(u)) if device is None: device = model_management.vae_device() self.device = device offload_device = model_management.vae_offload_device() if dtype is None: dtype = model_management.vae_dtype(self.device, self.working_dtypes) self.vae_dtype = dtype self.first_stage_model.to(self.vae_dtype) self.output_device = model_management.intermediate_device() self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype)) def vae_encode_crop_pixels(self, pixels): dims = pixels.shape[1:-1] for d in range(len(dims)): x = (dims[d] x_offset = (dims[d] % self.downscale_ratio) if x != dims[d]: pixels = pixels.narrow(d + 1, x_offset, x) return pixels def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y pbar = comfy.utils.ProgressBar(steps) decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() output = self.process_output( (comfy.utils.tiled_scale(samples, decode_fn, tile_x comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar)) / 3.0) return output def decode_tiled_1d(self, samples, tile_x=128, overlap=32): decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device) def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y pbar = comfy.utils.ProgressBar(steps) encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x samples /= 3.0 return samples def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048): encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device) def decode(self, samples_in): try: memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) model_management.load_models_gpu([self.patcher], memory_required=memory_used) free_memory = model_management.get_free_memory(self.device) batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) pixel_samples = torch.empty((samples_in.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples_in.shape[2:])), device=self.output_device) for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float()) except model_management.OOM_EXCEPTION as e: logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") if len(samples_in.shape) == 3: pixel_samples = self.decode_tiled_1d(samples_in) else: pixel_samples = self.decode_tiled_(samples_in) pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) return pixel_samples def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16): model_management.load_model_gpu(self.patcher) output = self.decode_tiled_(samples, tile_x, tile_y, overlap) return output.movedim(1,-1) def encode(self, pixel_samples): pixel_samples = self.vae_encode_crop_pixels(pixel_samples) pixel_samples = pixel_samples.movedim(-1,1) try: memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) model_management.load_models_gpu([self.patcher], memory_required=memory_used) free_memory = model_management.get_free_memory(self.device) batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a for x in range(0, pixel_samples.shape[0], batch_number): pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device) samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float() except model_management.OOM_EXCEPTION as e: logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") if len(pixel_samples.shape) == 3: samples = self.encode_tiled_1d(pixel_samples) else: samples = self.encode_tiled_(pixel_samples) return samples def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): pixel_samples = self.vae_encode_crop_pixels(pixel_samples) model_management.load_model_gpu(self.patcher) pixel_samples = pixel_samples.movedim(-1,1) samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap) return samples def get_sd(self): return self.first_stage_model.state_dict() class StyleModel: def __init__(self, model, device="cpu"): self.model = model def get_cond(self, input): return self.model(input.last_hidden_state) def load_style_model(ckpt_path): model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) keys = model_data.keys() if "style_embedding" in keys: model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) else: raise Exception("invalid style model {}".format(ckpt_path)) model.load_state_dict(model_data) return StyleModel(model) class CLIPType(Enum): STABLE_DIFFUSION = 1 STABLE_CASCADE = 2 SD3 = 3 STABLE_AUDIO = 4 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION): clip_data = [] for p in ckpt_paths: clip_data.append(comfy.utils.load_torch_file(p, safe_load=True)) class EmptyClass: pass for i in range(len(clip_data)): if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "") else: if "text_projection" in clip_data[i]: clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) clip_target = EmptyClass() clip_target.params = {} if len(clip_data) == 1: if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]: if clip_type == CLIPType.STABLE_CASCADE: clip_target.clip = sdxl_clip.StableCascadeClipModel clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer else: clip_target.clip = sdxl_clip.SDXLRefinerClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]: clip_target.clip = sd2_clip.SD2ClipModel clip_target.tokenizer = sd2_clip.SD2Tokenizer elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]: dtype_t5 = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"].dtype clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5) clip_target.tokenizer = sd3_clip.SD3Tokenizer elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]: clip_target.clip = sa_t5.SAT5Model clip_target.tokenizer = sa_t5.SAT5Tokenizer else: clip_target.clip = sd1_clip.SD1ClipModel clip_target.tokenizer = sd1_clip.SD1Tokenizer elif len(clip_data) == 2: if clip_type == CLIPType.SD3: clip_target.clip = sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False) clip_target.tokenizer = sd3_clip.SD3Tokenizer else: clip_target.clip = sdxl_clip.SDXLClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer elif len(clip_data) == 3: clip_target.clip = sd3_clip.SD3ClipModel clip_target.tokenizer = sd3_clip.SD3Tokenizer clip = CLIP(clip_target, embedding_directory=embedding_directory) for c in clip_data: m, u = clip.load_sd(c) if len(m) > 0: logging.warning("clip missing: {}".format(m)) if len(u) > 0: logging.debug("clip unexpected: {}".format(u)) return clip def load_gligen(ckpt_path): data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) model = gligen.load_gligen(data) if model_management.should_use_fp16(): model = model.half() return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.") model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True) if config is None: with open(config_path, 'r') as stream: config = yaml.safe_load(stream) model_config_params = config['model']['params'] clip_config = model_config_params['cond_stage_config'] scale_factor = model_config_params['scale_factor'] if "parameterization" in model_config_params: if model_config_params["parameterization"] == "v": m = model.clone() class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION): pass m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config)) model = m layer_idx = clip_config.get("params", {}).get("layer_idx", None) if layer_idx is not None: clip.clip_layer(layer_idx) return (model, clip, vae) def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True): sd = comfy.utils.load_torch_file(ckpt_path) sd_keys = sd.keys() clip = None clipvision = None vae = None model = None model_patcher = None clip_target = None diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix) load_device = model_management.get_torch_device() model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix) unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes) manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) if model_config is None: raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) if model_config.clip_vision_prefix is not None: if output_clipvision: clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) if output_model: inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype) offload_device = model_management.unet_offload_device() model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device) model.load_model_weights(sd, diffusion_model_prefix) if output_vae: vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) vae_sd = model_config.process_vae_state_dict(vae_sd) vae = VAE(sd=vae_sd) if output_clip: clip_target = model_config.clip_target(state_dict=sd) if clip_target is not None: clip_sd = model_config.process_clip_state_dict(sd) if len(clip_sd) > 0: clip = CLIP(clip_target, embedding_directory=embedding_directory) m, u = clip.load_sd(clip_sd, full_model=True) if len(m) > 0: m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m)) if len(m_filter) > 0: logging.warning("clip missing: {}".format(m)) else: logging.debug("clip missing: {}".format(m)) if len(u) > 0: logging.debug("clip unexpected {}:".format(u)) else: logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") left_over = sd.keys() if len(left_over) > 0: logging.debug("left over keys: {}".format(left_over)) if output_model: model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device) if inital_load_device != torch.device("cpu"): logging.info("loaded straight to GPU") model_management.load_model_gpu(model_patcher) return (model_patcher, clip, vae, clipvision) def load_unet_state_dict(sd): parameters = comfy.utils.calculate_parameters(sd) unet_dtype = model_management.unet_dtype(model_params=parameters) load_device = model_management.get_torch_device() if 'transformer_blocks.0.attn.add_q_proj.weight' in sd: new_sd = model_detection.convert_diffusers_mmdit(sd, "") if new_sd is None: return None model_config = model_detection.model_config_from_unet(new_sd, "") if model_config is None: return None elif "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: model_config = model_detection.model_config_from_unet(sd, "") if model_config is None: return None new_sd = sd else: model_config = model_detection.model_config_from_diffusers_unet(sd) if model_config is None: return None diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config) new_sd = {} for k in diffusers_keys: if k in sd: new_sd[diffusers_keys[k]] = sd.pop(k) else: logging.warning("{} {}".format(diffusers_keys[k], k)) offload_device = model_management.unet_offload_device() unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes) manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) model = model_config.get_model(new_sd, "") model = model.to(offload_device) model.load_model_weights(new_sd, "") left_over = sd.keys() if len(left_over) > 0: logging.info("left over keys in unet: {}".format(left_over)) return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) def load_unet(unet_path): sd = comfy.utils.load_torch_file(unet_path) model = load_unet_state_dict(sd) if model is None: logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path)) raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) return model def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}): clip_sd = None load_models = [model] if clip is not None: load_models.append(clip.load_model()) clip_sd = clip.get_sd() model_management.load_models_gpu(load_models, force_patch_weights=True) clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd) for k in extra_keys: sd[k] = extra_keys[k] comfy.utils.save_torch_file(sd, output_path, metadata=metadata)
import os from transformers import CLIPTokenizer import comfy.ops import torch import traceback import zipfile from . import model_management import comfy.clip_model import json import logging import numbers def gen_empty_tokens(special_tokens, length): start_token = special_tokens.get("start", None) end_token = special_tokens.get("end", None) pad_token = special_tokens.get("pad") output = [] if start_token is not None: output.append(start_token) if end_token is not None: output.append(end_token) output += [pad_token] * (length - len(output)) return output class ClipTokenWeightEncoder: def encode_token_weights(self, token_weight_pairs): to_encode = list() max_token_len = 0 has_weights = False for x in token_weight_pairs: tokens = list(map(lambda a: a[0], x)) max_token_len = max(len(tokens), max_token_len) has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) to_encode.append(tokens) sections = len(to_encode) if has_weights or sections == 0: to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) out, pooled = self.encode(to_encode) if pooled is not None: first_pooled = pooled[0:1].to(model_management.intermediate_device()) else: first_pooled = pooled output = [] for k in range(0, sections): z = out[k:k+1] if has_weights: z_empty = out[-1] for i in range(len(z)): for j in range(len(z[i])): weight = token_weight_pairs[k][j][1] if weight != 1.0: z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] output.append(z) if (len(output) == 0): return out[-1:].to(model_management.intermediate_device()), first_pooled return torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): """Uses the CLIP transformer encoder for text (from huggingface)""" LAYERS = [ "last", "pooled", "hidden" ] def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel, special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False, return_projected_pooled=True): super().__init__() assert layer in self.LAYERS if textmodel_json_config is None: textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") with open(textmodel_json_config) as f: config = json.load(f) self.transformer = model_class(config, dtype, device, comfy.ops.manual_cast) self.num_layers = self.transformer.num_layers self.max_length = max_length if freeze: self.freeze() self.layer = layer self.layer_idx = None self.special_tokens = special_tokens self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) self.enable_attention_masks = enable_attention_masks self.zero_out_masked = zero_out_masked self.layer_norm_hidden_state = layer_norm_hidden_state self.return_projected_pooled = return_projected_pooled if layer == "hidden": assert layer_idx is not None assert abs(layer_idx) < self.num_layers self.set_clip_options({"layer": layer_idx}) self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled) def freeze(self): self.transformer = self.transformer.eval() for param in self.parameters(): param.requires_grad = False def set_clip_options(self, options): layer_idx = options.get("layer", self.layer_idx) self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled) if layer_idx is None or abs(layer_idx) > self.num_layers: self.layer = "last" else: self.layer = "hidden" self.layer_idx = layer_idx def reset_clip_options(self): self.layer = self.options_default[0] self.layer_idx = self.options_default[1] self.return_projected_pooled = self.options_default[2] def set_up_textual_embeddings(self, tokens, current_embeds): out_tokens = [] next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1 embedding_weights = [] for x in tokens: tokens_temp = [] for y in x: if isinstance(y, numbers.Integral): if y == token_dict_size: y = -1 tokens_temp += [int(y)] else: if y.shape[0] == current_embeds.weight.shape[1]: embedding_weights += [y] tokens_temp += [next_new_token] next_new_token += 1 else: logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1])) while len(tokens_temp) < len(x): tokens_temp += [self.special_tokens["pad"]] out_tokens += [tokens_temp] n = token_dict_size if len(embedding_weights) > 0: new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype) new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1] for x in embedding_weights: new_embedding.weight[n] = x n += 1 new_embedding.weight[n] = current_embeds.weight[-1] self.transformer.set_input_embeddings(new_embedding) processed_tokens = [] for x in out_tokens: processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] return processed_tokens def forward(self, tokens): backup_embeds = self.transformer.get_input_embeddings() device = backup_embeds.weight.device tokens = self.set_up_textual_embeddings(tokens, backup_embeds) tokens = torch.LongTensor(tokens).to(device) attention_mask = None if self.enable_attention_masks: attention_mask = torch.zeros_like(tokens) end_token = self.special_tokens.get("end", -1) for x in range(attention_mask.shape[0]): for y in range(attention_mask.shape[1]): attention_mask[x, y] = 1 if tokens[x, y] == end_token: break outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state) self.transformer.set_input_embeddings(backup_embeds) if self.layer == "last": z = outputs[0].float() else: z = outputs[1].float() if self.zero_out_masked and attention_mask is not None: z *= attention_mask.unsqueeze(-1).float() pooled_output = None if len(outputs) >= 3: if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None: pooled_output = outputs[3].float() elif outputs[2] is not None: pooled_output = outputs[2].float() return z, pooled_output def encode(self, tokens): return self(tokens) def load_sd(self, sd): return self.transformer.load_state_dict(sd, strict=False) def parse_parentheses(string): result = [] current_item = "" nesting_level = 0 for char in string: if char == "(": if nesting_level == 0: if current_item: result.append(current_item) current_item = "(" else: current_item = "(" else: current_item += char nesting_level += 1 elif char == ")": nesting_level -= 1 if nesting_level == 0: result.append(current_item + ")") current_item = "" else: current_item += char else: current_item += char if current_item: result.append(current_item) return result def token_weights(string, current_weight): a = parse_parentheses(string) out = [] for x in a: weight = current_weight if len(x) >= 2 and x[-1] == ')' and x[0] == '(': x = x[1:-1] xx = x.rfind(":") weight *= 1.1 if xx > 0: try: weight = float(x[xx+1:]) x = x[:xx] except: pass out += token_weights(x, weight) else: out += [(x, current_weight)] return out def escape_important(text): text = text.replace("\\)", "\0\1") text = text.replace("\\(", "\0\2") return text def unescape_important(text): text = text.replace("\0\1", ")") text = text.replace("\0\2", "(") return text def safe_load_embed_zip(embed_path): with zipfile.ZipFile(embed_path) as myzip: names = list(filter(lambda a: "data/" in a, myzip.namelist())) names.reverse() for n in names: with myzip.open(n) as myfile: data = myfile.read() number = len(data) length_embed = 1024 if number < 768: continue if number % 768 == 0: length_embed = 768 num_embeds = number embed = torch.frombuffer(data, dtype=torch.float) out = embed.reshape((num_embeds, length_embed)).clone() del embed return out def expand_directory_list(directories): dirs = set() for x in directories: dirs.add(x) for root, subdir, file in os.walk(x, followlinks=True): dirs.add(root) return list(dirs) def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None): if isinstance(embedding_directory, str): embedding_directory = [embedding_directory] embedding_directory = expand_directory_list(embedding_directory) valid_file = None for embed_dir in embedding_directory: embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name)) embed_dir = os.path.abspath(embed_dir) try: if os.path.commonpath((embed_dir, embed_path)) != embed_dir: continue except: continue if not os.path.isfile(embed_path): extensions = ['.safetensors', '.pt', '.bin'] for x in extensions: t = embed_path + x if os.path.isfile(t): valid_file = t break else: valid_file = embed_path if valid_file is not None: break if valid_file is None: return None embed_path = valid_file embed_out = None try: if embed_path.lower().endswith(".safetensors"): import safetensors.torch embed = safetensors.torch.load_file(embed_path, device="cpu") else: if 'weights_only' in torch.load.__code__.co_varnames: try: embed = torch.load(embed_path, weights_only=True, map_location="cpu") except: embed_out = safe_load_embed_zip(embed_path) else: embed = torch.load(embed_path, map_location="cpu") except Exception as e: logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name)) return None if embed_out is None: if 'string_to_param' in embed: values = embed['string_to_param'].values() embed_out = next(iter(values)) elif isinstance(embed, list): out_list = [] for x in range(len(embed)): for k in embed[x]: t = embed[x][k] if t.shape[-1] != embedding_size: continue out_list.append(t.reshape(-1, t.shape[-1])) embed_out = torch.cat(out_list, dim=0) elif embed_key is not None and embed_key in embed: embed_out = embed[embed_key] else: values = embed.values() embed_out = next(iter(values)) return embed_out class SDTokenizer: def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None): if tokenizer_path is None: tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path) self.max_length = max_length self.min_length = min_length empty = self.tokenizer('')["input_ids"] if has_start_token: self.tokens_start = 1 self.start_token = empty[0] self.end_token = empty[1] else: self.tokens_start = 0 self.start_token = None self.end_token = empty[0] self.pad_with_end = pad_with_end self.pad_to_max_length = pad_to_max_length vocab = self.tokenizer.get_vocab() self.inv_vocab = {v: k for k, v in vocab.items()} self.embedding_directory = embedding_directory self.max_word_length = 8 self.embedding_identifier = "embedding:" self.embedding_size = embedding_size self.embedding_key = embedding_key def _try_get_embedding(self, embedding_name:str): ''' Takes a potential embedding name and tries to retrieve it. Returns a Tuple consisting of the embedding and any leftover string, embedding can be None. ''' embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key) if embed is None: stripped = embedding_name.strip(',') if len(stripped) < len(embedding_name): embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key) return (embed, embedding_name[len(stripped):]) return (embed, "") def tokenize_with_weights(self, text:str, return_word_ids=False): ''' Takes a prompt and converts it to a list of (token, weight, word id) elements. Tokens can both be integer tokens and pre computed CLIP tensors. Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. Returned list has the dimensions NxM where M is the input size of CLIP ''' if self.pad_with_end: pad_token = self.end_token else: pad_token = 0 text = escape_important(text) parsed_weights = token_weights(text, 1.0) tokens = [] for weighted_segment, weight in parsed_weights: to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ') to_tokenize = [x for x in to_tokenize if x != ""] for word in to_tokenize: if word.startswith(self.embedding_identifier) and self.embedding_directory is not None: embedding_name = word[len(self.embedding_identifier):].strip('\n') embed, leftover = self._try_get_embedding(embedding_name) if embed is None: logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring") else: if len(embed.shape) == 1: tokens.append([(embed, weight)]) else: tokens.append([(embed[x], weight) for x in range(embed.shape[0])]) if leftover != "": word = leftover else: continue tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]]) batched_tokens = [] batch = [] if self.start_token is not None: batch.append((self.start_token, 1.0, 0)) batched_tokens.append(batch) for i, t_group in enumerate(tokens): is_large = len(t_group) >= self.max_word_length while len(t_group) > 0: if len(t_group) + len(batch) > self.max_length - 1: remaining_length = self.max_length - len(batch) - 1 if is_large: batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]]) batch.append((self.end_token, 1.0, 0)) t_group = t_group[remaining_length:] else: batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: batch.extend([(pad_token, 1.0, 0)] * (remaining_length)) batch = [] if self.start_token is not None: batch.append((self.start_token, 1.0, 0)) batched_tokens.append(batch) else: batch.extend([(t,w,i+1) for t,w in t_group]) t_group = [] batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch))) if self.min_length is not None and len(batch) < self.min_length: batch.extend([(pad_token, 1.0, 0)] * (self.min_length - len(batch))) if not return_word_ids: batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] return batched_tokens def untokenize(self, token_weight_pair): return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) class SD1Tokenizer: def __init__(self, embedding_directory=None, clip_name="l", tokenizer=SDTokenizer): self.clip_name = clip_name self.clip = "clip_{}".format(self.clip_name) setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory)) def tokenize_with_weights(self, text:str, return_word_ids=False): out = {} out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids) return out def untokenize(self, token_weight_pair): return getattr(self, self.clip).untokenize(token_weight_pair) class SD1ClipModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, **kwargs): super().__init__() self.clip_name = clip_name self.clip = "clip_{}".format(self.clip_name) setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs)) self.dtypes = set() if dtype is not None: self.dtypes.add(dtype) def set_clip_options(self, options): getattr(self, self.clip).set_clip_options(options) def reset_clip_options(self): getattr(self, self.clip).reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs = token_weight_pairs[self.clip_name] out, pooled = getattr(self, self.clip).encode_token_weights(token_weight_pairs) return out, pooled def load_sd(self, sd): return getattr(self, self.clip).load_sd(sd)
{ "_name_or_path": "openai/clip-vit-large-patch14", "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 2, "hidden_act": "quick_gelu", "hidden_size": 768, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 1, "projection_dim": 768, "torch_dtype": "float32", "transformers_version": "4.24.0", "vocab_size": 49408 }
from comfy import sd1_clip import os class SD2ClipHModel(sd1_clip.SDClipModel): def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None): if layer == "penultimate": layer="hidden" layer_idx=-2 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json") super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}) class SD2ClipHTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None): super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024) class SD2Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None): super().__init__(embedding_directory=embedding_directory, clip_name="h", tokenizer=SD2ClipHTokenizer) class SD2ClipModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, **kwargs): super().__init__(device=device, dtype=dtype, clip_name="h", clip_model=SD2ClipHModel, **kwargs)
{ "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 2, "hidden_act": "gelu", "hidden_size": 1024, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 16, "num_hidden_layers": 24, "pad_token_id": 1, "projection_dim": 1024, "torch_dtype": "float32", "vocab_size": 49408 }
from comfy import sd1_clip from comfy import sdxl_clip from transformers import T5TokenizerFast import comfy.t5 import torch import os import comfy.model_management import logging class T5XXLModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json") super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5) class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77) class SDT5XXLTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None): super().__init__(embedding_directory=embedding_directory, clip_name="t5xxl", tokenizer=T5XXLTokenizer) class SDT5XXLModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, **kwargs): super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, **kwargs) class SD3Tokenizer: def __init__(self, embedding_directory=None): self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory) self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory) self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) def tokenize_with_weights(self, text:str, return_word_ids=False): out = {} out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) return out def untokenize(self, token_weight_pair): return self.clip_g.untokenize(token_weight_pair) class SD3ClipModel(torch.nn.Module): def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None): super().__init__() self.dtypes = set() if clip_l: self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False) self.dtypes.add(dtype) else: self.clip_l = None if clip_g: self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype) self.dtypes.add(dtype) else: self.clip_g = None if t5: if dtype_t5 is None: dtype_t5 = dtype elif comfy.model_management.dtype_size(dtype_t5) > comfy.model_management.dtype_size(dtype): dtype_t5 = dtype if not comfy.model_management.supports_cast(device, dtype_t5): dtype_t5 = dtype self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5) self.dtypes.add(dtype_t5) else: self.t5xxl = None logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5)) def set_clip_options(self, options): if self.clip_l is not None: self.clip_l.set_clip_options(options) if self.clip_g is not None: self.clip_g.set_clip_options(options) if self.t5xxl is not None: self.t5xxl.set_clip_options(options) def reset_clip_options(self): if self.clip_l is not None: self.clip_l.reset_clip_options() if self.clip_g is not None: self.clip_g.reset_clip_options() if self.t5xxl is not None: self.t5xxl.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_l = token_weight_pairs["l"] token_weight_pairs_g = token_weight_pairs["g"] token_weight_pars_t5 = token_weight_pairs["t5xxl"] lg_out = None pooled = None out = None if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: if self.clip_l is not None: lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) else: l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) if self.clip_g is not None: g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) if lg_out is not None: lg_out = torch.cat([lg_out, g_out], dim=-1) else: lg_out = torch.nn.functional.pad(g_out, (768, 0)) else: g_out = None g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) if lg_out is not None: lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) out = lg_out pooled = torch.cat((l_pooled, g_pooled), dim=-1) if self.t5xxl is not None: t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5) if lg_out is not None: out = torch.cat([lg_out, t5_out], dim=-2) else: out = t5_out if out is None: out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device()) if pooled is None: pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) return out, pooled def load_sd(self, sd): if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: return self.clip_g.load_sd(sd) elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: return self.clip_l.load_sd(sd) else: return self.t5xxl.load_sd(sd) def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None): class SD3ClipModel_(SD3ClipModel): def __init__(self, device="cpu", dtype=None): super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype) return SD3ClipModel_
from comfy import sd1_clip import torch import os class SDXLClipG(sd1_clip.SDClipModel): def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None): if layer == "penultimate": layer="hidden" layer_idx=-2 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False) def load_sd(self, sd): return super().load_sd(sd) class SDXLClipGTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None): super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') class SDXLTokenizer: def __init__(self, embedding_directory=None): self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory) self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory) def tokenize_with_weights(self, text:str, return_word_ids=False): out = {} out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) return out def untokenize(self, token_weight_pair): return self.clip_g.untokenize(token_weight_pair) class SDXLClipModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None): super().__init__() self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False) self.clip_g = SDXLClipG(device=device, dtype=dtype) self.dtypes = set([dtype]) def set_clip_options(self, options): self.clip_l.set_clip_options(options) self.clip_g.set_clip_options(options) def reset_clip_options(self): self.clip_g.reset_clip_options() self.clip_l.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_g = token_weight_pairs["g"] token_weight_pairs_l = token_weight_pairs["l"] g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) return torch.cat([l_out, g_out], dim=-1), g_pooled def load_sd(self, sd): if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: return self.clip_g.load_sd(sd) else: return self.clip_l.load_sd(sd) class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None): super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG) class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None): super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') class StableCascadeTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None): super().__init__(embedding_directory=embedding_directory, clip_name="g", tokenizer=StableCascadeClipGTokenizer) class StableCascadeClipG(sd1_clip.SDClipModel): def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True) def load_sd(self, sd): return super().load_sd(sd) class StableCascadeClipModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None): super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=StableCascadeClipG)
import torch from . import model_base from . import utils from . import sd1_clip from . import sd2_clip from . import sdxl_clip from . import sd3_clip from . import sa_t5 from . import supported_models_base from . import latent_formats from . import diffusers_convert class SD15(supported_models_base.BASE): unet_config = { "context_dim": 768, "model_channels": 320, "use_linear_in_transformer": False, "adm_in_channels": None, "use_temporal_attention": False, } unet_extra_config = { "num_heads": 8, "num_head_channels": -1, } latent_format = latent_formats.SD15 def process_clip_state_dict(self, state_dict): k = list(state_dict.keys()) for x in k: if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") state_dict[y] = state_dict.pop(x) if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict: ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] if ids.dtype == torch.float32: state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() replace_prefix = {} replace_prefix["cond_stage_model."] = "clip_l." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) return state_dict def process_clip_state_dict_for_saving(self, state_dict): pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] for p in pop_keys: if p in state_dict: state_dict.pop(p) replace_prefix = {"clip_l.": "cond_stage_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel) class SD20(supported_models_base.BASE): unet_config = { "context_dim": 1024, "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": None, "use_temporal_attention": False, } unet_extra_config = { "num_heads": -1, "num_head_channels": 64, "attn_precision": torch.float32, } latent_format = latent_formats.SD15 def model_type(self, state_dict, prefix=""): if self.unet_config["in_channels"] == 4: k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) out = state_dict.get(k, None) if out is not None and torch.std(out, unbiased=False) > 0.09: return model_base.ModelType.V_PREDICTION return model_base.ModelType.EPS def process_clip_state_dict(self, state_dict): replace_prefix = {} replace_prefix["conditioner.embedders.0.model."] = "clip_h." replace_prefix["cond_stage_model.model."] = "clip_h." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.") return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {} replace_prefix["clip_h"] = "cond_stage_model.model" state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) return state_dict def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel) class SD21UnclipL(SD20): unet_config = { "context_dim": 1024, "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": 1536, "use_temporal_attention": False, } clip_vision_prefix = "embedder.model.visual." noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768} class SD21UnclipH(SD20): unet_config = { "context_dim": 1024, "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": 2048, "use_temporal_attention": False, } clip_vision_prefix = "embedder.model.visual." noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024} class SDXLRefiner(supported_models_base.BASE): unet_config = { "model_channels": 384, "use_linear_in_transformer": True, "context_dim": 1280, "adm_in_channels": 2560, "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0], "use_temporal_attention": False, } latent_format = latent_formats.SDXL def get_model(self, state_dict, prefix="", device=None): return model_base.SDXLRefiner(self, device=device) def process_clip_state_dict(self, state_dict): keys_to_replace = {} replace_prefix = {} replace_prefix["conditioner.embedders.0.model."] = "clip_g." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {} state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") replace_prefix["clip_g"] = "conditioner.embedders.0.model" state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) return state_dict_g def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel) class SDXL(supported_models_base.BASE): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 2, 2, 10, 10], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } latent_format = latent_formats.SDXL def model_type(self, state_dict, prefix=""): if 'edm_mean' in state_dict and 'edm_std' in state_dict: self.latent_format = latent_formats.SDXL_Playground_2_5() self.sampling_settings["sigma_data"] = 0.5 self.sampling_settings["sigma_max"] = 80.0 self.sampling_settings["sigma_min"] = 0.002 return model_base.ModelType.EDM elif "edm_vpred.sigma_max" in state_dict: self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item()) if "edm_vpred.sigma_min" in state_dict: self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item()) return model_base.ModelType.V_PREDICTION_EDM elif "v_pred" in state_dict: return model_base.ModelType.V_PREDICTION else: return model_base.ModelType.EPS def get_model(self, state_dict, prefix="", device=None): out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) if self.inpaint_model(): out.set_inpaint() return out def process_clip_state_dict(self, state_dict): keys_to_replace = {} replace_prefix = {} replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model" replace_prefix["conditioner.embedders.1.model."] = "clip_g." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {} keys_to_replace = {} state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") for k in state_dict: if k.startswith("clip_l"): state_dict_g[k] = state_dict[k] state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1)) pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] for p in pop_keys: if p in state_dict_g: state_dict_g.pop(p) replace_prefix["clip_g"] = "conditioner.embedders.1.model" replace_prefix["clip_l"] = "conditioner.embedders.0" state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) return state_dict_g def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel) class SSD1B(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 2, 2, 4, 4], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } class Segmind_Vega(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 1, 1, 2, 2], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } class KOALA_700M(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 2, 5], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } class KOALA_1B(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 2, 6], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } class SVD_img2vid(supported_models_base.BASE): unet_config = { "model_channels": 320, "in_channels": 8, "use_linear_in_transformer": True, "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], "context_dim": 1024, "adm_in_channels": 768, "use_temporal_attention": True, "use_temporal_resblock": True } unet_extra_config = { "num_heads": -1, "num_head_channels": 64, "attn_precision": torch.float32, } clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual." latent_format = latent_formats.SD15 sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002} def get_model(self, state_dict, prefix="", device=None): out = model_base.SVD_img2vid(self, device=device) return out def clip_target(self, state_dict={}): return None class SV3D_u(SVD_img2vid): unet_config = { "model_channels": 320, "in_channels": 8, "use_linear_in_transformer": True, "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], "context_dim": 1024, "adm_in_channels": 256, "use_temporal_attention": True, "use_temporal_resblock": True } vae_key_prefix = ["conditioner.embedders.1.encoder."] def get_model(self, state_dict, prefix="", device=None): out = model_base.SV3D_u(self, device=device) return out class SV3D_p(SV3D_u): unet_config = { "model_channels": 320, "in_channels": 8, "use_linear_in_transformer": True, "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], "context_dim": 1024, "adm_in_channels": 1280, "use_temporal_attention": True, "use_temporal_resblock": True } def get_model(self, state_dict, prefix="", device=None): out = model_base.SV3D_p(self, device=device) return out class Stable_Zero123(supported_models_base.BASE): unet_config = { "context_dim": 768, "model_channels": 320, "use_linear_in_transformer": False, "adm_in_channels": None, "use_temporal_attention": False, "in_channels": 8, } unet_extra_config = { "num_heads": 8, "num_head_channels": -1, } required_keys = { "cc_projection.weight": None, "cc_projection.bias": None, } clip_vision_prefix = "cond_stage_model.model.visual." latent_format = latent_formats.SD15 def get_model(self, state_dict, prefix="", device=None): out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"]) return out def clip_target(self, state_dict={}): return None class SD_X4Upscaler(SD20): unet_config = { "context_dim": 1024, "model_channels": 256, 'in_channels': 7, "use_linear_in_transformer": True, "adm_in_channels": None, "use_temporal_attention": False, } unet_extra_config = { "disable_self_attentions": [True, True, True, False], "num_classes": 1000, "num_heads": 8, "num_head_channels": -1, } latent_format = latent_formats.SD_X4 sampling_settings = { "linear_start": 0.0001, "linear_end": 0.02, } def get_model(self, state_dict, prefix="", device=None): out = model_base.SD_X4Upscaler(self, device=device) return out class Stable_Cascade_C(supported_models_base.BASE): unet_config = { "stable_cascade_stage": 'c', } unet_extra_config = {} latent_format = latent_formats.SC_Prior supported_inference_dtypes = [torch.bfloat16, torch.float32] sampling_settings = { "shift": 2.0, } vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoder."] clip_vision_prefix = "clip_l_vision." def process_unet_state_dict(self, state_dict): key_list = list(state_dict.keys()) for y in ["weight", "bias"]: suffix = "in_proj_{}".format(y) keys = filter(lambda a: a.endswith(suffix), key_list) for k_from in keys: weights = state_dict.pop(k_from) prefix = k_from[:-(len(suffix) + 1)] shape_from = weights.shape[0] for x in range(3): p = ["to_q", "to_k", "to_v"] k_to = "{}.{}.{}".format(prefix, p[x], y) state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)] return state_dict def process_clip_state_dict(self, state_dict): state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) if "clip_g.text_projection" in state_dict: state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1) return state_dict def get_model(self, state_dict, prefix="", device=None): out = model_base.StableCascade_C(self, device=device) return out def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel) class Stable_Cascade_B(Stable_Cascade_C): unet_config = { "stable_cascade_stage": 'b', } unet_extra_config = {} latent_format = latent_formats.SC_B supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] sampling_settings = { "shift": 1.0, } clip_vision_prefix = None def get_model(self, state_dict, prefix="", device=None): out = model_base.StableCascade_B(self, device=device) return out class SD15_instructpix2pix(SD15): unet_config = { "context_dim": 768, "model_channels": 320, "use_linear_in_transformer": False, "adm_in_channels": None, "use_temporal_attention": False, "in_channels": 8, } def get_model(self, state_dict, prefix="", device=None): return model_base.SD15_instructpix2pix(self, device=device) class SDXL_instructpix2pix(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 2, 2, 10, 10], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, "in_channels": 8, } def get_model(self, state_dict, prefix="", device=None): return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device) class SD3(supported_models_base.BASE): unet_config = { "in_channels": 16, "pos_embed_scaling_factor": None, } sampling_settings = { "shift": 3.0, } unet_extra_config = {} latent_format = latent_formats.SD3 text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.SD3(self, device=device) return out def clip_target(self, state_dict={}): clip_l = False clip_g = False t5 = False dtype_t5 = None pref = self.text_encoder_key_prefix[0] if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: clip_l = True if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: clip_g = True t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref) if t5_key in state_dict: t5 = True dtype_t5 = state_dict[t5_key].dtype return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5)) class StableAudio(supported_models_base.BASE): unet_config = { "audio_model": "dit1.0", } sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03} unet_extra_config = {} latent_format = latent_formats.StableAudio1 text_encoder_key_prefix = ["text_encoders."] vae_key_prefix = ["pretransform.model."] def get_model(self, state_dict, prefix="", device=None): seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True) seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True) return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device) def process_unet_state_dict(self, state_dict): for k in list(state_dict.keys()): if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): state_dict.pop(k) return state_dict def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model) models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio] models += [SVD_img2vid]
import torch from . import model_base from . import utils from . import latent_formats class ClipTarget: def __init__(self, tokenizer, clip): self.clip = clip self.tokenizer = tokenizer self.params = {} class BASE: unet_config = {} unet_extra_config = { "num_heads": -1, "num_head_channels": 64, } required_keys = {} clip_prefix = [] clip_vision_prefix = None noise_aug_config = None sampling_settings = {} latent_format = latent_formats.LatentFormat vae_key_prefix = ["first_stage_model."] text_encoder_key_prefix = ["cond_stage_model."] supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] manual_cast_dtype = None @classmethod def matches(s, unet_config, state_dict=None): for k in s.unet_config: if k not in unet_config or s.unet_config[k] != unet_config[k]: return False if state_dict is not None: for k in s.required_keys: if k not in state_dict: return False return True def model_type(self, state_dict, prefix=""): return model_base.ModelType.EPS def inpaint_model(self): return self.unet_config["in_channels"] > 4 def __init__(self, unet_config): self.unet_config = unet_config.copy() self.sampling_settings = self.sampling_settings.copy() self.latent_format = self.latent_format() for x in self.unet_extra_config: self.unet_config[x] = self.unet_extra_config[x] def get_model(self, state_dict, prefix="", device=None): if self.noise_aug_config is not None: out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) else: out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) if self.inpaint_model(): out.set_inpaint() return out def process_clip_state_dict(self, state_dict): state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) return state_dict def process_unet_state_dict(self, state_dict): return state_dict def process_vae_state_dict(self, state_dict): return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {"": self.text_encoder_key_prefix[0]} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_clip_vision_state_dict_for_saving(self, state_dict): replace_prefix = {} if self.clip_vision_prefix is not None: replace_prefix[""] = self.clip_vision_prefix return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_unet_state_dict_for_saving(self, state_dict): replace_prefix = {"": "model.diffusion_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_vae_state_dict_for_saving(self, state_dict): replace_prefix = {"": self.vae_key_prefix[0]} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def set_inference_dtype(self, dtype, manual_cast_dtype): self.unet_config['dtype'] = dtype self.manual_cast_dtype = manual_cast_dtype
import torch import math from comfy.ldm.modules.attention import optimized_attention_for_device class T5LayerNorm(torch.nn.Module): def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None): super().__init__() self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device)) self.variance_epsilon = eps def forward(self, x): variance = x.pow(2).mean(-1, keepdim=True) x = x * torch.rsqrt(variance + self.variance_epsilon) return self.weight.to(device=x.device, dtype=x.dtype) * x class T5DenseActDense(torch.nn.Module): def __init__(self, model_dim, ff_dim, dtype, device, operations): super().__init__() self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) def forward(self, x): x = torch.nn.functional.relu(self.wi(x)) x = self.wo(x) return x class T5DenseGatedActDense(torch.nn.Module): def __init__(self, model_dim, ff_dim, dtype, device, operations): super().__init__() self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) def forward(self, x): hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh") hidden_linear = self.wi_1(x) x = hidden_gelu * hidden_linear x = self.wo(x) return x class T5LayerFF(torch.nn.Module): def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): super().__init__() if ff_activation == "gelu_pytorch_tanh": self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device, operations) elif ff_activation == "relu": self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, dtype, device, operations) self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) def forward(self, x): forwarded_states = self.layer_norm(x) forwarded_states = self.DenseReluDense(forwarded_states) x += forwarded_states return x class T5Attention(torch.nn.Module): def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations): super().__init__() self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device) self.num_heads = num_heads self.relative_attention_bias = None if relative_attention_bias: self.relative_attention_num_buckets = 32 self.relative_attention_max_distance = 128 self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device) @staticmethod def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """ Adapted from Mesh Tensorflow: https: Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ relative_buckets = 0 if bidirectional: num_buckets relative_buckets += (relative_position > 0).to(torch.long) * num_buckets relative_position = torch.abs(relative_position) else: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) max_exact = num_buckets is_small = relative_position < max_exact relative_position_if_large = max_exact + ( torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) relative_position_if_large = torch.min( relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) ) relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) return relative_buckets def compute_bias(self, query_length, key_length, device): """Compute binned relative position bias""" context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] relative_position = memory_position - context_position relative_position_bucket = self._relative_position_bucket( relative_position, bidirectional=True, num_buckets=self.relative_attention_num_buckets, max_distance=self.relative_attention_max_distance, ) values = self.relative_attention_bias(relative_position_bucket) values = values.permute([2, 0, 1]).unsqueeze(0) return values def forward(self, x, mask=None, past_bias=None, optimized_attention=None): q = self.q(x) k = self.k(x) v = self.v(x) if self.relative_attention_bias is not None: past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device) if past_bias is not None: if mask is not None: mask = mask + past_bias else: mask = past_bias out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask) return self.o(out), past_bias class T5LayerSelfAttention(torch.nn.Module): def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations): super().__init__() self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations) self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) def forward(self, x, mask=None, past_bias=None, optimized_attention=None): normed_hidden_states = self.layer_norm(x) output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention) x += output return x, past_bias class T5Block(torch.nn.Module): def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias, dtype, device, operations): super().__init__() self.layer = torch.nn.ModuleList() self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations)) self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, dtype, device, operations)) def forward(self, x, mask=None, past_bias=None, optimized_attention=None): x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention) x = self.layer[-1](x) return x, past_bias class T5Stack(torch.nn.Module): def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, num_heads, dtype, device, operations): super().__init__() self.block = torch.nn.ModuleList( [T5Block(model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)] ) self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True): mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) intermediate = None optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True) past_bias = None for i, l in enumerate(self.block): x, past_bias = l(x, mask, past_bias, optimized_attention) if i == intermediate_output: intermediate = x.clone() x = self.final_layer_norm(x) if intermediate is not None and final_layer_norm_intermediate: intermediate = self.final_layer_norm(intermediate) return x, intermediate class T5(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.num_layers = config_dict["num_layers"] model_dim = config_dict["d_model"] self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["num_heads"], dtype, device, operations) self.dtype = dtype self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device) def get_input_embeddings(self): return self.shared def set_input_embeddings(self, embeddings): self.shared = embeddings def forward(self, input_ids, *args, **kwargs): x = self.shared(input_ids) return self.encoder(x, *args, **kwargs)
{ "d_ff": 3072, "d_kv": 64, "d_model": 768, "decoder_start_token_id": 0, "dropout_rate": 0.1, "eos_token_id": 1, "dense_act_fn": "relu", "initializer_factor": 1.0, "is_encoder_decoder": true, "layer_norm_epsilon": 1e-06, "model_type": "t5", "num_decoder_layers": 12, "num_heads": 12, "num_layers": 12, "output_past": true, "pad_token_id": 0, "relative_attention_num_buckets": 32, "tie_word_embeddings": false, "vocab_size": 32128 }
{ "d_ff": 10240, "d_kv": 64, "d_model": 4096, "decoder_start_token_id": 0, "dropout_rate": 0.1, "eos_token_id": 1, "dense_act_fn": "gelu_pytorch_tanh", "initializer_factor": 1.0, "is_encoder_decoder": true, "layer_norm_epsilon": 1e-06, "model_type": "t5", "num_decoder_layers": 24, "num_heads": 64, "num_layers": 24, "output_past": true, "pad_token_id": 0, "relative_attention_num_buckets": 32, "tie_word_embeddings": false, "vocab_size": 32128 }
import torch from typing import Callable, Protocol, TypedDict, Optional, List class UnetApplyFunction(Protocol): """Function signature protocol on comfy.model_base.BaseModel.apply_model""" def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor: pass class UnetApplyConds(TypedDict): """Optional conditions for unet apply function.""" c_concat: Optional[torch.Tensor] c_crossattn: Optional[torch.Tensor] control: Optional[torch.Tensor] transformer_options: Optional[dict] class UnetParams(TypedDict): input: torch.Tensor timestep: torch.Tensor c: UnetApplyConds cond_or_uncond: List[int] UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor]
import torch import math import struct import comfy.checkpoint_pickle import safetensors.torch import numpy as np from PIL import Image import logging import itertools def load_torch_file(ckpt, safe_load=False, device=None): if device is None: device = torch.device("cpu") if ckpt.lower().endswith(".safetensors"): sd = safetensors.torch.load_file(ckpt, device=device.type) else: if safe_load: if not 'weights_only' in torch.load.__code__.co_varnames: logging.warning("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.") safe_load = False if safe_load: pl_sd = torch.load(ckpt, map_location=device, weights_only=True) else: pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle) if "global_step" in pl_sd: logging.debug(f"Global Step: {pl_sd['global_step']}") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd return sd def save_torch_file(sd, ckpt, metadata=None): if metadata is not None: safetensors.torch.save_file(sd, ckpt, metadata=metadata) else: safetensors.torch.save_file(sd, ckpt) def calculate_parameters(sd, prefix=""): params = 0 for k in sd.keys(): if k.startswith(prefix): params += sd[k].nelement() return params def state_dict_key_replace(state_dict, keys_to_replace): for x in keys_to_replace: if x in state_dict: state_dict[keys_to_replace[x]] = state_dict.pop(x) return state_dict def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False): if filter_keys: out = {} else: out = state_dict for rp in replace_prefix: replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) for x in replace: w = state_dict.pop(x[0]) out[x[1]] = w return out def transformers_convert(sd, prefix_from, prefix_to, number): keys_to_replace = { "{}positional_embedding": "{}embeddings.position_embedding.weight", "{}token_embedding.weight": "{}embeddings.token_embedding.weight", "{}ln_final.weight": "{}final_layer_norm.weight", "{}ln_final.bias": "{}final_layer_norm.bias", } for k in keys_to_replace: x = k.format(prefix_from) if x in sd: sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x) resblock_to_replace = { "ln_1": "layer_norm1", "ln_2": "layer_norm2", "mlp.c_fc": "mlp.fc1", "mlp.c_proj": "mlp.fc2", "attn.out_proj": "self_attn.out_proj", } for resblock in range(number): for x in resblock_to_replace: for y in ["weight", "bias"]: k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y) k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y) if k in sd: sd[k_to] = sd.pop(k) for y in ["weight", "bias"]: k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y) if k_from in sd: weights = sd.pop(k_from) shape_from = weights.shape[0] for x in range(3): p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] return sd def clip_text_transformers_convert(sd, prefix_from, prefix_to): sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32) tp = "{}text_projection.weight".format(prefix_from) if tp in sd: sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp) tp = "{}text_projection".format(prefix_from) if tp in sd: sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous() return sd UNET_MAP_ATTENTIONS = { "proj_in.weight", "proj_in.bias", "proj_out.weight", "proj_out.bias", "norm.weight", "norm.bias", } TRANSFORMER_BLOCKS = { "norm1.weight", "norm1.bias", "norm2.weight", "norm2.bias", "norm3.weight", "norm3.bias", "attn1.to_q.weight", "attn1.to_k.weight", "attn1.to_v.weight", "attn1.to_out.0.weight", "attn1.to_out.0.bias", "attn2.to_q.weight", "attn2.to_k.weight", "attn2.to_v.weight", "attn2.to_out.0.weight", "attn2.to_out.0.bias", "ff.net.0.proj.weight", "ff.net.0.proj.bias", "ff.net.2.weight", "ff.net.2.bias", } UNET_MAP_RESNET = { "in_layers.2.weight": "conv1.weight", "in_layers.2.bias": "conv1.bias", "emb_layers.1.weight": "time_emb_proj.weight", "emb_layers.1.bias": "time_emb_proj.bias", "out_layers.3.weight": "conv2.weight", "out_layers.3.bias": "conv2.bias", "skip_connection.weight": "conv_shortcut.weight", "skip_connection.bias": "conv_shortcut.bias", "in_layers.0.weight": "norm1.weight", "in_layers.0.bias": "norm1.bias", "out_layers.0.weight": "norm2.weight", "out_layers.0.bias": "norm2.bias", } UNET_MAP_BASIC = { ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), ("input_blocks.0.0.weight", "conv_in.weight"), ("input_blocks.0.0.bias", "conv_in.bias"), ("out.0.weight", "conv_norm_out.weight"), ("out.0.bias", "conv_norm_out.bias"), ("out.2.weight", "conv_out.weight"), ("out.2.bias", "conv_out.bias"), ("time_embed.0.weight", "time_embedding.linear_1.weight"), ("time_embed.0.bias", "time_embedding.linear_1.bias"), ("time_embed.2.weight", "time_embedding.linear_2.weight"), ("time_embed.2.bias", "time_embedding.linear_2.bias") } def unet_to_diffusers(unet_config): if "num_res_blocks" not in unet_config: return {} num_res_blocks = unet_config["num_res_blocks"] channel_mult = unet_config["channel_mult"] transformer_depth = unet_config["transformer_depth"][:] transformer_depth_output = unet_config["transformer_depth_output"][:] num_blocks = len(channel_mult) transformers_mid = unet_config.get("transformer_depth_middle", None) diffusers_unet_map = {} for x in range(num_blocks): n = 1 + (num_res_blocks[x] + 1) * x for i in range(num_res_blocks[x]): for b in UNET_MAP_RESNET: diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) num_transformers = transformer_depth.pop(0) if num_transformers > 0: for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) for t in range(num_transformers): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) n += 1 for k in ["weight", "bias"]: diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) i = 0 for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) for t in range(transformers_mid): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) for i, n in enumerate([0, 2]): for b in UNET_MAP_RESNET: diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) num_res_blocks = list(reversed(num_res_blocks)) for x in range(num_blocks): n = (num_res_blocks[x] + 1) * x l = num_res_blocks[x] + 1 for i in range(l): c = 0 for b in UNET_MAP_RESNET: diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) c += 1 num_transformers = transformer_depth_output.pop() if num_transformers > 0: c += 1 for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) for t in range(num_transformers): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) if i == l - 1: for k in ["weight", "bias"]: diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) n += 1 for k in UNET_MAP_BASIC: diffusers_unet_map[k[1]] = k[0] return diffusers_unet_map def swap_scale_shift(weight): shift, scale = weight.chunk(2, dim=0) new_weight = torch.cat([scale, shift], dim=0) return new_weight MMDIT_MAP_BASIC = { ("context_embedder.bias", "context_embedder.bias"), ("context_embedder.weight", "context_embedder.weight"), ("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"), ("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"), ("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"), ("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"), ("x_embedder.proj.bias", "pos_embed.proj.bias"), ("x_embedder.proj.weight", "pos_embed.proj.weight"), ("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"), ("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"), ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"), ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"), ("pos_embed", "pos_embed.pos_embed"), ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), ("final_layer.linear.bias", "proj_out.bias"), ("final_layer.linear.weight", "proj_out.weight"), } MMDIT_MAP_BLOCK = { ("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"), ("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"), ("context_block.attn.proj.bias", "attn.to_add_out.bias"), ("context_block.attn.proj.weight", "attn.to_add_out.weight"), ("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"), ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"), ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"), ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"), ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"), ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"), ("x_block.attn.proj.bias", "attn.to_out.0.bias"), ("x_block.attn.proj.weight", "attn.to_out.0.weight"), ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"), ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"), ("x_block.mlp.fc2.bias", "ff.net.2.bias"), ("x_block.mlp.fc2.weight", "ff.net.2.weight"), } def mmdit_to_diffusers(mmdit_config, output_prefix=""): key_map = {} depth = mmdit_config.get("depth", 0) for i in range(depth): block_from = "transformer_blocks.{}".format(i) block_to = "{}joint_blocks.{}".format(output_prefix, i) offset = depth * 64 for end in ("weight", "bias"): k = "{}.attn.".format(block_from) qkv = "{}.x_block.attn.qkv.{}".format(block_to, end) key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset)) key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset)) key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) qkv = "{}.context_block.attn.qkv.{}".format(block_to, end) key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset)) key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset)) key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) for k in MMDIT_MAP_BLOCK: key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0]) map_basic = MMDIT_MAP_BASIC.copy() map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift)) map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift)) for k in map_basic: if len(k) > 2: key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) else: key_map[k[1]] = "{}{}".format(output_prefix, k[0]) return key_map def repeat_to_batch_size(tensor, batch_size, dim=0): if tensor.shape[dim] > batch_size: return tensor.narrow(dim, 0, batch_size) elif tensor.shape[dim] < batch_size: return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size) return tensor def resize_to_batch_size(tensor, batch_size): in_batch_size = tensor.shape[0] if in_batch_size == batch_size: return tensor if batch_size <= 1: return tensor[:batch_size] output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device) if batch_size < in_batch_size: scale = (in_batch_size - 1) / (batch_size - 1) for i in range(batch_size): output[i] = tensor[min(round(i * scale), in_batch_size - 1)] else: scale = in_batch_size / batch_size for i in range(batch_size): output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)] return output def convert_sd_to(state_dict, dtype): keys = list(state_dict.keys()) for k in keys: state_dict[k] = state_dict[k].to(dtype) return state_dict def safetensors_header(safetensors_path, max_size=100*1024*1024): with open(safetensors_path, "rb") as f: header = f.read(8) length_of_header = struct.unpack('<Q', header)[0] if length_of_header > max_size: return None return f.read(length_of_header) def set_attr(obj, attr, value): attrs = attr.split(".") for name in attrs[:-1]: obj = getattr(obj, name) prev = getattr(obj, attrs[-1]) setattr(obj, attrs[-1], value) return prev def set_attr_param(obj, attr, value): return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False)) def copy_to_param(obj, attr, value): attrs = attr.split(".") for name in attrs[:-1]: obj = getattr(obj, name) prev = getattr(obj, attrs[-1]) prev.data.copy_(value) def get_attr(obj, attr): attrs = attr.split(".") for name in attrs: obj = getattr(obj, name) return obj def bislerp(samples, width, height): def slerp(b1, b2, r): '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' c = b1.shape[-1] b1_norms = torch.norm(b1, dim=-1, keepdim=True) b2_norms = torch.norm(b2, dim=-1, keepdim=True) b1_normalized = b1 / b1_norms b2_normalized = b2 / b2_norms b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0 b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0 dot = (b1_normalized*b2_normalized).sum(1) omega = torch.acos(dot) so = torch.sin(omega) res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c) res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] return res def generate_bilinear_data(length_old, length_new, device): coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") ratios = coords_1 - coords_1.floor() coords_1 = coords_1.to(torch.int64) coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1 coords_2[:,:,:,-1] -= 1 coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") coords_2 = coords_2.to(torch.int64) return ratios, coords_1, coords_2 orig_dtype = samples.dtype samples = samples.float() n,c,h,w = samples.shape h_new, w_new = (height, width) ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) coords_1 = coords_1.expand((n, c, h, -1)) coords_2 = coords_2.expand((n, c, h, -1)) ratios = ratios.expand((n, 1, h, -1)) pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c)) pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c)) ratios = ratios.movedim(1, -1).reshape((-1,1)) result = slerp(pass_1, pass_2, ratios) result = result.reshape(n, h, w_new, c).movedim(-1, 1) ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device) coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c)) pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c)) ratios = ratios.movedim(1, -1).reshape((-1,1)) result = slerp(pass_1, pass_2, ratios) result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) return result.to(orig_dtype) def lanczos(samples, width, height): images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples] images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images] images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images] result = torch.stack(images) return result.to(samples.device, samples.dtype) def common_upscale(samples, width, height, upscale_method, crop): if crop == "center": old_width = samples.shape[3] old_height = samples.shape[2] old_aspect = old_width / old_height new_aspect = width / height x = 0 y = 0 if old_aspect > new_aspect: x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) elif old_aspect < new_aspect: y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) s = samples[:,:,y:old_height-y,x:old_width-x] else: s = samples if upscale_method == "bislerp": return bislerp(s, width, height) elif upscale_method == "lanczos": return lanczos(s, width, height) else: return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap))) @torch.inference_mode() def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): dims = len(tile) output = torch.empty([samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:])), device=output_device) for b in range(samples.shape[0]): s = samples[b:b+1] out = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device) out_div = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device) for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))): s_in = s upscaled = [] for d in range(dims): pos = max(0, min(s.shape[d + 2] - overlap, it[d])) l = min(tile[d], s.shape[d + 2] - pos) s_in = s_in.narrow(d + 2, pos, l) upscaled.append(round(pos * upscale_amount)) ps = function(s_in).to(output_device) mask = torch.ones_like(ps) feather = round(overlap * upscale_amount) for t in range(feather): for d in range(2, dims + 2): m = mask.narrow(d, t, 1) m *= ((1.0/feather) * (t + 1)) m = mask.narrow(d, mask.shape[d] -1 -t, 1) m *= ((1.0/feather) * (t + 1)) o = out o_d = out_div for d in range(dims): o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2]) o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2]) o += ps * mask o_d += mask if pbar is not None: pbar.update(1) output[b:b+1] = out/out_div return output def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels, output_device, pbar) PROGRESS_BAR_ENABLED = True def set_progress_bar_enabled(enabled): global PROGRESS_BAR_ENABLED PROGRESS_BAR_ENABLED = enabled PROGRESS_BAR_HOOK = None def set_progress_bar_global_hook(function): global PROGRESS_BAR_HOOK PROGRESS_BAR_HOOK = function class ProgressBar: def __init__(self, total): global PROGRESS_BAR_HOOK self.total = total self.current = 0 self.hook = PROGRESS_BAR_HOOK def update_absolute(self, value, total=None, preview=None): if total is not None: self.total = total if value > self.total: value = self.total self.current = value if self.hook is not None: self.hook(self.current, self.total, preview) def update(self, value): self.update_absolute(self.current + value)
import torch import torch as th import torch.nn as nn from ..ldm.modules.diffusionmodules.util import ( zero_module, timestep_embedding, ) from ..ldm.modules.attention import SpatialTransformer from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample from ..ldm.util import exists import comfy.ops class ControlledUnetModel(UNetModel): pass class ControlNet(nn.Module): def __init__( self, image_size, in_channels, model_channels, hint_channels, num_res_blocks, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, dtype=torch.float32, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, transformer_depth=1, context_dim=None, n_embed=None, legacy=True, disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, use_linear_in_transformer=False, adm_in_channels=None, transformer_depth_middle=None, transformer_depth_output=None, attn_precision=None, device=None, operations=comfy.ops.disable_weight_init, **kwargs, ): super().__init__() assert use_spatial_transformer == True, "use_spatial_transformer has to be true" if use_spatial_transformer: assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' if context_dim is not None: assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' if num_heads_upsample == -1: num_heads_upsample = num_heads if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.dims = dims self.image_size = image_size self.in_channels = in_channels self.model_channels = model_channels if isinstance(num_res_blocks, int): self.num_res_blocks = len(channel_mult) * [num_res_blocks] else: if len(num_res_blocks) != len(channel_mult): raise ValueError("provide num_res_blocks either as an int (globally constant) or " "as a list/tuple (per-level) with the same length as channel_mult") self.num_res_blocks = num_res_blocks if disable_self_attentions is not None: assert len(disable_self_attentions) == len(channel_mult) if num_attention_blocks is not None: assert len(num_attention_blocks) == len(self.num_res_blocks) assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) transformer_depth = transformer_depth[:] self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = dtype self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.predict_codebook_ids = n_embed is not None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) if self.num_classes is not None: if isinstance(self.num_classes, int): self.label_emb = nn.Embedding(num_classes, time_embed_dim) elif self.num_classes == "continuous": print("setting up linear c_adm embedding layer") self.label_emb = nn.Linear(1, time_embed_dim) elif self.num_classes == "sequential": assert adm_in_channels is not None self.label_emb = nn.Sequential( nn.Sequential( operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) ) else: raise ValueError() self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) ] ) self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)]) self.input_hint_block = TimestepEmbedSequential( operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for nr in range(self.num_res_blocks[level]): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations, ) ] ch = mult * model_channels num_transformers = transformer_depth.pop(0) if num_transformers > 0: if num_head_channels == -1: dim_head = ch else: num_heads = ch dim_head = num_head_channels if legacy: dim_head = ch if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: layers.append( SpatialTransformer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations ) ) ) ch = out_ch input_block_chans.append(ch) self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch else: num_heads = ch dim_head = num_head_channels if legacy: dim_head = ch mid_block = [ ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] if transformer_depth_middle >= 0: mid_block += [SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] self.middle_block = TimestepEmbedSequential(*mid_block) self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device) self._feature_size += ch def make_zero_conv(self, channels, operations=None, dtype=None, device=None): return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device)) def forward(self, x, hint, timesteps, context, y=None, **kwargs): t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) guided_hint = self.input_hint_block(hint, emb, context) outs = [] hs = [] if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) h = x for module, zero_conv in zip(self.input_blocks, self.zero_convs): if guided_hint is not None: h = module(h, emb, context) h += guided_hint guided_hint = None else: h = module(h, emb, context) outs.append(zero_conv(h, emb, context)) h = self.middle_block(h, emb, context) outs.append(self.middle_block_out(h, emb, context)) return outs
import torch import torch.nn.functional as F import math from tqdm.auto import trange, tqdm class NoiseScheduleVP: def __init__( self, schedule='discrete', betas=None, alphas_cumprod=None, continuous_beta_0=0.1, continuous_beta_1=20., ): """Create a wrapper class for the forward SDE (VP type). *** Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. *** The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: log_alpha_t = self.marginal_log_mean_coeff(t) sigma_t = self.marginal_std(t) lambda_t = self.marginal_lambda(t) Moreover, as lambda(t) is an invertible function, we also support its inverse function: t = self.inverse_lambda(lambda_t) =============================================================== We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). 1. For discrete-time DPMs: For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: t_i = (i + 1) / N e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. Args: betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. **Important**: Please pay special attention for the args for `alphas_cumprod`: The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have alpha_{t_n} = \sqrt{\hat{alpha_n}}, and log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). 2. For continuous-time DPMs: We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise schedule are the default settings in DDPM and improved-DDPM: Args: beta_min: A `float` number. The smallest beta for the linear schedule. beta_max: A `float` number. The largest beta for the linear schedule. cosine_s: A `float` number. The hyperparameter in the cosine schedule. cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. T: A `float` number. The ending time of the forward process. =============================================================== Args: schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, 'linear' or 'cosine' for continuous-time DPMs. Returns: A wrapper object of the forward SDE (VP type). =============================================================== Example: >>> ns = NoiseScheduleVP('discrete', betas=betas) >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) """ if schedule not in ['discrete', 'linear', 'cosine']: raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) self.schedule = schedule if schedule == 'discrete': if betas is not None: log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) else: assert alphas_cumprod is not None log_alphas = 0.5 * torch.log(alphas_cumprod) self.total_N = len(log_alphas) self.T = 1. self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) self.log_alpha_array = log_alphas.reshape((1, -1,)) else: self.total_N = 1000 self.beta_0 = continuous_beta_0 self.beta_1 = continuous_beta_1 self.cosine_s = 0.008 self.cosine_beta_max = 999. self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) self.schedule = schedule if schedule == 'cosine': self.T = 0.9946 else: self.T = 1. def marginal_log_mean_coeff(self, t): """ Compute log(alpha_t) of a given continuous-time label t in [0, T]. """ if self.schedule == 'discrete': return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) elif self.schedule == 'linear': return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 elif self.schedule == 'cosine': log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 return log_alpha_t def marginal_alpha(self, t): """ Compute alpha_t of a given continuous-time label t in [0, T]. """ return torch.exp(self.marginal_log_mean_coeff(t)) def marginal_std(self, t): """ Compute sigma_t of a given continuous-time label t in [0, T]. """ return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) def marginal_lambda(self, t): """ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. """ log_mean_coeff = self.marginal_log_mean_coeff(t) log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) return log_mean_coeff - log_std def inverse_lambda(self, lamb): """ Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. """ if self.schedule == 'linear': tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) Delta = self.beta_0**2 + tmp return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) elif self.schedule == 'discrete': log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) return t.reshape((-1,)) else: log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s t = t_fn(log_alpha) return t def model_wrapper( model, noise_schedule, model_type="noise", model_kwargs={}, guidance_type="uncond", condition=None, unconditional_condition=None, guidance_scale=1., classifier_fn=None, classifier_kwargs={}, ): """Create a wrapper function for the noise prediction model. DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. We support four types of the diffusion model by setting `model_type`: 1. "noise": noise prediction model. (Trained by predicting noise). 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). 3. "v": velocity prediction model. (Trained by predicting the velocity). The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." arXiv preprint arXiv:2202.00512 (2022). [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." arXiv preprint arXiv:2210.02303 (2022). 4. "score": marginal score function. (Trained by denoising score matching). Note that the score function and the noise prediction model follows a simple relationship: ``` noise(x_t, t) = -sigma_t * score(x_t, t) ``` We support three types of guided sampling by DPMs by setting `guidance_type`: 1. "uncond": unconditional sampling by DPMs. The input `model` has the following format: `` model(x, t_input, **model_kwargs) -> noise | x_start | v | score `` 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. The input `model` has the following format: `` model(x, t_input, **model_kwargs) -> noise | x_start | v | score `` The input `classifier_fn` has the following format: `` classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) `` [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. The input `model` has the following format: `` model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score `` And if cond == `unconditional_condition`, the model output is the unconditional DPM output. [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." arXiv preprint arXiv:2207.12598 (2022). The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) or continuous-time labels (i.e. epsilon to T). We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: `` def model_fn(x, t_continuous) -> noise: t_input = get_model_input_time(t_continuous) return noise_pred(model, x, t_input, **model_kwargs) `` where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. =============================================================== Args: model: A diffusion model with the corresponding format described above. noise_schedule: A noise schedule object, such as NoiseScheduleVP. model_type: A `str`. The parameterization type of the diffusion model. "noise" or "x_start" or "v" or "score". model_kwargs: A `dict`. A dict for the other inputs of the model function. guidance_type: A `str`. The type of the guidance for sampling. "uncond" or "classifier" or "classifier-free". condition: A pytorch tensor. The condition for the guided sampling. Only used for "classifier" or "classifier-free" guidance type. unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. Only used for "classifier-free" guidance type. guidance_scale: A `float`. The scale for the guided sampling. classifier_fn: A classifier function. Only used for the classifier guidance. classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. Returns: A noise prediction model that accepts the noised data and the continuous time as the inputs. """ def get_model_input_time(t_continuous): """ Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. For continuous-time DPMs, we just use `t_continuous`. """ if noise_schedule.schedule == 'discrete': return (t_continuous - 1. / noise_schedule.total_N) * 1000. else: return t_continuous def noise_pred_fn(x, t_continuous, cond=None): if t_continuous.reshape((-1,)).shape[0] == 1: t_continuous = t_continuous.expand((x.shape[0])) t_input = get_model_input_time(t_continuous) output = model(x, t_input, **model_kwargs) if model_type == "noise": return output elif model_type == "x_start": alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) dims = x.dim() return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) elif model_type == "v": alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) dims = x.dim() return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x elif model_type == "score": sigma_t = noise_schedule.marginal_std(t_continuous) dims = x.dim() return -expand_dims(sigma_t, dims) * output def cond_grad_fn(x, t_input): """ Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). """ with torch.enable_grad(): x_in = x.detach().requires_grad_(True) log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) return torch.autograd.grad(log_prob.sum(), x_in)[0] def model_fn(x, t_continuous): """ The noise predicition model function that is used for DPM-Solver. """ if t_continuous.reshape((-1,)).shape[0] == 1: t_continuous = t_continuous.expand((x.shape[0])) if guidance_type == "uncond": return noise_pred_fn(x, t_continuous) elif guidance_type == "classifier": assert classifier_fn is not None t_input = get_model_input_time(t_continuous) cond_grad = cond_grad_fn(x, t_input) sigma_t = noise_schedule.marginal_std(t_continuous) noise = noise_pred_fn(x, t_continuous) return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad elif guidance_type == "classifier-free": if guidance_scale == 1. or unconditional_condition is None: return noise_pred_fn(x, t_continuous, cond=condition) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t_continuous] * 2) c_in = torch.cat([unconditional_condition, condition]) noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) return noise_uncond + guidance_scale * (noise - noise_uncond) assert model_type in ["noise", "x_start", "v"] assert guidance_type in ["uncond", "classifier", "classifier-free"] return model_fn class UniPC: def __init__( self, model_fn, noise_schedule, predict_x0=True, thresholding=False, max_val=1., variant='bh1', ): """Construct a UniPC. We support both data_prediction and noise_prediction. """ self.model = model_fn self.noise_schedule = noise_schedule self.variant = variant self.predict_x0 = predict_x0 self.thresholding = thresholding self.max_val = max_val def dynamic_thresholding_fn(self, x0, t=None): """ The dynamic thresholding method. """ dims = x0.dim() p = self.dynamic_thresholding_ratio s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) x0 = torch.clamp(x0, -s, s) / s return x0 def noise_prediction_fn(self, x, t): """ Return the noise prediction model. """ return self.model(x, t) def data_prediction_fn(self, x, t): """ Return the data prediction model (with thresholding). """ noise = self.noise_prediction_fn(x, t) dims = x.dim() alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) if self.thresholding: p = 0.995 s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) x0 = torch.clamp(x0, -s, s) / s return x0 def model_fn(self, x, t): """ Convert the model to the noise prediction model or the data prediction model. """ if self.predict_x0: return self.data_prediction_fn(x, t) else: return self.noise_prediction_fn(x, t) def get_time_steps(self, skip_type, t_T, t_0, N, device): """Compute the intermediate time steps for sampling. """ if skip_type == 'logSNR': lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) return self.noise_schedule.inverse_lambda(logSNR_steps) elif skip_type == 'time_uniform': return torch.linspace(t_T, t_0, N + 1).to(device) elif skip_type == 'time_quadratic': t_order = 2 t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) return t else: raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): """ Get the order of each step for sampling by the singlestep DPM-Solver. """ if order == 3: K = steps if steps % 3 == 0: orders = [3,] * (K - 2) + [2, 1] elif steps % 3 == 1: orders = [3,] * (K - 1) + [1] else: orders = [3,] * (K - 1) + [2] elif order == 2: if steps % 2 == 0: K = steps orders = [2,] * K else: K = steps orders = [2,] * (K - 1) + [1] elif order == 1: K = steps orders = [1,] * steps else: raise ValueError("'order' must be '1' or '2' or '3'.") if skip_type == 'logSNR': timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) else: timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] return timesteps_outer, orders def denoise_to_zero_fn(self, x, s): """ Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. """ return self.data_prediction_fn(x, s) def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): if len(t.shape) == 0: t = t.view(-1) if 'bh' in self.variant: return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) else: assert self.variant == 'vary_coeff' return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') ns = self.noise_schedule assert order <= len(model_prev_list) t_prev_0 = t_prev_list[-1] lambda_prev_0 = ns.marginal_lambda(t_prev_0) lambda_t = ns.marginal_lambda(t) model_prev_0 = model_prev_list[-1] sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) log_alpha_t = ns.marginal_log_mean_coeff(t) alpha_t = torch.exp(log_alpha_t) h = lambda_t - lambda_prev_0 rks = [] D1s = [] for i in range(1, order): t_prev_i = t_prev_list[-(i + 1)] model_prev_i = model_prev_list[-(i + 1)] lambda_prev_i = ns.marginal_lambda(t_prev_i) rk = (lambda_prev_i - lambda_prev_0) / h rks.append(rk) D1s.append((model_prev_i - model_prev_0) / rk) rks.append(1.) rks = torch.tensor(rks, device=x.device) K = len(rks) C = [] col = torch.ones_like(rks) for k in range(1, K + 1): C.append(col) col = col * rks / (k + 1) C = torch.stack(C, dim=1) if len(D1s) > 0: D1s = torch.stack(D1s, dim=1) C_inv_p = torch.linalg.inv(C[:-1, :-1]) A_p = C_inv_p if use_corrector: print('using corrector') C_inv = torch.linalg.inv(C) A_c = C_inv hh = -h if self.predict_x0 else h h_phi_1 = torch.expm1(hh) h_phi_ks = [] factorial_k = 1 h_phi_k = h_phi_1 for k in range(1, K + 2): h_phi_ks.append(h_phi_k) h_phi_k = h_phi_k / hh - 1 / factorial_k factorial_k *= (k + 1) model_t = None if self.predict_x0: x_t_ = ( sigma_t / sigma_prev_0 * x - alpha_t * h_phi_1 * model_prev_0 ) x_t = x_t_ if len(D1s) > 0: for k in range(K - 1): x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) if use_corrector: model_t = self.model_fn(x_t, t) D1_t = (model_t - model_prev_0) x_t = x_t_ k = 0 for k in range(K - 1): x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) else: log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) x_t_ = ( (torch.exp(log_alpha_t - log_alpha_prev_0)) * x - (sigma_t * h_phi_1) * model_prev_0 ) x_t = x_t_ if len(D1s) > 0: for k in range(K - 1): x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) if use_corrector: model_t = self.model_fn(x_t, t) D1_t = (model_t - model_prev_0) x_t = x_t_ k = 0 for k in range(K - 1): x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) return x_t, model_t def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): ns = self.noise_schedule assert order <= len(model_prev_list) dims = x.dim() t_prev_0 = t_prev_list[-1] lambda_prev_0 = ns.marginal_lambda(t_prev_0) lambda_t = ns.marginal_lambda(t) model_prev_0 = model_prev_list[-1] sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) alpha_t = torch.exp(log_alpha_t) h = lambda_t - lambda_prev_0 rks = [] D1s = [] for i in range(1, order): t_prev_i = t_prev_list[-(i + 1)] model_prev_i = model_prev_list[-(i + 1)] lambda_prev_i = ns.marginal_lambda(t_prev_i) rk = ((lambda_prev_i - lambda_prev_0) / h)[0] rks.append(rk) D1s.append((model_prev_i - model_prev_0) / rk) rks.append(1.) rks = torch.tensor(rks, device=x.device) R = [] b = [] hh = -h[0] if self.predict_x0 else h[0] h_phi_1 = torch.expm1(hh) h_phi_k = h_phi_1 / hh - 1 factorial_i = 1 if self.variant == 'bh1': B_h = hh elif self.variant == 'bh2': B_h = torch.expm1(hh) else: raise NotImplementedError() for i in range(1, order + 1): R.append(torch.pow(rks, i - 1)) b.append(h_phi_k * factorial_i / B_h) factorial_i *= (i + 1) h_phi_k = h_phi_k / hh - 1 / factorial_i R = torch.stack(R) b = torch.tensor(b, device=x.device) use_predictor = len(D1s) > 0 and x_t is None if len(D1s) > 0: D1s = torch.stack(D1s, dim=1) if x_t is None: if order == 2: rhos_p = torch.tensor([0.5], device=b.device) else: rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) else: D1s = None if use_corrector: if order == 1: rhos_c = torch.tensor([0.5], device=b.device) else: rhos_c = torch.linalg.solve(R, b) model_t = None if self.predict_x0: x_t_ = ( expand_dims(sigma_t / sigma_prev_0, dims) * x - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 ) if x_t is None: if use_predictor: pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) else: pred_res = 0 x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res if use_corrector: model_t = self.model_fn(x_t, t) if D1s is not None: corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) else: corr_res = 0 D1_t = (model_t - model_prev_0) x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) else: x_t_ = ( expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 ) if x_t is None: if use_predictor: pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) else: pred_res = 0 x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res if use_corrector: model_t = self.model_fn(x_t, t) if D1s is not None: corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) else: corr_res = 0 D1_t = (model_t - model_prev_0) x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) return x_t, model_t def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False ): device = x.device steps = len(timesteps) - 1 if method == 'multistep': assert steps >= order assert timesteps.shape[0] - 1 == steps for step_index in trange(steps, disable=disable_pbar): if step_index == 0: vec_t = timesteps[0].expand((x.shape[0])) model_prev_list = [self.model_fn(x, vec_t)] t_prev_list = [vec_t] elif step_index < order: init_order = step_index vec_t = timesteps[init_order].expand(x.shape[0]) x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) if model_x is None: model_x = self.model_fn(x, vec_t) model_prev_list.append(model_x) t_prev_list.append(vec_t) else: extra_final_step = 0 if step_index == (steps - 1): extra_final_step = 1 for step in range(step_index, step_index + 1 + extra_final_step): vec_t = timesteps[step].expand(x.shape[0]) if lower_order_final: step_order = min(order, steps + 1 - step) else: step_order = order if step == steps: use_corrector = False else: use_corrector = True x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) for i in range(order - 1): t_prev_list[i] = t_prev_list[i + 1] model_prev_list[i] = model_prev_list[i + 1] t_prev_list[-1] = vec_t if step < steps: if model_x is None: model_x = self.model_fn(x, vec_t) model_prev_list[-1] = model_x if callback is not None: callback({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]}) else: raise NotImplementedError() return x def interpolate_fn(x, xp, yp): """ A piecewise linear function y = f(x), using xp and yp as keypoints. We implement f(x) in a differentiable way (i.e. applicable for autograd). The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) Args: x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. yp: PyTorch tensor with shape [C, K]. Returns: The function values f(x), with shape [N, C]. """ N, K = x.shape[0], xp.shape[1] all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) sorted_all_x, x_indices = torch.sort(all_x, dim=2) x_idx = torch.argmin(x_indices, dim=2) cand_start_idx = x_idx - 1 start_idx = torch.where( torch.eq(x_idx, 0), torch.tensor(1, device=x.device), torch.where( torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, ), ) end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) start_idx2 = torch.where( torch.eq(x_idx, 0), torch.tensor(0, device=x.device), torch.where( torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, ), ) y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) return cand def expand_dims(v, dims): """ Expand the tensor `v` to the dim `dims`. Args: `v`: a PyTorch tensor with shape [N]. `dim`: a `int`. Returns: a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. """ return v[(...,) + (None,)*(dims - 1)] class SigmaConvert: schedule = "" def marginal_log_mean_coeff(self, sigma): return 0.5 * torch.log(1 / ((sigma * sigma) + 1)) def marginal_alpha(self, t): return torch.exp(self.marginal_log_mean_coeff(t)) def marginal_std(self, t): return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) def marginal_lambda(self, t): """ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. """ log_mean_coeff = self.marginal_log_mean_coeff(t) log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) return log_mean_coeff - log_std def predict_eps_sigma(model, input, sigma_in, **kwargs): sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1)) input = input * ((sigma ** 2 + 1.0) ** 0.5) return (input - model(input, sigma_in, **kwargs)) / sigma def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'): timesteps = sigmas.clone() if sigmas[-1] == 0: timesteps = sigmas[:] timesteps[-1] = 0.001 else: timesteps = sigmas.clone() ns = SigmaConvert() noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0) model_type = "noise" model_fn = model_wrapper( lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs), ns, model_type=model_type, guidance_type="uncond", model_kwargs=extra_args, ) order = min(3, len(timesteps) - 2) uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=variant) x = uni_pc.sample(noise, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) x /= ns.marginal_alpha(timesteps[-1]) return x def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False): return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2')
import math from scipy import integrate import torch from torch import nn import torchsde from tqdm.auto import trange, tqdm from . import utils def append_zero(x): return torch.cat([x, x.new_zeros([1])]) def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): """Constructs the noise schedule of Karras et al. (2022).""" ramp = torch.linspace(0, 1, n, device=device) min_inv_rho = sigma_min ** (1 / rho) max_inv_rho = sigma_max ** (1 / rho) sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho return append_zero(sigmas).to(device) def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): """Constructs an exponential noise schedule.""" sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() return append_zero(sigmas) def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): """Constructs an polynomial in log sigma noise schedule.""" ramp = torch.linspace(1, 0, n, device=device) ** rho sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) return append_zero(sigmas) def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): """Constructs a continuous VP noise schedule.""" t = torch.linspace(1, eps_s, n, device=device) sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1) return append_zero(sigmas) def to_d(x, sigma, denoised): """Converts a denoiser output to a Karras ODE derivative.""" return (x - denoised) / utils.append_dims(sigma, x.ndim) def get_ancestral_step(sigma_from, sigma_to, eta=1.): """Calculates the noise level (sigma_down) to step down to and the amount of noise to add (sigma_up) when doing an ancestral sampling step.""" if not eta: return sigma_to, 0. sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 return sigma_down, sigma_up def default_noise_sampler(x): return lambda sigma, sigma_next: torch.randn_like(x) class BatchedBrownianTree: """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" def __init__(self, x, t0, t1, seed=None, **kwargs): self.cpu_tree = True if "cpu" in kwargs: self.cpu_tree = kwargs.pop("cpu") t0, t1, self.sign = self.sort(t0, t1) w0 = kwargs.get('w0', torch.zeros_like(x)) if seed is None: seed = torch.randint(0, 2 ** 63 - 1, []).item() self.batched = True try: assert len(seed) == x.shape[0] w0 = w0[0] except TypeError: seed = [seed] self.batched = False if self.cpu_tree: self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed] else: self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] @staticmethod def sort(a, b): return (a, b, 1) if a < b else (b, a, -1) def __call__(self, t0, t1): t0, t1, sign = self.sort(t0, t1) if self.cpu_tree: w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign) else: w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) return w if self.batched else w[0] class BrownianTreeNoiseSampler: """A noise sampler backed by a torchsde.BrownianTree. Args: x (Tensor): The tensor whose shape, device and dtype to use to generate random samples. sigma_min (float): The low end of the valid interval. sigma_max (float): The high end of the valid interval. seed (int or List[int]): The random seed. If a list of seeds is supplied instead of a single integer, then the noise sampler will use one BrownianTree per batch item, each with its own seed. transform (callable): A function that maps sigma to the sampler's internal timestep. """ def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): self.transform = transform t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) def __call__(self, sigma, sigma_next): t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) return self.tree(t0, t1) / (t1 - t0).abs().sqrt() @torch.no_grad() def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): if s_churn > 0: gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. sigma_hat = sigmas[i] * (gamma + 1) else: gamma = 0 sigma_hat = sigmas[i] if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat x = x + d * dt return x @torch.no_grad() def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with Euler method steps.""" extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d = to_d(x, sigmas[i], denoised) dt = sigma_down - sigmas[i] x = x + d * dt if sigmas[i + 1] > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x @torch.no_grad() def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): if s_churn > 0: gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. sigma_hat = sigmas[i] * (gamma + 1) else: gamma = 0 sigma_hat = sigmas[i] sigma_hat = sigmas[i] * (gamma + 1) if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat if sigmas[i + 1] == 0: x = x + d * dt else: x_2 = x + d * dt denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) d_2 = to_d(x_2, sigmas[i + 1], denoised_2) d_prime = (d + d_2) / 2 x = x + d_prime * dt return x @torch.no_grad() def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): if s_churn > 0: gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. sigma_hat = sigmas[i] * (gamma + 1) else: gamma = 0 sigma_hat = sigmas[i] if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) if sigmas[i + 1] == 0: dt = sigmas[i + 1] - sigma_hat x = x + d * dt else: sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() dt_1 = sigma_mid - sigma_hat dt_2 = sigmas[i + 1] - sigma_hat x_2 = x + d * dt_1 denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) d_2 = to_d(x_2, sigma_mid, denoised_2) x = x + d_2 * dt_2 return x @torch.no_grad() def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with DPM-Solver second-order steps.""" extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d = to_d(x, sigmas[i], denoised) if sigma_down == 0: dt = sigma_down - sigmas[i] x = x + d * dt else: sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() dt_1 = sigma_mid - sigmas[i] dt_2 = sigma_down - sigmas[i] x_2 = x + d * dt_1 denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) d_2 = to_d(x_2, sigma_mid, denoised_2) x = x + d_2 * dt_2 x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x def linear_multistep_coeff(order, t, i, j): if order - 1 > i: raise ValueError(f'Order {order} too high for step {i}') def fn(tau): prod = 1. for k in range(order): if j == k: continue prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) return prod return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] @torch.no_grad() def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) sigmas_cpu = sigmas.detach().cpu().numpy() ds = [] for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) d = to_d(x, sigmas[i], denoised) ds.append(d) if len(ds) > order: ds.pop(0) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) cur_order = min(i + 1, order) coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) return x class PIDStepSizeController: """A PID controller for ODE adaptive step size control.""" def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): self.h = h self.b1 = (pcoeff + icoeff + dcoeff) / order self.b2 = -(pcoeff + 2 * dcoeff) / order self.b3 = dcoeff / order self.accept_safety = accept_safety self.eps = eps self.errs = [] def limiter(self, x): return 1 + math.atan(x - 1) def propose_step(self, error): inv_error = 1 / (float(error) + self.eps) if not self.errs: self.errs = [inv_error, inv_error, inv_error] self.errs[0] = inv_error factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 factor = self.limiter(factor) accept = factor >= self.accept_safety if accept: self.errs[2] = self.errs[1] self.errs[1] = self.errs[0] self.h *= factor return accept class DPMSolver(nn.Module): """DPM-Solver. See https: def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): super().__init__() self.model = model self.extra_args = {} if extra_args is None else extra_args self.eps_callback = eps_callback self.info_callback = info_callback def t(self, sigma): return -sigma.log() def sigma(self, t): return t.neg().exp() def eps(self, eps_cache, key, x, t, *args, **kwargs): if key in eps_cache: return eps_cache[key], eps_cache sigma = self.sigma(t) * x.new_ones([x.shape[0]]) eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) if self.eps_callback is not None: self.eps_callback() return eps, {key: eps, **eps_cache} def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): eps_cache = {} if eps_cache is None else eps_cache h = t_next - t eps, eps_cache = self.eps(eps_cache, 'eps', x, t) x_1 = x - self.sigma(t_next) * h.expm1() * eps return x_1, eps_cache def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): eps_cache = {} if eps_cache is None else eps_cache h = t_next - t eps, eps_cache = self.eps(eps_cache, 'eps', x, t) s1 = t + r1 * h u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) return x_2, eps_cache def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): eps_cache = {} if eps_cache is None else eps_cache h = t_next - t eps, eps_cache = self.eps(eps_cache, 'eps', x, t) s1 = t + r1 * h s2 = t + r2 * h u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) return x_3, eps_cache def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler if not t_end > t_start and eta: raise ValueError('eta must be 0 for reverse sampling') m = math.floor(nfe / 3) + 1 ts = torch.linspace(t_start, t_end, m + 1, device=x.device) if nfe % 3 == 0: orders = [3] * (m - 2) + [2, 1] else: orders = [3] * (m - 1) + [nfe % 3] for i in range(len(orders)): eps_cache = {} t, t_next = ts[i], ts[i + 1] if eta: sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) t_next_ = torch.minimum(t_end, self.t(sd)) su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 else: t_next_, su = t_next, 0. eps, eps_cache = self.eps(eps_cache, 'eps', x, t) denoised = x - self.sigma(t) * eps if self.info_callback is not None: self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) if orders[i] == 1: x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) elif orders[i] == 2: x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) else: x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) return x def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler if order not in {2, 3}: raise ValueError('order should be 2 or 3') forward = t_end > t_start if not forward and eta: raise ValueError('eta must be 0 for reverse sampling') h_init = abs(h_init) * (1 if forward else -1) atol = torch.tensor(atol) rtol = torch.tensor(rtol) s = t_start x_prev = x accept = True pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} while s < t_end - 1e-5 if forward else s > t_end + 1e-5: eps_cache = {} t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) if eta: sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) t_ = torch.minimum(t_end, self.t(sd)) su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 else: t_, su = t, 0. eps, eps_cache = self.eps(eps_cache, 'eps', x, s) denoised = x - self.sigma(s) * eps if order == 2: x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) else: x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 accept = pid.propose_step(error) if accept: x_prev = x_low x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) s = t info['n_accept'] += 1 else: info['n_reject'] += 1 info['nfe'] += order info['steps'] += 1 if self.info_callback is not None: self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) return x, info @torch.no_grad() def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): """DPM-Solver-Fast (fixed step size). See https: if sigma_min <= 0 or sigma_max <= 0: raise ValueError('sigma_min and sigma_max must not be 0') with tqdm(total=n, disable=disable) as pbar: dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) if callback is not None: dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) @torch.no_grad() def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False): """DPM-Solver-12 and 23 (adaptive step size). See https: if sigma_min <= 0 or sigma_max <= 0: raise ValueError('sigma_min and sigma_max must not be 0') with tqdm(disable=disable) as pbar: dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) if callback is not None: dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) if return_info: return x, info return x @torch.no_grad() def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigma_down == 0: d = to_d(x, sigmas[i], denoised) dt = sigma_down - sigmas[i] x = x + d * dt else: t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) r = 1 / 2 h = t_next - t s = t + r * h x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 if sigmas[i + 1] > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x @torch.no_grad() def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): """DPM-Solver++ (stochastic).""" if len(sigmas) <= 1: return x sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() seed = extra_args.get("seed", None) noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: d = to_d(x, sigmas[i], denoised) dt = sigmas[i + 1] - sigmas[i] x = x + d * dt else: t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) h = t_next - t s = t + h * r fac = 1 / (2 * r) sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) s_ = t_fn(sd) x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta) t_next_ = t_fn(sd) denoised_d = (1 - fac) * denoised + fac * denoised_2 x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su return x @torch.no_grad() def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): """DPM-Solver++(2M).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() old_denoised = None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) h = t_next - t if old_denoised is None or sigmas[i + 1] == 0: x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised else: h_last = t - t_fn(sigmas[i - 1]) r = h_last / h denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d old_denoised = denoised return x @torch.no_grad() def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): """DPM-Solver++(2M) SDE.""" if len(sigmas) <= 1: return x if solver_type not in {'heun', 'midpoint'}: raise ValueError('solver_type must be \'heun\' or \'midpoint\'') seed = extra_args.get("seed", None) sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) old_denoised = None h_last = None h = None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: x = denoised else: t, s = -sigmas[i].log(), -sigmas[i + 1].log() h = s - t eta_h = eta * h x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised if old_denoised is not None: r = h_last / h if solver_type == 'heun': x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised) elif solver_type == 'midpoint': x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised) if eta: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise old_denoised = denoised h_last = h return x @torch.no_grad() def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """DPM-Solver++(3M) SDE.""" if len(sigmas) <= 1: return x seed = extra_args.get("seed", None) sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) denoised_1, denoised_2 = None, None h, h_1, h_2 = None, None, None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: x = denoised else: t, s = -sigmas[i].log(), -sigmas[i + 1].log() h = s - t h_eta = h * (eta + 1) x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised if h_2 is not None: r0 = h_1 / h r1 = h_2 / h d1_0 = (denoised - denoised_1) / r0 d1_1 = (denoised_1 - denoised_2) / r1 d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1) d2 = (d1_0 - d1_1) / (r0 + r1) phi_2 = h_eta.neg().expm1() / h_eta + 1 phi_3 = phi_2 / h_eta - 0.5 x = x + phi_2 * d1 - phi_3 * d2 elif h_1 is not None: r = h_1 / h d = (denoised - denoised_1) / r phi_2 = h_eta.neg().expm1() / h_eta + 1 x = x + phi_2 * d if eta: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise denoised_1, denoised_2 = denoised, denoised_1 h_1, h_2 = h, h_1 return x @torch.no_grad() def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): if len(sigmas) <= 1: return x sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler) @torch.no_grad() def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): if len(sigmas) <= 1: return x sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) @torch.no_grad() def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): if len(sigmas) <= 1: return x sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler): alpha_cumprod = 1 / ((sigma * sigma) + 1) alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1) alpha = (alpha_cumprod / alpha_cumprod_prev) mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt()) if sigma_prev > 0: mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev) return mu def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None): extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler) if sigmas[i + 1] != 0: x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0) return x @torch.no_grad() def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step) @torch.no_grad() def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) x = denoised if sigmas[i + 1] > 0: x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x) return x @torch.no_grad() def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) s_end = sigmas[-1] for i in trange(len(sigmas) - 1, disable=disable): gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. eps = torch.randn_like(x) * s_noise sigma_hat = sigmas[i] * (gamma + 1) if gamma > 0: x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat if sigmas[i + 1] == s_end: x = x + d * dt elif sigmas[i + 2] == s_end: x_2 = x + d * dt denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) d_2 = to_d(x_2, sigmas[i + 1], denoised_2) w = 2 * sigmas[0] w2 = sigmas[i+1]/w w1 = 1 - w2 d_prime = d * w1 + d_2 * w2 x = x + d_prime * dt else: x_2 = x + d * dt denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) d_2 = to_d(x_2, sigmas[i + 1], denoised_2) dt_2 = sigmas[i + 2] - sigmas[i + 1] x_3 = x_2 + d_2 * dt_2 denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args) d_3 = to_d(x_3, sigmas[i + 2], denoised_3) w = 3 * sigmas[0] w2 = sigmas[i + 1] / w w3 = sigmas[i + 2] / w w1 = 1 - w2 - w3 d_prime = w1 * d + w2 * d_2 + w3 * d_3 x = x + d_prime * dt return x def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) x_next = x buffer_model = [] for i in trange(len(sigmas) - 1, disable=disable): t_cur = sigmas[i] t_next = sigmas[i + 1] x_cur = x_next denoised = model(x_cur, t_cur * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d_cur = (x_cur - denoised) / t_cur order = min(max_order, i+1) if order == 1: x_next = x_cur + (t_next - t_cur) * d_cur elif order == 2: x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2 elif order == 3: x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12 elif order == 4: x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24 if len(buffer_model) == max_order - 1: for k in range(max_order - 2): buffer_model[k] = buffer_model[k+1] buffer_model[-1] = d_cur else: buffer_model.append(d_cur) return x_next def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) x_next = x t_steps = sigmas buffer_model = [] for i in trange(len(sigmas) - 1, disable=disable): t_cur = sigmas[i] t_next = sigmas[i + 1] x_cur = x_next denoised = model(x_cur, t_cur * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d_cur = (x_cur - denoised) / t_cur order = min(max_order, i+1) if order == 1: x_next = x_cur + (t_next - t_cur) * d_cur elif order == 2: h_n = (t_next - t_cur) h_n_1 = (t_cur - t_steps[i-1]) coeff1 = (2 + (h_n / h_n_1)) / 2 coeff2 = -(h_n / h_n_1) / 2 x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1]) elif order == 3: h_n = (t_next - t_cur) h_n_1 = (t_cur - t_steps[i-1]) h_n_2 = (t_steps[i-1] - t_steps[i-2]) temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 coeff1 = (2 + (h_n / h_n_1)) / 2 + temp coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp coeff3 = temp * h_n_1 / h_n_2 x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2]) elif order == 4: h_n = (t_next - t_cur) h_n_1 = (t_cur - t_steps[i-1]) h_n_2 = (t_steps[i-1] - t_steps[i-2]) h_n_3 = (t_steps[i-2] - t_steps[i-3]) temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \ * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3)) coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2 coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2 coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2 coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2 x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3]) if len(buffer_model) == max_order - 1: for k in range(max_order - 2): buffer_model[k] = buffer_model[k+1] buffer_model[-1] = d_cur.detach() else: buffer_model.append(d_cur.detach()) return x_next
from contextlib import contextmanager import hashlib import math from pathlib import Path import shutil import urllib import warnings from PIL import Image import torch from torch import nn, optim from torch.utils import data def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'): """Apply passed in transforms for HuggingFace Datasets.""" images = [transform(image.convert(mode)) for image in examples[image_key]] return {image_key: images} def append_dims(x, target_dims): """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" dims_to_append = target_dims - x.ndim if dims_to_append < 0: raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') expanded = x[(...,) + (None,) * dims_to_append] return expanded.detach().clone() if expanded.device.type == 'mps' else expanded def n_params(module): """Returns the number of trainable parameters in a module.""" return sum(p.numel() for p in module.parameters()) def download_file(path, url, digest=None): """Downloads a file if it does not exist, optionally checking its SHA-256 hash.""" path = Path(path) path.parent.mkdir(parents=True, exist_ok=True) if not path.exists(): with urllib.request.urlopen(url) as response, open(path, 'wb') as f: shutil.copyfileobj(response, f) if digest is not None: file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest() if digest != file_digest: raise OSError(f'hash of {path} (url: {url}) failed to validate') return path @contextmanager def train_mode(model, mode=True): """A context manager that places a model into training mode and restores the previous mode on exit.""" modes = [module.training for module in model.modules()] try: yield model.train(mode) finally: for i, module in enumerate(model.modules()): module.training = modes[i] def eval_mode(model): """A context manager that places a model into evaluation mode and restores the previous mode on exit.""" return train_mode(model, False) @torch.no_grad() def ema_update(model, averaged_model, decay): """Incorporates updated model parameters into an exponential moving averaged version of a model. It should be called after each optimizer step.""" model_params = dict(model.named_parameters()) averaged_params = dict(averaged_model.named_parameters()) assert model_params.keys() == averaged_params.keys() for name, param in model_params.items(): averaged_params[name].mul_(decay).add_(param, alpha=1 - decay) model_buffers = dict(model.named_buffers()) averaged_buffers = dict(averaged_model.named_buffers()) assert model_buffers.keys() == averaged_buffers.keys() for name, buf in model_buffers.items(): averaged_buffers[name].copy_(buf) class EMAWarmup: """Implements an EMA warmup using an inverse decay schedule. If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are good values for models you plan to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at 215.4k steps). Args: inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. power (float): Exponential factor of EMA warmup. Default: 1. min_value (float): The minimum EMA decay rate. Default: 0. max_value (float): The maximum EMA decay rate. Default: 1. start_at (int): The epoch to start averaging at. Default: 0. last_epoch (int): The index of last epoch. Default: 0. """ def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0, last_epoch=0): self.inv_gamma = inv_gamma self.power = power self.min_value = min_value self.max_value = max_value self.start_at = start_at self.last_epoch = last_epoch def state_dict(self): """Returns the state of the class as a :class:`dict`.""" return dict(self.__dict__.items()) def load_state_dict(self, state_dict): """Loads the class's state. Args: state_dict (dict): scaler state. Should be an object returned from a call to :meth:`state_dict`. """ self.__dict__.update(state_dict) def get_value(self): """Gets the current EMA decay rate.""" epoch = max(0, self.last_epoch - self.start_at) value = 1 - (1 + epoch / self.inv_gamma) ** -self.power return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value)) def step(self): """Updates the step count.""" self.last_epoch += 1 class InverseLR(optim.lr_scheduler._LRScheduler): """Implements an inverse decay learning rate schedule with an optional exponential warmup. When last_epoch=-1, sets initial lr as lr. inv_gamma is the number of steps/epochs required for the learning rate to decay to (1 / 2)**power of its original value. Args: optimizer (Optimizer): Wrapped optimizer. inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. power (float): Exponential factor of learning rate decay. Default: 1. warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) Default: 0. min_lr (float): The minimum learning rate. Default: 0. last_epoch (int): The index of last epoch. Default: -1. verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False``. """ def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0., last_epoch=-1, verbose=False): self.inv_gamma = inv_gamma self.power = power if not 0. <= warmup < 1: raise ValueError('Invalid value for warmup') self.warmup = warmup self.min_lr = min_lr super().__init__(optimizer, last_epoch, verbose) def get_lr(self): if not self._get_lr_called_within_step: warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.") return self._get_closed_form_lr() def _get_closed_form_lr(self): warmup = 1 - self.warmup ** (self.last_epoch + 1) lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power return [warmup * max(self.min_lr, base_lr * lr_mult) for base_lr in self.base_lrs] class ExponentialLR(optim.lr_scheduler._LRScheduler): """Implements an exponential learning rate schedule with an optional exponential warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate continuously by decay (default 0.5) every num_steps steps. Args: optimizer (Optimizer): Wrapped optimizer. num_steps (float): The number of steps to decay the learning rate by decay in. decay (float): The factor by which to decay the learning rate every num_steps steps. Default: 0.5. warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) Default: 0. min_lr (float): The minimum learning rate. Default: 0. last_epoch (int): The index of last epoch. Default: -1. verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False``. """ def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0., last_epoch=-1, verbose=False): self.num_steps = num_steps self.decay = decay if not 0. <= warmup < 1: raise ValueError('Invalid value for warmup') self.warmup = warmup self.min_lr = min_lr super().__init__(optimizer, last_epoch, verbose) def get_lr(self): if not self._get_lr_called_within_step: warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.") return self._get_closed_form_lr() def _get_closed_form_lr(self): warmup = 1 - self.warmup ** (self.last_epoch + 1) lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch return [warmup * max(self.min_lr, base_lr * lr_mult) for base_lr in self.base_lrs] def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): """Draws samples from an lognormal distribution.""" return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp() def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): """Draws samples from an optionally truncated log-logistic distribution.""" min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64) max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64) min_cdf = min_value.log().sub(loc).div(scale).sigmoid() max_cdf = max_value.log().sub(loc).div(scale).sigmoid() u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf return u.logit().mul(scale).add(loc).exp().to(dtype) def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32): """Draws samples from an log-uniform distribution.""" min_value = math.log(min_value) max_value = math.log(max_value) return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp() def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): """Draws samples from a truncated v-diffusion training timestep distribution.""" min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf return torch.tan(u * math.pi / 2) * sigma_data def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32): """Draws samples from a split lognormal distribution.""" n = torch.randn(shape, device=device, dtype=dtype).abs() u = torch.rand(shape, device=device, dtype=dtype) n_left = n * -scale_1 + loc n_right = n * scale_2 + loc ratio = scale_1 / (scale_1 + scale_2) return torch.where(u < ratio, n_left, n_right).exp() class FolderOfImages(data.Dataset): """Recursively finds all images in a directory. It does not support classes/targets.""" IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} def __init__(self, root, transform=None): super().__init__() self.root = Path(root) self.transform = nn.Identity() if transform is None else transform self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS) def __repr__(self): return f'FolderOfImages(root="{self.root}", len: {len(self)})' def __len__(self): return len(self.paths) def __getitem__(self, key): path = self.paths[key] with open(path, 'rb') as f: image = Image.open(f).convert('RGB') image = self.transform(image) return image, class CSVLogger: def __init__(self, filename, columns): self.filename = Path(filename) self.columns = columns if self.filename.exists(): self.file = open(self.filename, 'a') else: self.file = open(self.filename, 'w') self.write(*self.columns) def write(self, *args): print(*args, sep=',', file=self.file, flush=True) @contextmanager def tf32_mode(cudnn=None, matmul=None): """A context manager that sets whether TF32 is allowed on cuDNN or matmul.""" cudnn_old = torch.backends.cudnn.allow_tf32 matmul_old = torch.backends.cuda.matmul.allow_tf32 try: if cudnn is not None: torch.backends.cudnn.allow_tf32 = cudnn if matmul is not None: torch.backends.cuda.matmul.allow_tf32 = matmul yield finally: if cudnn is not None: torch.backends.cudnn.allow_tf32 = cudnn_old if matmul is not None: torch.backends.cuda.matmul.allow_tf32 = matmul_old
import importlib import torch from torch import optim import numpy as np from inspect import isfunction from PIL import Image, ImageDraw, ImageFont def log_txt_as_img(wh, xc, size=10): b = len(xc) txts = list() for bi in range(b): txt = Image.new("RGB", wh, color="white") draw = ImageDraw.Draw(txt) font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) nc = int(40 * (wh[0] / 256)) lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) try: draw.text((0, 0), lines, fill="black", font=font) except UnicodeEncodeError: print("Cant encode string for logging. Skipping.") txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 txts.append(txt) txts = np.stack(txts) txts = torch.tensor(txts) return txts def ismap(x): if not isinstance(x, torch.Tensor): return False return (len(x.shape) == 4) and (x.shape[1] > 3) def isimage(x): if not isinstance(x,torch.Tensor): return False return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) def exists(x): return x is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d def mean_flat(tensor): """ https: Take the mean over all non-batch dimensions. """ return tensor.mean(dim=list(range(1, len(tensor.shape)))) def count_params(model, verbose=False): total_params = sum(p.numel() for p in model.parameters()) if verbose: print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") return total_params def instantiate_from_config(config): if not "target" in config: if config == '__is_first_stage__': return None elif config == "__is_unconditional__": return None raise KeyError("Expected key `target` to instantiate.") return get_obj_from_str(config["target"])(**config.get("params", dict())) def get_obj_from_str(string, reload=False): module, cls = string.rsplit(".", 1) if reload: module_imp = importlib.import_module(module) importlib.reload(module_imp) return getattr(importlib.import_module(module, package=None), cls) class AdamWwithEMAandWings(optim.Optimizer): def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, ema_power=1., param_names=()): """AdamW that saves EMA versions of the parameters.""" if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) if not 0.0 <= ema_decay <= 1.0: raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, ema_power=ema_power, param_names=param_names) super().__init__(params, defaults) def __setstate__(self, state): super().__setstate__(state) for group in self.param_groups: group.setdefault('amsgrad', False) @torch.no_grad() def step(self, closure=None): """Performs a single optimization step. Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: params_with_grad = [] grads = [] exp_avgs = [] exp_avg_sqs = [] ema_params_with_grad = [] state_sums = [] max_exp_avg_sqs = [] state_steps = [] amsgrad = group['amsgrad'] beta1, beta2 = group['betas'] ema_decay = group['ema_decay'] ema_power = group['ema_power'] for p in group['params']: if p.grad is None: continue params_with_grad.append(p) if p.grad.is_sparse: raise RuntimeError('AdamW does not support sparse gradients') grads.append(p.grad) state = self.state[p] if len(state) == 0: state['step'] = 0 state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) if amsgrad: state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) state['param_exp_avg'] = p.detach().float().clone() exp_avgs.append(state['exp_avg']) exp_avg_sqs.append(state['exp_avg_sq']) ema_params_with_grad.append(state['param_exp_avg']) if amsgrad: max_exp_avg_sqs.append(state['max_exp_avg_sq']) state['step'] += 1 state_steps.append(state['step']) optim._functional.adamw(params_with_grad, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad=amsgrad, beta1=beta1, beta2=beta2, lr=group['lr'], weight_decay=group['weight_decay'], eps=group['eps'], maximize=False) cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) for param, ema_param in zip(params_with_grad, ema_params_with_grad): ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) return loss
import torch from torch import nn from typing import Literal, Dict, Any import math import comfy.ops ops = comfy.ops.disable_weight_init def vae_sample(mean, scale): stdev = nn.functional.softplus(scale) + 1e-4 var = stdev * stdev logvar = torch.log(var) latents = torch.randn_like(mean) * stdev + mean kl = (mean * mean + var - logvar - 1).sum(1).mean() return latents, kl class VAEBottleneck(nn.Module): def __init__(self): super().__init__() self.is_discrete = False def encode(self, x, return_info=False, **kwargs): info = {} mean, scale = x.chunk(2, dim=1) x, kl = vae_sample(mean, scale) info["kl"] = kl if return_info: return x, info else: return x def decode(self, x): return x def snake_beta(x, alpha, beta): return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2) class SnakeBeta(nn.Module): def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True): super(SnakeBeta, self).__init__() self.in_features = in_features self.alpha_logscale = alpha_logscale if self.alpha_logscale: self.alpha = nn.Parameter(torch.zeros(in_features) * alpha) self.beta = nn.Parameter(torch.zeros(in_features) * alpha) else: self.alpha = nn.Parameter(torch.ones(in_features) * alpha) self.beta = nn.Parameter(torch.ones(in_features) * alpha) self.no_div_by_zero = 0.000000001 def forward(self, x): alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device) if self.alpha_logscale: alpha = torch.exp(alpha) beta = torch.exp(beta) x = snake_beta(x, alpha, beta) return x def WNConv1d(*args, **kwargs): try: return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs)) except: return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) def WNConvTranspose1d(*args, **kwargs): try: return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) except: return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module: if activation == "elu": act = torch.nn.ELU() elif activation == "snake": act = SnakeBeta(channels) elif activation == "none": act = torch.nn.Identity() else: raise ValueError(f"Unknown activation {activation}") if antialias: act = Activation1d(act) return act class ResidualUnit(nn.Module): def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False): super().__init__() self.dilation = dilation padding = (dilation * (7-1)) self.layers = nn.Sequential( get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), WNConv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=7, dilation=dilation, padding=padding), get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), WNConv1d(in_channels=out_channels, out_channels=out_channels, kernel_size=1) ) def forward(self, x): res = x x = self.layers(x) return x + res class EncoderBlock(nn.Module): def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False): super().__init__() self.layers = nn.Sequential( ResidualUnit(in_channels=in_channels, out_channels=in_channels, dilation=1, use_snake=use_snake), ResidualUnit(in_channels=in_channels, out_channels=in_channels, dilation=3, use_snake=use_snake), ResidualUnit(in_channels=in_channels, out_channels=in_channels, dilation=9, use_snake=use_snake), get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), WNConv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)), ) def forward(self, x): return self.layers(x) class DecoderBlock(nn.Module): def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False): super().__init__() if use_nearest_upsample: upsample_layer = nn.Sequential( nn.Upsample(scale_factor=stride, mode="nearest"), WNConv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=2*stride, stride=1, bias=False, padding='same') ) else: upsample_layer = WNConvTranspose1d(in_channels=in_channels, out_channels=out_channels, kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)) self.layers = nn.Sequential( get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), upsample_layer, ResidualUnit(in_channels=out_channels, out_channels=out_channels, dilation=1, use_snake=use_snake), ResidualUnit(in_channels=out_channels, out_channels=out_channels, dilation=3, use_snake=use_snake), ResidualUnit(in_channels=out_channels, out_channels=out_channels, dilation=9, use_snake=use_snake), ) def forward(self, x): return self.layers(x) class OobleckEncoder(nn.Module): def __init__(self, in_channels=2, channels=128, latent_dim=32, c_mults = [1, 2, 4, 8], strides = [2, 4, 8, 8], use_snake=False, antialias_activation=False ): super().__init__() c_mults = [1] + c_mults self.depth = len(c_mults) layers = [ WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3) ] for i in range(self.depth-1): layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)] layers += [ get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels), WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1) ] self.layers = nn.Sequential(*layers) def forward(self, x): return self.layers(x) class OobleckDecoder(nn.Module): def __init__(self, out_channels=2, channels=128, latent_dim=32, c_mults = [1, 2, 4, 8], strides = [2, 4, 8, 8], use_snake=False, antialias_activation=False, use_nearest_upsample=False, final_tanh=True): super().__init__() c_mults = [1] + c_mults self.depth = len(c_mults) layers = [ WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3), ] for i in range(self.depth-1, 0, -1): layers += [DecoderBlock( in_channels=c_mults[i]*channels, out_channels=c_mults[i-1]*channels, stride=strides[i-1], use_snake=use_snake, antialias_activation=antialias_activation, use_nearest_upsample=use_nearest_upsample ) ] layers += [ get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels), WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False), nn.Tanh() if final_tanh else nn.Identity() ] self.layers = nn.Sequential(*layers) def forward(self, x): return self.layers(x) class AudioOobleckVAE(nn.Module): def __init__(self, in_channels=2, channels=128, latent_dim=64, c_mults = [1, 2, 4, 8, 16], strides = [2, 4, 4, 8, 8], use_snake=True, antialias_activation=False, use_nearest_upsample=False, final_tanh=False): super().__init__() self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation) self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation, use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh) self.bottleneck = VAEBottleneck() def encode(self, x): return self.bottleneck.encode(self.encoder(x)) def decode(self, x): return self.decoder(self.bottleneck.decode(x))
from comfy.ldm.modules.attention import optimized_attention import typing as tp import torch from einops import rearrange from torch import nn from torch.nn import functional as F import math class FourierFeatures(nn.Module): def __init__(self, in_features, out_features, std=1., dtype=None, device=None): super().__init__() assert out_features % 2 == 0 self.weight = nn.Parameter(torch.empty( [out_features def forward(self, input): f = 2 * math.pi * input @ self.weight.T.to(dtype=input.dtype, device=input.device) return torch.cat([f.cos(), f.sin()], dim=-1) class LayerNorm(nn.Module): def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None): """ bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less """ super().__init__() self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) if bias: self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) else: self.beta = None def forward(self, x): beta = self.beta if self.beta is not None: beta = beta.to(dtype=x.dtype, device=x.device) return F.layer_norm(x, x.shape[-1:], weight=self.gamma.to(dtype=x.dtype, device=x.device), bias=beta) class GLU(nn.Module): def __init__( self, dim_in, dim_out, activation, use_conv = False, conv_kernel_size = 3, dtype=None, device=None, operations=None, ): super().__init__() self.act = activation self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size self.use_conv = use_conv def forward(self, x): if self.use_conv: x = rearrange(x, 'b n d -> b d n') x = self.proj(x) x = rearrange(x, 'b d n -> b n d') else: x = self.proj(x) x, gate = x.chunk(2, dim = -1) return x * self.act(gate) class AbsolutePositionalEmbedding(nn.Module): def __init__(self, dim, max_seq_len): super().__init__() self.scale = dim ** -0.5 self.max_seq_len = max_seq_len self.emb = nn.Embedding(max_seq_len, dim) def forward(self, x, pos = None, seq_start_pos = None): seq_len, device = x.shape[1], x.device assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}' if pos is None: pos = torch.arange(seq_len, device = device) if seq_start_pos is not None: pos = (pos - seq_start_pos[..., None]).clamp(min = 0) pos_emb = self.emb(pos) pos_emb = pos_emb * self.scale return pos_emb class ScaledSinusoidalEmbedding(nn.Module): def __init__(self, dim, theta = 10000): super().__init__() assert (dim % 2) == 0, 'dimension must be divisible by 2' self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5) half_dim = dim freq_seq = torch.arange(half_dim).float() / half_dim inv_freq = theta ** -freq_seq self.register_buffer('inv_freq', inv_freq, persistent = False) def forward(self, x, pos = None, seq_start_pos = None): seq_len, device = x.shape[1], x.device if pos is None: pos = torch.arange(seq_len, device = device) if seq_start_pos is not None: pos = pos - seq_start_pos[..., None] emb = torch.einsum('i, j -> i j', pos, self.inv_freq) emb = torch.cat((emb.sin(), emb.cos()), dim = -1) return emb * self.scale class RotaryEmbedding(nn.Module): def __init__( self, dim, use_xpos = False, scale_base = 512, interpolation_factor = 1., base = 10000, base_rescale_factor = 1. ): super().__init__() base *= base_rescale_factor ** (dim / (dim - 2)) inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer('inv_freq', inv_freq) assert interpolation_factor >= 1. self.interpolation_factor = interpolation_factor if not use_xpos: self.register_buffer('scale', None) return scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) self.scale_base = scale_base self.register_buffer('scale', scale) def forward_from_seq_len(self, seq_len, device, dtype): t = torch.arange(seq_len, device=device, dtype=dtype) return self.forward(t) def forward(self, t): device = t.device dtype = t.dtype t = t / self.interpolation_factor freqs = torch.einsum('i , j -> i j', t, self.inv_freq.to(dtype=dtype, device=device)) freqs = torch.cat((freqs, freqs), dim = -1) if self.scale is None: return freqs, 1. power = (torch.arange(seq_len, device = device) - (seq_len scale = self.scale.to(dtype=dtype, device=device) ** rearrange(power, 'n -> n 1') scale = torch.cat((scale, scale), dim = -1) return freqs, scale def rotate_half(x): x = rearrange(x, '... (j d) -> ... j d', j = 2) x1, x2 = x.unbind(dim = -2) return torch.cat((-x2, x1), dim = -1) def apply_rotary_pos_emb(t, freqs, scale = 1): out_dtype = t.dtype dtype = t.dtype rot_dim, seq_len = freqs.shape[-1], t.shape[-2] freqs, t = freqs.to(dtype), t.to(dtype) freqs = freqs[-seq_len:, :] if t.ndim == 4 and freqs.ndim == 3: freqs = rearrange(freqs, 'b n d -> b 1 n d') t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:] t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale) t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype) return torch.cat((t, t_unrotated), dim = -1) class FeedForward(nn.Module): def __init__( self, dim, dim_out = None, mult = 4, no_bias = False, glu = True, use_conv = False, conv_kernel_size = 3, zero_init_output = True, dtype=None, device=None, operations=None, ): super().__init__() inner_dim = int(dim * mult) activation = nn.SiLU() dim_out = dim if dim_out is None else dim_out if glu: linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations) else: linear_in = nn.Sequential( Rearrange('b n d -> b d n') if use_conv else nn.Identity(), operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size Rearrange('b n d -> b d n') if use_conv else nn.Identity(), activation ) linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size self.ff = nn.Sequential( linear_in, Rearrange('b d n -> b n d') if use_conv else nn.Identity(), linear_out, Rearrange('b n d -> b d n') if use_conv else nn.Identity(), ) def forward(self, x): return self.ff(x) class Attention(nn.Module): def __init__( self, dim, dim_heads = 64, dim_context = None, causal = False, zero_init_output=True, qk_norm = False, natten_kernel_size = None, dtype=None, device=None, operations=None, ): super().__init__() self.dim = dim self.dim_heads = dim_heads self.causal = causal dim_kv = dim_context if dim_context is not None else dim self.num_heads = dim self.kv_heads = dim_kv if dim_context is not None: self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device) else: self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device) self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) self.qk_norm = qk_norm def forward( self, x, context = None, mask = None, context_mask = None, rotary_pos_emb = None, causal = None ): h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None kv_input = context if has_context else x if hasattr(self, 'to_q'): q = self.to_q(x) q = rearrange(q, 'b n (h d) -> b h n d', h = h) k, v = self.to_kv(kv_input).chunk(2, dim=-1) k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v)) else: q, k, v = self.to_qkv(x).chunk(3, dim=-1) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v)) if self.qk_norm: q = F.normalize(q, dim=-1) k = F.normalize(k, dim=-1) if rotary_pos_emb is not None and not has_context: freqs, _ = rotary_pos_emb q_dtype = q.dtype k_dtype = k.dtype q = q.to(torch.float32) k = k.to(torch.float32) freqs = freqs.to(torch.float32) q = apply_rotary_pos_emb(q, freqs) k = apply_rotary_pos_emb(k, freqs) q = q.to(q_dtype) k = k.to(k_dtype) input_mask = context_mask if input_mask is None and not has_context: input_mask = mask masks = [] final_attn_mask = None if input_mask is not None: input_mask = rearrange(input_mask, 'b j -> b 1 1 j') masks.append(~input_mask) if len(masks) > 0: final_attn_mask = ~or_reduce(masks) n, device = q.shape[-2], q.device causal = self.causal if causal is None else causal if n == 1 and causal: causal = False if h != kv_h: heads_per_kv_head = h k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v)) out = optimized_attention(q, k, v, h, skip_reshape=True) out = self.to_out(out) if mask is not None: mask = rearrange(mask, 'b n -> b n 1') out = out.masked_fill(~mask, 0.) return out class ConformerModule(nn.Module): def __init__( self, dim, norm_kwargs = {}, ): super().__init__() self.dim = dim self.in_norm = LayerNorm(dim, **norm_kwargs) self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False) self.glu = GLU(dim, dim, nn.SiLU()) self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False) self.mid_norm = LayerNorm(dim, **norm_kwargs) self.swish = nn.SiLU() self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False) def forward(self, x): x = self.in_norm(x) x = rearrange(x, 'b n d -> b d n') x = self.pointwise_conv(x) x = rearrange(x, 'b d n -> b n d') x = self.glu(x) x = rearrange(x, 'b n d -> b d n') x = self.depthwise_conv(x) x = rearrange(x, 'b d n -> b n d') x = self.mid_norm(x) x = self.swish(x) x = rearrange(x, 'b n d -> b d n') x = self.pointwise_conv_2(x) x = rearrange(x, 'b d n -> b n d') return x class TransformerBlock(nn.Module): def __init__( self, dim, dim_heads = 64, cross_attend = False, dim_context = None, global_cond_dim = None, causal = False, zero_init_branch_outputs = True, conformer = False, layer_ix = -1, remove_norms = False, attn_kwargs = {}, ff_kwargs = {}, norm_kwargs = {}, dtype=None, device=None, operations=None, ): super().__init__() self.dim = dim self.dim_heads = dim_heads self.cross_attend = cross_attend self.dim_context = dim_context self.causal = causal self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() self.self_attn = Attention( dim, dim_heads = dim_heads, causal = causal, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations, **attn_kwargs ) if cross_attend: self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() self.cross_attn = Attention( dim, dim_heads = dim_heads, dim_context=dim_context, causal = causal, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations, **attn_kwargs ) self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs) self.layer_ix = layer_ix self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None self.global_cond_dim = global_cond_dim if global_cond_dim is not None: self.to_scale_shift_gate = nn.Sequential( nn.SiLU(), nn.Linear(global_cond_dim, dim * 6, bias=False) ) nn.init.zeros_(self.to_scale_shift_gate[1].weight) def forward( self, x, context = None, global_cond=None, mask = None, context_mask = None, rotary_pos_emb = None ): if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None: scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1) residual = x x = self.pre_norm(x) x = x * (1 + scale_self) + shift_self x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb) x = x * torch.sigmoid(1 - gate_self) x = x + residual if context is not None: x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) if self.conformer is not None: x = x + self.conformer(x) residual = x x = self.ff_norm(x) x = x * (1 + scale_ff) + shift_ff x = self.ff(x) x = x * torch.sigmoid(1 - gate_ff) x = x + residual else: x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb) if context is not None: x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) if self.conformer is not None: x = x + self.conformer(x) x = x + self.ff(self.ff_norm(x)) return x class ContinuousTransformer(nn.Module): def __init__( self, dim, depth, *, dim_in = None, dim_out = None, dim_heads = 64, cross_attend=False, cond_token_dim=None, global_cond_dim=None, causal=False, rotary_pos_emb=True, zero_init_branch_outputs=True, conformer=False, use_sinusoidal_emb=False, use_abs_pos_emb=False, abs_pos_emb_max_length=10000, dtype=None, device=None, operations=None, **kwargs ): super().__init__() self.dim = dim self.depth = depth self.causal = causal self.layers = nn.ModuleList([]) self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity() self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity() if rotary_pos_emb: self.rotary_pos_emb = RotaryEmbedding(max(dim_heads else: self.rotary_pos_emb = None self.use_sinusoidal_emb = use_sinusoidal_emb if use_sinusoidal_emb: self.pos_emb = ScaledSinusoidalEmbedding(dim) self.use_abs_pos_emb = use_abs_pos_emb if use_abs_pos_emb: self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length) for i in range(depth): self.layers.append( TransformerBlock( dim, dim_heads = dim_heads, cross_attend = cross_attend, dim_context = cond_token_dim, global_cond_dim = global_cond_dim, causal = causal, zero_init_branch_outputs = zero_init_branch_outputs, conformer=conformer, layer_ix=i, dtype=dtype, device=device, operations=operations, **kwargs ) ) def forward( self, x, mask = None, prepend_embeds = None, prepend_mask = None, global_cond = None, return_info = False, **kwargs ): batch, seq, device = *x.shape[:2], x.device info = { "hidden_states": [], } x = self.project_in(x) if prepend_embeds is not None: prepend_length, prepend_dim = prepend_embeds.shape[1:] assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension' x = torch.cat((prepend_embeds, x), dim = -2) if prepend_mask is not None or mask is not None: mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool) prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool) mask = torch.cat((prepend_mask, mask), dim = -1) if self.rotary_pos_emb is not None: rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device) else: rotary_pos_emb = None if self.use_sinusoidal_emb or self.use_abs_pos_emb: x = x + self.pos_emb(x) for layer in self.layers: x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs) if return_info: info["hidden_states"].append(x) x = self.project_out(x) if return_info: return x, info return x class AudioDiffusionTransformer(nn.Module): def __init__(self, io_channels=64, patch_size=1, embed_dim=1536, cond_token_dim=768, project_cond_tokens=False, global_cond_dim=1536, project_global_cond=True, input_concat_dim=0, prepend_cond_dim=0, depth=24, num_heads=24, transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer", global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend", audio_model="", dtype=None, device=None, operations=None, **kwargs): super().__init__() self.dtype = dtype self.cond_token_dim = cond_token_dim timestep_features_dim = 256 self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device) self.to_timestep_embed = nn.Sequential( operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device), nn.SiLU(), operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device), ) if cond_token_dim > 0: cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim self.to_cond_embed = nn.Sequential( operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device), nn.SiLU(), operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device) ) else: cond_embed_dim = 0 if global_cond_dim > 0: global_embed_dim = global_cond_dim if not project_global_cond else embed_dim self.to_global_embed = nn.Sequential( operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device), nn.SiLU(), operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device) ) if prepend_cond_dim > 0: self.to_prepend_embed = nn.Sequential( operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device), nn.SiLU(), operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) ) self.input_concat_dim = input_concat_dim dim_in = io_channels + self.input_concat_dim self.patch_size = patch_size self.transformer_type = transformer_type self.global_cond_type = global_cond_type if self.transformer_type == "continuous_transformer": global_dim = None if self.global_cond_type == "adaLN": global_dim = embed_dim self.transformer = ContinuousTransformer( dim=embed_dim, depth=depth, dim_heads=embed_dim dim_in=dim_in * patch_size, dim_out=io_channels * patch_size, cross_attend = cond_token_dim > 0, cond_token_dim = cond_embed_dim, global_cond_dim=global_dim, dtype=dtype, device=device, operations=operations, **kwargs ) else: raise ValueError(f"Unknown transformer type: {self.transformer_type}") self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device) self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device) def _forward( self, x, t, mask=None, cross_attn_cond=None, cross_attn_cond_mask=None, input_concat_cond=None, global_embed=None, prepend_cond=None, prepend_cond_mask=None, return_info=False, **kwargs): if cross_attn_cond is not None: cross_attn_cond = self.to_cond_embed(cross_attn_cond) if global_embed is not None: global_embed = self.to_global_embed(global_embed) prepend_inputs = None prepend_mask = None prepend_length = 0 if prepend_cond is not None: prepend_cond = self.to_prepend_embed(prepend_cond) prepend_inputs = prepend_cond if prepend_cond_mask is not None: prepend_mask = prepend_cond_mask if input_concat_cond is not None: if input_concat_cond.shape[2] != x.shape[2]: input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest') x = torch.cat([x, input_concat_cond], dim=1) timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) if global_embed is not None: global_embed = global_embed + timestep_embed else: global_embed = timestep_embed if self.global_cond_type == "prepend": if prepend_inputs is None: prepend_inputs = global_embed.unsqueeze(1) prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool) else: prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1) prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1) prepend_length = prepend_inputs.shape[1] x = self.preprocess_conv(x) + x x = rearrange(x, "b c t -> b t c") extra_args = {} if self.global_cond_type == "adaLN": extra_args["global_cond"] = global_embed if self.patch_size > 1: x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size) if self.transformer_type == "x-transformers": output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs) elif self.transformer_type == "continuous_transformer": output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs) if return_info: output, info = output elif self.transformer_type == "mm_transformer": output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs) output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:] if self.patch_size > 1: output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size) output = self.postprocess_conv(output) + output if return_info: return output, info return output def forward( self, x, timestep, context=None, context_mask=None, input_concat_cond=None, global_embed=None, negative_global_embed=None, prepend_cond=None, prepend_cond_mask=None, mask=None, return_info=False, control=None, transformer_options={}, **kwargs): return self._forward( x, timestep, cross_attn_cond=context, cross_attn_cond_mask=context_mask, input_concat_cond=input_concat_cond, global_embed=global_embed, prepend_cond=prepend_cond, prepend_cond_mask=prepend_cond_mask, mask=mask, return_info=return_info, **kwargs )
import torch import torch.nn as nn from torch import Tensor, einsum from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union from einops import rearrange import math import comfy.ops class LearnedPositionalEmbedding(nn.Module): """Used for continuous time""" def __init__(self, dim: int): super().__init__() assert (dim % 2) == 0 half_dim = dim self.weights = nn.Parameter(torch.empty(half_dim)) def forward(self, x: Tensor) -> Tensor: x = rearrange(x, "b -> b 1") freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1) fouriered = torch.cat((x, fouriered), dim=-1) return fouriered def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module: return nn.Sequential( LearnedPositionalEmbedding(dim), comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features), ) class NumberEmbedder(nn.Module): def __init__( self, features: int, dim: int = 256, ): super().__init__() self.features = features self.embedding = TimePositionalEmbedding(dim=dim, out_features=features) def forward(self, x: Union[List[float], Tensor]) -> Tensor: if not torch.is_tensor(x): device = next(self.embedding.parameters()).device x = torch.tensor(x, device=device) assert isinstance(x, Tensor) shape = x.shape x = rearrange(x, "... -> (...)") embedding = self.embedding(x) x = embedding.view(*shape, self.features) return x class Conditioner(nn.Module): def __init__( self, dim: int, output_dim: int, project_out: bool = False ): super().__init__() self.dim = dim self.output_dim = output_dim self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity() def forward(self, x): raise NotImplementedError() class NumberConditioner(Conditioner): ''' Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings ''' def __init__(self, output_dim: int, min_val: float=0, max_val: float=1 ): super().__init__(output_dim, output_dim) self.min_val = min_val self.max_val = max_val self.embedder = NumberEmbedder(features=output_dim) def forward(self, floats, device=None): floats = [float(x) for x in floats] if device is None: device = next(self.embedder.parameters()).device floats = torch.tensor(floats).to(device) floats = floats.clamp(self.min_val, self.max_val) normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val) embedder_dtype = next(self.embedder.parameters()).dtype normalized_floats = normalized_floats.to(embedder_dtype) float_embeds = self.embedder(normalized_floats).unsqueeze(1) return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)]
""" This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https: """ import torch import torch.nn as nn from comfy.ldm.modules.attention import optimized_attention class Linear(torch.nn.Linear): def reset_parameters(self): return None class Conv2d(torch.nn.Conv2d): def reset_parameters(self): return None class OptimizedAttention(nn.Module): def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.heads = nhead self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device) self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device) self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device) self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) def forward(self, q, k, v): q = self.to_q(q) k = self.to_k(k) v = self.to_v(v) out = optimized_attention(q, k, v, self.heads) return self.out_proj(out) class Attention2D(nn.Module): def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations) def forward(self, x, kv, self_attn=False): orig_shape = x.shape x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) if self_attn: kv = torch.cat([x, kv], dim=1) x = self.attn(x, kv, kv) x = x.permute(0, 2, 1).view(*orig_shape) return x def LayerNorm2d_op(operations): class LayerNorm2d(operations.LayerNorm): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def forward(self, x): return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) return LayerNorm2d class GlobalResponseNorm(nn.Module): "from https: def __init__(self, dim, dtype=None, device=None): super().__init__() self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device)) self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device)) def forward(self, x): Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True) Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) return self.gamma.to(device=x.device, dtype=x.dtype) * (x * Nx) + self.beta.to(device=x.device, dtype=x.dtype) + x class ResBlock(nn.Module): def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.channelwise = nn.Sequential( operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device), nn.GELU(), GlobalResponseNorm(c * 4, dtype=dtype, device=device), nn.Dropout(dropout), operations.Linear(c * 4, c, dtype=dtype, device=device) ) def forward(self, x, x_skip=None): x_res = x x = self.norm(self.depthwise(x)) if x_skip is not None: x = torch.cat([x, x_skip], dim=1) x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) return x + x_res class AttnBlock(nn.Module): def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.self_attn = self_attn self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations) self.kv_mapper = nn.Sequential( nn.SiLU(), operations.Linear(c_cond, c, dtype=dtype, device=device) ) def forward(self, x, kv): kv = self.kv_mapper(kv) x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn) return x class FeedForwardBlock(nn.Module): def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None): super().__init__() self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.channelwise = nn.Sequential( operations.Linear(c, c * 4, dtype=dtype, device=device), nn.GELU(), GlobalResponseNorm(c * 4, dtype=dtype, device=device), nn.Dropout(dropout), operations.Linear(c * 4, c, dtype=dtype, device=device) ) def forward(self, x): x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2) return x class TimestepBlock(nn.Module): def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None): super().__init__() self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device) self.conds = conds for cname in conds: setattr(self, f"mapper_{cname}", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)) def forward(self, x, t): t = t.chunk(len(self.conds) + 1, dim=1) a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1) for i, c in enumerate(self.conds): ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1) a, b = a + ac, b + bc return x * (1 + a) + b
""" This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https: """ import torch import torchvision from torch import nn from .common import LayerNorm2d_op class CNetResBlock(nn.Module): def __init__(self, c, dtype=None, device=None, operations=None): super().__init__() self.blocks = nn.Sequential( LayerNorm2d_op(operations)(c, dtype=dtype, device=device), nn.GELU(), operations.Conv2d(c, c, kernel_size=3, padding=1), LayerNorm2d_op(operations)(c, dtype=dtype, device=device), nn.GELU(), operations.Conv2d(c, c, kernel_size=3, padding=1), ) def forward(self, x): return x + self.blocks(x) class ControlNet(nn.Module): def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn): super().__init__() if bottleneck_mode is None: bottleneck_mode = 'effnet' self.proj_blocks = proj_blocks if bottleneck_mode == 'effnet': embd_channels = 1280 self.backbone = torchvision.models.efficientnet_v2_s().features.eval() if c_in != 3: in_weights = self.backbone[0][0].weight.data self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device) if c_in > 3: self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone() else: self.backbone[0][0].weight.data = in_weights[:, :c_in].clone() elif bottleneck_mode == 'simple': embd_channels = c_in self.backbone = nn.Sequential( operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device), nn.LeakyReLU(0.2, inplace=True), operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device), ) elif bottleneck_mode == 'large': self.backbone = nn.Sequential( operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device), nn.LeakyReLU(0.2, inplace=True), operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device), *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)], operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device), ) embd_channels = 1280 else: raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}') self.projections = nn.ModuleList() for _ in range(len(proj_blocks)): self.projections.append(nn.Sequential( operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device), nn.LeakyReLU(0.2, inplace=True), operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device), )) self.xl = False self.input_channels = c_in self.unshuffle_amount = 8 def forward(self, x): x = self.backbone(x) proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)] for i, idx in enumerate(self.proj_blocks): proj_outputs[idx] = self.projections[i](x) return proj_outputs
""" This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https: """ import torch from torch import nn from torch.autograd import Function class vector_quantize(Function): @staticmethod def forward(ctx, x, codebook): with torch.no_grad(): codebook_sqr = torch.sum(codebook ** 2, dim=1) x_sqr = torch.sum(x ** 2, dim=1, keepdim=True) dist = torch.addmm(codebook_sqr + x_sqr, x, codebook.t(), alpha=-2.0, beta=1.0) _, indices = dist.min(dim=1) ctx.save_for_backward(indices, codebook) ctx.mark_non_differentiable(indices) nn = torch.index_select(codebook, 0, indices) return nn, indices @staticmethod def backward(ctx, grad_output, grad_indices): grad_inputs, grad_codebook = None, None if ctx.needs_input_grad[0]: grad_inputs = grad_output.clone() if ctx.needs_input_grad[1]: indices, codebook = ctx.saved_tensors grad_codebook = torch.zeros_like(codebook) grad_codebook.index_add_(0, indices, grad_output) return (grad_inputs, grad_codebook) class VectorQuantize(nn.Module): def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False): """ Takes an input of variable size (as long as the last dimension matches the embedding size). Returns one tensor containing the nearest neigbour embeddings to each of the inputs, with the same size as the input, vq and commitment components for the loss as a touple in the second output and the indices of the quantized vectors in the third: quantized, (vq_loss, commit_loss), indices """ super(VectorQuantize, self).__init__() self.codebook = nn.Embedding(k, embedding_size) self.codebook.weight.data.uniform_(-1./k, 1./k) self.vq = vector_quantize.apply self.ema_decay = ema_decay self.ema_loss = ema_loss if ema_loss: self.register_buffer('ema_element_count', torch.ones(k)) self.register_buffer('ema_weight_sum', torch.zeros_like(self.codebook.weight)) def _laplace_smoothing(self, x, epsilon): n = torch.sum(x) return ((x + epsilon) / (n + x.size(0) * epsilon) * n) def _updateEMA(self, z_e_x, indices): mask = nn.functional.one_hot(indices, self.ema_element_count.size(0)).float() elem_count = mask.sum(dim=0) weight_sum = torch.mm(mask.t(), z_e_x) self.ema_element_count = (self.ema_decay * self.ema_element_count) + ((1-self.ema_decay) * elem_count) self.ema_element_count = self._laplace_smoothing(self.ema_element_count, 1e-5) self.ema_weight_sum = (self.ema_decay * self.ema_weight_sum) + ((1-self.ema_decay) * weight_sum) self.codebook.weight.data = self.ema_weight_sum / self.ema_element_count.unsqueeze(-1) def idx2vq(self, idx, dim=-1): q_idx = self.codebook(idx) if dim != -1: q_idx = q_idx.movedim(-1, dim) return q_idx def forward(self, x, get_losses=True, dim=-1): if dim != -1: x = x.movedim(dim, -1) z_e_x = x.contiguous().view(-1, x.size(-1)) if len(x.shape) > 2 else x z_q_x, indices = self.vq(z_e_x, self.codebook.weight.detach()) vq_loss, commit_loss = None, None if self.ema_loss and self.training: self._updateEMA(z_e_x.detach(), indices.detach()) z_q_x_grd = torch.index_select(self.codebook.weight, dim=0, index=indices) if get_losses: vq_loss = (z_q_x_grd - z_e_x.detach()).pow(2).mean() commit_loss = (z_e_x - z_q_x_grd.detach()).pow(2).mean() z_q_x = z_q_x.view(x.shape) if dim != -1: z_q_x = z_q_x.movedim(-1, dim) return z_q_x, (vq_loss, commit_loss), indices.view(x.shape[:-1]) class ResBlock(nn.Module): def __init__(self, c, c_hidden): super().__init__() self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) self.depthwise = nn.Sequential( nn.ReplicationPad2d(1), nn.Conv2d(c, c, kernel_size=3, groups=c) ) self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) self.channelwise = nn.Sequential( nn.Linear(c, c_hidden), nn.GELU(), nn.Linear(c_hidden, c), ) self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True) def _basic_init(module): if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): torch.nn.init.xavier_uniform_(module.weight) if module.bias is not None: nn.init.constant_(module.bias, 0) self.apply(_basic_init) def _norm(self, x, norm): return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) def forward(self, x): mods = self.gammas x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1] try: x = x + self.depthwise(x_temp) * mods[2] except: x_temp = self.depthwise[0](x_temp.float()).to(x.dtype) x = x + self.depthwise[1](x_temp) * mods[2] x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4] x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5] return x class StageA(nn.Module): def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192): super().__init__() self.c_latent = c_latent c_levels = [c_hidden self.in_block = nn.Sequential( nn.PixelUnshuffle(2), nn.Conv2d(3 * 4, c_levels[0], kernel_size=1) ) down_blocks = [] for i in range(levels): if i > 0: down_blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1)) block = ResBlock(c_levels[i], c_levels[i] * 4) down_blocks.append(block) down_blocks.append(nn.Sequential( nn.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False), nn.BatchNorm2d(c_latent), )) self.down_blocks = nn.Sequential(*down_blocks) self.down_blocks[0] self.codebook_size = codebook_size self.vquantizer = VectorQuantize(c_latent, k=codebook_size) up_blocks = [nn.Sequential( nn.Conv2d(c_latent, c_levels[-1], kernel_size=1) )] for i in range(levels): for j in range(bottleneck_blocks if i == 0 else 1): block = ResBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4) up_blocks.append(block) if i < levels - 1: up_blocks.append( nn.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, padding=1)) self.up_blocks = nn.Sequential(*up_blocks) self.out_block = nn.Sequential( nn.Conv2d(c_levels[0], 3 * 4, kernel_size=1), nn.PixelShuffle(2), ) def encode(self, x, quantize=False): x = self.in_block(x) x = self.down_blocks(x) if quantize: qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1) return qe, x, indices, vq_loss + commit_loss * 0.25 else: return x def decode(self, x): x = self.up_blocks(x) x = self.out_block(x) return x def forward(self, x, quantize=False): qe, x, _, vq_loss = self.encode(x, quantize) x = self.decode(qe) return x, vq_loss class Discriminator(nn.Module): def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6): super().__init__() d = max(depth - 3, 3) layers = [ nn.utils.spectral_norm(nn.Conv2d(c_in, c_hidden nn.LeakyReLU(0.2), ] for i in range(depth - 1): c_in = c_hidden c_out = c_hidden layers.append(nn.utils.spectral_norm(nn.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1))) layers.append(nn.InstanceNorm2d(c_out)) layers.append(nn.LeakyReLU(0.2)) self.encoder = nn.Sequential(*layers) self.shuffle = nn.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1) self.logits = nn.Sigmoid() def forward(self, x, cond=None): x = self.encoder(x) if cond is not None: cond = cond.view(cond.size(0), cond.size(1), 1, 1, ).expand(-1, -1, x.size(-2), x.size(-1)) x = torch.cat([x, cond], dim=1) x = self.shuffle(x) x = self.logits(x) return x
""" This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https: """ import math import torch from torch import nn from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock class StageB(nn.Module): def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280, c_hidden=[320, 640, 1280, 1280], nhead=[-1, -1, 20, 20], blocks=[[2, 6, 28, 6], [6, 28, 6, 2]], block_repeat=[[1, 1, 1, 1], [3, 3, 2, 2]], level_config=['CT', 'CT', 'CTA', 'CTA'], c_clip=1280, c_clip_seq=4, c_effnet=16, c_pixels=3, kernel_size=3, dropout=[0, 0, 0.0, 0.0], self_attn=True, t_conds=['sca'], stable_cascade_stage=None, dtype=None, device=None, operations=None): super().__init__() self.dtype = dtype self.c_r = c_r self.t_conds = t_conds self.c_clip_seq = c_clip_seq if not isinstance(dropout, list): dropout = [dropout] * len(c_hidden) if not isinstance(self_attn, list): self_attn = [self_attn] * len(c_hidden) self.effnet_mapper = nn.Sequential( operations.Conv2d(c_effnet, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), nn.GELU(), operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) ) self.pixels_mapper = nn.Sequential( operations.Conv2d(c_pixels, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), nn.GELU(), operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) ) self.clip_mapper = operations.Linear(c_clip, c_cond * c_clip_seq, dtype=dtype, device=device) self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.embedding = nn.Sequential( nn.PixelUnshuffle(patch_size), operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) ) def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): if block_type == 'C': return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'A': return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'F': return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'T': return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) else: raise Exception(f'Block type {block_type} not supported') self.down_blocks = nn.ModuleList() self.down_downscalers = nn.ModuleList() self.down_repeat_mappers = nn.ModuleList() for i in range(len(c_hidden)): if i > 0: self.down_downscalers.append(nn.Sequential( LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), operations.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2, dtype=dtype, device=device), )) else: self.down_downscalers.append(nn.Identity()) down_block = nn.ModuleList() for _ in range(blocks[0][i]): for block_type in level_config[i]: block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) down_block.append(block) self.down_blocks.append(down_block) if block_repeat is not None: block_repeat_mappers = nn.ModuleList() for _ in range(block_repeat[0][i] - 1): block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) self.down_repeat_mappers.append(block_repeat_mappers) self.up_blocks = nn.ModuleList() self.up_upscalers = nn.ModuleList() self.up_repeat_mappers = nn.ModuleList() for i in reversed(range(len(c_hidden))): if i > 0: self.up_upscalers.append(nn.Sequential( LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), operations.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2, dtype=dtype, device=device), )) else: self.up_upscalers.append(nn.Identity()) up_block = nn.ModuleList() for j in range(blocks[1][::-1][i]): for k, block_type in enumerate(level_config[i]): c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], self_attn=self_attn[i]) up_block.append(block) self.up_blocks.append(up_block) if block_repeat is not None: block_repeat_mappers = nn.ModuleList() for _ in range(block_repeat[1][::-1][i] - 1): block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) self.up_repeat_mappers.append(block_repeat_mappers) self.clf = nn.Sequential( LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), nn.PixelShuffle(patch_size), ) def gen_r_embedding(self, r, max_positions=10000): r = r * max_positions half_dim = self.c_r emb = math.log(max_positions) / (half_dim - 1) emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() emb = r[:, None] * emb[None, :] emb = torch.cat([emb.sin(), emb.cos()], dim=1) if self.c_r % 2 == 1: emb = nn.functional.pad(emb, (0, 1), mode='constant') return emb def gen_c_embeddings(self, clip): if len(clip.shape) == 2: clip = clip.unsqueeze(1) clip = self.clip_mapper(clip).view(clip.size(0), clip.size(1) * self.c_clip_seq, -1) clip = self.clip_norm(clip) return clip def _down_encode(self, x, r_embed, clip): level_outputs = [] block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) for down_block, downscaler, repmap in block_group: x = downscaler(x) for i in range(len(repmap) + 1): for block in down_block: if isinstance(block, ResBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, ResBlock)): x = block(x) elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): x = block(x, clip) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): x = block(x, r_embed) else: x = block(x) if i < len(repmap): x = repmap[i](x) level_outputs.insert(0, x) return level_outputs def _up_decode(self, level_outputs, r_embed, clip): x = level_outputs[0] block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) for i, (up_block, upscaler, repmap) in enumerate(block_group): for j in range(len(repmap) + 1): for k, block in enumerate(up_block): if isinstance(block, ResBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, ResBlock)): skip = level_outputs[i] if k == 0 and i > 0 else None if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', align_corners=True) x = block(x, skip) elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): x = block(x, clip) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): x = block(x, r_embed) else: x = block(x) if j < len(repmap): x = repmap[j](x) x = upscaler(x) return x def forward(self, x, r, effnet, clip, pixels=None, **kwargs): if pixels is None: pixels = x.new_zeros(x.size(0), 3, 8, 8) r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) for c in self.t_conds: t_cond = kwargs.get(c, torch.zeros_like(r)) r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) clip = self.gen_c_embeddings(clip) x = self.embedding(x) x = x + self.effnet_mapper( nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True)) x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear', align_corners=True) level_outputs = self._down_encode(x, r_embed, clip) x = self._up_decode(level_outputs, r_embed, clip) return self.clf(x) def update_weights_ema(self, src_model, beta=0.999): for self_params, src_params in zip(self.parameters(), src_model.parameters()): self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)
""" This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https: """ import torch from torch import nn import math from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock class UpDownBlock2d(nn.Module): def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None): super().__init__() assert mode in ['up', 'down'] interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear', align_corners=True) if enabled else nn.Identity() mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device) self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation]) def forward(self, x): for block in self.blocks: x = block(x) return x class StageC(nn.Module): def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32], blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'], c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3, dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None, dtype=None, device=None, operations=None): super().__init__() self.dtype = dtype self.c_r = c_r self.t_conds = t_conds self.c_clip_seq = c_clip_seq if not isinstance(dropout, list): dropout = [dropout] * len(c_hidden) if not isinstance(self_attn, list): self_attn = [self_attn] * len(c_hidden) self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device) self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device) self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device) self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.embedding = nn.Sequential( nn.PixelUnshuffle(patch_size), operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6) ) def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): if block_type == 'C': return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'A': return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'F': return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) elif block_type == 'T': return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) else: raise Exception(f'Block type {block_type} not supported') self.down_blocks = nn.ModuleList() self.down_downscalers = nn.ModuleList() self.down_repeat_mappers = nn.ModuleList() for i in range(len(c_hidden)): if i > 0: self.down_downscalers.append(nn.Sequential( LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6), UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) )) else: self.down_downscalers.append(nn.Identity()) down_block = nn.ModuleList() for _ in range(blocks[0][i]): for block_type in level_config[i]: block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) down_block.append(block) self.down_blocks.append(down_block) if block_repeat is not None: block_repeat_mappers = nn.ModuleList() for _ in range(block_repeat[0][i] - 1): block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) self.down_repeat_mappers.append(block_repeat_mappers) self.up_blocks = nn.ModuleList() self.up_upscalers = nn.ModuleList() self.up_repeat_mappers = nn.ModuleList() for i in reversed(range(len(c_hidden))): if i > 0: self.up_upscalers.append(nn.Sequential( LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6), UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) )) else: self.up_upscalers.append(nn.Identity()) up_block = nn.ModuleList() for j in range(blocks[1][::-1][i]): for k, block_type in enumerate(level_config[i]): c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], self_attn=self_attn[i]) up_block.append(block) self.up_blocks.append(up_block) if block_repeat is not None: block_repeat_mappers = nn.ModuleList() for _ in range(block_repeat[1][::-1][i] - 1): block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) self.up_repeat_mappers.append(block_repeat_mappers) self.clf = nn.Sequential( LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), nn.PixelShuffle(patch_size), ) def gen_r_embedding(self, r, max_positions=10000): r = r * max_positions half_dim = self.c_r emb = math.log(max_positions) / (half_dim - 1) emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() emb = r[:, None] * emb[None, :] emb = torch.cat([emb.sin(), emb.cos()], dim=1) if self.c_r % 2 == 1: emb = nn.functional.pad(emb, (0, 1), mode='constant') return emb def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img): clip_txt = self.clip_txt_mapper(clip_txt) if len(clip_txt_pooled.shape) == 2: clip_txt_pooled = clip_txt_pooled.unsqueeze(1) if len(clip_img.shape) == 2: clip_img = clip_img.unsqueeze(1) clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1) clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1) clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1) clip = self.clip_norm(clip) return clip def _down_encode(self, x, r_embed, clip, cnet=None): level_outputs = [] block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) for down_block, downscaler, repmap in block_group: x = downscaler(x) for i in range(len(repmap) + 1): for block in down_block: if isinstance(block, ResBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, ResBlock)): if cnet is not None: next_cnet = cnet.pop() if next_cnet is not None: x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', align_corners=True).to(x.dtype) x = block(x) elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): x = block(x, clip) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): x = block(x, r_embed) else: x = block(x) if i < len(repmap): x = repmap[i](x) level_outputs.insert(0, x) return level_outputs def _up_decode(self, level_outputs, r_embed, clip, cnet=None): x = level_outputs[0] block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) for i, (up_block, upscaler, repmap) in enumerate(block_group): for j in range(len(repmap) + 1): for k, block in enumerate(up_block): if isinstance(block, ResBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, ResBlock)): skip = level_outputs[i] if k == 0 and i > 0 else None if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', align_corners=True) if cnet is not None: next_cnet = cnet.pop() if next_cnet is not None: x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', align_corners=True).to(x.dtype) x = block(x, skip) elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): x = block(x, clip) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): x = block(x, r_embed) else: x = block(x) if j < len(repmap): x = repmap[j](x) x = upscaler(x) return x def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs): r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) for c in self.t_conds: t_cond = kwargs.get(c, torch.zeros_like(r)) r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img) if control is not None: cnet = control.get("input") else: cnet = None x = self.embedding(x) level_outputs = self._down_encode(x, r_embed, clip, cnet) x = self._up_decode(level_outputs, r_embed, clip, cnet) return self.clf(x) def update_weights_ema(self, src_model, beta=0.999): for self_params, src_params in zip(self.parameters(), src_model.parameters()): self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)
""" This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https: """ import torch import torchvision from torch import nn class EfficientNetEncoder(nn.Module): def __init__(self, c_latent=16): super().__init__() self.backbone = torchvision.models.efficientnet_v2_s().features.eval() self.mapper = nn.Sequential( nn.Conv2d(1280, c_latent, kernel_size=1, bias=False), nn.BatchNorm2d(c_latent, affine=False), ) self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406])) self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225])) def forward(self, x): x = x * 0.5 + 0.5 x = (x - self.mean.view([3,1,1])) / self.std.view([3,1,1]) o = self.mapper(self.backbone(x)) return o class Previewer(nn.Module): def __init__(self, c_in=16, c_hidden=512, c_out=3): super().__init__() self.blocks = nn.Sequential( nn.Conv2d(c_in, c_hidden, kernel_size=1), nn.GELU(), nn.BatchNorm2d(c_hidden), nn.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1), nn.GELU(), nn.BatchNorm2d(c_hidden), nn.ConvTranspose2d(c_hidden, c_hidden nn.GELU(), nn.BatchNorm2d(c_hidden nn.Conv2d(c_hidden nn.GELU(), nn.BatchNorm2d(c_hidden nn.ConvTranspose2d(c_hidden nn.GELU(), nn.BatchNorm2d(c_hidden nn.Conv2d(c_hidden nn.GELU(), nn.BatchNorm2d(c_hidden nn.ConvTranspose2d(c_hidden nn.GELU(), nn.BatchNorm2d(c_hidden nn.Conv2d(c_hidden nn.GELU(), nn.BatchNorm2d(c_hidden nn.Conv2d(c_hidden ) def forward(self, x): return (self.blocks(x) - 0.5) * 2.0 class StageC_coder(nn.Module): def __init__(self): super().__init__() self.previewer = Previewer() self.encoder = EfficientNetEncoder() def encode(self, x): return self.encoder(x) def decode(self, x): return self.previewer(x)
import torch from contextlib import contextmanager from typing import Any, Dict, List, Optional, Tuple, Union from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution from comfy.ldm.util import instantiate_from_config from comfy.ldm.modules.ema import LitEma import comfy.ops class DiagonalGaussianRegularizer(torch.nn.Module): def __init__(self, sample: bool = True): super().__init__() self.sample = sample def get_trainable_parameters(self) -> Any: yield from () def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: log = dict() posterior = DiagonalGaussianDistribution(z) if self.sample: z = posterior.sample() else: z = posterior.mode() kl_loss = posterior.kl() kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] log["kl_loss"] = kl_loss return z, log class AbstractAutoencoder(torch.nn.Module): """ This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators, unCLIP models, etc. Hence, it is fairly general, and specific features (e.g. discriminator training, encoding, decoding) must be implemented in subclasses. """ def __init__( self, ema_decay: Union[None, float] = None, monitor: Union[None, str] = None, input_key: str = "jpg", **kwargs, ): super().__init__() self.input_key = input_key self.use_ema = ema_decay is not None if monitor is not None: self.monitor = monitor if self.use_ema: self.model_ema = LitEma(self, decay=ema_decay) logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") def get_input(self, batch) -> Any: raise NotImplementedError() def on_train_batch_end(self, *args, **kwargs): if self.use_ema: self.model_ema(self) @contextmanager def ema_scope(self, context=None): if self.use_ema: self.model_ema.store(self.parameters()) self.model_ema.copy_to(self) if context is not None: logpy.info(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.parameters()) if context is not None: logpy.info(f"{context}: Restored training weights") def encode(self, *args, **kwargs) -> torch.Tensor: raise NotImplementedError("encode()-method of abstract base class called") def decode(self, *args, **kwargs) -> torch.Tensor: raise NotImplementedError("decode()-method of abstract base class called") def instantiate_optimizer_from_config(self, params, lr, cfg): logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config") return get_obj_from_str(cfg["target"])( params, lr=lr, **cfg.get("params", dict()) ) def configure_optimizers(self) -> Any: raise NotImplementedError() class AutoencodingEngine(AbstractAutoencoder): """ Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL (we also restore them explicitly as special cases for legacy reasons). Regularizations such as KL or VQ are moved to the regularizer class. """ def __init__( self, *args, encoder_config: Dict, decoder_config: Dict, regularizer_config: Dict, **kwargs, ): super().__init__(*args, **kwargs) self.encoder: torch.nn.Module = instantiate_from_config(encoder_config) self.decoder: torch.nn.Module = instantiate_from_config(decoder_config) self.regularization: AbstractRegularizer = instantiate_from_config( regularizer_config ) def get_last_layer(self): return self.decoder.get_last_layer() def encode( self, x: torch.Tensor, return_reg_log: bool = False, unregularized: bool = False, ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: z = self.encoder(x) if unregularized: return z, dict() z, reg_log = self.regularization(z) if return_reg_log: return z, reg_log return z def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor: x = self.decoder(z, **kwargs) return x def forward( self, x: torch.Tensor, **additional_decode_kwargs ) -> Tuple[torch.Tensor, torch.Tensor, dict]: z, reg_log = self.encode(x, return_reg_log=True) dec = self.decode(z, **additional_decode_kwargs) return z, dec, reg_log class AutoencodingEngineLegacy(AutoencodingEngine): def __init__(self, embed_dim: int, **kwargs): self.max_batch_size = kwargs.pop("max_batch_size", None) ddconfig = kwargs.pop("ddconfig") super().__init__( encoder_config={ "target": "comfy.ldm.modules.diffusionmodules.model.Encoder", "params": ddconfig, }, decoder_config={ "target": "comfy.ldm.modules.diffusionmodules.model.Decoder", "params": ddconfig, }, **kwargs, ) self.quant_conv = comfy.ops.disable_weight_init.Conv2d( (1 + ddconfig["double_z"]) * ddconfig["z_channels"], (1 + ddconfig["double_z"]) * embed_dim, 1, ) self.post_quant_conv = comfy.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim def get_autoencoder_params(self) -> list: params = super().get_autoencoder_params() return params def encode( self, x: torch.Tensor, return_reg_log: bool = False ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: if self.max_batch_size is None: z = self.encoder(x) z = self.quant_conv(z) else: N = x.shape[0] bs = self.max_batch_size n_batches = int(math.ceil(N / bs)) z = list() for i_batch in range(n_batches): z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs]) z_batch = self.quant_conv(z_batch) z.append(z_batch) z = torch.cat(z, 0) z, reg_log = self.regularization(z) if return_reg_log: return z, reg_log return z def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor: if self.max_batch_size is None: dec = self.post_quant_conv(z) dec = self.decoder(dec, **decoder_kwargs) else: N = z.shape[0] bs = self.max_batch_size n_batches = int(math.ceil(N / bs)) dec = list() for i_batch in range(n_batches): dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs]) dec_batch = self.decoder(dec_batch, **decoder_kwargs) dec.append(dec_batch) dec = torch.cat(dec, 0) return dec class AutoencoderKL(AutoencodingEngineLegacy): def __init__(self, **kwargs): if "lossconfig" in kwargs: kwargs["loss_config"] = kwargs.pop("lossconfig") super().__init__( regularizer_config={ "target": ( "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer" ) }, **kwargs, )
import math import torch import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat from typing import Optional import logging from .diffusionmodules.util import AlphaBlender, timestep_embedding from .sub_quadratic_attention import efficient_dot_product_attention from comfy import model_management if model_management.xformers_enabled(): import xformers import xformers.ops from comfy.cli_args import args import comfy.ops ops = comfy.ops.disable_weight_init FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype() def get_attn_precision(attn_precision): if args.dont_upcast_attention: return None if FORCE_UPCAST_ATTENTION_DTYPE is not None: return FORCE_UPCAST_ATTENTION_DTYPE return attn_precision def exists(val): return val is not None def uniq(arr): return{el: True for el in arr}.keys() def default(val, d): if exists(val): return val return d def max_neg_value(t): return -torch.finfo(t.dtype).max def init_(tensor): dim = tensor.shape[-1] std = 1 / math.sqrt(dim) tensor.uniform_(-std, std) return tensor class GEGLU(nn.Module): def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops): super().__init__() self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) return x * F.gelu(gate) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( operations.Linear(dim, inner_dim, dtype=dtype, device=device), nn.GELU() ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations) self.net = nn.Sequential( project_in, nn.Dropout(dropout), operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) ) def forward(self, x): return self.net(x) def Normalize(in_channels, dtype=None, device=None): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): attn_precision = get_attn_precision(attn_precision) if skip_reshape: b, _, _, dim_head = q.shape else: b, _, dim_head = q.shape dim_head scale = dim_head ** -0.5 h = heads if skip_reshape: q, k, v = map( lambda t: t.reshape(b * heads, -1, dim_head), (q, k, v), ) else: q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, -1, heads, dim_head) .permute(0, 2, 1, 3) .reshape(b * heads, -1, dim_head) .contiguous(), (q, k, v), ) if attn_precision == torch.float32: sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale else: sim = einsum('b i d, b j d -> b i j', q, k) * scale del q, k if exists(mask): if mask.dtype == torch.bool: mask = rearrange(mask, 'b ... -> b (...)') max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) else: if len(mask.shape) == 2: bs = 1 else: bs = mask.shape[0] mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) sim.add_(mask) sim = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) out = ( out.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) return out def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False): attn_precision = get_attn_precision(attn_precision) if skip_reshape: b, _, _, dim_head = query.shape else: b, _, dim_head = query.shape dim_head scale = dim_head ** -0.5 if skip_reshape: query = query.reshape(b * heads, -1, dim_head) value = value.reshape(b * heads, -1, dim_head) key = key.reshape(b * heads, -1, dim_head).movedim(1, 2) else: query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1) dtype = query.dtype upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32 if upcast_attention: bytes_per_token = torch.finfo(torch.float32).bits else: bytes_per_token = torch.finfo(query.dtype).bits batch_x_heads, q_tokens, _ = query.shape _, _, k_tokens = key.shape qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True) kv_chunk_size_min = None kv_chunk_size = None query_chunk_size = None for x in [4096, 2048, 1024, 512, 256]: count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0) if count >= k_tokens: kv_chunk_size = k_tokens query_chunk_size = x break if query_chunk_size is None: query_chunk_size = 512 if mask is not None: if len(mask.shape) == 2: bs = 1 else: bs = mask.shape[0] mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) hidden_states = efficient_dot_product_attention( query, key, value, query_chunk_size=query_chunk_size, kv_chunk_size=kv_chunk_size, kv_chunk_size_min=kv_chunk_size_min, use_checkpoint=False, upcast_attention=upcast_attention, mask=mask, ) hidden_states = hidden_states.to(dtype) hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) return hidden_states def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): attn_precision = get_attn_precision(attn_precision) if skip_reshape: b, _, _, dim_head = q.shape else: b, _, dim_head = q.shape dim_head scale = dim_head ** -0.5 h = heads if skip_reshape: q, k, v = map( lambda t: t.reshape(b * heads, -1, dim_head), (q, k, v), ) else: q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, -1, heads, dim_head) .permute(0, 2, 1, 3) .reshape(b * heads, -1, dim_head) .contiguous(), (q, k, v), ) r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) mem_free_total = model_management.get_free_memory(q.device) if attn_precision == torch.float32: element_size = 4 upcast = True else: element_size = q.element_size() upcast = False gb = 1024 ** 3 tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size modifier = 3 mem_required = tensor_size * modifier steps = 1 if mem_required > mem_free_total: steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) if steps > 64: max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') if mask is not None: if len(mask.shape) == 2: bs = 1 else: bs = mask.shape[0] mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) first_op_done = False cleared_cache = False while True: try: slice_size = q.shape[1] for i in range(0, q.shape[1], slice_size): end = i + slice_size if upcast: with torch.autocast(enabled=False, device_type = 'cuda'): s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale else: s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale if mask is not None: if len(mask.shape) == 2: s1 += mask[i:end] else: s1 += mask[:, i:end] s2 = s1.softmax(dim=-1).to(v.dtype) del s1 first_op_done = True r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) del s2 break except model_management.OOM_EXCEPTION as e: if first_op_done == False: model_management.soft_empty_cache(True) if cleared_cache == False: cleared_cache = True logging.warning("out of memory error, emptying cache and trying again") continue steps *= 2 if steps > 64: raise e logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) else: raise e del q, k, v r1 = ( r1.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) return r1 BROKEN_XFORMERS = False try: x_vers = xformers.__version__ BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20") except: pass def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): if skip_reshape: b, _, _, dim_head = q.shape else: b, _, dim_head = q.shape dim_head disabled_xformers = False if BROKEN_XFORMERS: if b * heads > 65535: disabled_xformers = True if not disabled_xformers: if torch.jit.is_tracing() or torch.jit.is_scripting(): disabled_xformers = True if disabled_xformers: return attention_pytorch(q, k, v, heads, mask) if skip_reshape: q, k, v = map( lambda t: t.reshape(b * heads, -1, dim_head), (q, k, v), ) else: q, k, v = map( lambda t: t.reshape(b, -1, heads, dim_head), (q, k, v), ) if mask is not None: pad = 8 - q.shape[1] % 8 mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device) mask_out[:, :, :mask.shape[-1]] = mask mask = mask_out[:, :, :mask.shape[-1]] out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) if skip_reshape: out = ( out.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) else: out = ( out.reshape(b, -1, heads * dim_head) ) return out def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): if skip_reshape: b, _, _, dim_head = q.shape else: b, _, dim_head = q.shape dim_head q, k, v = map( lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), (q, k, v), ) out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) out = ( out.transpose(1, 2).reshape(b, -1, heads * dim_head) ) return out optimized_attention = attention_basic if model_management.xformers_enabled(): logging.info("Using xformers cross attention") optimized_attention = attention_xformers elif model_management.pytorch_attention_enabled(): logging.info("Using pytorch cross attention") optimized_attention = attention_pytorch else: if args.use_split_cross_attention: logging.info("Using split optimization for cross attention") optimized_attention = attention_split else: logging.info("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention") optimized_attention = attention_sub_quad optimized_attention_masked = optimized_attention def optimized_attention_for_device(device, mask=False, small_input=False): if small_input: if model_management.pytorch_attention_enabled(): return attention_pytorch else: return attention_basic if device == torch.device("cpu"): return attention_sub_quad if mask: return optimized_attention_masked return optimized_attention class CrossAttention(nn.Module): def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) self.attn_precision = attn_precision self.heads = heads self.dim_head = dim_head self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) def forward(self, x, context=None, value=None, mask=None): q = self.to_q(x) context = default(context, x) k = self.to_k(context) if value is not None: v = self.to_v(value) del value else: v = self.to_v(context) if mask is None: out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) else: out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) return self.to_out(out) class BasicTransformerBlock(nn.Module): def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None, disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops): super().__init__() self.ff_in = ff_in or inner_dim is not None if inner_dim is None: inner_dim = dim self.is_res = inner_dim == dim self.attn_precision = attn_precision if self.ff_in: self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device) self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) self.disable_self_attn = disable_self_attn self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout, context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) if disable_temporal_crossattention: if switch_temporal_ca_to_sa: raise ValueError else: self.attn2 = None else: context_dim_attn2 = None if not switch_temporal_ca_to_sa: context_dim_attn2 = context_dim self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2, heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.n_heads = n_heads self.d_head = d_head self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa def forward(self, x, context=None, transformer_options={}): extra_options = {} block = transformer_options.get("block", None) block_index = transformer_options.get("block_index", 0) transformer_patches = {} transformer_patches_replace = {} for k in transformer_options: if k == "patches": transformer_patches = transformer_options[k] elif k == "patches_replace": transformer_patches_replace = transformer_options[k] else: extra_options[k] = transformer_options[k] extra_options["n_heads"] = self.n_heads extra_options["dim_head"] = self.d_head extra_options["attn_precision"] = self.attn_precision if self.ff_in: x_skip = x x = self.ff_in(self.norm_in(x)) if self.is_res: x += x_skip n = self.norm1(x) if self.disable_self_attn: context_attn1 = context else: context_attn1 = None value_attn1 = None if "attn1_patch" in transformer_patches: patch = transformer_patches["attn1_patch"] if context_attn1 is None: context_attn1 = n value_attn1 = context_attn1 for p in patch: n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options) if block is not None: transformer_block = (block[0], block[1], block_index) else: transformer_block = None attn1_replace_patch = transformer_patches_replace.get("attn1", {}) block_attn1 = transformer_block if block_attn1 not in attn1_replace_patch: block_attn1 = block if block_attn1 in attn1_replace_patch: if context_attn1 is None: context_attn1 = n value_attn1 = n n = self.attn1.to_q(n) context_attn1 = self.attn1.to_k(context_attn1) value_attn1 = self.attn1.to_v(value_attn1) n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options) n = self.attn1.to_out(n) else: n = self.attn1(n, context=context_attn1, value=value_attn1) if "attn1_output_patch" in transformer_patches: patch = transformer_patches["attn1_output_patch"] for p in patch: n = p(n, extra_options) x += n if "middle_patch" in transformer_patches: patch = transformer_patches["middle_patch"] for p in patch: x = p(x, extra_options) if self.attn2 is not None: n = self.norm2(x) if self.switch_temporal_ca_to_sa: context_attn2 = n else: context_attn2 = context value_attn2 = None if "attn2_patch" in transformer_patches: patch = transformer_patches["attn2_patch"] value_attn2 = context_attn2 for p in patch: n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options) attn2_replace_patch = transformer_patches_replace.get("attn2", {}) block_attn2 = transformer_block if block_attn2 not in attn2_replace_patch: block_attn2 = block if block_attn2 in attn2_replace_patch: if value_attn2 is None: value_attn2 = context_attn2 n = self.attn2.to_q(n) context_attn2 = self.attn2.to_k(context_attn2) value_attn2 = self.attn2.to_v(value_attn2) n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) n = self.attn2.to_out(n) else: n = self.attn2(n, context=context_attn2, value=value_attn2) if "attn2_output_patch" in transformer_patches: patch = transformer_patches["attn2_output_patch"] for p in patch: n = p(n, extra_options) x += n if self.is_res: x_skip = x x = self.ff(self.norm3(x)) if self.is_res: x += x_skip return x class SpatialTransformer(nn.Module): """ Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transformer action. Finally, reshape to image NEW: use_linear for more efficiency instead of the 1x1 convs """ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, disable_self_attn=False, use_linear=False, use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops): super().__init__() if exists(context_dim) and not isinstance(context_dim, list): context_dim = [context_dim] * depth self.in_channels = in_channels inner_dim = n_heads * d_head self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) if not use_linear: self.proj_in = operations.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device) else: self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) self.transformer_blocks = nn.ModuleList( [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) for d in range(depth)] ) if not use_linear: self.proj_out = operations.Conv2d(inner_dim,in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device) else: self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) self.use_linear = use_linear def forward(self, x, context=None, transformer_options={}): if not isinstance(context, list): context = [context] * len(self.transformer_blocks) b, c, h, w = x.shape x_in = x x = self.norm(x) if not self.use_linear: x = self.proj_in(x) x = x.movedim(1, 3).flatten(1, 2).contiguous() if self.use_linear: x = self.proj_in(x) for i, block in enumerate(self.transformer_blocks): transformer_options["block_index"] = i x = block(x, context=context[i], transformer_options=transformer_options) if self.use_linear: x = self.proj_out(x) x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous() if not self.use_linear: x = self.proj_out(x) return x + x_in class SpatialVideoTransformer(SpatialTransformer): def __init__( self, in_channels, n_heads, d_head, depth=1, dropout=0.0, use_linear=False, context_dim=None, use_spatial_context=False, timesteps=None, merge_strategy: str = "fixed", merge_factor: float = 0.5, time_context_dim=None, ff_in=False, checkpoint=False, time_depth=1, disable_self_attn=False, disable_temporal_crossattention=False, max_time_embed_period: int = 10000, attn_precision=None, dtype=None, device=None, operations=ops ): super().__init__( in_channels, n_heads, d_head, depth=depth, dropout=dropout, use_checkpoint=checkpoint, context_dim=context_dim, use_linear=use_linear, disable_self_attn=disable_self_attn, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations ) self.time_depth = time_depth self.depth = depth self.max_time_embed_period = max_time_embed_period time_mix_d_head = d_head n_time_mix_heads = n_heads time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads) inner_dim = n_heads * d_head if use_spatial_context: time_context_dim = context_dim self.time_stack = nn.ModuleList( [ BasicTransformerBlock( inner_dim, n_time_mix_heads, time_mix_d_head, dropout=dropout, context_dim=time_context_dim, checkpoint=checkpoint, ff_in=ff_in, inner_dim=time_mix_inner_dim, disable_self_attn=disable_self_attn, disable_temporal_crossattention=disable_temporal_crossattention, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations ) for _ in range(self.depth) ] ) assert len(self.time_stack) == len(self.transformer_blocks) self.use_spatial_context = use_spatial_context self.in_channels = in_channels time_embed_dim = self.in_channels * 4 self.time_pos_embed = nn.Sequential( operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device), ) self.time_mixer = AlphaBlender( alpha=merge_factor, merge_strategy=merge_strategy ) def forward( self, x: torch.Tensor, context: Optional[torch.Tensor] = None, time_context: Optional[torch.Tensor] = None, timesteps: Optional[int] = None, image_only_indicator: Optional[torch.Tensor] = None, transformer_options={} ) -> torch.Tensor: _, _, h, w = x.shape x_in = x spatial_context = None if exists(context): spatial_context = context if self.use_spatial_context: assert ( context.ndim == 3 ), f"n dims of spatial context should be 3 but are {context.ndim}" if time_context is None: time_context = context time_context_first_timestep = time_context[::timesteps] time_context = repeat( time_context_first_timestep, "b ... -> (b n) ...", n=h * w ) elif time_context is not None and not self.use_spatial_context: time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w) if time_context.ndim == 2: time_context = rearrange(time_context, "b c -> b 1 c") x = self.norm(x) if not self.use_linear: x = self.proj_in(x) x = rearrange(x, "b c h w -> b (h w) c") if self.use_linear: x = self.proj_in(x) num_frames = torch.arange(timesteps, device=x.device) num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] num_frames = rearrange(num_frames, "b t -> (b t)") t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype) emb = self.time_pos_embed(t_emb) emb = emb[:, None, :] for it_, (block, mix_block) in enumerate( zip(self.transformer_blocks, self.time_stack) ): transformer_options["block_index"] = it_ x = block( x, context=spatial_context, transformer_options=transformer_options, ) x_mix = x x_mix = x_mix + emb B, S, C = x_mix.shape x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps) x_mix = mix_block(x_mix, context=time_context) x_mix = rearrange( x_mix, "(b s) t c -> (b t) s c", s=S, b=B ) x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator) if self.use_linear: x = self.proj_out(x) x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) if not self.use_linear: x = self.proj_out(x) out = x + x_in return out
import torch from torch import nn class LitEma(nn.Module): def __init__(self, model, decay=0.9999, use_num_upates=True): super().__init__() if decay < 0.0 or decay > 1.0: raise ValueError('Decay must be between 0 and 1') self.m_name2s_name = {} self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates else torch.tensor(-1, dtype=torch.int)) for name, p in model.named_parameters(): if p.requires_grad: s_name = name.replace('.', '') self.m_name2s_name.update({name: s_name}) self.register_buffer(s_name, p.clone().detach().data) self.collected_params = [] def reset_num_updates(self): del self.num_updates self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) def forward(self, model): decay = self.decay if self.num_updates >= 0: self.num_updates += 1 decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) one_minus_decay = 1.0 - decay with torch.no_grad(): m_param = dict(model.named_parameters()) shadow_params = dict(self.named_buffers()) for key in m_param: if m_param[key].requires_grad: sname = self.m_name2s_name[key] shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) else: assert not key in self.m_name2s_name def copy_to(self, model): m_param = dict(model.named_parameters()) shadow_params = dict(self.named_buffers()) for key in m_param: if m_param[key].requires_grad: m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) else: assert not key in self.m_name2s_name def store(self, parameters): """ Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored. """ self.collected_params = [param.clone() for param in parameters] def restore(self, parameters): """ Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the original optimization process. Store the parameters before the `copy_to` method. After validation (or model saving), use this to restore the former parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored parameters. """ for c_param, param in zip(self.collected_params, parameters): param.data.copy_(c_param.data)
from functools import partial import torch from torch import Tensor from torch.utils.checkpoint import checkpoint import math import logging try: from typing import Optional, NamedTuple, List, Protocol except ImportError: from typing import Optional, NamedTuple, List from typing_extensions import Protocol from torch import Tensor from typing import List from comfy import model_management def dynamic_slice( x: Tensor, starts: List[int], sizes: List[int], ) -> Tensor: slicing = [slice(start, start + size) for start, size in zip(starts, sizes)] return x[slicing] class AttnChunk(NamedTuple): exp_values: Tensor exp_weights_sum: Tensor max_score: Tensor class SummarizeChunk(Protocol): @staticmethod def __call__( query: Tensor, key_t: Tensor, value: Tensor, ) -> AttnChunk: ... class ComputeQueryChunkAttn(Protocol): @staticmethod def __call__( query: Tensor, key_t: Tensor, value: Tensor, ) -> Tensor: ... def _summarize_chunk( query: Tensor, key_t: Tensor, value: Tensor, scale: float, upcast_attention: bool, mask, ) -> AttnChunk: if upcast_attention: with torch.autocast(enabled=False, device_type = 'cuda'): query = query.float() key_t = key_t.float() attn_weights = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) else: attn_weights = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) max_score, _ = torch.max(attn_weights, -1, keepdim=True) max_score = max_score.detach() attn_weights -= max_score if mask is not None: attn_weights += mask torch.exp(attn_weights, out=attn_weights) exp_weights = attn_weights.to(value.dtype) exp_values = torch.bmm(exp_weights, value) max_score = max_score.squeeze(-1) return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score) def _query_chunk_attention( query: Tensor, key_t: Tensor, value: Tensor, summarize_chunk: SummarizeChunk, kv_chunk_size: int, mask, ) -> Tensor: batch_x_heads, k_channels_per_head, k_tokens = key_t.shape _, _, v_channels_per_head = value.shape def chunk_scanner(chunk_idx: int, mask) -> AttnChunk: key_chunk = dynamic_slice( key_t, (0, 0, chunk_idx), (batch_x_heads, k_channels_per_head, kv_chunk_size) ) value_chunk = dynamic_slice( value, (0, chunk_idx, 0), (batch_x_heads, kv_chunk_size, v_channels_per_head) ) if mask is not None: mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size] return summarize_chunk(query, key_chunk, value_chunk, mask=mask) chunks: List[AttnChunk] = [ chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size) ] acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks))) chunk_values, chunk_weights, chunk_max = acc_chunk global_max, _ = torch.max(chunk_max, 0, keepdim=True) max_diffs = torch.exp(chunk_max - global_max) chunk_values *= torch.unsqueeze(max_diffs, -1) chunk_weights *= max_diffs all_values = chunk_values.sum(dim=0) all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0) return all_values / all_weights def _get_attention_scores_no_kv_chunking( query: Tensor, key_t: Tensor, value: Tensor, scale: float, upcast_attention: bool, mask, ) -> Tensor: if upcast_attention: with torch.autocast(enabled=False, device_type = 'cuda'): query = query.float() key_t = key_t.float() attn_scores = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) else: attn_scores = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) if mask is not None: attn_scores += mask try: attn_probs = attn_scores.softmax(dim=-1) del attn_scores except model_management.OOM_EXCEPTION: logging.warning("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead") attn_scores -= attn_scores.max(dim=-1, keepdim=True).values torch.exp(attn_scores, out=attn_scores) summed = torch.sum(attn_scores, dim=-1, keepdim=True) attn_scores /= summed attn_probs = attn_scores hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value) return hidden_states_slice class ScannedChunk(NamedTuple): chunk_idx: int attn_chunk: AttnChunk def efficient_dot_product_attention( query: Tensor, key_t: Tensor, value: Tensor, query_chunk_size=1024, kv_chunk_size: Optional[int] = None, kv_chunk_size_min: Optional[int] = None, use_checkpoint=True, upcast_attention=False, mask = None, ): """Computes efficient dot-product attention given query, transposed key, and value. This is efficient version of attention presented in https: Args: query: queries for calculating attention with shape of `[batch * num_heads, tokens, channels_per_head]`. key_t: keys for calculating attention with shape of `[batch * num_heads, channels_per_head, tokens]`. value: values to be used in attention with shape of `[batch * num_heads, tokens, channels_per_head]`. query_chunk_size: int: query chunks size kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens) kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done). use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference) Returns: Output of shape `[batch * num_heads, query_tokens, channels_per_head]`. """ batch_x_heads, q_tokens, q_channels_per_head = query.shape _, _, k_tokens = key_t.shape scale = q_channels_per_head ** -0.5 kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens) if kv_chunk_size_min is not None: kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min) if mask is not None and len(mask.shape) == 2: mask = mask.unsqueeze(0) def get_query_chunk(chunk_idx: int) -> Tensor: return dynamic_slice( query, (0, chunk_idx, 0), (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head) ) def get_mask_chunk(chunk_idx: int) -> Tensor: if mask is None: return None chunk = min(query_chunk_size, q_tokens) return mask[:,chunk_idx:chunk_idx + chunk] summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention) summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk compute_query_chunk_attn: ComputeQueryChunkAttn = partial( _get_attention_scores_no_kv_chunking, scale=scale, upcast_attention=upcast_attention ) if k_tokens <= kv_chunk_size else ( partial( _query_chunk_attention, kv_chunk_size=kv_chunk_size, summarize_chunk=summarize_chunk, ) ) if q_tokens <= query_chunk_size: return compute_query_chunk_attn( query=query, key_t=key_t, value=value, mask=mask, ) res = torch.cat([ compute_query_chunk_attn( query=get_query_chunk(i * query_chunk_size), key_t=key_t, value=value, mask=get_mask_chunk(i * query_chunk_size) ) for i in range(math.ceil(q_tokens / query_chunk_size)) ], dim=1) return res
import functools from typing import Callable, Iterable, Union import torch from einops import rearrange, repeat import comfy.ops ops = comfy.ops.disable_weight_init from .diffusionmodules.model import ( AttnBlock, Decoder, ResnetBlock, ) from .diffusionmodules.openaimodel import ResBlock, timestep_embedding from .attention import BasicTransformerBlock def partialclass(cls, *args, **kwargs): class NewCls(cls): __init__ = functools.partialmethod(cls.__init__, *args, **kwargs) return NewCls class VideoResBlock(ResnetBlock): def __init__( self, out_channels, *args, dropout=0.0, video_kernel_size=3, alpha=0.0, merge_strategy="learned", **kwargs, ): super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs) if video_kernel_size is None: video_kernel_size = [3, 1, 1] self.time_stack = ResBlock( channels=out_channels, emb_channels=0, dropout=dropout, dims=3, use_scale_shift_norm=False, use_conv=False, up=False, down=False, kernel_size=video_kernel_size, use_checkpoint=False, skip_t_emb=True, ) self.merge_strategy = merge_strategy if self.merge_strategy == "fixed": self.register_buffer("mix_factor", torch.Tensor([alpha])) elif self.merge_strategy == "learned": self.register_parameter( "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) ) else: raise ValueError(f"unknown merge strategy {self.merge_strategy}") def get_alpha(self, bs): if self.merge_strategy == "fixed": return self.mix_factor elif self.merge_strategy == "learned": return torch.sigmoid(self.mix_factor) else: raise NotImplementedError() def forward(self, x, temb, skip_video=False, timesteps=None): b, c, h, w = x.shape if timesteps is None: timesteps = b x = super().forward(x, temb) if not skip_video: x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) x = self.time_stack(x, temb) alpha = self.get_alpha(bs=b x = alpha * x + (1.0 - alpha) * x_mix x = rearrange(x, "b c t h w -> (b t) c h w") return x class AE3DConv(ops.Conv2d): def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs): super().__init__(in_channels, out_channels, *args, **kwargs) if isinstance(video_kernel_size, Iterable): padding = [int(k else: padding = int(video_kernel_size self.time_mix_conv = ops.Conv3d( in_channels=out_channels, out_channels=out_channels, kernel_size=video_kernel_size, padding=padding, ) def forward(self, input, timesteps=None, skip_video=False): if timesteps is None: timesteps = input.shape[0] x = super().forward(input) if skip_video: return x x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) x = self.time_mix_conv(x) return rearrange(x, "b c t h w -> (b t) c h w") class AttnVideoBlock(AttnBlock): def __init__( self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" ): super().__init__(in_channels) self.time_mix_block = BasicTransformerBlock( dim=in_channels, n_heads=1, d_head=in_channels, checkpoint=False, ff_in=True, ) time_embed_dim = self.in_channels * 4 self.video_time_embed = torch.nn.Sequential( ops.Linear(self.in_channels, time_embed_dim), torch.nn.SiLU(), ops.Linear(time_embed_dim, self.in_channels), ) self.merge_strategy = merge_strategy if self.merge_strategy == "fixed": self.register_buffer("mix_factor", torch.Tensor([alpha])) elif self.merge_strategy == "learned": self.register_parameter( "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) ) else: raise ValueError(f"unknown merge strategy {self.merge_strategy}") def forward(self, x, timesteps=None, skip_time_block=False): if skip_time_block: return super().forward(x) if timesteps is None: timesteps = x.shape[0] x_in = x x = self.attention(x) h, w = x.shape[2:] x = rearrange(x, "b c h w -> b (h w) c") x_mix = x num_frames = torch.arange(timesteps, device=x.device) num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] num_frames = rearrange(num_frames, "b t -> (b t)") t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) emb = self.video_time_embed(t_emb) emb = emb[:, None, :] x_mix = x_mix + emb alpha = self.get_alpha().to(x.device) x_mix = self.time_mix_block(x_mix, timesteps=timesteps) x = alpha * x + (1.0 - alpha) * x_mix x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) x = self.proj_out(x) return x_in + x def get_alpha( self, ): if self.merge_strategy == "fixed": return self.mix_factor elif self.merge_strategy == "learned": return torch.sigmoid(self.mix_factor) else: raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") def make_time_attn( in_channels, attn_type="vanilla", attn_kwargs=None, alpha: float = 0, merge_strategy: str = "learned", ): return partialclass( AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy ) class Conv2DWrapper(torch.nn.Conv2d): def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: return super().forward(input) class VideoDecoder(Decoder): available_time_modes = ["all", "conv-only", "attn-only"] def __init__( self, *args, video_kernel_size: Union[int, list] = 3, alpha: float = 0.0, merge_strategy: str = "learned", time_mode: str = "conv-only", **kwargs, ): self.video_kernel_size = video_kernel_size self.alpha = alpha self.merge_strategy = merge_strategy self.time_mode = time_mode assert ( self.time_mode in self.available_time_modes ), f"time_mode parameter has to be in {self.available_time_modes}" if self.time_mode != "attn-only": kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size) if self.time_mode not in ["conv-only", "only-last-conv"]: kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy) if self.time_mode not in ["attn-only", "only-last-conv"]: kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy) super().__init__(*args, **kwargs) def get_last_layer(self, skip_time_mix=False, **kwargs): if self.time_mode == "attn-only": raise NotImplementedError("TODO") else: return ( self.conv_out.time_mix_conv.weight if not skip_time_mix else self.conv_out.weight )
import logging import math from typing import Dict, Optional import numpy as np import torch import torch.nn as nn from .. import attention from einops import rearrange, repeat def default(x, y): if x is not None: return x return y class Mlp(nn.Module): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, norm_layer=None, bias=True, drop=0., use_conv=False, dtype=None, device=None, operations=None, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features drop_probs = drop linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs) self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device) self.drop2 = nn.Dropout(drop_probs) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop1(x) x = self.norm(x) x = self.fc2(x) x = self.drop2(x) return x class PatchEmbed(nn.Module): """ 2D Image to Patch Embedding """ dynamic_img_pad: torch.jit.Final[bool] def __init__( self, img_size: Optional[int] = 224, patch_size: int = 16, in_chans: int = 3, embed_dim: int = 768, norm_layer = None, flatten: bool = True, bias: bool = True, strict_img_size: bool = True, dynamic_img_pad: bool = True, dtype=None, device=None, operations=None, ): super().__init__() self.patch_size = (patch_size, patch_size) if img_size is not None: self.img_size = (img_size, img_size) self.grid_size = tuple([s self.num_patches = self.grid_size[0] * self.grid_size[1] else: self.img_size = None self.grid_size = None self.num_patches = None self.flatten = flatten self.strict_img_size = strict_img_size self.dynamic_img_pad = dynamic_img_pad self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x): B, C, H, W = x.shape if self.dynamic_img_pad: pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0] pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1] x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect') x = self.proj(x) if self.flatten: x = x.flatten(2).transpose(1, 2) x = self.norm(x) return x def modulate(x, shift, scale): if shift is None: shift = torch.zeros_like(scale) return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) def get_2d_sincos_pos_embed( embed_dim, grid_size, cls_token=False, extra_tokens=0, scaling_factor=None, offset=None, ): """ grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) """ grid_h = np.arange(grid_size, dtype=np.float32) grid_w = np.arange(grid_size, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) grid = np.stack(grid, axis=0) if scaling_factor is not None: grid = grid / scaling_factor if offset is not None: grid = grid - offset grid = grid.reshape([2, 1, grid_size, grid_size]) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) if cls_token and extra_tokens > 0: pos_embed = np.concatenate( [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0 ) return pos_embed def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): assert embed_dim % 2 == 0 emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim emb = np.concatenate([emb_h, emb_w], axis=1) return emb def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) """ assert embed_dim % 2 == 0 omega = np.arange(embed_dim omega /= embed_dim / 2.0 omega = 1.0 / 10000**omega pos = pos.reshape(-1) out = np.einsum("m,d->md", pos, omega) emb_sin = np.sin(out) emb_cos = np.cos(out) emb = np.concatenate([emb_sin, emb_cos], axis=1) return emb def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32): omega = torch.arange(embed_dim omega /= embed_dim / 2.0 omega = 1.0 / 10000**omega pos = pos.reshape(-1) out = torch.einsum("m,d->md", pos, omega) emb_sin = torch.sin(out) emb_cos = torch.cos(out) emb = torch.cat([emb_sin, emb_cos], dim=1) return emb def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32): small = min(h, w) val_h = (h / small) * val_magnitude val_w = (w / small) * val_magnitude grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij') emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim emb = torch.cat([emb_w, emb_h], dim=1) return emb class TimestepEmbedder(nn.Module): """ Embeds scalar timesteps into vector representations. """ def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): super().__init__() self.mlp = nn.Sequential( operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), nn.SiLU(), operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), ) self.frequency_embedding_size = frequency_embedding_size @staticmethod def timestep_embedding(t, dim, max_period=10000): """ Create sinusoidal timestep embeddings. :param t: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an (N, D) Tensor of positional embeddings. """ half = dim freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half ) args = t[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat( [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 ) if torch.is_floating_point(t): embedding = embedding.to(dtype=t.dtype) return embedding def forward(self, t, dtype, **kwargs): t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype) t_emb = self.mlp(t_freq) return t_emb class VectorEmbedder(nn.Module): """ Embeds a flat vector of dimension input_dim """ def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None): super().__init__() self.mlp = nn.Sequential( operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device), nn.SiLU(), operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), ) def forward(self, x: torch.Tensor) -> torch.Tensor: emb = self.mlp(x) return emb def split_qkv(qkv, head_dim): qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0) return qkv[0], qkv[1], qkv[2] def optimized_attention(qkv, num_heads): return attention.optimized_attention(qkv[0], qkv[1], qkv[2], num_heads) class SelfAttention(nn.Module): ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") def __init__( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, qk_scale: Optional[float] = None, proj_drop: float = 0.0, attn_mode: str = "xformers", pre_only: bool = False, qk_norm: Optional[str] = None, rmsnorm: bool = False, dtype=None, device=None, operations=None, ): super().__init__() self.num_heads = num_heads self.head_dim = dim self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) if not pre_only: self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) self.proj_drop = nn.Dropout(proj_drop) assert attn_mode in self.ATTENTION_MODES self.attn_mode = attn_mode self.pre_only = pre_only if qk_norm == "rms": self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) elif qk_norm == "ln": self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) elif qk_norm is None: self.ln_q = nn.Identity() self.ln_k = nn.Identity() else: raise ValueError(qk_norm) def pre_attention(self, x: torch.Tensor) -> torch.Tensor: B, L, C = x.shape qkv = self.qkv(x) q, k, v = split_qkv(qkv, self.head_dim) q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1) k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1) return (q, k, v) def post_attention(self, x: torch.Tensor) -> torch.Tensor: assert not self.pre_only x = self.proj(x) x = self.proj_drop(x) return x def forward(self, x: torch.Tensor) -> torch.Tensor: qkv = self.pre_attention(x) x = optimized_attention( qkv, num_heads=self.num_heads ) x = self.post_attention(x) return x class RMSNorm(torch.nn.Module): def __init__( self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None ): """ Initialize the RMSNorm normalization layer. Args: dim (int): The dimension of the input tensor. eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. Attributes: eps (float): A small value added to the denominator for numerical stability. weight (nn.Parameter): Learnable scaling parameter. """ super().__init__() self.eps = eps self.learnable_scale = elementwise_affine if self.learnable_scale: self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) else: self.register_parameter("weight", None) def _norm(self, x): """ Apply the RMSNorm normalization to the input tensor. Args: x (torch.Tensor): The input tensor. Returns: torch.Tensor: The normalized tensor. """ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x): """ Forward pass through the RMSNorm layer. Args: x (torch.Tensor): The input tensor. Returns: torch.Tensor: The output tensor after applying RMSNorm. """ x = self._norm(x) if self.learnable_scale: return x * self.weight.to(device=x.device, dtype=x.dtype) else: return x class SwiGLUFeedForward(nn.Module): def __init__( self, dim: int, hidden_dim: int, multiple_of: int, ffn_dim_multiplier: Optional[float] = None, ): """ Initialize the FeedForward module. Args: dim (int): Input dimension. hidden_dim (int): Hidden dimension of the feedforward layer. multiple_of (int): Value to ensure hidden dimension is a multiple of this value. ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None. Attributes: w1 (ColumnParallelLinear): Linear transformation for the first layer. w2 (RowParallelLinear): Linear transformation for the second layer. w3 (ColumnParallelLinear): Linear transformation for the third layer. """ super().__init__() hidden_dim = int(2 * hidden_dim / 3) if ffn_dim_multiplier is not None: hidden_dim = int(ffn_dim_multiplier * hidden_dim) hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) self.w1 = nn.Linear(dim, hidden_dim, bias=False) self.w2 = nn.Linear(hidden_dim, dim, bias=False) self.w3 = nn.Linear(dim, hidden_dim, bias=False) def forward(self, x): return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x)) class DismantledBlock(nn.Module): """ A DiT block with gated adaptive layer norm (adaLN) conditioning. """ ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") def __init__( self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0, attn_mode: str = "xformers", qkv_bias: bool = False, pre_only: bool = False, rmsnorm: bool = False, scale_mod_only: bool = False, swiglu: bool = False, qk_norm: Optional[str] = None, dtype=None, device=None, operations=None, **block_kwargs, ): super().__init__() assert attn_mode in self.ATTENTION_MODES if not rmsnorm: self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) else: self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.attn = SelfAttention( dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=pre_only, qk_norm=qk_norm, rmsnorm=rmsnorm, dtype=dtype, device=device, operations=operations ) if not pre_only: if not rmsnorm: self.norm2 = operations.LayerNorm( hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device ) else: self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) mlp_hidden_dim = int(hidden_size * mlp_ratio) if not pre_only: if not swiglu: self.mlp = Mlp( in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations ) else: self.mlp = SwiGLUFeedForward( dim=hidden_size, hidden_dim=mlp_hidden_dim, multiple_of=256, ) self.scale_mod_only = scale_mod_only if not scale_mod_only: n_mods = 6 if not pre_only else 2 else: n_mods = 4 if not pre_only else 1 self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device) ) self.pre_only = pre_only def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: if not self.pre_only: if not self.scale_mod_only: ( shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, ) = self.adaLN_modulation(c).chunk(6, dim=1) else: shift_msa = None shift_mlp = None ( scale_msa, gate_msa, scale_mlp, gate_mlp, ) = self.adaLN_modulation( c ).chunk(4, dim=1) qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) return qkv, ( x, gate_msa, shift_mlp, scale_mlp, gate_mlp, ) else: if not self.scale_mod_only: ( shift_msa, scale_msa, ) = self.adaLN_modulation( c ).chunk(2, dim=1) else: shift_msa = None scale_msa = self.adaLN_modulation(c) qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) return qkv, None def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp): assert not self.pre_only x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn) x = x + gate_mlp.unsqueeze(1) * self.mlp( modulate(self.norm2(x), shift_mlp, scale_mlp) ) return x def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: assert not self.pre_only qkv, intermediates = self.pre_attention(x, c) attn = optimized_attention( qkv, num_heads=self.attn.num_heads, ) return self.post_attention(attn, *intermediates) def block_mixing(*args, use_checkpoint=True, **kwargs): if use_checkpoint: return torch.utils.checkpoint.checkpoint( _block_mixing, *args, use_reentrant=False, **kwargs ) else: return _block_mixing(*args, **kwargs) def _block_mixing(context, x, context_block, x_block, c): context_qkv, context_intermediates = context_block.pre_attention(context, c) x_qkv, x_intermediates = x_block.pre_attention(x, c) o = [] for t in range(3): o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1)) qkv = tuple(o) attn = optimized_attention( qkv, num_heads=x_block.attn.num_heads, ) context_attn, x_attn = ( attn[:, : context_qkv[0].shape[1]], attn[:, context_qkv[0].shape[1] :], ) if not context_block.pre_only: context = context_block.post_attention(context_attn, *context_intermediates) else: context = None x = x_block.post_attention(x_attn, *x_intermediates) return context, x class JointBlock(nn.Module): """just a small wrapper to serve as a fsdp unit""" def __init__( self, *args, **kwargs, ): super().__init__() pre_only = kwargs.pop("pre_only") qk_norm = kwargs.pop("qk_norm", None) self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs) self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, **kwargs) def forward(self, *args, **kwargs): return block_mixing( *args, context_block=self.context_block, x_block=self.x_block, **kwargs ) class FinalLayer(nn.Module): """ The final layer of DiT. """ def __init__( self, hidden_size: int, patch_size: int, out_channels: int, total_out_channels: Optional[int] = None, dtype=None, device=None, operations=None, ): super().__init__() self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = ( operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) if (total_out_channels is None) else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device) ) self.adaLN_modulation = nn.Sequential( nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) ) def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) x = modulate(self.norm_final(x), shift, scale) x = self.linear(x) return x class SelfAttentionContext(nn.Module): def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None): super().__init__() dim_head = dim inner_dim = dim self.heads = heads self.dim_head = dim_head self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device) self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device) def forward(self, x): qkv = self.qkv(x) q, k, v = split_qkv(qkv, self.dim_head) x = optimized_attention((q.reshape(q.shape[0], q.shape[1], -1), k, v), self.heads) return self.proj(x) class ContextProcessorBlock(nn.Module): def __init__(self, context_size, dtype=None, device=None, operations=None): super().__init__() self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations) self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations) def forward(self, x): x += self.attn(self.norm1(x)) x += self.mlp(self.norm2(x)) return x class ContextProcessor(nn.Module): def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None): super().__init__() self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)]) self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) def forward(self, x): for i, l in enumerate(self.layers): x = l(x) return self.norm(x) class MMDiT(nn.Module): """ Diffusion model with a Transformer backbone. """ def __init__( self, input_size: int = 32, patch_size: int = 2, in_channels: int = 4, depth: int = 28, mlp_ratio: float = 4.0, learn_sigma: bool = False, adm_in_channels: Optional[int] = None, context_embedder_config: Optional[Dict] = None, compile_core: bool = False, use_checkpoint: bool = False, register_length: int = 0, attn_mode: str = "torch", rmsnorm: bool = False, scale_mod_only: bool = False, swiglu: bool = False, out_channels: Optional[int] = None, pos_embed_scaling_factor: Optional[float] = None, pos_embed_offset: Optional[float] = None, pos_embed_max_size: Optional[int] = None, num_patches = None, qk_norm: Optional[str] = None, qkv_bias: bool = True, context_processor_layers = None, context_size = 4096, dtype = None, device = None, operations = None, ): super().__init__() self.dtype = dtype self.learn_sigma = learn_sigma self.in_channels = in_channels default_out_channels = in_channels * 2 if learn_sigma else in_channels self.out_channels = default(out_channels, default_out_channels) self.patch_size = patch_size self.pos_embed_scaling_factor = pos_embed_scaling_factor self.pos_embed_offset = pos_embed_offset self.pos_embed_max_size = pos_embed_max_size self.hidden_size = 64 * depth num_heads = depth self.num_heads = num_heads self.x_embedder = PatchEmbed( input_size, patch_size, in_channels, self.hidden_size, bias=True, strict_img_size=self.pos_embed_max_size is None, dtype=dtype, device=device, operations=operations ) self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations) self.y_embedder = None if adm_in_channels is not None: assert isinstance(adm_in_channels, int) self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations) if context_processor_layers is not None: self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations) else: self.context_processor = None self.context_embedder = nn.Identity() if context_embedder_config is not None: if context_embedder_config["target"] == "torch.nn.Linear": self.context_embedder = operations.Linear(**context_embedder_config["params"], dtype=dtype, device=device) self.register_length = register_length if self.register_length > 0: self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device)) if num_patches is not None: self.register_buffer( "pos_embed", torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device), ) else: self.pos_embed = None self.use_checkpoint = use_checkpoint self.joint_blocks = nn.ModuleList( [ JointBlock( self.hidden_size, num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=i == depth - 1, rmsnorm=rmsnorm, scale_mod_only=scale_mod_only, swiglu=swiglu, qk_norm=qk_norm, dtype=dtype, device=device, operations=operations ) for i in range(depth) ] ) self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) if compile_core: assert False self.forward_core_with_concat = torch.compile(self.forward_core_with_concat) def cropped_pos_embed(self, hw, device=None): p = self.x_embedder.patch_size[0] h, w = hw h = (h + 1) w = (w + 1) if self.pos_embed is None: return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device) assert self.pos_embed_max_size is not None assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size) assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size) top = (self.pos_embed_max_size - h) left = (self.pos_embed_max_size - w) spatial_pos_embed = rearrange( self.pos_embed, "1 (h w) c -> 1 h w c", h=self.pos_embed_max_size, w=self.pos_embed_max_size, ) spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :] spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c") return spatial_pos_embed def unpatchify(self, x, hw=None): """ x: (N, T, patch_size**2 * C) imgs: (N, H, W, C) """ c = self.out_channels p = self.x_embedder.patch_size[0] if hw is None: h = w = int(x.shape[1] ** 0.5) else: h, w = hw h = (h + 1) w = (w + 1) assert h * w == x.shape[1] x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) x = torch.einsum("nhwpqc->nchpwq", x) imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) return imgs def forward_core_with_concat( self, x: torch.Tensor, c_mod: torch.Tensor, context: Optional[torch.Tensor] = None, ) -> torch.Tensor: if self.register_length > 0: context = torch.cat( ( repeat(self.register, "1 ... -> b ...", b=x.shape[0]), default(context, torch.Tensor([]).type_as(x)), ), 1, ) for block in self.joint_blocks: context, x = block( context, x, c=c_mod, use_checkpoint=self.use_checkpoint, ) x = self.final_layer(x, c_mod) return x def forward( self, x: torch.Tensor, t: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ Forward pass of DiT. x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) t: (N,) tensor of diffusion timesteps y: (N,) tensor of class labels """ if self.context_processor is not None: context = self.context_processor(context) hw = x.shape[-2:] x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device) c = self.t_embedder(t, dtype=x.dtype) if y is not None and self.y_embedder is not None: y = self.y_embedder(y) c = c + y if context is not None: context = self.context_embedder(context) x = self.forward_core_with_concat(x, c, context) x = self.unpatchify(x, hw=hw) return x[:,:,:hw[-2],:hw[-1]] class OpenAISignatureMMDITWrapper(MMDiT): def forward( self, x: torch.Tensor, timesteps: torch.Tensor, context: Optional[torch.Tensor] = None, y: Optional[torch.Tensor] = None, **kwargs, ) -> torch.Tensor: return super().forward(x, timesteps, context=context, y=y)
import math import torch import torch.nn as nn import numpy as np from typing import Optional, Any import logging from comfy import model_management import comfy.ops ops = comfy.ops.disable_weight_init if model_management.xformers_enabled_vae(): import xformers import xformers.ops def get_timestep_embedding(timesteps, embedding_dim): """ This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ assert len(timesteps.shape) == 1 half_dim = embedding_dim emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) emb = emb.to(device=timesteps.device) emb = timesteps.float()[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: emb = torch.nn.functional.pad(emb, (0,1,0,0)) return emb def nonlinearity(x): return x*torch.sigmoid(x) def Normalize(in_channels, num_groups=32): return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) class Upsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: self.conv = ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): try: x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") except: b, c, h, w = x.shape out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device) split = 8 l = out.shape[1] for i in range(0, out.shape[1], l): out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype) del x x = out if self.with_conv: x = self.conv(x) return x class Downsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: self.conv = ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) def forward(self, x): if self.with_conv: pad = (0,1,0,1) x = torch.nn.functional.pad(x, pad, mode="constant", value=0) x = self.conv(x) else: x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) return x class ResnetBlock(nn.Module): def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512): super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.use_conv_shortcut = conv_shortcut self.swish = torch.nn.SiLU(inplace=True) self.norm1 = Normalize(in_channels) self.conv1 = ops.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels > 0: self.temb_proj = ops.Linear(temb_channels, out_channels) self.norm2 = Normalize(out_channels) self.dropout = torch.nn.Dropout(dropout, inplace=True) self.conv2 = ops.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = ops.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) else: self.nin_shortcut = ops.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) def forward(self, x, temb): h = x h = self.norm1(h) h = self.swish(h) h = self.conv1(h) if temb is not None: h = h + self.temb_proj(self.swish(temb))[:,:,None,None] h = self.norm2(h) h = self.swish(h) h = self.dropout(h) h = self.conv2(h) if self.in_channels != self.out_channels: if self.use_conv_shortcut: x = self.conv_shortcut(x) else: x = self.nin_shortcut(x) return x+h def slice_attention(q, k, v): r1 = torch.zeros_like(k, device=q.device) scale = (int(q.shape[-1])**(-0.5)) mem_free_total = model_management.get_free_memory(q.device) gb = 1024 ** 3 tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() modifier = 3 if q.element_size() == 2 else 2.5 mem_required = tensor_size * modifier steps = 1 if mem_required > mem_free_total: steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) while True: try: slice_size = q.shape[1] for i in range(0, q.shape[1], slice_size): end = i + slice_size s1 = torch.bmm(q[:, i:end], k) * scale s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1) del s1 r1[:, :, i:end] = torch.bmm(v, s2) del s2 break except model_management.OOM_EXCEPTION as e: model_management.soft_empty_cache(True) steps *= 2 if steps > 128: raise e logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) return r1 def normal_attention(q, k, v): b,c,h,w = q.shape q = q.reshape(b,c,h*w) q = q.permute(0,2,1) k = k.reshape(b,c,h*w) v = v.reshape(b,c,h*w) r1 = slice_attention(q, k, v) h_ = r1.reshape(b,c,h,w) del r1 return h_ def xformers_attention(q, k, v): B, C, H, W = q.shape q, k, v = map( lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), (q, k, v), ) try: out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) out = out.transpose(1, 2).reshape(B, C, H, W) except NotImplementedError as e: out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) return out def pytorch_attention(q, k, v): B, C, H, W = q.shape q, k, v = map( lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(), (q, k, v), ) try: out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) out = out.transpose(2, 3).reshape(B, C, H, W) except model_management.OOM_EXCEPTION as e: logging.warning("scaled_dot_product_attention OOMed: switched to slice attention") out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) return out class AttnBlock(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = Normalize(in_channels) self.q = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) if model_management.xformers_enabled_vae(): logging.info("Using xformers attention in VAE") self.optimized_attention = xformers_attention elif model_management.pytorch_attention_enabled(): logging.info("Using pytorch attention in VAE") self.optimized_attention = pytorch_attention else: logging.info("Using split attention in VAE") self.optimized_attention = normal_attention def forward(self, x): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) h_ = self.optimized_attention(q, k, v) h_ = self.proj_out(h_) return x+h_ def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): return AttnBlock(in_channels) class Model(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = self.ch*4 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.use_timestep = use_timestep if self.use_timestep: self.temb = nn.Module() self.temb.dense = nn.ModuleList([ ops.Linear(self.ch, self.temb_ch), ops.Linear(self.temb_ch, self.temb_ch), ]) self.conv_in = ops.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) curr_res = resolution in_ch_mult = (1,)+tuple(ch_mult) self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch*in_ch_mult[i_level] block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions-1: down.downsample = Downsample(block_in, resamp_with_conv) curr_res = curr_res self.down.append(down) self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch*ch_mult[i_level] skip_in = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks+1): if i_block == self.num_res_blocks: skip_in = ch*in_ch_mult[i_level] block.append(ResnetBlock(in_channels=block_in+skip_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) self.norm_out = Normalize(block_in) self.conv_out = ops.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, x, t=None, context=None): if context is not None: x = torch.cat((x, context), dim=1) if self.use_timestep: assert t is not None temb = get_timestep_embedding(t, self.ch) temb = self.temb.dense[0](temb) temb = nonlinearity(temb) temb = self.temb.dense[1](temb) else: temb = None hs = [self.conv_in(x)] for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](hs[-1], temb) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) hs.append(h) if i_level != self.num_resolutions-1: hs.append(self.down[i_level].downsample(hs[-1])) h = hs[-1] h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks+1): h = self.up[i_level].block[i_block]( torch.cat([h, hs.pop()], dim=1), temb) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: h = self.up[i_level].upsample(h) h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h def get_last_layer(self): return self.conv_out.weight class Encoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", **ignore_kwargs): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.conv_in = ops.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) curr_res = resolution in_ch_mult = (1,)+tuple(ch_mult) self.in_ch_mult = in_ch_mult self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch*in_ch_mult[i_level] block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions-1: down.downsample = Downsample(block_in, resamp_with_conv) curr_res = curr_res self.down.append(down) self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.norm_out = Normalize(block_in) self.conv_out = ops.Conv2d(block_in, 2*z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): temb = None h = self.conv_in(x) for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](h, temb) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) if i_level != self.num_resolutions-1: h = self.down[i_level].downsample(h) h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class Decoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, conv_out_op=ops.Conv2d, resnet_op=ResnetBlock, attn_op=AttnBlock, **ignorekwargs): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.give_pre_end = give_pre_end self.tanh_out = tanh_out in_ch_mult = (1,)+tuple(ch_mult) block_in = ch*ch_mult[self.num_resolutions-1] curr_res = resolution self.z_shape = (1,z_channels,curr_res,curr_res) logging.debug("Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape))) self.conv_in = ops.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) self.mid = nn.Module() self.mid.block_1 = resnet_op(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = attn_op(block_in) self.mid.block_2 = resnet_op(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks+1): block.append(resnet_op(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(attn_op(block_in)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) self.norm_out = Normalize(block_in) self.conv_out = conv_out_op(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, z, **kwargs): self.last_z_shape = z.shape temb = None h = self.conv_in(z) h = self.mid.block_1(h, temb, **kwargs) h = self.mid.attn_1(h, **kwargs) h = self.mid.block_2(h, temb, **kwargs) for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks+1): h = self.up[i_level].block[i_block](h, temb, **kwargs) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h, **kwargs) if i_level != 0: h = self.up[i_level].upsample(h) if self.give_pre_end: return h h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h, **kwargs) if self.tanh_out: h = torch.tanh(h) return h
from abc import abstractmethod import torch as th import torch.nn as nn import torch.nn.functional as F from einops import rearrange import logging from .util import ( checkpoint, avg_pool_nd, zero_module, timestep_embedding, AlphaBlender, ) from ..attention import SpatialTransformer, SpatialVideoTransformer, default from comfy.ldm.util import exists import comfy.ops ops = comfy.ops.disable_weight_init class TimestepBlock(nn.Module): """ Any module where forward() takes timestep embeddings as a second argument. """ @abstractmethod def forward(self, x, emb): """ Apply the module to `x` given `emb` timestep embeddings. """ def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None): for layer in ts: if isinstance(layer, VideoResBlock): x = layer(x, emb, num_video_frames, image_only_indicator) elif isinstance(layer, TimestepBlock): x = layer(x, emb) elif isinstance(layer, SpatialVideoTransformer): x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options) if "transformer_index" in transformer_options: transformer_options["transformer_index"] += 1 elif isinstance(layer, SpatialTransformer): x = layer(x, context, transformer_options) if "transformer_index" in transformer_options: transformer_options["transformer_index"] += 1 elif isinstance(layer, Upsample): x = layer(x, output_shape=output_shape) else: x = layer(x) return x class TimestepEmbedSequential(nn.Sequential, TimestepBlock): """ A sequential module that passes timestep embeddings to the children that support it as an extra input. """ def forward(self, *args, **kwargs): return forward_timestep_embed(self, *args, **kwargs) class Upsample(nn.Module): """ An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then upsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device) def forward(self, x, output_shape=None): assert x.shape[1] == self.channels if self.dims == 3: shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2] if output_shape is not None: shape[1] = output_shape[3] shape[2] = output_shape[4] else: shape = [x.shape[2] * 2, x.shape[3] * 2] if output_shape is not None: shape[0] = output_shape[2] shape[1] = output_shape[3] x = F.interpolate(x, size=shape, mode="nearest") if self.use_conv: x = self.conv(x) return x class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = operations.conv_nd( dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device ) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels return self.op(x) class ResBlock(TimestepBlock): """ A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels. :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param use_conv: if True and out_channels is specified, use a spatial convolution instead of a smaller 1x1 convolution to change the channels in the skip connection. :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for downsampling. """ def __init__( self, channels, emb_channels, dropout, out_channels=None, use_conv=False, use_scale_shift_norm=False, dims=2, use_checkpoint=False, up=False, down=False, kernel_size=3, exchange_temb_dims=False, skip_t_emb=False, dtype=None, device=None, operations=ops ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_conv = use_conv self.use_checkpoint = use_checkpoint self.use_scale_shift_norm = use_scale_shift_norm self.exchange_temb_dims = exchange_temb_dims if isinstance(kernel_size, list): padding = [k else: padding = kernel_size self.in_layers = nn.Sequential( operations.GroupNorm(32, channels, dtype=dtype, device=device), nn.SiLU(), operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device), ) self.updown = up or down if up: self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device) self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device) elif down: self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device) self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device) else: self.h_upd = self.x_upd = nn.Identity() self.skip_t_emb = skip_t_emb if self.skip_t_emb: self.emb_layers = None self.exchange_temb_dims = False else: self.emb_layers = nn.Sequential( nn.SiLU(), operations.Linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device ), ) self.out_layers = nn.Sequential( operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device), nn.SiLU(), nn.Dropout(p=dropout), operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device) , ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = operations.conv_nd( dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device ) else: self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) def forward(self, x, emb): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ return checkpoint( self._forward, (x, emb), self.parameters(), self.use_checkpoint ) def _forward(self, x, emb): if self.updown: in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] h = in_rest(x) h = self.h_upd(h) x = self.x_upd(x) h = in_conv(h) else: h = self.in_layers(x) emb_out = None if not self.skip_t_emb: emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] h = out_norm(h) if emb_out is not None: scale, shift = th.chunk(emb_out, 2, dim=1) h *= (1 + scale) h += shift h = out_rest(h) else: if emb_out is not None: if self.exchange_temb_dims: emb_out = emb_out.movedim(1, 2) h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class VideoResBlock(ResBlock): def __init__( self, channels: int, emb_channels: int, dropout: float, video_kernel_size=3, merge_strategy: str = "fixed", merge_factor: float = 0.5, out_channels=None, use_conv: bool = False, use_scale_shift_norm: bool = False, dims: int = 2, use_checkpoint: bool = False, up: bool = False, down: bool = False, dtype=None, device=None, operations=ops ): super().__init__( channels, emb_channels, dropout, out_channels=out_channels, use_conv=use_conv, use_scale_shift_norm=use_scale_shift_norm, dims=dims, use_checkpoint=use_checkpoint, up=up, down=down, dtype=dtype, device=device, operations=operations ) self.time_stack = ResBlock( default(out_channels, channels), emb_channels, dropout=dropout, dims=3, out_channels=default(out_channels, channels), use_scale_shift_norm=False, use_conv=False, up=False, down=False, kernel_size=video_kernel_size, use_checkpoint=use_checkpoint, exchange_temb_dims=True, dtype=dtype, device=device, operations=operations ) self.time_mixer = AlphaBlender( alpha=merge_factor, merge_strategy=merge_strategy, rearrange_pattern="b t -> b 1 t 1 1", ) def forward( self, x: th.Tensor, emb: th.Tensor, num_video_frames: int, image_only_indicator = None, ) -> th.Tensor: x = super().forward(x, emb) x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) x = self.time_stack( x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames) ) x = self.time_mixer( x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator ) x = rearrange(x, "b c t h w -> (b t) c h w") return x class Timestep(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim def forward(self, t): return timestep_embedding(t, self.dim) def apply_control(h, control, name): if control is not None and name in control and len(control[name]) > 0: ctrl = control[name].pop() if ctrl is not None: try: h += ctrl except: logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape)) return h class UNetModel(nn.Module): """ The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this model will be class-conditional with `num_classes` classes. :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use a fixed channel width per attention head. :param num_heads_upsample: works with num_heads to set a different number of heads for upsampling. Deprecated. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, image_size, in_channels, model_channels, out_channels, num_res_blocks, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, dtype=th.float32, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, transformer_depth=1, context_dim=None, n_embed=None, legacy=True, disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, use_linear_in_transformer=False, adm_in_channels=None, transformer_depth_middle=None, transformer_depth_output=None, use_temporal_resblock=False, use_temporal_attention=False, time_context_dim=None, extra_ff_mix_layer=False, use_spatial_context=False, merge_strategy=None, merge_factor=0.0, video_kernel_size=None, disable_temporal_crossattention=False, max_ddpm_temb_period=10000, attn_precision=None, device=None, operations=ops, ): super().__init__() if context_dim is not None: assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' if num_heads_upsample == -1: num_heads_upsample = num_heads if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels if isinstance(num_res_blocks, int): self.num_res_blocks = len(channel_mult) * [num_res_blocks] else: if len(num_res_blocks) != len(channel_mult): raise ValueError("provide num_res_blocks either as an int (globally constant) or " "as a list/tuple (per-level) with the same length as channel_mult") self.num_res_blocks = num_res_blocks if disable_self_attentions is not None: assert len(disable_self_attentions) == len(channel_mult) if num_attention_blocks is not None: assert len(num_attention_blocks) == len(self.num_res_blocks) transformer_depth = transformer_depth[:] transformer_depth_output = transformer_depth_output[:] self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = dtype self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.use_temporal_resblocks = use_temporal_resblock self.predict_codebook_ids = n_embed is not None self.default_num_video_frames = None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) if self.num_classes is not None: if isinstance(self.num_classes, int): self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device) elif self.num_classes == "continuous": logging.debug("setting up linear c_adm embedding layer") self.label_emb = nn.Linear(1, time_embed_dim) elif self.num_classes == "sequential": assert adm_in_channels is not None self.label_emb = nn.Sequential( nn.Sequential( operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) ) else: raise ValueError() self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 def get_attention_layer( ch, num_heads, dim_head, depth=1, context_dim=None, use_checkpoint=False, disable_self_attn=False, ): if use_temporal_attention: return SpatialVideoTransformer( ch, num_heads, dim_head, depth=depth, context_dim=context_dim, time_context_dim=time_context_dim, dropout=dropout, ff_in=extra_ff_mix_layer, use_spatial_context=use_spatial_context, merge_strategy=merge_strategy, merge_factor=merge_factor, checkpoint=use_checkpoint, use_linear=use_linear_in_transformer, disable_self_attn=disable_self_attn, disable_temporal_crossattention=disable_temporal_crossattention, max_time_embed_period=max_ddpm_temb_period, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ) else: return SpatialTransformer( ch, num_heads, dim_head, depth=depth, context_dim=context_dim, disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ) def get_resblock( merge_factor, merge_strategy, video_kernel_size, ch, time_embed_dim, dropout, out_channels, dims, use_checkpoint, use_scale_shift_norm, down=False, up=False, dtype=None, device=None, operations=ops ): if self.use_temporal_resblocks: return VideoResBlock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, channels=ch, emb_channels=time_embed_dim, dropout=dropout, out_channels=out_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=down, up=up, dtype=dtype, device=device, operations=operations ) else: return ResBlock( channels=ch, emb_channels=time_embed_dim, dropout=dropout, out_channels=out_channels, use_checkpoint=use_checkpoint, dims=dims, use_scale_shift_norm=use_scale_shift_norm, down=down, up=up, dtype=dtype, device=device, operations=operations ) for level, mult in enumerate(channel_mult): for nr in range(self.num_res_blocks[level]): layers = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations, ) ] ch = mult * model_channels num_transformers = transformer_depth.pop(0) if num_transformers > 0: if num_head_channels == -1: dim_head = ch else: num_heads = ch dim_head = num_head_channels if legacy: dim_head = ch if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: layers.append(get_attention_layer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch else: num_heads = ch dim_head = num_head_channels if legacy: dim_head = ch mid_block = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=None, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] self.middle_block = None if transformer_depth_middle >= -1: if transformer_depth_middle >= 0: mid_block += [get_attention_layer( ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint ), get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=None, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] self.middle_block = TimestepEmbedSequential(*mid_block) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(self.num_res_blocks[level] + 1): ich = input_block_chans.pop() layers = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch + ich, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=model_channels * mult, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations ) ] ch = model_channels * mult num_transformers = transformer_depth_output.pop() if num_transformers > 0: if num_head_channels == -1: dim_head = ch else: num_heads = ch dim_head = num_head_channels if legacy: dim_head = ch if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or i < num_attention_blocks[level]: layers.append( get_attention_layer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint ) ) if level and i == self.num_res_blocks[level]: out_ch = ch layers.append( get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations) ) ds self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( operations.GroupNorm(32, ch, dtype=self.dtype, device=device), nn.SiLU(), zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( operations.GroupNorm(32, ch, dtype=self.dtype, device=device), operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), ) def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param context: conditioning plugged in via crossattn :param y: an [N] Tensor of labels, if class-conditional. :return: an [N x C x ...] Tensor of outputs. """ transformer_options["original_shape"] = list(x.shape) transformer_options["transformer_index"] = 0 transformer_patches = transformer_options.get("patches", {}) num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) image_only_indicator = kwargs.get("image_only_indicator", None) time_context = kwargs.get("time_context", None) assert (y is not None) == ( self.num_classes is not None ), "must specify y if and only if the model is class-conditional" hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) h = x for id, module in enumerate(self.input_blocks): transformer_options["block"] = ("input", id) h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'input') if "input_block_patch" in transformer_patches: patch = transformer_patches["input_block_patch"] for p in patch: h = p(h, transformer_options) hs.append(h) if "input_block_patch_after_skip" in transformer_patches: patch = transformer_patches["input_block_patch_after_skip"] for p in patch: h = p(h, transformer_options) transformer_options["block"] = ("middle", 0) if self.middle_block is not None: h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'middle') for id, module in enumerate(self.output_blocks): transformer_options["block"] = ("output", id) hsp = hs.pop() hsp = apply_control(hsp, control, 'output') if "output_block_patch" in transformer_patches: patch = transformer_patches["output_block_patch"] for p in patch: h, hsp = p(h, hsp, transformer_options) h = th.cat([h, hsp], dim=1) del hsp if len(hs) > 0: output_shape = hs[-1].shape else: output_shape = None h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) else: return self.out(h)
import torch import torch.nn as nn import numpy as np from functools import partial from .util import extract_into_tensor, make_beta_schedule from comfy.ldm.util import default class AbstractLowScaleModel(nn.Module): def __init__(self, noise_schedule_config=None): super(AbstractLowScaleModel, self).__init__() if noise_schedule_config is not None: self.register_schedule(**noise_schedule_config) def register_schedule(self, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) def q_sample(self, x_start, t, noise=None, seed=None): if noise is None: if seed is None: noise = torch.randn_like(x_start) else: noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device) return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise) def forward(self, x): return x, None def decode(self, x): return x class SimpleImageConcat(AbstractLowScaleModel): def __init__(self): super(SimpleImageConcat, self).__init__(noise_schedule_config=None) self.max_noise_level = 0 def forward(self, x): return x, torch.zeros(x.shape[0], device=x.device).long() class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): super().__init__(noise_schedule_config=noise_schedule_config) self.max_noise_level = max_noise_level def forward(self, x, noise_level=None, seed=None): if noise_level is None: noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() else: assert isinstance(noise_level, torch.Tensor) z = self.q_sample(x, noise_level, seed=seed) return z, noise_level
import os import math import torch import torch.nn as nn import numpy as np from einops import repeat, rearrange from comfy.ldm.util import instantiate_from_config class AlphaBlender(nn.Module): strategies = ["learned", "fixed", "learned_with_images"] def __init__( self, alpha: float, merge_strategy: str = "learned_with_images", rearrange_pattern: str = "b t -> (b t) 1 1", ): super().__init__() self.merge_strategy = merge_strategy self.rearrange_pattern = rearrange_pattern assert ( merge_strategy in self.strategies ), f"merge_strategy needs to be in {self.strategies}" if self.merge_strategy == "fixed": self.register_buffer("mix_factor", torch.Tensor([alpha])) elif ( self.merge_strategy == "learned" or self.merge_strategy == "learned_with_images" ): self.register_parameter( "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) ) else: raise ValueError(f"unknown merge strategy {self.merge_strategy}") def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor: if self.merge_strategy == "fixed": alpha = self.mix_factor.to(device) elif self.merge_strategy == "learned": alpha = torch.sigmoid(self.mix_factor.to(device)) elif self.merge_strategy == "learned_with_images": if image_only_indicator is None: alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1") else: alpha = torch.where( image_only_indicator.bool(), torch.ones(1, 1, device=image_only_indicator.device), rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"), ) alpha = rearrange(alpha, self.rearrange_pattern) else: raise NotImplementedError() return alpha def forward( self, x_spatial, x_temporal, image_only_indicator=None, ) -> torch.Tensor: alpha = self.get_alpha(image_only_indicator, x_spatial.device) x = ( alpha.to(x_spatial.dtype) * x_spatial + (1.0 - alpha).to(x_spatial.dtype) * x_temporal ) return x def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if schedule == "linear": betas = ( torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 ) elif schedule == "cosine": timesteps = ( torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s ) alphas = timesteps / (1 + cosine_s) * np.pi / 2 alphas = torch.cos(alphas).pow(2) alphas = alphas / alphas[0] betas = 1 - alphas[1:] / alphas[:-1] betas = torch.clamp(betas, min=0, max=0.999) elif schedule == "squaredcos_cap_v2": return betas_for_alpha_bar( n_timestep, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, ) elif schedule == "sqrt_linear": betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) elif schedule == "sqrt": betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 else: raise ValueError(f"schedule '{schedule}' unknown.") return betas def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): if ddim_discr_method == 'uniform': c = num_ddpm_timesteps ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) elif ddim_discr_method == 'quad': ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) else: raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') steps_out = ddim_timesteps + 1 if verbose: print(f'Selected timesteps for ddim sampler: {steps_out}') return steps_out def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): alphas = alphacums[ddim_timesteps] alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) if verbose: print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') print(f'For the chosen value of eta, which is {eta}, ' f'this results in the following sigma_t schedule for ddim sampler {sigmas}') return sigmas, alphas, alphas_prev def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): """ Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. :param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t from 0 to 1 and produces the cumulative product of (1-beta) up to that part of the diffusion process. :param max_beta: the maximum beta to use; use values lower than 1 to prevent singularities. """ betas = [] for i in range(num_diffusion_timesteps): t1 = i / num_diffusion_timesteps t2 = (i + 1) / num_diffusion_timesteps betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) return np.array(betas) def extract_into_tensor(a, t, x_shape): b, *_ = t.shape out = a.gather(-1, t) return out.reshape(b, *((1,) * (len(x_shape) - 1))) def checkpoint(func, inputs, params, flag): """ Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pass. :param func: the function to evaluate. :param inputs: the argument sequence to pass to `func`. :param params: a sequence of parameters `func` depends on but does not explicitly take as arguments. :param flag: if False, disable gradient checkpointing. """ if flag: args = tuple(inputs) + tuple(params) return CheckpointFunction.apply(func, len(inputs), *args) else: return func(*inputs) class CheckpointFunction(torch.autograd.Function): @staticmethod def forward(ctx, run_function, length, *args): ctx.run_function = run_function ctx.input_tensors = list(args[:length]) ctx.input_params = list(args[length:]) ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), "dtype": torch.get_autocast_gpu_dtype(), "cache_enabled": torch.is_autocast_cache_enabled()} with torch.no_grad(): output_tensors = ctx.run_function(*ctx.input_tensors) return output_tensors @staticmethod def backward(ctx, *output_grads): ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] with torch.enable_grad(), \ torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): shallow_copies = [x.view_as(x) for x in ctx.input_tensors] output_tensors = ctx.run_function(*shallow_copies) input_grads = torch.autograd.grad( output_tensors, ctx.input_tensors + ctx.input_params, output_grads, allow_unused=True, ) del ctx.input_tensors del ctx.input_params del output_tensors return (None, None) + input_grads def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): """ Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings. """ if not repeat_only: half = dim freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half ) args = timesteps[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) else: embedding = repeat(timesteps, 'b -> b d', d=dim) return embedding def zero_module(module): """ Zero out the parameters of a module and return it. """ for p in module.parameters(): p.detach().zero_() return module def scale_module(module, scale): """ Scale the parameters of a module and return it. """ for p in module.parameters(): p.detach().mul_(scale) return module def mean_flat(tensor): """ Take the mean over all non-batch dimensions. """ return tensor.mean(dim=list(range(1, len(tensor.shape)))) def avg_pool_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D average pooling module. """ if dims == 1: return nn.AvgPool1d(*args, **kwargs) elif dims == 2: return nn.AvgPool2d(*args, **kwargs) elif dims == 3: return nn.AvgPool3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") class HybridConditioner(nn.Module): def __init__(self, c_concat_config, c_crossattn_config): super().__init__() self.concat_conditioner = instantiate_from_config(c_concat_config) self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) def forward(self, c_concat, c_crossattn): c_concat = self.concat_conditioner(c_concat) c_crossattn = self.crossattn_conditioner(c_crossattn) return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} def noise_like(shape, device, repeat=False): repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) noise = lambda: torch.randn(shape, device=device) return repeat_noise() if repeat else noise()

import torch import numpy as np class AbstractDistribution: def sample(self): raise NotImplementedError() def mode(self): raise NotImplementedError() class DiracDistribution(AbstractDistribution): def __init__(self, value): self.value = value def sample(self): return self.value def mode(self): return self.value class DiagonalGaussianDistribution(object): def __init__(self, parameters, deterministic=False): self.parameters = parameters self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) self.logvar = torch.clamp(self.logvar, -30.0, 20.0) self.deterministic = deterministic self.std = torch.exp(0.5 * self.logvar) self.var = torch.exp(self.logvar) if self.deterministic: self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) def sample(self): x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) return x def kl(self, other=None): if self.deterministic: return torch.Tensor([0.]) else: if other is None: return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3]) else: return 0.5 * torch.sum( torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar, dim=[1, 2, 3]) def nll(self, sample, dims=[1,2,3]): if self.deterministic: return torch.Tensor([0.]) logtwopi = np.log(2.0 * np.pi) return 0.5 * torch.sum( logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims) def mode(self): return self.mean def normal_kl(mean1, logvar1, mean2, logvar2): """ source: https: Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other use cases. """ tensor = None for obj in (mean1, logvar1, mean2, logvar2): if isinstance(obj, torch.Tensor): tensor = obj break assert tensor is not None, "at least one argument must be a Tensor" logvar1, logvar2 = [ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) for x in (logvar1, logvar2) ] return 0.5 * ( -1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) )

from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation from ..diffusionmodules.openaimodel import Timestep import torch class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs): super().__init__(*args, **kwargs) if clip_stats_path is None: clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) else: clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu") self.register_buffer("data_mean", clip_mean[None, :], persistent=False) self.register_buffer("data_std", clip_std[None, :], persistent=False) self.time_embed = Timestep(timestep_dim) def scale(self, x): x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device) return x def unscale(self, x): x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device) return x def forward(self, x, noise_level=None, seed=None): if noise_level is None: noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() else: assert isinstance(noise_level, torch.Tensor) x = self.scale(x) z = self.q_sample(x, noise_level, seed=seed) z = self.unscale(z) noise_level = self.time_embed(noise_level) return z, noise_level

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import torch import torch.nn as nn from collections import OrderedDict def conv_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D convolution module. """ if dims == 1: return nn.Conv1d(*args, **kwargs) elif dims == 2: return nn.Conv2d(*args, **kwargs) elif dims == 3: return nn.Conv3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") def avg_pool_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D average pooling module. """ if dims == 1: return nn.AvgPool1d(*args, **kwargs) elif dims == 2: return nn.AvgPool2d(*args, **kwargs) elif dims == 3: return nn.AvgPool3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = conv_nd( dims, self.channels, self.out_channels, 3, stride=stride, padding=padding ) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels if not self.use_conv: padding = [x.shape[2] % 2, x.shape[3] % 2] self.op.padding = padding x = self.op(x) return x class ResnetBlock(nn.Module): def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): super().__init__() ps = ksize if in_c != out_c or sk == False: self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) else: self.in_conv = None self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) self.act = nn.ReLU() self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) if sk == False: self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) else: self.skep = None self.down = down if self.down == True: self.down_opt = Downsample(in_c, use_conv=use_conv) def forward(self, x): if self.down == True: x = self.down_opt(x) if self.in_conv is not None: x = self.in_conv(x) h = self.block1(x) h = self.act(h) h = self.block2(h) if self.skep is not None: return h + self.skep(x) else: return h + x class Adapter(nn.Module): def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True): super(Adapter, self).__init__() self.unshuffle_amount = 8 resblock_no_downsample = [] resblock_downsample = [3, 2, 1] self.xl = xl if self.xl: self.unshuffle_amount = 16 resblock_no_downsample = [1] resblock_downsample = [2] self.input_channels = cin self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) self.channels = channels self.nums_rb = nums_rb self.body = [] for i in range(len(channels)): for j in range(nums_rb): if (i in resblock_downsample) and (j == 0): self.body.append( ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv)) elif (i in resblock_no_downsample) and (j == 0): self.body.append( ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) else: self.body.append( ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) self.body = nn.ModuleList(self.body) self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) def forward(self, x): x = self.unshuffle(x) features = [] x = self.conv_in(x) for i in range(len(self.channels)): for j in range(self.nums_rb): idx = i * self.nums_rb + j x = self.body[idx](x) if self.xl: features.append(None) if i == 0: features.append(None) features.append(None) if i == 2: features.append(None) else: features.append(None) features.append(None) features.append(x) return features class LayerNorm(nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16.""" def forward(self, x: torch.Tensor): orig_type = x.dtype ret = super().forward(x.type(torch.float32)) return ret.type(orig_type) class QuickGELU(nn.Module): def forward(self, x: torch.Tensor): return x * torch.sigmoid(1.702 * x) class ResidualAttentionBlock(nn.Module): def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): super().__init__() self.attn = nn.MultiheadAttention(d_model, n_head) self.ln_1 = LayerNorm(d_model) self.mlp = nn.Sequential( OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), ("c_proj", nn.Linear(d_model * 4, d_model))])) self.ln_2 = LayerNorm(d_model) self.attn_mask = attn_mask def attention(self, x: torch.Tensor): self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] def forward(self, x: torch.Tensor): x = x + self.attention(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x class StyleAdapter(nn.Module): def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4): super().__init__() scale = width ** -0.5 self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)]) self.num_token = num_token self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale) self.ln_post = LayerNorm(width) self.ln_pre = LayerNorm(width) self.proj = nn.Parameter(scale * torch.randn(width, context_dim)) def forward(self, x): style_embedding = self.style_embedding + torch.zeros( (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device) x = torch.cat([x, style_embedding], dim=1) x = self.ln_pre(x) x = x.permute(1, 0, 2) x = self.transformer_layes(x) x = x.permute(1, 0, 2) x = self.ln_post(x[:, -self.num_token:, :]) x = x @ self.proj return x class ResnetBlock_light(nn.Module): def __init__(self, in_c): super().__init__() self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1) self.act = nn.ReLU() self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1) def forward(self, x): h = self.block1(x) h = self.act(h) h = self.block2(h) return h + x class extractor(nn.Module): def __init__(self, in_c, inter_c, out_c, nums_rb, down=False): super().__init__() self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0) self.body = [] for _ in range(nums_rb): self.body.append(ResnetBlock_light(inter_c)) self.body = nn.Sequential(*self.body) self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0) self.down = down if self.down == True: self.down_opt = Downsample(in_c, use_conv=False) def forward(self, x): if self.down == True: x = self.down_opt(x) x = self.in_conv(x) x = self.body(x) x = self.out_conv(x) return x class Adapter_light(nn.Module): def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64): super(Adapter_light, self).__init__() self.unshuffle_amount = 8 self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) self.input_channels = cin self.channels = channels self.nums_rb = nums_rb self.body = [] self.xl = False for i in range(len(channels)): if i == 0: self.body.append(extractor(in_c=cin, inter_c=channels[i] else: self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i] self.body = nn.ModuleList(self.body) def forward(self, x): x = self.unshuffle(x) features = [] for i in range(len(self.channels)): x = self.body[i](x) features.append(None) features.append(None) features.append(x) return features
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""" Tiny AutoEncoder for Stable Diffusion (DNN for encoding / decoding SD's latent space) """ import torch import torch.nn as nn import comfy.utils import comfy.ops def conv(n_in, n_out, **kwargs): return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) class Clamp(nn.Module): def forward(self, x): return torch.tanh(x / 3) * 3 class Block(nn.Module): def __init__(self, n_in, n_out): super().__init__() self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() self.fuse = nn.ReLU() def forward(self, x): return self.fuse(self.conv(x) + self.skip(x)) def Encoder(latent_channels=4): return nn.Sequential( conv(3, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, latent_channels), ) def Decoder(latent_channels=4): return nn.Sequential( Clamp(), conv(latent_channels, 64), nn.ReLU(), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), conv(64, 3), ) class TAESD(nn.Module): latent_magnitude = 3 latent_shift = 0.5 def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4): """Initialize pretrained TAESD on the given device from the given checkpoints.""" super().__init__() self.taesd_encoder = Encoder(latent_channels=latent_channels) self.taesd_decoder = Decoder(latent_channels=latent_channels) self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) self.vae_shift = torch.nn.Parameter(torch.tensor(0.0)) if encoder_path is not None: self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True)) if decoder_path is not None: self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True)) @staticmethod def scale_latents(x): """raw latents -> [0, 1]""" return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) @staticmethod def unscale_latents(x): """[0, 1] -> raw latents""" return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) def decode(self, x): x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale) x_sample = x_sample.sub(0.5).mul(2) return x_sample def encode(self, x): return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift
import comfy.samplers import comfy.utils import torch import numpy as np from tqdm.auto import trange, tqdm import math @torch.no_grad() def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None): extra_args = {} if extra_args is None else extra_args if upscale_steps is None: upscale_steps = max(len(sigmas) else: upscale_steps += 1 upscale_steps = min(upscale_steps, len(sigmas) + 1) upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:] orig_shape = x.size() s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) x = denoised if i < len(upscales): x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled") if sigmas[i + 1] > 0: x += sigmas[i + 1] * torch.randn_like(x) return x class SamplerLCMUpscale: upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"] @classmethod def INPUT_TYPES(s): return {"required": {"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}), "scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}), "upscale_method": (s.upscale_methods,), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, scale_ratio, scale_steps, upscale_method): if scale_steps < 0: scale_steps = None sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method}) return (sampler, ) NODE_CLASS_MAPPINGS = { "SamplerLCMUpscale": SamplerLCMUpscale, }
import numpy as np import torch def loglinear_interp(t_steps, num_steps): """ Performs log-linear interpolation of a given array of decreasing numbers. """ xs = np.linspace(0, 1, len(t_steps)) ys = np.log(t_steps[::-1]) new_xs = np.linspace(0, 1, num_steps) new_ys = np.interp(new_xs, xs, ys) interped_ys = np.exp(new_ys)[::-1].copy() return interped_ys NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582], "SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582], "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]} class AlignYourStepsScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"model_type": (["SD1", "SDXL", "SVD"], ), "steps": ("INT", {"default": 10, "min": 10, "max": 10000}), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, model_type, steps, denoise): total_steps = steps if denoise < 1.0: if denoise <= 0.0: return (torch.FloatTensor([]),) total_steps = round(steps * denoise) sigmas = NOISE_LEVELS[model_type][:] if (steps + 1) != len(sigmas): sigmas = loglinear_interp(sigmas, steps + 1) sigmas = sigmas[-(total_steps + 1):] sigmas[-1] = 0 return (torch.FloatTensor(sigmas), ) NODE_CLASS_MAPPINGS = { "AlignYourStepsScheduler": AlignYourStepsScheduler, }
def attention_multiply(attn, model, q, k, v, out): m = model.clone() sd = model.model_state_dict() for key in sd: if key.endswith("{}.to_q.bias".format(attn)) or key.endswith("{}.to_q.weight".format(attn)): m.add_patches({key: (None,)}, 0.0, q) if key.endswith("{}.to_k.bias".format(attn)) or key.endswith("{}.to_k.weight".format(attn)): m.add_patches({key: (None,)}, 0.0, k) if key.endswith("{}.to_v.bias".format(attn)) or key.endswith("{}.to_v.weight".format(attn)): m.add_patches({key: (None,)}, 0.0, v) if key.endswith("{}.to_out.0.bias".format(attn)) or key.endswith("{}.to_out.0.weight".format(attn)): m.add_patches({key: (None,)}, 0.0, out) return m class UNetSelfAttentionMultiply: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing/attention_experiments" def patch(self, model, q, k, v, out): m = attention_multiply("attn1", model, q, k, v, out) return (m, ) class UNetCrossAttentionMultiply: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing/attention_experiments" def patch(self, model, q, k, v, out): m = attention_multiply("attn2", model, q, k, v, out) return (m, ) class CLIPAttentionMultiply: @classmethod def INPUT_TYPES(s): return {"required": { "clip": ("CLIP",), "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("CLIP",) FUNCTION = "patch" CATEGORY = "_for_testing/attention_experiments" def patch(self, clip, q, k, v, out): m = clip.clone() sd = m.patcher.model_state_dict() for key in sd: if key.endswith("self_attn.q_proj.weight") or key.endswith("self_attn.q_proj.bias"): m.add_patches({key: (None,)}, 0.0, q) if key.endswith("self_attn.k_proj.weight") or key.endswith("self_attn.k_proj.bias"): m.add_patches({key: (None,)}, 0.0, k) if key.endswith("self_attn.v_proj.weight") or key.endswith("self_attn.v_proj.bias"): m.add_patches({key: (None,)}, 0.0, v) if key.endswith("self_attn.out_proj.weight") or key.endswith("self_attn.out_proj.bias"): m.add_patches({key: (None,)}, 0.0, out) return (m, ) class UNetTemporalAttentionMultiply: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "self_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "self_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "cross_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "cross_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing/attention_experiments" def patch(self, model, self_structural, self_temporal, cross_structural, cross_temporal): m = model.clone() sd = model.model_state_dict() for k in sd: if (k.endswith("attn1.to_out.0.bias") or k.endswith("attn1.to_out.0.weight")): if '.time_stack.' in k: m.add_patches({k: (None,)}, 0.0, self_temporal) else: m.add_patches({k: (None,)}, 0.0, self_structural) elif (k.endswith("attn2.to_out.0.bias") or k.endswith("attn2.to_out.0.weight")): if '.time_stack.' in k: m.add_patches({k: (None,)}, 0.0, cross_temporal) else: m.add_patches({k: (None,)}, 0.0, cross_structural) return (m, ) NODE_CLASS_MAPPINGS = { "UNetSelfAttentionMultiply": UNetSelfAttentionMultiply, "UNetCrossAttentionMultiply": UNetCrossAttentionMultiply, "CLIPAttentionMultiply": CLIPAttentionMultiply, "UNetTemporalAttentionMultiply": UNetTemporalAttentionMultiply, }
import torchaudio import torch import comfy.model_management import folder_paths import os class EmptyLatentAudio: def __init__(self): self.device = comfy.model_management.intermediate_device() @classmethod def INPUT_TYPES(s): return {"required": {}} RETURN_TYPES = ("LATENT",) FUNCTION = "generate" CATEGORY = "_for_testing/audio" def generate(self): batch_size = 1 latent = torch.zeros([batch_size, 64, 1024], device=self.device) return ({"samples":latent, "type": "audio"}, ) class VAEEncodeAudio: @classmethod def INPUT_TYPES(s): return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}} RETURN_TYPES = ("LATENT",) FUNCTION = "encode" CATEGORY = "_for_testing/audio" def encode(self, vae, audio): t = vae.encode(audio["waveform"].movedim(1, -1)) return ({"samples":t}, ) class VAEDecodeAudio: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} RETURN_TYPES = ("AUDIO",) FUNCTION = "decode" CATEGORY = "_for_testing/audio" def decode(self, vae, samples): audio = vae.decode(samples["samples"]).movedim(-1, 1) return ({"waveform": audio, "sample_rate": 44100}, ) class SaveAudio: def __init__(self): self.output_dir = folder_paths.get_output_directory() self.type = "output" self.prefix_append = "" self.compress_level = 4 @classmethod def INPUT_TYPES(s): return {"required": { "audio": ("AUDIO", ), "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save_audio" OUTPUT_NODE = True CATEGORY = "_for_testing/audio" def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) results = list() for (batch_number, waveform) in enumerate(audio["waveform"]): filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) file = f"{filename_with_batch_num}_{counter:05}_.flac" torchaudio.save(os.path.join(full_output_folder, file), waveform, audio["sample_rate"], format="FLAC") results.append({ "filename": file, "subfolder": subfolder, "type": self.type }) counter += 1 return { "ui": { "audio": results } } class LoadAudio: @classmethod def INPUT_TYPES(s): input_dir = folder_paths.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] return {"required": {"audio": [sorted(files), ]}, } CATEGORY = "_for_testing/audio" RETURN_TYPES = ("AUDIO", ) FUNCTION = "load" def load(self, audio): audio_path = folder_paths.get_annotated_filepath(audio) waveform, sample_rate = torchaudio.load(audio_path) multiplier = 1.0 audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate} return (audio, ) @classmethod def IS_CHANGED(s, audio): image_path = folder_paths.get_annotated_filepath(audio) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) return m.digest().hex() @classmethod def VALIDATE_INPUTS(s, audio): if not folder_paths.exists_annotated_filepath(audio): return "Invalid audio file: {}".format(audio) return True NODE_CLASS_MAPPINGS = { "EmptyLatentAudio": EmptyLatentAudio, "VAEEncodeAudio": VAEEncodeAudio, "VAEDecodeAudio": VAEDecodeAudio, "SaveAudio": SaveAudio, "LoadAudio": LoadAudio, }
from kornia.filters import canny import comfy.model_management class Canny: @classmethod def INPUT_TYPES(s): return {"required": {"image": ("IMAGE",), "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}), "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01}) }} RETURN_TYPES = ("IMAGE",) FUNCTION = "detect_edge" CATEGORY = "image/preprocessors" def detect_edge(self, image, low_threshold, high_threshold): output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) return (img_out,) NODE_CLASS_MAPPINGS = { "Canny": Canny, }
import torch from nodes import MAX_RESOLUTION class CLIPTextEncodeSDXLRefiner: @classmethod def INPUT_TYPES(s): return {"required": { "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "advanced/conditioning" def encode(self, clip, ascore, width, height, text): tokens = clip.tokenize(text) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return ([[cond, {"pooled_output": pooled, "aesthetic_score": ascore, "width": width,"height": height}]], ) class CLIPTextEncodeSDXL: @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), "text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "advanced/conditioning" def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l): tokens = clip.tokenize(text_g) tokens["l"] = clip.tokenize(text_l)["l"] if len(tokens["l"]) != len(tokens["g"]): empty = clip.tokenize("") while len(tokens["l"]) < len(tokens["g"]): tokens["l"] += empty["l"] while len(tokens["l"]) > len(tokens["g"]): tokens["g"] += empty["g"] cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return ([[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]], ) NODE_CLASS_MAPPINGS = { "CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner, "CLIPTextEncodeSDXL": CLIPTextEncodeSDXL, }
import numpy as np import torch import comfy.utils from enum import Enum def resize_mask(mask, shape): return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1) class PorterDuffMode(Enum): ADD = 0 CLEAR = 1 DARKEN = 2 DST = 3 DST_ATOP = 4 DST_IN = 5 DST_OUT = 6 DST_OVER = 7 LIGHTEN = 8 MULTIPLY = 9 OVERLAY = 10 SCREEN = 11 SRC = 12 SRC_ATOP = 13 SRC_IN = 14 SRC_OUT = 15 SRC_OVER = 16 XOR = 17 def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode): src_alpha = 1 - src_alpha dst_alpha = 1 - dst_alpha src_image = src_image * src_alpha dst_image = dst_image * dst_alpha if mode == PorterDuffMode.ADD: out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1) out_image = torch.clamp(src_image + dst_image, 0, 1) elif mode == PorterDuffMode.CLEAR: out_alpha = torch.zeros_like(dst_alpha) out_image = torch.zeros_like(dst_image) elif mode == PorterDuffMode.DARKEN: out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image) elif mode == PorterDuffMode.DST: out_alpha = dst_alpha out_image = dst_image elif mode == PorterDuffMode.DST_ATOP: out_alpha = src_alpha out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image elif mode == PorterDuffMode.DST_IN: out_alpha = src_alpha * dst_alpha out_image = dst_image * src_alpha elif mode == PorterDuffMode.DST_OUT: out_alpha = (1 - src_alpha) * dst_alpha out_image = (1 - src_alpha) * dst_image elif mode == PorterDuffMode.DST_OVER: out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha out_image = dst_image + (1 - dst_alpha) * src_image elif mode == PorterDuffMode.LIGHTEN: out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image) elif mode == PorterDuffMode.MULTIPLY: out_alpha = src_alpha * dst_alpha out_image = src_image * dst_image elif mode == PorterDuffMode.OVERLAY: out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image, src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image)) elif mode == PorterDuffMode.SCREEN: out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha out_image = src_image + dst_image - src_image * dst_image elif mode == PorterDuffMode.SRC: out_alpha = src_alpha out_image = src_image elif mode == PorterDuffMode.SRC_ATOP: out_alpha = dst_alpha out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image elif mode == PorterDuffMode.SRC_IN: out_alpha = src_alpha * dst_alpha out_image = src_image * dst_alpha elif mode == PorterDuffMode.SRC_OUT: out_alpha = (1 - dst_alpha) * src_alpha out_image = (1 - dst_alpha) * src_image elif mode == PorterDuffMode.SRC_OVER: out_alpha = src_alpha + (1 - src_alpha) * dst_alpha out_image = src_image + (1 - src_alpha) * dst_image elif mode == PorterDuffMode.XOR: out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image else: return None, None out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image)) out_image = torch.clamp(out_image, 0, 1) out_alpha = 1 - out_alpha return out_image, out_alpha class PorterDuffImageComposite: @classmethod def INPUT_TYPES(s): return { "required": { "source": ("IMAGE",), "source_alpha": ("MASK",), "destination": ("IMAGE",), "destination_alpha": ("MASK",), "mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}), }, } RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "composite" CATEGORY = "mask/compositing" def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode): batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha)) out_images = [] out_alphas = [] for i in range(batch_size): src_image = source[i] dst_image = destination[i] assert src_image.shape[2] == dst_image.shape[2] src_alpha = source_alpha[i].unsqueeze(2) dst_alpha = destination_alpha[i].unsqueeze(2) if dst_alpha.shape[:2] != dst_image.shape[:2]: upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2) upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) if src_image.shape != dst_image.shape: upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2) upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0) if src_alpha.shape != dst_alpha.shape: upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2) upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center') src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode]) out_images.append(out_image) out_alphas.append(out_alpha.squeeze(2)) result = (torch.stack(out_images), torch.stack(out_alphas)) return result class SplitImageWithAlpha: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), } } CATEGORY = "mask/compositing" RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "split_image_with_alpha" def split_image_with_alpha(self, image: torch.Tensor): out_images = [i[:,:,:3] for i in image] out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image] result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas)) return result class JoinImageWithAlpha: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "alpha": ("MASK",), } } CATEGORY = "mask/compositing" RETURN_TYPES = ("IMAGE",) FUNCTION = "join_image_with_alpha" def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor): batch_size = min(len(image), len(alpha)) out_images = [] alpha = 1.0 - resize_mask(alpha, image.shape[1:]) for i in range(batch_size): out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2)) result = (torch.stack(out_images),) return result NODE_CLASS_MAPPINGS = { "PorterDuffImageComposite": PorterDuffImageComposite, "SplitImageWithAlpha": SplitImageWithAlpha, "JoinImageWithAlpha": JoinImageWithAlpha, } NODE_DISPLAY_NAME_MAPPINGS = { "PorterDuffImageComposite": "Porter-Duff Image Composite", "SplitImageWithAlpha": "Split Image with Alpha", "JoinImageWithAlpha": "Join Image with Alpha", }
class CLIPTextEncodeControlnet: @classmethod def INPUT_TYPES(s): return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "_for_testing/conditioning" def encode(self, clip, conditioning, text): tokens = clip.tokenize(text) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) c = [] for t in conditioning: n = [t[0], t[1].copy()] n[1]['cross_attn_controlnet'] = cond n[1]['pooled_output_controlnet'] = pooled c.append(n) return (c, ) NODE_CLASS_MAPPINGS = { "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet }
import comfy.samplers import comfy.sample from comfy.k_diffusion import sampling as k_diffusion_sampling import latent_preview import torch import comfy.utils import node_helpers class BasicScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "scheduler": (comfy.samplers.SCHEDULER_NAMES, ), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, model, scheduler, steps, denoise): total_steps = steps if denoise < 1.0: if denoise <= 0.0: return (torch.FloatTensor([]),) total_steps = int(steps/denoise) sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu() sigmas = sigmas[-(steps + 1):] return (sigmas, ) class KarrasScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, steps, sigma_max, sigma_min, rho): sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) return (sigmas, ) class ExponentialScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, steps, sigma_max, sigma_min): sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max) return (sigmas, ) class PolyexponentialScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, steps, sigma_max, sigma_min, rho): sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) return (sigmas, ) class SDTurboScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "steps": ("INT", {"default": 1, "min": 1, "max": 10}), "denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, model, steps, denoise): start_step = 10 - int(10 * denoise) timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps] sigmas = model.get_model_object("model_sampling").sigma(timesteps) sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) return (sigmas, ) class VPScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, steps, beta_d, beta_min, eps_s): sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s) return (sigmas, ) class SplitSigmas: @classmethod def INPUT_TYPES(s): return {"required": {"sigmas": ("SIGMAS", ), "step": ("INT", {"default": 0, "min": 0, "max": 10000}), } } RETURN_TYPES = ("SIGMAS","SIGMAS") RETURN_NAMES = ("high_sigmas", "low_sigmas") CATEGORY = "sampling/custom_sampling/sigmas" FUNCTION = "get_sigmas" def get_sigmas(self, sigmas, step): sigmas1 = sigmas[:step + 1] sigmas2 = sigmas[step:] return (sigmas1, sigmas2) class SplitSigmasDenoise: @classmethod def INPUT_TYPES(s): return {"required": {"sigmas": ("SIGMAS", ), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS","SIGMAS") RETURN_NAMES = ("high_sigmas", "low_sigmas") CATEGORY = "sampling/custom_sampling/sigmas" FUNCTION = "get_sigmas" def get_sigmas(self, sigmas, denoise): steps = max(sigmas.shape[-1] - 1, 0) total_steps = round(steps * denoise) sigmas1 = sigmas[:-(total_steps)] sigmas2 = sigmas[-(total_steps + 1):] return (sigmas1, sigmas2) class FlipSigmas: @classmethod def INPUT_TYPES(s): return {"required": {"sigmas": ("SIGMAS", ), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/sigmas" FUNCTION = "get_sigmas" def get_sigmas(self, sigmas): if len(sigmas) == 0: return (sigmas,) sigmas = sigmas.flip(0) if sigmas[0] == 0: sigmas[0] = 0.0001 return (sigmas,) class KSamplerSelect: @classmethod def INPUT_TYPES(s): return {"required": {"sampler_name": (comfy.samplers.SAMPLER_NAMES, ), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, sampler_name): sampler = comfy.samplers.sampler_object(sampler_name) return (sampler, ) class SamplerDPMPP_3M_SDE: @classmethod def INPUT_TYPES(s): return {"required": {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "noise_device": (['gpu', 'cpu'], ), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, eta, s_noise, noise_device): if noise_device == 'cpu': sampler_name = "dpmpp_3m_sde" else: sampler_name = "dpmpp_3m_sde_gpu" sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise}) return (sampler, ) class SamplerDPMPP_2M_SDE: @classmethod def INPUT_TYPES(s): return {"required": {"solver_type": (['midpoint', 'heun'], ), "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "noise_device": (['gpu', 'cpu'], ), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, solver_type, eta, s_noise, noise_device): if noise_device == 'cpu': sampler_name = "dpmpp_2m_sde" else: sampler_name = "dpmpp_2m_sde_gpu" sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type}) return (sampler, ) class SamplerDPMPP_SDE: @classmethod def INPUT_TYPES(s): return {"required": {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "noise_device": (['gpu', 'cpu'], ), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, eta, s_noise, r, noise_device): if noise_device == 'cpu': sampler_name = "dpmpp_sde" else: sampler_name = "dpmpp_sde_gpu" sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r}) return (sampler, ) class SamplerEulerAncestral: @classmethod def INPUT_TYPES(s): return {"required": {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, eta, s_noise): sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise}) return (sampler, ) class SamplerLMS: @classmethod def INPUT_TYPES(s): return {"required": {"order": ("INT", {"default": 4, "min": 1, "max": 100}), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, order): sampler = comfy.samplers.ksampler("lms", {"order": order}) return (sampler, ) class SamplerDPMAdaptative: @classmethod def INPUT_TYPES(s): return {"required": {"order": ("INT", {"default": 3, "min": 2, "max": 3}), "rtol": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "atol": ("FLOAT", {"default": 0.0078, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "h_init": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "pcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "icoeff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "dcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "accept_safety": ("FLOAT", {"default": 0.81, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "eta": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise): sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff, "icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta, "s_noise":s_noise }) return (sampler, ) class Noise_EmptyNoise: def __init__(self): self.seed = 0 def generate_noise(self, input_latent): latent_image = input_latent["samples"] return torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") class Noise_RandomNoise: def __init__(self, seed): self.seed = seed def generate_noise(self, input_latent): latent_image = input_latent["samples"] batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds) class SamplerCustom: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "add_noise": ("BOOLEAN", {"default": True}), "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "sampler": ("SAMPLER", ), "sigmas": ("SIGMAS", ), "latent_image": ("LATENT", ), } } RETURN_TYPES = ("LATENT","LATENT") RETURN_NAMES = ("output", "denoised_output") FUNCTION = "sample" CATEGORY = "sampling/custom_sampling" def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image): latent = latent_image latent_image = latent["samples"] latent = latent.copy() latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) latent["samples"] = latent_image if not add_noise: noise = Noise_EmptyNoise().generate_noise(latent) else: noise = Noise_RandomNoise(noise_seed).generate_noise(latent) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] x0_output = {} callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output) disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed) out = latent.copy() out["samples"] = samples if "x0" in x0_output: out_denoised = latent.copy() out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu()) else: out_denoised = out return (out, out_denoised) class Guider_Basic(comfy.samplers.CFGGuider): def set_conds(self, positive): self.inner_set_conds({"positive": positive}) class BasicGuider: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "conditioning": ("CONDITIONING", ), } } RETURN_TYPES = ("GUIDER",) FUNCTION = "get_guider" CATEGORY = "sampling/custom_sampling/guiders" def get_guider(self, model, conditioning): guider = Guider_Basic(model) guider.set_conds(conditioning) return (guider,) class CFGGuider: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), } } RETURN_TYPES = ("GUIDER",) FUNCTION = "get_guider" CATEGORY = "sampling/custom_sampling/guiders" def get_guider(self, model, positive, negative, cfg): guider = comfy.samplers.CFGGuider(model) guider.set_conds(positive, negative) guider.set_cfg(cfg) return (guider,) class Guider_DualCFG(comfy.samplers.CFGGuider): def set_cfg(self, cfg1, cfg2): self.cfg1 = cfg1 self.cfg2 = cfg2 def set_conds(self, positive, middle, negative): middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"}) self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative}) def predict_noise(self, x, timestep, model_options={}, seed=None): negative_cond = self.conds.get("negative", None) middle_cond = self.conds.get("middle", None) out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, self.conds.get("positive", None)], x, timestep, model_options) return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1 class DualCFGGuider: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "cond1": ("CONDITIONING", ), "cond2": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), } } RETURN_TYPES = ("GUIDER",) FUNCTION = "get_guider" CATEGORY = "sampling/custom_sampling/guiders" def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative): guider = Guider_DualCFG(model) guider.set_conds(cond1, cond2, negative) guider.set_cfg(cfg_conds, cfg_cond2_negative) return (guider,) class DisableNoise: @classmethod def INPUT_TYPES(s): return {"required":{ } } RETURN_TYPES = ("NOISE",) FUNCTION = "get_noise" CATEGORY = "sampling/custom_sampling/noise" def get_noise(self): return (Noise_EmptyNoise(),) class RandomNoise(DisableNoise): @classmethod def INPUT_TYPES(s): return {"required":{ "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), } } def get_noise(self, noise_seed): return (Noise_RandomNoise(noise_seed),) class SamplerCustomAdvanced: @classmethod def INPUT_TYPES(s): return {"required": {"noise": ("NOISE", ), "guider": ("GUIDER", ), "sampler": ("SAMPLER", ), "sigmas": ("SIGMAS", ), "latent_image": ("LATENT", ), } } RETURN_TYPES = ("LATENT","LATENT") RETURN_NAMES = ("output", "denoised_output") FUNCTION = "sample" CATEGORY = "sampling/custom_sampling" def sample(self, noise, guider, sampler, sigmas, latent_image): latent = latent_image latent_image = latent["samples"] latent = latent.copy() latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image) latent["samples"] = latent_image noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] x0_output = {} callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output) disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed) samples = samples.to(comfy.model_management.intermediate_device()) out = latent.copy() out["samples"] = samples if "x0" in x0_output: out_denoised = latent.copy() out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu()) else: out_denoised = out return (out, out_denoised) class AddNoise: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "noise": ("NOISE", ), "sigmas": ("SIGMAS", ), "latent_image": ("LATENT", ), } } RETURN_TYPES = ("LATENT",) FUNCTION = "add_noise" CATEGORY = "_for_testing/custom_sampling/noise" def add_noise(self, model, noise, sigmas, latent_image): if len(sigmas) == 0: return latent_image latent = latent_image latent_image = latent["samples"] noisy = noise.generate_noise(latent) model_sampling = model.get_model_object("model_sampling") process_latent_out = model.get_model_object("process_latent_out") process_latent_in = model.get_model_object("process_latent_in") if len(sigmas) > 1: scale = torch.abs(sigmas[0] - sigmas[-1]) else: scale = sigmas[0] if torch.count_nonzero(latent_image) > 0: latent_image = process_latent_in(latent_image) noisy = model_sampling.noise_scaling(scale, noisy, latent_image) noisy = process_latent_out(noisy) noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0) out = latent.copy() out["samples"] = noisy return (out,) NODE_CLASS_MAPPINGS = { "SamplerCustom": SamplerCustom, "BasicScheduler": BasicScheduler, "KarrasScheduler": KarrasScheduler, "ExponentialScheduler": ExponentialScheduler, "PolyexponentialScheduler": PolyexponentialScheduler, "VPScheduler": VPScheduler, "SDTurboScheduler": SDTurboScheduler, "KSamplerSelect": KSamplerSelect, "SamplerEulerAncestral": SamplerEulerAncestral, "SamplerLMS": SamplerLMS, "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE, "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, "SamplerDPMPP_SDE": SamplerDPMPP_SDE, "SamplerDPMAdaptative": SamplerDPMAdaptative, "SplitSigmas": SplitSigmas, "SplitSigmasDenoise": SplitSigmasDenoise, "FlipSigmas": FlipSigmas, "CFGGuider": CFGGuider, "DualCFGGuider": DualCFGGuider, "BasicGuider": BasicGuider, "RandomNoise": RandomNoise, "DisableNoise": DisableNoise, "AddNoise": AddNoise, "SamplerCustomAdvanced": SamplerCustomAdvanced, }
import torch class DifferentialDiffusion(): @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL", ), }} RETURN_TYPES = ("MODEL",) FUNCTION = "apply" CATEGORY = "_for_testing" INIT = False def apply(self, model): model = model.clone() model.set_model_denoise_mask_function(self.forward) return (model,) def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): model = extra_options["model"] step_sigmas = extra_options["sigmas"] sigma_to = model.inner_model.model_sampling.sigma_min if step_sigmas[-1] > sigma_to: sigma_to = step_sigmas[-1] sigma_from = step_sigmas[0] ts_from = model.inner_model.model_sampling.timestep(sigma_from) ts_to = model.inner_model.model_sampling.timestep(sigma_to) current_ts = model.inner_model.model_sampling.timestep(sigma[0]) threshold = (current_ts - ts_to) / (ts_from - ts_to) return (denoise_mask >= threshold).to(denoise_mask.dtype) NODE_CLASS_MAPPINGS = { "DifferentialDiffusion": DifferentialDiffusion, } NODE_DISPLAY_NAME_MAPPINGS = { "DifferentialDiffusion": "Differential Diffusion", }
import torch import logging def Fourier_filter(x, threshold, scale): x_freq = torch.fft.fftn(x.float(), dim=(-2, -1)) x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1)) B, C, H, W = x_freq.shape mask = torch.ones((B, C, H, W), device=x.device) crow, ccol = H mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale x_freq = x_freq * mask x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1)) x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real return x_filtered.to(x.dtype) class FreeU: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}), "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}), "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "model_patches" def patch(self, model, b1, b2, s1, s2): model_channels = model.model.model_config.unet_config["model_channels"] scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} on_cpu_devices = {} def output_block_patch(h, hsp, transformer_options): scale = scale_dict.get(int(h.shape[1]), None) if scale is not None: h[:,:h.shape[1] if hsp.device not in on_cpu_devices: try: hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) except: logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) on_cpu_devices[hsp.device] = True hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) else: hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) return h, hsp m = model.clone() m.set_model_output_block_patch(output_block_patch) return (m, ) class FreeU_V2: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}), "b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}), "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "model_patches" def patch(self, model, b1, b2, s1, s2): model_channels = model.model.model_config.unet_config["model_channels"] scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} on_cpu_devices = {} def output_block_patch(h, hsp, transformer_options): scale = scale_dict.get(int(h.shape[1]), None) if scale is not None: hidden_mean = h.mean(1).unsqueeze(1) B = hidden_mean.shape[0] hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) h[:,:h.shape[1] if hsp.device not in on_cpu_devices: try: hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) except: logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) on_cpu_devices[hsp.device] = True hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) else: hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) return h, hsp m = model.clone() m.set_model_output_block_patch(output_block_patch) return (m, ) NODE_CLASS_MAPPINGS = { "FreeU": FreeU, "FreeU_V2": FreeU_V2, }
import numpy as np import torch def loglinear_interp(t_steps, num_steps): """ Performs log-linear interpolation of a given array of decreasing numbers. """ xs = np.linspace(0, 1, len(t_steps)) ys = np.log(t_steps[::-1]) new_xs = np.linspace(0, 1, num_steps) new_ys = np.interp(new_xs, xs, ys) interped_ys = np.exp(new_ys)[::-1].copy() return interped_ys NOISE_LEVELS = { 0.80: [ [14.61464119, 7.49001646, 0.02916753], [14.61464119, 11.54541874, 6.77309084, 0.02916753], [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753], [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.75677586, 2.84484982, 1.78698075, 0.803307, 0.02916753], ], 0.85: [ [14.61464119, 7.49001646, 0.02916753], [14.61464119, 7.49001646, 1.84880662, 0.02916753], [14.61464119, 11.54541874, 6.77309084, 1.56271636, 0.02916753], [14.61464119, 11.54541874, 7.11996698, 3.07277966, 1.24153244, 0.02916753], [14.61464119, 11.54541874, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753], [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753], [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753], [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 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0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], ], } class GITSScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}), "steps": ("INT", {"default": 10, "min": 2, "max": 1000}), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, coeff, steps, denoise): total_steps = steps if denoise < 1.0: if denoise <= 0.0: return (torch.FloatTensor([]),) total_steps = round(steps * denoise) if steps <= 20: sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:] else: sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:] sigmas = loglinear_interp(sigmas, steps + 1) sigmas = sigmas[-(total_steps + 1):] sigmas[-1] = 0 return (torch.FloatTensor(sigmas), ) NODE_CLASS_MAPPINGS = { "GITSScheduler": GITSScheduler, }
import comfy.utils import folder_paths import torch import logging def load_hypernetwork_patch(path, strength): sd = comfy.utils.load_torch_file(path, safe_load=True) activation_func = sd.get('activation_func', 'linear') is_layer_norm = sd.get('is_layer_norm', False) use_dropout = sd.get('use_dropout', False) activate_output = sd.get('activate_output', False) last_layer_dropout = sd.get('last_layer_dropout', False) valid_activation = { "linear": torch.nn.Identity, "relu": torch.nn.ReLU, "leakyrelu": torch.nn.LeakyReLU, "elu": torch.nn.ELU, "swish": torch.nn.Hardswish, "tanh": torch.nn.Tanh, "sigmoid": torch.nn.Sigmoid, "softsign": torch.nn.Softsign, "mish": torch.nn.Mish, } if activation_func not in valid_activation: logging.error("Unsupported Hypernetwork format, if you report it I might implement it. {} {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout)) return None out = {} for d in sd: try: dim = int(d) except: continue output = [] for index in [0, 1]: attn_weights = sd[dim][index] keys = attn_weights.keys() linears = filter(lambda a: a.endswith(".weight"), keys) linears = list(map(lambda a: a[:-len(".weight")], linears)) layers = [] i = 0 while i < len(linears): lin_name = linears[i] last_layer = (i == (len(linears) - 1)) penultimate_layer = (i == (len(linears) - 2)) lin_weight = attn_weights['{}.weight'.format(lin_name)] lin_bias = attn_weights['{}.bias'.format(lin_name)] layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0]) layer.load_state_dict({"weight": lin_weight, "bias": lin_bias}) layers.append(layer) if activation_func != "linear": if (not last_layer) or (activate_output): layers.append(valid_activation[activation_func]()) if is_layer_norm: i += 1 ln_name = linears[i] ln_weight = attn_weights['{}.weight'.format(ln_name)] ln_bias = attn_weights['{}.bias'.format(ln_name)] ln = torch.nn.LayerNorm(ln_weight.shape[0]) ln.load_state_dict({"weight": ln_weight, "bias": ln_bias}) layers.append(ln) if use_dropout: if (not last_layer) and (not penultimate_layer or last_layer_dropout): layers.append(torch.nn.Dropout(p=0.3)) i += 1 output.append(torch.nn.Sequential(*layers)) out[dim] = torch.nn.ModuleList(output) class hypernetwork_patch: def __init__(self, hypernet, strength): self.hypernet = hypernet self.strength = strength def __call__(self, q, k, v, extra_options): dim = k.shape[-1] if dim in self.hypernet: hn = self.hypernet[dim] k = k + hn[0](k) * self.strength v = v + hn[1](v) * self.strength return q, k, v def to(self, device): for d in self.hypernet.keys(): self.hypernet[d] = self.hypernet[d].to(device) return self return hypernetwork_patch(out, strength) class HypernetworkLoader: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "hypernetwork_name": (folder_paths.get_filename_list("hypernetworks"), ), "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "load_hypernetwork" CATEGORY = "loaders" def load_hypernetwork(self, model, hypernetwork_name, strength): hypernetwork_path = folder_paths.get_full_path("hypernetworks", hypernetwork_name) model_hypernetwork = model.clone() patch = load_hypernetwork_patch(hypernetwork_path, strength) if patch is not None: model_hypernetwork.set_model_attn1_patch(patch) model_hypernetwork.set_model_attn2_patch(patch) return (model_hypernetwork,) NODE_CLASS_MAPPINGS = { "HypernetworkLoader": HypernetworkLoader }
import math from einops import rearrange from torch import randint def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int: min_value = min(min_value, value) divisors = [i for i in range(min_value, value + 1) if value % i == 0] ns = [value if len(ns) - 1 > 0: idx = randint(low=0, high=len(ns) - 1, size=(1,)).item() else: idx = 0 return ns[idx] class HyperTile: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}), "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}), "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}), "scale_depth": ("BOOLEAN", {"default": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "model_patches" def patch(self, model, tile_size, swap_size, max_depth, scale_depth): model_channels = model.model.model_config.unet_config["model_channels"] latent_tile_size = max(32, tile_size) self.temp = None def hypertile_in(q, k, v, extra_options): model_chans = q.shape[-2] orig_shape = extra_options['original_shape'] apply_to = [] for i in range(max_depth + 1): apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i))) if model_chans in apply_to: shape = extra_options["original_shape"] aspect_ratio = shape[-1] / shape[-2] hw = q.size(1) h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1 nh = random_divisor(h, latent_tile_size * factor, swap_size) nw = random_divisor(w, latent_tile_size * factor, swap_size) if nh * nw > 1: q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h self.temp = (nh, nw, h, w) return q, k, v return q, k, v def hypertile_out(out, extra_options): if self.temp is not None: nh, nw, h, w = self.temp self.temp = None out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h return out m = model.clone() m.set_model_attn1_patch(hypertile_in) m.set_model_attn1_output_patch(hypertile_out) return (m, ) NODE_CLASS_MAPPINGS = { "HyperTile": HyperTile, }
import nodes import folder_paths from comfy.cli_args import args from PIL import Image from PIL.PngImagePlugin import PngInfo import numpy as np import json import os MAX_RESOLUTION = nodes.MAX_RESOLUTION class ImageCrop: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "crop" CATEGORY = "image/transform" def crop(self, image, width, height, x, y): x = min(x, image.shape[2] - 1) y = min(y, image.shape[1] - 1) to_x = width + x to_y = height + y img = image[:,y:to_y, x:to_x, :] return (img,) class RepeatImageBatch: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "amount": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "repeat" CATEGORY = "image/batch" def repeat(self, image, amount): s = image.repeat((amount, 1,1,1)) return (s,) class ImageFromBatch: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}), "length": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "frombatch" CATEGORY = "image/batch" def frombatch(self, image, batch_index, length): s_in = image batch_index = min(s_in.shape[0] - 1, batch_index) length = min(s_in.shape[0] - batch_index, length) s = s_in[batch_index:batch_index + length].clone() return (s,) class SaveAnimatedWEBP: def __init__(self): self.output_dir = folder_paths.get_output_directory() self.type = "output" self.prefix_append = "" methods = {"default": 4, "fastest": 0, "slowest": 6} @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), "filename_prefix": ("STRING", {"default": "ComfyUI"}), "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), "lossless": ("BOOLEAN", {"default": True}), "quality": ("INT", {"default": 80, "min": 0, "max": 100}), "method": (list(s.methods.keys()),), }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = "image/animation" def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None): method = self.methods.get(method) filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() pil_images = [] for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) pil_images.append(img) metadata = pil_images[0].getexif() if not args.disable_metadata: if prompt is not None: metadata[0x0110] = "prompt:{}".format(json.dumps(prompt)) if extra_pnginfo is not None: inital_exif = 0x010f for x in extra_pnginfo: metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x])) inital_exif -= 1 if num_frames == 0: num_frames = len(pil_images) c = len(pil_images) for i in range(0, c, num_frames): file = f"{filename}_{counter:05}_.webp" pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method) results.append({ "filename": file, "subfolder": subfolder, "type": self.type }) counter += 1 animated = num_frames != 1 return { "ui": { "images": results, "animated": (animated,) } } class SaveAnimatedPNG: def __init__(self): self.output_dir = folder_paths.get_output_directory() self.type = "output" self.prefix_append = "" @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), "filename_prefix": ("STRING", {"default": "ComfyUI"}), "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), "compress_level": ("INT", {"default": 4, "min": 0, "max": 9}) }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = "image/animation" def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() pil_images = [] for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) pil_images.append(img) metadata = None if not args.disable_metadata: metadata = PngInfo() if prompt is not None: metadata.add(b"comf", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True) if extra_pnginfo is not None: for x in extra_pnginfo: metadata.add(b"comf", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True) file = f"{filename}_{counter:05}_.png" pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:]) results.append({ "filename": file, "subfolder": subfolder, "type": self.type }) return { "ui": { "images": results, "animated": (True,)} } NODE_CLASS_MAPPINGS = { "ImageCrop": ImageCrop, "RepeatImageBatch": RepeatImageBatch, "ImageFromBatch": ImageFromBatch, "SaveAnimatedWEBP": SaveAnimatedWEBP, "SaveAnimatedPNG": SaveAnimatedPNG, }
import torch class InstructPixToPixConditioning: @classmethod def INPUT_TYPES(s): return {"required": {"positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "vae": ("VAE", ), "pixels": ("IMAGE", ), }} RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/instructpix2pix" def encode(self, positive, negative, pixels, vae): x = (pixels.shape[1] y = (pixels.shape[2] if pixels.shape[1] != x or pixels.shape[2] != y: x_offset = (pixels.shape[1] % 8) y_offset = (pixels.shape[2] % 8) pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] concat_latent = vae.encode(pixels) out_latent = {} out_latent["samples"] = torch.zeros_like(concat_latent) out = [] for conditioning in [positive, negative]: c = [] for t in conditioning: d = t[1].copy() d["concat_latent_image"] = concat_latent n = [t[0], d] c.append(n) out.append(c) return (out[0], out[1], out_latent) NODE_CLASS_MAPPINGS = { "InstructPixToPixConditioning": InstructPixToPixConditioning, }
import comfy.utils import torch def reshape_latent_to(target_shape, latent): if latent.shape[1:] != target_shape[1:]: latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center") return comfy.utils.repeat_to_batch_size(latent, target_shape[0]) class LatentAdd: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples1, samples2): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] s2 = reshape_latent_to(s1.shape, s2) samples_out["samples"] = s1 + s2 return (samples_out,) class LatentSubtract: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples1, samples2): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] s2 = reshape_latent_to(s1.shape, s2) samples_out["samples"] = s1 - s2 return (samples_out,) class LatentMultiply: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples, multiplier): samples_out = samples.copy() s1 = samples["samples"] samples_out["samples"] = s1 * multiplier return (samples_out,) class LatentInterpolate: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",), "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples1, samples2, ratio): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] s2 = reshape_latent_to(s1.shape, s2) m1 = torch.linalg.vector_norm(s1, dim=(1)) m2 = torch.linalg.vector_norm(s2, dim=(1)) s1 = torch.nan_to_num(s1 / m1) s2 = torch.nan_to_num(s2 / m2) t = (s1 * ratio + s2 * (1.0 - ratio)) mt = torch.linalg.vector_norm(t, dim=(1)) st = torch.nan_to_num(t / mt) samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio)) return (samples_out,) class LatentBatch: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} RETURN_TYPES = ("LATENT",) FUNCTION = "batch" CATEGORY = "latent/batch" def batch(self, samples1, samples2): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] if s1.shape[1:] != s2.shape[1:]: s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center") s = torch.cat((s1, s2), dim=0) samples_out["samples"] = s samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])]) return (samples_out,) class LatentBatchSeedBehavior: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "seed_behavior": (["random", "fixed"],{"default": "fixed"}),}} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples, seed_behavior): samples_out = samples.copy() latent = samples["samples"] if seed_behavior == "random": if 'batch_index' in samples_out: samples_out.pop('batch_index') elif seed_behavior == "fixed": batch_number = samples_out.get("batch_index", [0])[0] samples_out["batch_index"] = [batch_number] * latent.shape[0] return (samples_out,) NODE_CLASS_MAPPINGS = { "LatentAdd": LatentAdd, "LatentSubtract": LatentSubtract, "LatentMultiply": LatentMultiply, "LatentInterpolate": LatentInterpolate, "LatentBatch": LatentBatch, "LatentBatchSeedBehavior": LatentBatchSeedBehavior, }
import numpy as np import scipy.ndimage import torch import comfy.utils from nodes import MAX_RESOLUTION def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): source = source.to(destination.device) if resize_source: source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") source = comfy.utils.repeat_to_batch_size(source, destination.shape[0]) x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) left, top = (x right, bottom = (left + source.shape[3], top + source.shape[2],) if mask is None: mask = torch.ones_like(source) else: mask = mask.to(destination.device, copy=True) mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0]) visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) mask = mask[:, :, :visible_height, :visible_width] inverse_mask = torch.ones_like(mask) - mask source_portion = mask * source[:, :, :visible_height, :visible_width] destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] destination[:, :, top:bottom, left:right] = source_portion + destination_portion return destination class LatentCompositeMasked: @classmethod def INPUT_TYPES(s): return { "required": { "destination": ("LATENT",), "source": ("LATENT",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "resize_source": ("BOOLEAN", {"default": False}), }, "optional": { "mask": ("MASK",), } } RETURN_TYPES = ("LATENT",) FUNCTION = "composite" CATEGORY = "latent" def composite(self, destination, source, x, y, resize_source, mask = None): output = destination.copy() destination = destination["samples"].clone() source = source["samples"] output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) return (output,) class ImageCompositeMasked: @classmethod def INPUT_TYPES(s): return { "required": { "destination": ("IMAGE",), "source": ("IMAGE",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "resize_source": ("BOOLEAN", {"default": False}), }, "optional": { "mask": ("MASK",), } } RETURN_TYPES = ("IMAGE",) FUNCTION = "composite" CATEGORY = "image" def composite(self, destination, source, x, y, resize_source, mask = None): destination = destination.clone().movedim(-1, 1) output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) return (output,) class MaskToImage: @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), } } CATEGORY = "mask" RETURN_TYPES = ("IMAGE",) FUNCTION = "mask_to_image" def mask_to_image(self, mask): result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) return (result,) class ImageToMask: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "channel": (["red", "green", "blue", "alpha"],), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "image_to_mask" def image_to_mask(self, image, channel): channels = ["red", "green", "blue", "alpha"] mask = image[:, :, :, channels.index(channel)] return (mask,) class ImageColorToMask: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "image_to_mask" def image_to_mask(self, image, color): temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int) temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2] mask = torch.where(temp == color, 255, 0).float() return (mask,) class SolidMask: @classmethod def INPUT_TYPES(cls): return { "required": { "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "solid" def solid(self, value, width, height): out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") return (out,) class InvertMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "invert" def invert(self, mask): out = 1.0 - mask return (out,) class CropMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "crop" def crop(self, mask, x, y, width, height): mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) out = mask[:, y:y + height, x:x + width] return (out,) class MaskComposite: @classmethod def INPUT_TYPES(cls): return { "required": { "destination": ("MASK",), "source": ("MASK",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "operation": (["multiply", "add", "subtract", "and", "or", "xor"],), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "combine" def combine(self, destination, source, x, y, operation): output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() source = source.reshape((-1, source.shape[-2], source.shape[-1])) left, top = (x, y,) right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) visible_width, visible_height = (right - left, bottom - top,) source_portion = source[:, :visible_height, :visible_width] destination_portion = destination[:, top:bottom, left:right] if operation == "multiply": output[:, top:bottom, left:right] = destination_portion * source_portion elif operation == "add": output[:, top:bottom, left:right] = destination_portion + source_portion elif operation == "subtract": output[:, top:bottom, left:right] = destination_portion - source_portion elif operation == "and": output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float() elif operation == "or": output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float() elif operation == "xor": output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float() output = torch.clamp(output, 0.0, 1.0) return (output,) class FeatherMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "feather" def feather(self, mask, left, top, right, bottom): output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone() left = min(left, output.shape[-1]) right = min(right, output.shape[-1]) top = min(top, output.shape[-2]) bottom = min(bottom, output.shape[-2]) for x in range(left): feather_rate = (x + 1.0) / left output[:, :, x] *= feather_rate for x in range(right): feather_rate = (x + 1) / right output[:, :, -x] *= feather_rate for y in range(top): feather_rate = (y + 1) / top output[:, y, :] *= feather_rate for y in range(bottom): feather_rate = (y + 1) / bottom output[:, -y, :] *= feather_rate return (output,) class GrowMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), "tapered_corners": ("BOOLEAN", {"default": True}), }, } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "expand_mask" def expand_mask(self, mask, expand, tapered_corners): c = 0 if tapered_corners else 1 kernel = np.array([[c, 1, c], [1, 1, 1], [c, 1, c]]) mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) out = [] for m in mask: output = m.numpy() for _ in range(abs(expand)): if expand < 0: output = scipy.ndimage.grey_erosion(output, footprint=kernel) else: output = scipy.ndimage.grey_dilation(output, footprint=kernel) output = torch.from_numpy(output) out.append(output) return (torch.stack(out, dim=0),) class ThresholdMask: @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "image_to_mask" def image_to_mask(self, mask, value): mask = (mask > value).float() return (mask,) NODE_CLASS_MAPPINGS = { "LatentCompositeMasked": LatentCompositeMasked, "ImageCompositeMasked": ImageCompositeMasked, "MaskToImage": MaskToImage, "ImageToMask": ImageToMask, "ImageColorToMask": ImageColorToMask, "SolidMask": SolidMask, "InvertMask": InvertMask, "CropMask": CropMask, "MaskComposite": MaskComposite, "FeatherMask": FeatherMask, "GrowMask": GrowMask, "ThresholdMask": ThresholdMask, } NODE_DISPLAY_NAME_MAPPINGS = { "ImageToMask": "Convert Image to Mask", "MaskToImage": "Convert Mask to Image", }
import folder_paths import comfy.sd import comfy.model_sampling import comfy.latent_formats import torch class LCM(comfy.model_sampling.EPS): def calculate_denoised(self, sigma, model_output, model_input): timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) x0 = model_input - model_output * sigma sigma_data = 0.5 scaled_timestep = timestep * 10.0 c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2) c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5 return c_out * x0 + c_skip * model_input class X0(comfy.model_sampling.EPS): def calculate_denoised(self, sigma, model_output, model_input): return model_output class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete): original_timesteps = 50 def __init__(self, model_config=None): super().__init__(model_config) self.skip_steps = self.num_timesteps sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32) for x in range(self.original_timesteps): sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps] self.set_sigmas(sigmas_valid) def timestep(self, sigma): log_sigma = sigma.log() dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device) def sigma(self, timestep): t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) low_idx = t.floor().long() high_idx = t.ceil().long() w = t.frac() log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] return log_sigma.exp().to(timestep.device) def rescale_zero_terminal_snr_sigmas(sigmas): alphas_cumprod = 1 / ((sigmas * sigmas) + 1) alphas_bar_sqrt = alphas_cumprod.sqrt() alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() alphas_bar_sqrt -= (alphas_bar_sqrt_T) alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) alphas_bar = alphas_bar_sqrt**2 alphas_bar[-1] = 4.8973451890853435e-08 return ((1 - alphas_bar) / alphas_bar) ** 0.5 class ModelSamplingDiscrete: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "sampling": (["eps", "v_prediction", "lcm", "x0"],), "zsnr": ("BOOLEAN", {"default": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, sampling, zsnr): m = model.clone() sampling_base = comfy.model_sampling.ModelSamplingDiscrete if sampling == "eps": sampling_type = comfy.model_sampling.EPS elif sampling == "v_prediction": sampling_type = comfy.model_sampling.V_PREDICTION elif sampling == "lcm": sampling_type = LCM sampling_base = ModelSamplingDiscreteDistilled elif sampling == "x0": sampling_type = X0 class ModelSamplingAdvanced(sampling_base, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) if zsnr: model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas)) m.add_object_patch("model_sampling", model_sampling) return (m, ) class ModelSamplingStableCascade: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, shift): m = model.clone() sampling_base = comfy.model_sampling.StableCascadeSampling sampling_type = comfy.model_sampling.EPS class ModelSamplingAdvanced(sampling_base, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling.set_parameters(shift) m.add_object_patch("model_sampling", model_sampling) return (m, ) class ModelSamplingSD3: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, shift): m = model.clone() sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow sampling_type = comfy.model_sampling.CONST class ModelSamplingAdvanced(sampling_base, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling.set_parameters(shift=shift) m.add_object_patch("model_sampling", model_sampling) return (m, ) class ModelSamplingContinuousEDM: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],), "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() latent_format = None sigma_data = 1.0 if sampling == "eps": sampling_type = comfy.model_sampling.EPS elif sampling == "v_prediction": sampling_type = comfy.model_sampling.V_PREDICTION elif sampling == "edm_playground_v2.5": sampling_type = comfy.model_sampling.EDM sigma_data = 0.5 latent_format = comfy.latent_formats.SDXL_Playground_2_5() class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) m.add_object_patch("model_sampling", model_sampling) if latent_format is not None: m.add_object_patch("latent_format", latent_format) return (m, ) class ModelSamplingContinuousV: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "sampling": (["v_prediction"],), "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() latent_format = None sigma_data = 1.0 if sampling == "v_prediction": sampling_type = comfy.model_sampling.V_PREDICTION class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) m.add_object_patch("model_sampling", model_sampling) return (m, ) class RescaleCFG: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, multiplier): def rescale_cfg(args): cond = args["cond"] uncond = args["uncond"] cond_scale = args["cond_scale"] sigma = args["sigma"] sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1)) x_orig = args["input"] x = x_orig / (sigma * sigma + 1.0) cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) x_cfg = uncond + cond_scale * (cond - uncond) ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True) ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True) x_rescaled = x_cfg * (ro_pos / ro_cfg) x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5) m = model.clone() m.set_model_sampler_cfg_function(rescale_cfg) return (m, ) NODE_CLASS_MAPPINGS = { "ModelSamplingDiscrete": ModelSamplingDiscrete, "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, "ModelSamplingContinuousV": ModelSamplingContinuousV, "ModelSamplingStableCascade": ModelSamplingStableCascade, "ModelSamplingSD3": ModelSamplingSD3, "RescaleCFG": RescaleCFG, }
import torch import comfy.utils class PatchModelAddDownscale: upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}), "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}), "downscale_after_skip": ("BOOLEAN", {"default": True}), "downscale_method": (s.upscale_methods,), "upscale_method": (s.upscale_methods,), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method): model_sampling = model.get_model_object("model_sampling") sigma_start = model_sampling.percent_to_sigma(start_percent) sigma_end = model_sampling.percent_to_sigma(end_percent) def input_block_patch(h, transformer_options): if transformer_options["block"][1] == block_number: sigma = transformer_options["sigmas"][0].item() if sigma <= sigma_start and sigma >= sigma_end: h = comfy.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled") return h def output_block_patch(h, hsp, transformer_options): if h.shape[2] != hsp.shape[2]: h = comfy.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled") return h, hsp m = model.clone() if downscale_after_skip: m.set_model_input_block_patch_after_skip(input_block_patch) else: m.set_model_input_block_patch(input_block_patch) m.set_model_output_block_patch(output_block_patch) return (m, ) NODE_CLASS_MAPPINGS = { "PatchModelAddDownscale": PatchModelAddDownscale, } NODE_DISPLAY_NAME_MAPPINGS = { "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)", }
import comfy.sd import comfy.utils import comfy.model_base import comfy.model_management import comfy.model_sampling import torch import folder_paths import json import os from comfy.cli_args import args class ModelMergeSimple: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2, ratio): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") for k in kp: m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) return (m, ) class ModelSubtract: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2, multiplier): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") for k in kp: m.add_patches({k: kp[k]}, - multiplier, multiplier) return (m, ) class ModelAdd: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") for k in kp: m.add_patches({k: kp[k]}, 1.0, 1.0) return (m, ) class CLIPMergeSimple: @classmethod def INPUT_TYPES(s): return {"required": { "clip1": ("CLIP",), "clip2": ("CLIP",), "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("CLIP",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, clip1, clip2, ratio): m = clip1.clone() kp = clip2.get_key_patches() for k in kp: if k.endswith(".position_ids") or k.endswith(".logit_scale"): continue m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) return (m, ) class CLIPSubtract: @classmethod def INPUT_TYPES(s): return {"required": { "clip1": ("CLIP",), "clip2": ("CLIP",), "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("CLIP",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, clip1, clip2, multiplier): m = clip1.clone() kp = clip2.get_key_patches() for k in kp: if k.endswith(".position_ids") or k.endswith(".logit_scale"): continue m.add_patches({k: kp[k]}, - multiplier, multiplier) return (m, ) class CLIPAdd: @classmethod def INPUT_TYPES(s): return {"required": { "clip1": ("CLIP",), "clip2": ("CLIP",), }} RETURN_TYPES = ("CLIP",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, clip1, clip2): m = clip1.clone() kp = clip2.get_key_patches() for k in kp: if k.endswith(".position_ids") or k.endswith(".logit_scale"): continue m.add_patches({k: kp[k]}, 1.0, 1.0) return (m, ) class ModelMergeBlocks: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), "input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2, **kwargs): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") default_ratio = next(iter(kwargs.values())) for k in kp: ratio = default_ratio k_unet = k[len("diffusion_model."):] last_arg_size = 0 for arg in kwargs: if k_unet.startswith(arg) and last_arg_size < len(arg): ratio = kwargs[arg] last_arg_size = len(arg) m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) return (m, ) def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None): full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir) prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = {} enable_modelspec = True if isinstance(model.model, comfy.model_base.SDXL): if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix): metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit" else: metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" elif isinstance(model.model, comfy.model_base.SDXLRefiner): metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" elif isinstance(model.model, comfy.model_base.SVD_img2vid): metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1" elif isinstance(model.model, comfy.model_base.SD3): metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" else: enable_modelspec = False if enable_modelspec: metadata["modelspec.sai_model_spec"] = "1.0.0" metadata["modelspec.implementation"] = "sgm" metadata["modelspec.title"] = "{} {}".format(filename, counter) extra_keys = {} model_sampling = model.get_model_object("model_sampling") if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM): if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION): extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float() extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float() if model.model.model_type == comfy.model_base.ModelType.EPS: metadata["modelspec.predict_key"] = "epsilon" elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION: metadata["modelspec.predict_key"] = "v" if not args.disable_metadata: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys) class CheckpointSave: def __init__(self): self.output_dir = folder_paths.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "clip": ("CLIP",), "vae": ("VAE",), "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "advanced/model_merging" def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) return {} class CLIPSave: def __init__(self): self.output_dir = folder_paths.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "clip": ("CLIP",), "filename_prefix": ("STRING", {"default": "clip/ComfyUI"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "advanced/model_merging" def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None): prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = {} if not args.disable_metadata: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True) clip_sd = clip.get_sd() for prefix in ["clip_l.", "clip_g.", ""]: k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys())) current_clip_sd = {} for x in k: current_clip_sd[x] = clip_sd.pop(x) if len(current_clip_sd) == 0: continue p = prefix[:-1] replace_prefix = {} filename_prefix_ = filename_prefix if len(p) > 0: filename_prefix_ = "{}_{}".format(filename_prefix_, p) replace_prefix[prefix] = "" replace_prefix["transformer."] = "" full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir) output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) current_clip_sd = comfy.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix) comfy.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata) return {} class VAESave: def __init__(self): self.output_dir = folder_paths.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "vae": ("VAE",), "filename_prefix": ("STRING", {"default": "vae/ComfyUI_vae"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "advanced/model_merging" def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = {} if not args.disable_metadata: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) comfy.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata) return {} NODE_CLASS_MAPPINGS = { "ModelMergeSimple": ModelMergeSimple, "ModelMergeBlocks": ModelMergeBlocks, "ModelMergeSubtract": ModelSubtract, "ModelMergeAdd": ModelAdd, "CheckpointSave": CheckpointSave, "CLIPMergeSimple": CLIPMergeSimple, "CLIPMergeSubtract": CLIPSubtract, "CLIPMergeAdd": CLIPAdd, "CLIPSave": CLIPSave, "VAESave": VAESave, }
import comfy_extras.nodes_model_merging class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks): CATEGORY = "advanced/model_merging/model_specific" @classmethod def INPUT_TYPES(s): arg_dict = { "model1": ("MODEL",), "model2": ("MODEL",)} argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) arg_dict["time_embed."] = argument arg_dict["label_emb."] = argument for i in range(12): arg_dict["input_blocks.{}.".format(i)] = argument for i in range(3): arg_dict["middle_block.{}.".format(i)] = argument for i in range(12): arg_dict["output_blocks.{}.".format(i)] = argument arg_dict["out."] = argument return {"required": arg_dict} class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): CATEGORY = "advanced/model_merging/model_specific" @classmethod def INPUT_TYPES(s): arg_dict = { "model1": ("MODEL",), "model2": ("MODEL",)} argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) arg_dict["time_embed."] = argument arg_dict["label_emb."] = argument for i in range(9): arg_dict["input_blocks.{}".format(i)] = argument for i in range(3): arg_dict["middle_block.{}".format(i)] = argument for i in range(9): arg_dict["output_blocks.{}".format(i)] = argument arg_dict["out."] = argument return {"required": arg_dict} class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): CATEGORY = "advanced/model_merging/model_specific" @classmethod def INPUT_TYPES(s): arg_dict = { "model1": ("MODEL",), "model2": ("MODEL",)} argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) arg_dict["pos_embed."] = argument arg_dict["x_embedder."] = argument arg_dict["context_embedder."] = argument arg_dict["y_embedder."] = argument arg_dict["t_embedder."] = argument for i in range(24): arg_dict["joint_blocks.{}.".format(i)] = argument arg_dict["final_layer."] = argument return {"required": arg_dict} NODE_CLASS_MAPPINGS = { "ModelMergeSD1": ModelMergeSD1, "ModelMergeSD2": ModelMergeSD1, "ModelMergeSDXL": ModelMergeSDXL, "ModelMergeSD3_2B": ModelMergeSD3_2B, }
import torch import comfy.model_management from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat class Morphology: @classmethod def INPUT_TYPES(s): return {"required": {"image": ("IMAGE",), "operation": (["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],), "kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "process" CATEGORY = "image/postprocessing" def process(self, image, operation, kernel_size): device = comfy.model_management.get_torch_device() kernel = torch.ones(kernel_size, kernel_size, device=device) image_k = image.to(device).movedim(-1, 1) if operation == "erode": output = erosion(image_k, kernel) elif operation == "dilate": output = dilation(image_k, kernel) elif operation == "open": output = opening(image_k, kernel) elif operation == "close": output = closing(image_k, kernel) elif operation == "gradient": output = gradient(image_k, kernel) elif operation == "top_hat": output = top_hat(image_k, kernel) elif operation == "bottom_hat": output = bottom_hat(image_k, kernel) else: raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'") img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1) return (img_out,) NODE_CLASS_MAPPINGS = { "Morphology": Morphology, } NODE_DISPLAY_NAME_MAPPINGS = { "Morphology": "ImageMorphology", }
import comfy.model_patcher import comfy.samplers class PerturbedAttentionGuidance: @classmethod def INPUT_TYPES(s): return { "required": { "model": ("MODEL",), "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), } } RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, scale): unet_block = "middle" unet_block_id = 0 m = model.clone() def perturbed_attention(q, k, v, extra_options, mask=None): return v def post_cfg_function(args): model = args["model"] cond_pred = args["cond_denoised"] cond = args["cond"] cfg_result = args["denoised"] sigma = args["sigma"] model_options = args["model_options"].copy() x = args["input"] if scale == 0: return cfg_result model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, perturbed_attention, "attn1", unet_block, unet_block_id) (pag,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options) return cfg_result + (cond_pred - pag) * scale m.set_model_sampler_post_cfg_function(post_cfg_function) return (m,) NODE_CLASS_MAPPINGS = { "PerturbedAttentionGuidance": PerturbedAttentionGuidance, }
import torch import comfy.model_management import comfy.sampler_helpers import comfy.samplers import comfy.utils import node_helpers def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale): pos = noise_pred_pos - noise_pred_nocond neg = noise_pred_neg - noise_pred_nocond perp = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos perp_neg = perp * neg_scale cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) return cfg_result class PerpNeg: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL", ), "empty_conditioning": ("CONDITIONING", ), "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, empty_conditioning, neg_scale): m = model.clone() nocond = comfy.sampler_helpers.convert_cond(empty_conditioning) def cfg_function(args): model = args["model"] noise_pred_pos = args["cond_denoised"] noise_pred_neg = args["uncond_denoised"] cond_scale = args["cond_scale"] x = args["input"] sigma = args["sigma"] model_options = args["model_options"] nocond_processed = comfy.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative") (noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options) cfg_result = x - perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale) return cfg_result m.set_model_sampler_cfg_function(cfg_function) return (m, ) class Guider_PerpNeg(comfy.samplers.CFGGuider): def set_conds(self, positive, negative, empty_negative_prompt): empty_negative_prompt = node_helpers.conditioning_set_values(empty_negative_prompt, {"prompt_type": "negative"}) self.inner_set_conds({"positive": positive, "empty_negative_prompt": empty_negative_prompt, "negative": negative}) def set_cfg(self, cfg, neg_scale): self.cfg = cfg self.neg_scale = neg_scale def predict_noise(self, x, timestep, model_options={}, seed=None): positive_cond = self.conds.get("positive", None) negative_cond = self.conds.get("negative", None) empty_cond = self.conds.get("empty_negative_prompt", None) (noise_pred_pos, noise_pred_neg, noise_pred_empty) = \ comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options) cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg) for fn in model_options.get("sampler_post_cfg_function", []): args = { "denoised": cfg_result, "cond": positive_cond, "uncond": negative_cond, "model": self.inner_model, "uncond_denoised": noise_pred_neg, "cond_denoised": noise_pred_pos, "sigma": timestep, "model_options": model_options, "input": x, "empty_cond": empty_cond, "empty_cond_denoised": noise_pred_empty,} cfg_result = fn(args) return cfg_result class PerpNegGuider: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "empty_conditioning": ("CONDITIONING", ), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), } } RETURN_TYPES = ("GUIDER",) FUNCTION = "get_guider" CATEGORY = "_for_testing" def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale): guider = Guider_PerpNeg(model) guider.set_conds(positive, negative, empty_conditioning) guider.set_cfg(cfg, neg_scale) return (guider,) NODE_CLASS_MAPPINGS = { "PerpNeg": PerpNeg, "PerpNegGuider": PerpNegGuider, } NODE_DISPLAY_NAME_MAPPINGS = { "PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)", }
import torch import torch.nn as nn import folder_paths import comfy.clip_model import comfy.clip_vision import comfy.ops VISION_CONFIG_DICT = { "hidden_size": 1024, "image_size": 224, "intermediate_size": 4096, "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 24, "patch_size": 14, "projection_dim": 768, "hidden_act": "quick_gelu", } class MLP(nn.Module): def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=comfy.ops): super().__init__() if use_residual: assert in_dim == out_dim self.layernorm = operations.LayerNorm(in_dim) self.fc1 = operations.Linear(in_dim, hidden_dim) self.fc2 = operations.Linear(hidden_dim, out_dim) self.use_residual = use_residual self.act_fn = nn.GELU() def forward(self, x): residual = x x = self.layernorm(x) x = self.fc1(x) x = self.act_fn(x) x = self.fc2(x) if self.use_residual: x = x + residual return x class FuseModule(nn.Module): def __init__(self, embed_dim, operations): super().__init__() self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations) self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations) self.layer_norm = operations.LayerNorm(embed_dim) def fuse_fn(self, prompt_embeds, id_embeds): stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1) stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds stacked_id_embeds = self.mlp2(stacked_id_embeds) stacked_id_embeds = self.layer_norm(stacked_id_embeds) return stacked_id_embeds def forward( self, prompt_embeds, id_embeds, class_tokens_mask, ) -> torch.Tensor: id_embeds = id_embeds.to(prompt_embeds.dtype) num_inputs = class_tokens_mask.sum().unsqueeze(0) batch_size, max_num_inputs = id_embeds.shape[:2] seq_length = prompt_embeds.shape[1] flat_id_embeds = id_embeds.view( -1, id_embeds.shape[-2], id_embeds.shape[-1] ) valid_id_mask = ( torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :] < num_inputs[:, None] ) valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()] prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1]) class_tokens_mask = class_tokens_mask.view(-1) valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1]) image_token_embeds = prompt_embeds[class_tokens_mask] stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds) assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}" prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype)) updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1) return updated_prompt_embeds class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection): def __init__(self): self.load_device = comfy.model_management.text_encoder_device() offload_device = comfy.model_management.text_encoder_offload_device() dtype = comfy.model_management.text_encoder_dtype(self.load_device) super().__init__(VISION_CONFIG_DICT, dtype, offload_device, comfy.ops.manual_cast) self.visual_projection_2 = comfy.ops.manual_cast.Linear(1024, 1280, bias=False) self.fuse_module = FuseModule(2048, comfy.ops.manual_cast) def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask): b, num_inputs, c, h, w = id_pixel_values.shape id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w) shared_id_embeds = self.vision_model(id_pixel_values)[2] id_embeds = self.visual_projection(shared_id_embeds) id_embeds_2 = self.visual_projection_2(shared_id_embeds) id_embeds = id_embeds.view(b, num_inputs, 1, -1) id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1) id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1) updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask) return updated_prompt_embeds class PhotoMakerLoader: @classmethod def INPUT_TYPES(s): return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}} RETURN_TYPES = ("PHOTOMAKER",) FUNCTION = "load_photomaker_model" CATEGORY = "_for_testing/photomaker" def load_photomaker_model(self, photomaker_model_name): photomaker_model_path = folder_paths.get_full_path("photomaker", photomaker_model_name) photomaker_model = PhotoMakerIDEncoder() data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True) if "id_encoder" in data: data = data["id_encoder"] photomaker_model.load_state_dict(data) return (photomaker_model,) class PhotoMakerEncode: @classmethod def INPUT_TYPES(s): return {"required": { "photomaker": ("PHOTOMAKER",), "image": ("IMAGE",), "clip": ("CLIP", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_photomaker" CATEGORY = "_for_testing/photomaker" def apply_photomaker(self, photomaker, image, clip, text): special_token = "photomaker" pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float() try: index = text.split(" ").index(special_token) + 1 except ValueError: index = -1 tokens = clip.tokenize(text, return_word_ids=True) out_tokens = {} for k in tokens: out_tokens[k] = [] for t in tokens[k]: f = list(filter(lambda x: x[2] != index, t)) while len(f) < len(t): f.append(t[-1]) out_tokens[k].append(f) cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True) if index > 0: token_index = index - 1 num_id_images = 1 class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)] out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device), class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0)) else: out = cond return ([[out, {"pooled_output": pooled}]], ) NODE_CLASS_MAPPINGS = { "PhotoMakerLoader": PhotoMakerLoader, "PhotoMakerEncode": PhotoMakerEncode, }
import numpy as np import torch import torch.nn.functional as F from PIL import Image import math import comfy.utils import comfy.model_management class Blend: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image1": ("IMAGE",), "image2": ("IMAGE",), "blend_factor": ("FLOAT", { "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01 }), "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "blend_images" CATEGORY = "image/postprocessing" def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): image2 = image2.to(image1.device) if image1.shape != image2.shape: image2 = image2.permute(0, 3, 1, 2) image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') image2 = image2.permute(0, 2, 3, 1) blended_image = self.blend_mode(image1, image2, blend_mode) blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor blended_image = torch.clamp(blended_image, 0, 1) return (blended_image,) def blend_mode(self, img1, img2, mode): if mode == "normal": return img2 elif mode == "multiply": return img1 * img2 elif mode == "screen": return 1 - (1 - img1) * (1 - img2) elif mode == "overlay": return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) elif mode == "soft_light": return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1)) elif mode == "difference": return img1 - img2 else: raise ValueError(f"Unsupported blend mode: {mode}") def g(self, x): return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) def gaussian_kernel(kernel_size: int, sigma: float, device=None): x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij") d = torch.sqrt(x * x + y * y) g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) return g / g.sum() class Blur: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "blur_radius": ("INT", { "default": 1, "min": 1, "max": 31, "step": 1 }), "sigma": ("FLOAT", { "default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1 }), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "blur" CATEGORY = "image/postprocessing" def blur(self, image: torch.Tensor, blur_radius: int, sigma: float): if blur_radius == 0: return (image,) image = image.to(comfy.model_management.get_torch_device()) batch_size, height, width, channels = image.shape kernel_size = blur_radius * 2 + 1 kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1) image = image.permute(0, 3, 1, 2) padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect') blurred = F.conv2d(padded_image, kernel, padding=kernel_size blurred = blurred.permute(0, 2, 3, 1) return (blurred.to(comfy.model_management.intermediate_device()),) class Quantize: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "colors": ("INT", { "default": 256, "min": 1, "max": 256, "step": 1 }), "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "quantize" CATEGORY = "image/postprocessing" def bayer(im, pal_im, order): def normalized_bayer_matrix(n): if n == 0: return np.zeros((1,1), "float32") else: q = 4 ** n m = q * normalized_bayer_matrix(n - 1) return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q num_colors = len(pal_im.getpalette()) spread = 2 * 256 / num_colors bayer_n = int(math.log2(order)) bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5) result = torch.from_numpy(np.array(im).astype(np.float32)) tw = math.ceil(result.shape[0] / bayer_matrix.shape[0]) th = math.ceil(result.shape[1] / bayer_matrix.shape[1]) tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1) result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255) result = result.to(dtype=torch.uint8) im = Image.fromarray(result.cpu().numpy()) im = im.quantize(palette=pal_im, dither=Image.Dither.NONE) return im def quantize(self, image: torch.Tensor, colors: int, dither: str): batch_size, height, width, _ = image.shape result = torch.zeros_like(image) for b in range(batch_size): im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB') pal_im = im.quantize(colors=colors) if dither == "none": quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE) elif dither == "floyd-steinberg": quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG) elif dither.startswith("bayer"): order = int(dither.split('-')[-1]) quantized_image = Quantize.bayer(im, pal_im, order) quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255 result[b] = quantized_array return (result,) class Sharpen: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "sharpen_radius": ("INT", { "default": 1, "min": 1, "max": 31, "step": 1 }), "sigma": ("FLOAT", { "default": 1.0, "min": 0.1, "max": 10.0, "step": 0.01 }), "alpha": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "sharpen" CATEGORY = "image/postprocessing" def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float): if sharpen_radius == 0: return (image,) batch_size, height, width, channels = image.shape image = image.to(comfy.model_management.get_torch_device()) kernel_size = sharpen_radius * 2 + 1 kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) center = kernel_size kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) tensor_image = image.permute(0, 3, 1, 2) tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] sharpened = sharpened.permute(0, 2, 3, 1) result = torch.clamp(sharpened, 0, 1) return (result.to(comfy.model_management.intermediate_device()),) class ImageScaleToTotalPixels: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] crop_methods = ["disabled", "center"] @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "upscale" CATEGORY = "image/upscaling" def upscale(self, image, upscale_method, megapixels): samples = image.movedim(-1,1) total = int(megapixels * 1024 * 1024) scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) width = round(samples.shape[3] * scale_by) height = round(samples.shape[2] * scale_by) s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") s = s.movedim(1,-1) return (s,) NODE_CLASS_MAPPINGS = { "ImageBlend": Blend, "ImageBlur": Blur, "ImageQuantize": Quantize, "ImageSharpen": Sharpen, "ImageScaleToTotalPixels": ImageScaleToTotalPixels, }
import torch class LatentRebatch: @classmethod def INPUT_TYPES(s): return {"required": { "latents": ("LATENT",), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("LATENT",) INPUT_IS_LIST = True OUTPUT_IS_LIST = (True, ) FUNCTION = "rebatch" CATEGORY = "latent/batch" @staticmethod def get_batch(latents, list_ind, offset): '''prepare a batch out of the list of latents''' samples = latents[list_ind]['samples'] shape = samples.shape mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu') if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]: torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear") if mask.shape[0] < samples.shape[0]: mask = mask.repeat((shape[0] - 1) if 'batch_index' in latents[list_ind]: batch_inds = latents[list_ind]['batch_index'] else: batch_inds = [x+offset for x in range(shape[0])] return samples, mask, batch_inds @staticmethod def get_slices(indexable, num, batch_size): '''divides an indexable object into num slices of length batch_size, and a remainder''' slices = [] for i in range(num): slices.append(indexable[i*batch_size:(i+1)*batch_size]) if num * batch_size < len(indexable): return slices, indexable[num * batch_size:] else: return slices, None @staticmethod def slice_batch(batch, num, batch_size): result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch] return list(zip(*result)) @staticmethod def cat_batch(batch1, batch2): if batch1[0] is None: return batch2 result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)] return result def rebatch(self, latents, batch_size): batch_size = batch_size[0] output_list = [] current_batch = (None, None, None) processed = 0 for i in range(len(latents)): next_batch = self.get_batch(latents, i, processed) processed += len(next_batch[2]) if current_batch[0] is None: current_batch = next_batch elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]: sliced, _ = self.slice_batch(current_batch, 1, batch_size) output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) current_batch = next_batch else: current_batch = self.cat_batch(current_batch, next_batch) if current_batch[0].shape[0] > batch_size: num = current_batch[0].shape[0] sliced, remainder = self.slice_batch(current_batch, num, batch_size) for i in range(num): output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]}) current_batch = remainder if current_batch[0] is not None: sliced, _ = self.slice_batch(current_batch, 1, batch_size) output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) for s in output_list: if s['noise_mask'].mean() == 1.0: del s['noise_mask'] return (output_list,) class ImageRebatch: @classmethod def INPUT_TYPES(s): return {"required": { "images": ("IMAGE",), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("IMAGE",) INPUT_IS_LIST = True OUTPUT_IS_LIST = (True, ) FUNCTION = "rebatch" CATEGORY = "image/batch" def rebatch(self, images, batch_size): batch_size = batch_size[0] output_list = [] all_images = [] for img in images: for i in range(img.shape[0]): all_images.append(img[i:i+1]) for i in range(0, len(all_images), batch_size): output_list.append(torch.cat(all_images[i:i+batch_size], dim=0)) return (output_list,) NODE_CLASS_MAPPINGS = { "RebatchLatents": LatentRebatch, "RebatchImages": ImageRebatch, } NODE_DISPLAY_NAME_MAPPINGS = { "RebatchLatents": "Rebatch Latents", "RebatchImages": "Rebatch Images", }
import torch from torch import einsum import torch.nn.functional as F import math from einops import rearrange, repeat from comfy.ldm.modules.attention import optimized_attention import comfy.samplers def attention_basic_with_sim(q, k, v, heads, mask=None, attn_precision=None): b, _, dim_head = q.shape dim_head scale = dim_head ** -0.5 h = heads q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, -1, heads, dim_head) .permute(0, 2, 1, 3) .reshape(b * heads, -1, dim_head) .contiguous(), (q, k, v), ) if attn_precision == torch.float32: sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale else: sim = einsum('b i d, b j d -> b i j', q, k) * scale del q, k if mask is not None: mask = rearrange(mask, 'b ... -> b (...)') max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) sim = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) out = ( out.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) return (out, sim) def create_blur_map(x0, attn, sigma=3.0, threshold=1.0): _, hw1, hw2 = attn.shape b, _, lh, lw = x0.shape attn = attn.reshape(b, -1, hw1, hw2) mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length() mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)] mask = ( mask.reshape(b, *mid_shape) .unsqueeze(1) .type(attn.dtype) ) mask = F.interpolate(mask, (lh, lw)) blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma) blurred = blurred * mask + x0 * (1 - mask) return blurred def gaussian_blur_2d(img, kernel_size, sigma): ksize_half = (kernel_size - 1) * 0.5 x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) pdf = torch.exp(-0.5 * (x / sigma).pow(2)) x_kernel = pdf / pdf.sum() x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) padding = [kernel_size img = F.pad(img, padding, mode="reflect") img = F.conv2d(img, kernel2d, groups=img.shape[-3]) return img class SelfAttentionGuidance: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.1}), "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, scale, blur_sigma): m = model.clone() attn_scores = None def attn_and_record(q, k, v, extra_options): nonlocal attn_scores heads = extra_options["n_heads"] cond_or_uncond = extra_options["cond_or_uncond"] b = q.shape[0] if 1 in cond_or_uncond: uncond_index = cond_or_uncond.index(1) (out, sim) = attention_basic_with_sim(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) n_slices = heads * b attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)] return out else: return optimized_attention(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) def post_cfg_function(args): nonlocal attn_scores uncond_attn = attn_scores sag_scale = scale sag_sigma = blur_sigma sag_threshold = 1.0 model = args["model"] uncond_pred = args["uncond_denoised"] uncond = args["uncond"] cfg_result = args["denoised"] sigma = args["sigma"] model_options = args["model_options"] x = args["input"] if min(cfg_result.shape[2:]) <= 4: return cfg_result degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold) degraded_noised = degraded + x - uncond_pred (sag,) = comfy.samplers.calc_cond_batch(model, [uncond], degraded_noised, sigma, model_options) return cfg_result + (degraded - sag) * sag_scale m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) m.set_model_attn1_replace(attn_and_record, "middle", 0, 0) return (m, ) NODE_CLASS_MAPPINGS = { "SelfAttentionGuidance": SelfAttentionGuidance, } NODE_DISPLAY_NAME_MAPPINGS = { "SelfAttentionGuidance": "Self-Attention Guidance", }
import folder_paths import comfy.sd import comfy.model_management import nodes import torch class TripleCLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), ) }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" CATEGORY = "advanced/loaders" def load_clip(self, clip_name1, clip_name2, clip_name3): clip_path1 = folder_paths.get_full_path("clip", clip_name1) clip_path2 = folder_paths.get_full_path("clip", clip_name2) clip_path3 = folder_paths.get_full_path("clip", clip_name3) clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings")) return (clip,) class EmptySD3LatentImage: def __init__(self): self.device = comfy.model_management.intermediate_device() @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} RETURN_TYPES = ("LATENT",) FUNCTION = "generate" CATEGORY = "latent/sd3" def generate(self, width, height, batch_size=1): latent = torch.ones([batch_size, 16, height return ({"samples":latent}, ) class CLIPTextEncodeSD3: @classmethod def INPUT_TYPES(s): return {"required": { "clip": ("CLIP", ), "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), "empty_padding": (["none", "empty_prompt"], ) }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "advanced/conditioning" def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding): no_padding = empty_padding == "none" tokens = clip.tokenize(clip_g) if len(clip_g) == 0 and no_padding: tokens["g"] = [] if len(clip_l) == 0 and no_padding: tokens["l"] = [] else: tokens["l"] = clip.tokenize(clip_l)["l"] if len(t5xxl) == 0 and no_padding: tokens["t5xxl"] = [] else: tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] if len(tokens["l"]) != len(tokens["g"]): empty = clip.tokenize("") while len(tokens["l"]) < len(tokens["g"]): tokens["l"] += empty["l"] while len(tokens["l"]) > len(tokens["g"]): tokens["g"] += empty["g"] cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return ([[cond, {"pooled_output": pooled}]], ) NODE_CLASS_MAPPINGS = { "TripleCLIPLoader": TripleCLIPLoader, "EmptySD3LatentImage": EmptySD3LatentImage, "CLIPTextEncodeSD3": CLIPTextEncodeSD3, }
import torch import comfy.utils class SD_4XUpscale_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "images": ("IMAGE",), "positive": ("CONDITIONING",), "negative": ("CONDITIONING",), "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}), "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/upscale_diffusion" def encode(self, images, positive, negative, scale_ratio, noise_augmentation): width = max(1, round(images.shape[-2] * scale_ratio)) height = max(1, round(images.shape[-3] * scale_ratio)) pixels = comfy.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width out_cp = [] out_cn = [] for t in positive: n = [t[0], t[1].copy()] n[1]['concat_image'] = pixels n[1]['noise_augmentation'] = noise_augmentation out_cp.append(n) for t in negative: n = [t[0], t[1].copy()] n[1]['concat_image'] = pixels n[1]['noise_augmentation'] = noise_augmentation out_cn.append(n) latent = torch.zeros([images.shape[0], 4, height return (out_cp, out_cn, {"samples":latent}) NODE_CLASS_MAPPINGS = { "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning, }
import torch import nodes import comfy.utils def camera_embeddings(elevation, azimuth): elevation = torch.as_tensor([elevation]) azimuth = torch.as_tensor([azimuth]) embeddings = torch.stack( [ torch.deg2rad( (90 - elevation) - (90) ), torch.sin(torch.deg2rad(azimuth)), torch.cos(torch.deg2rad(azimuth)), torch.deg2rad( 90 - torch.full_like(elevation, 0) ), ], dim=-1).unsqueeze(1) return embeddings class StableZero123_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/3d_models" def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] t = vae.encode(encode_pixels) cam_embeds = camera_embeddings(elevation, azimuth) cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1) positive = [[cond, {"concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([batch_size, 4, height return (positive, negative, {"samples":latent}) class StableZero123_Conditioning_Batched: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/3d_models" def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] t = vae.encode(encode_pixels) cam_embeds = [] for i in range(batch_size): cam_embeds.append(camera_embeddings(elevation, azimuth)) elevation += elevation_batch_increment azimuth += azimuth_batch_increment cam_embeds = torch.cat(cam_embeds, dim=0) cond = torch.cat([comfy.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1) positive = [[cond, {"concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([batch_size, 4, height return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size}) class SV3D_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}), "elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/3d_models" def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] t = vae.encode(encode_pixels) azimuth = 0 azimuth_increment = 360 / (max(video_frames, 2) - 1) elevations = [] azimuths = [] for i in range(video_frames): elevations.append(elevation) azimuths.append(azimuth) azimuth += azimuth_increment positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]] latent = torch.zeros([video_frames, 4, height return (positive, negative, {"samples":latent}) NODE_CLASS_MAPPINGS = { "StableZero123_Conditioning": StableZero123_Conditioning, "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched, "SV3D_Conditioning": SV3D_Conditioning, }
""" This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https: """ import torch import nodes import comfy.utils class StableCascade_EmptyLatentImage: def __init__(self, device="cpu"): self.device = device @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}) }} RETURN_TYPES = ("LATENT", "LATENT") RETURN_NAMES = ("stage_c", "stage_b") FUNCTION = "generate" CATEGORY = "latent/stable_cascade" def generate(self, width, height, compression, batch_size=1): c_latent = torch.zeros([batch_size, 16, height b_latent = torch.zeros([batch_size, 4, height return ({ "samples": c_latent, }, { "samples": b_latent, }) class StableCascade_StageC_VAEEncode: def __init__(self, device="cpu"): self.device = device @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "vae": ("VAE", ), "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), }} RETURN_TYPES = ("LATENT", "LATENT") RETURN_NAMES = ("stage_c", "stage_b") FUNCTION = "generate" CATEGORY = "latent/stable_cascade" def generate(self, image, vae, compression): width = image.shape[-2] height = image.shape[-3] out_width = (width out_height = (height s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1) c_latent = vae.encode(s[:,:,:,:3]) b_latent = torch.zeros([c_latent.shape[0], 4, (height return ({ "samples": c_latent, }, { "samples": b_latent, }) class StableCascade_StageB_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "conditioning": ("CONDITIONING",), "stage_c": ("LATENT",), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "set_prior" CATEGORY = "conditioning/stable_cascade" def set_prior(self, conditioning, stage_c): c = [] for t in conditioning: d = t[1].copy() d['stable_cascade_prior'] = stage_c['samples'] n = [t[0], d] c.append(n) return (c, ) class StableCascade_SuperResolutionControlnet: def __init__(self, device="cpu"): self.device = device @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "vae": ("VAE", ), }} RETURN_TYPES = ("IMAGE", "LATENT", "LATENT") RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b") FUNCTION = "generate" CATEGORY = "_for_testing/stable_cascade" def generate(self, image, vae): width = image.shape[-2] height = image.shape[-3] batch_size = image.shape[0] controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1) c_latent = torch.zeros([batch_size, 16, height b_latent = torch.zeros([batch_size, 4, height return (controlnet_input, { "samples": c_latent, }, { "samples": b_latent, }) NODE_CLASS_MAPPINGS = { "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage, "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning, "StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode, "StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet, }
import torch from typing import Tuple, Callable import math def do_nothing(x: torch.Tensor, mode:str=None): return x def mps_gather_workaround(input, dim, index): if input.shape[-1] == 1: return torch.gather( input.unsqueeze(-1), dim - 1 if dim < 0 else dim, index.unsqueeze(-1) ).squeeze(-1) else: return torch.gather(input, dim, index) def bipartite_soft_matching_random2d(metric: torch.Tensor, w: int, h: int, sx: int, sy: int, r: int, no_rand: bool = False) -> Tuple[Callable, Callable]: """ Partitions the tokens into src and dst and merges r tokens from src to dst. Dst tokens are partitioned by choosing one randomy in each (sx, sy) region. Args: - metric [B, N, C]: metric to use for similarity - w: image width in tokens - h: image height in tokens - sx: stride in the x dimension for dst, must divide w - sy: stride in the y dimension for dst, must divide h - r: number of tokens to remove (by merging) - no_rand: if true, disable randomness (use top left corner only) """ B, N, _ = metric.shape if r <= 0 or w == 1 or h == 1: return do_nothing, do_nothing gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather with torch.no_grad(): hsy, wsx = h if no_rand: rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64) else: rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device) idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64) idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx) if (hsy * sy) < h or (wsx * sx) < w: idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64) idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view else: idx_buffer = idx_buffer_view rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1) del idx_buffer, idx_buffer_view num_dst = hsy * wsx a_idx = rand_idx[:, num_dst:, :] b_idx = rand_idx[:, :num_dst, :] def split(x): C = x.shape[-1] src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C)) dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C)) return src, dst metric = metric / metric.norm(dim=-1, keepdim=True) a, b = split(metric) scores = a @ b.transpose(-1, -2) r = min(a.shape[1], r) node_max, node_idx = scores.max(dim=-1) edge_idx = node_max.argsort(dim=-1, descending=True)[..., None] unm_idx = edge_idx[..., r:, :] src_idx = edge_idx[..., :r, :] dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx) def merge(x: torch.Tensor, mode="mean") -> torch.Tensor: src, dst = split(x) n, t1, c = src.shape unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c)) src = gather(src, dim=-2, index=src_idx.expand(n, r, c)) dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode) return torch.cat([unm, dst], dim=1) def unmerge(x: torch.Tensor) -> torch.Tensor: unm_len = unm_idx.shape[1] unm, dst = x[..., :unm_len, :], x[..., unm_len:, :] _, _, c = unm.shape src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c)) out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype) out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst) out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm) out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src) return out return merge, unmerge def get_functions(x, ratio, original_shape): b, c, original_h, original_w = original_shape original_tokens = original_h * original_w downsample = int(math.ceil(math.sqrt(original_tokens stride_x = 2 stride_y = 2 max_downsample = 1 if downsample <= max_downsample: w = int(math.ceil(original_w / downsample)) h = int(math.ceil(original_h / downsample)) r = int(x.shape[1] * ratio) no_rand = False m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand) return m, u nothing = lambda y: y return nothing, nothing class TomePatchModel: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, ratio): self.u = None def tomesd_m(q, k, v, extra_options): m, self.u = get_functions(q, ratio, extra_options["original_shape"]) return m(q), k, v def tomesd_u(n, extra_options): return self.u(n) m = model.clone() m.set_model_attn1_patch(tomesd_m) m.set_model_attn1_output_patch(tomesd_u) return (m, ) NODE_CLASS_MAPPINGS = { "TomePatchModel": TomePatchModel, }
import os import logging from spandrel import ModelLoader, ImageModelDescriptor from comfy import model_management import torch import comfy.utils import folder_paths try: from spandrel_extra_arches import EXTRA_REGISTRY from spandrel import MAIN_REGISTRY MAIN_REGISTRY.add(*EXTRA_REGISTRY) logging.info("Successfully imported spandrel_extra_arches: support for non commercial upscale models.") except: pass class UpscaleModelLoader: @classmethod def INPUT_TYPES(s): return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ), }} RETURN_TYPES = ("UPSCALE_MODEL",) FUNCTION = "load_model" CATEGORY = "loaders" def load_model(self, model_name): model_path = folder_paths.get_full_path("upscale_models", model_name) sd = comfy.utils.load_torch_file(model_path, safe_load=True) if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""}) out = ModelLoader().load_from_state_dict(sd).eval() if not isinstance(out, ImageModelDescriptor): raise Exception("Upscale model must be a single-image model.") return (out, ) class ImageUpscaleWithModel: @classmethod def INPUT_TYPES(s): return {"required": { "upscale_model": ("UPSCALE_MODEL",), "image": ("IMAGE",), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "upscale" CATEGORY = "image/upscaling" def upscale(self, upscale_model, image): device = model_management.get_torch_device() memory_required = model_management.module_size(upscale_model.model) memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 memory_required += image.nelement() * image.element_size() model_management.free_memory(memory_required, device) upscale_model.to(device) in_img = image.movedim(-1,-3).to(device) tile = 512 overlap = 32 oom = True while oom: try: steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) pbar = comfy.utils.ProgressBar(steps) s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) oom = False except model_management.OOM_EXCEPTION as e: tile if tile < 128: raise e upscale_model.to("cpu") s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) return (s,) NODE_CLASS_MAPPINGS = { "UpscaleModelLoader": UpscaleModelLoader, "ImageUpscaleWithModel": ImageUpscaleWithModel }
import nodes import torch import comfy.utils import comfy.sd import folder_paths import comfy_extras.nodes_model_merging class ImageOnlyCheckpointLoader: @classmethod def INPUT_TYPES(s): return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), }} RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE") FUNCTION = "load_checkpoint" CATEGORY = "loaders/video_models" def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) return (out[0], out[3], out[2]) class SVD_img2vid_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}), "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}), "fps": ("INT", {"default": 6, "min": 1, "max": 1024}), "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}) }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/video_models" def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] if augmentation_level > 0: encode_pixels += torch.randn_like(pixels) * augmentation_level t = vae.encode(encode_pixels) positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([video_frames, 4, height return (positive, negative, {"samples":latent}) class VideoLinearCFGGuidance: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "sampling/video_models" def patch(self, model, min_cfg): def linear_cfg(args): cond = args["cond"] uncond = args["uncond"] cond_scale = args["cond_scale"] scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1)) return uncond + scale * (cond - uncond) m = model.clone() m.set_model_sampler_cfg_function(linear_cfg) return (m, ) class VideoTriangleCFGGuidance: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "sampling/video_models" def patch(self, model, min_cfg): def linear_cfg(args): cond = args["cond"] uncond = args["uncond"] cond_scale = args["cond_scale"] period = 1.0 values = torch.linspace(0, 1, cond.shape[0], device=cond.device) values = 2 * (values / period - torch.floor(values / period + 0.5)).abs() scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1)) return uncond + scale * (cond - uncond) m = model.clone() m.set_model_sampler_cfg_function(linear_cfg) return (m, ) class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave): CATEGORY = "_for_testing" @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "clip_vision": ("CLIP_VISION",), "vae": ("VAE",), "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None): comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) return {} NODE_CLASS_MAPPINGS = { "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader, "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning, "VideoLinearCFGGuidance": VideoLinearCFGGuidance, "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance, "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave, } NODE_DISPLAY_NAME_MAPPINGS = { "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)", }
import nodes import folder_paths MAX_RESOLUTION = nodes.MAX_RESOLUTION class WebcamCapture(nodes.LoadImage): @classmethod def INPUT_TYPES(s): return { "required": { "image": ("WEBCAM", {}), "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "capture_on_queue": ("BOOLEAN", {"default": True}), } } RETURN_TYPES = ("IMAGE",) FUNCTION = "load_capture" CATEGORY = "image" def load_capture(s, image, **kwargs): return super().load_image(folder_paths.get_annotated_filepath(image)) NODE_CLASS_MAPPINGS = { "WebcamCapture": WebcamCapture, } NODE_DISPLAY_NAME_MAPPINGS = { "WebcamCapture": "Webcam Capture", }
from spandrel import ModelLoader def load_state_dict(state_dict): print("WARNING: comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.") return ModelLoader().load_from_state_dict(state_dict).eval()
from PIL import Image, ImageOps from io import BytesIO import numpy as np import struct import comfy.utils import time class SaveImageWebsocket: @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ),} } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = "api/image" def save_images(self, images): pbar = comfy.utils.ProgressBar(images.shape[0]) step = 0 for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) pbar.update_absolute(step, images.shape[0], ("PNG", img, None)) step += 1 return {} def IS_CHANGED(s, images): return time.time() NODE_CLASS_MAPPINGS = { "SaveImageWebsocket": SaveImageWebsocket, }
model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder params: layer: "hidden" layer_idx: -2
model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder params: layer: "hidden" layer_idx: -2
model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_fp16: True image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder params: layer: "hidden" layer_idx: -2
model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_fp16: True image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
model: base_learning_rate: 7.5e-05 target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: hybrid monitor: val/loss_simple_ema scale_factor: 0.18215 finetune_keys: null scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 2500 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 9 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate"
model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: False image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate"
model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate"
model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: False image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate"
model: base_learning_rate: 5.0e-05 target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: hybrid scale_factor: 0.18215 monitor: val/loss_simple_ema finetune_keys: null use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True image_size: 32 in_channels: 9 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [ ] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: target: ldm.data.laion.WebDataModuleFromConfig params: tar_base: null p_unsafe_threshold: 0.1 filter_word_list: "data/filters.yaml" max_pwatermark: 0.45 batch_size: 8 num_workers: 6 multinode: True min_size: 512 train: shards: - "pipe:aws s3 cp s3: - "pipe:aws s3 cp s3: - "pipe:aws s3 cp s3: - "pipe:aws s3 cp s3: - "pipe:aws s3 cp s3: shuffle: 10000 image_key: jpg image_transforms: - target: torchvision.transforms.Resize params: size: 512 interpolation: 3 - target: torchvision.transforms.RandomCrop params: size: 512 postprocess: target: ldm.data.laion.AddMask params: mode: "512train-large" p_drop: 0.25 validation: shards: - "pipe:aws s3 cp s3: shuffle: 0 image_key: jpg image_transforms: - target: torchvision.transforms.Resize params: size: 512 interpolation: 3 - target: torchvision.transforms.CenterCrop params: size: 512 postprocess: target: ldm.data.laion.AddMask params: mode: "512train-large" p_drop: 0.25 lightning: find_unused_parameters: True modelcheckpoint: params: every_n_train_steps: 5000 callbacks: metrics_over_trainsteps_checkpoint: params: every_n_train_steps: 10000 image_logger: target: main.ImageLogger params: enable_autocast: False disabled: False batch_frequency: 1000 max_images: 4 increase_log_steps: False log_first_step: False log_images_kwargs: use_ema_scope: False inpaint: False plot_progressive_rows: False plot_diffusion_rows: False N: 4 unconditional_guidance_scale: 5.0 unconditional_guidance_label: [""] ddim_steps: 50 ddim_eta: 0.0 trainer: benchmark: True val_check_interval: 5000000 num_sanity_val_steps: 0 accumulate_grad_batches: 1
import json from urllib import request, parse import random prompt_text = """ { "3": { "class_type": "KSampler", "inputs": { "cfg": 8, "denoise": 1, "latent_image": [ "5", 0 ], "model": [ "4", 0 ], "negative": [ "7", 0 ], "positive": [ "6", 0 ], "sampler_name": "euler", "scheduler": "normal", "seed": 8566257, "steps": 20 } }, "4": { "class_type": "CheckpointLoaderSimple", "inputs": { "ckpt_name": "v1-5-pruned-emaonly.ckpt" } }, "5": { "class_type": "EmptyLatentImage", "inputs": { "batch_size": 1, "height": 512, "width": 512 } }, "6": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "masterpiece best quality girl" } }, "7": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "bad hands" } }, "8": { "class_type": "VAEDecode", "inputs": { "samples": [ "3", 0 ], "vae": [ "4", 2 ] } }, "9": { "class_type": "SaveImage", "inputs": { "filename_prefix": "ComfyUI", "images": [ "8", 0 ] } } } """ def queue_prompt(prompt): p = {"prompt": prompt} data = json.dumps(p).encode('utf-8') req = request.Request("http: request.urlopen(req) prompt = json.loads(prompt_text) prompt["6"]["inputs"]["text"] = "masterpiece best quality man" prompt["3"]["inputs"]["seed"] = 5 queue_prompt(prompt)
import websocket import uuid import json import urllib.request import urllib.parse server_address = "127.0.0.1:8188" client_id = str(uuid.uuid4()) def queue_prompt(prompt): p = {"prompt": prompt, "client_id": client_id} data = json.dumps(p).encode('utf-8') req = urllib.request.Request("http: return json.loads(urllib.request.urlopen(req).read()) def get_image(filename, subfolder, folder_type): data = {"filename": filename, "subfolder": subfolder, "type": folder_type} url_values = urllib.parse.urlencode(data) with urllib.request.urlopen("http: return response.read() def get_history(prompt_id): with urllib.request.urlopen("http: return json.loads(response.read()) def get_images(ws, prompt): prompt_id = queue_prompt(prompt)['prompt_id'] output_images = {} while True: out = ws.recv() if isinstance(out, str): message = json.loads(out) if message['type'] == 'executing': data = message['data'] if data['node'] is None and data['prompt_id'] == prompt_id: break else: continue history = get_history(prompt_id)[prompt_id] for o in history['outputs']: for node_id in history['outputs']: node_output = history['outputs'][node_id] if 'images' in node_output: images_output = [] for image in node_output['images']: image_data = get_image(image['filename'], image['subfolder'], image['type']) images_output.append(image_data) output_images[node_id] = images_output return output_images prompt_text = """ { "3": { "class_type": "KSampler", "inputs": { "cfg": 8, "denoise": 1, "latent_image": [ "5", 0 ], "model": [ "4", 0 ], "negative": [ "7", 0 ], "positive": [ "6", 0 ], "sampler_name": "euler", "scheduler": "normal", "seed": 8566257, "steps": 20 } }, "4": { "class_type": "CheckpointLoaderSimple", "inputs": { "ckpt_name": "v1-5-pruned-emaonly.ckpt" } }, "5": { "class_type": "EmptyLatentImage", "inputs": { "batch_size": 1, "height": 512, "width": 512 } }, "6": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "masterpiece best quality girl" } }, "7": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "bad hands" } }, "8": { "class_type": "VAEDecode", "inputs": { "samples": [ "3", 0 ], "vae": [ "4", 2 ] } }, "9": { "class_type": "SaveImage", "inputs": { "filename_prefix": "ComfyUI", "images": [ "8", 0 ] } } } """ prompt = json.loads(prompt_text) prompt["6"]["inputs"]["text"] = "masterpiece best quality man" prompt["3"]["inputs"]["seed"] = 5 ws = websocket.WebSocket() ws.connect("ws: images = get_images(ws, prompt)
import websocket import uuid import json import urllib.request import urllib.parse server_address = "127.0.0.1:8188" client_id = str(uuid.uuid4()) def queue_prompt(prompt): p = {"prompt": prompt, "client_id": client_id} data = json.dumps(p).encode('utf-8') req = urllib.request.Request("http: return json.loads(urllib.request.urlopen(req).read()) def get_image(filename, subfolder, folder_type): data = {"filename": filename, "subfolder": subfolder, "type": folder_type} url_values = urllib.parse.urlencode(data) with urllib.request.urlopen("http: return response.read() def get_history(prompt_id): with urllib.request.urlopen("http: return json.loads(response.read()) def get_images(ws, prompt): prompt_id = queue_prompt(prompt)['prompt_id'] output_images = {} current_node = "" while True: out = ws.recv() if isinstance(out, str): message = json.loads(out) if message['type'] == 'executing': data = message['data'] if data['prompt_id'] == prompt_id: if data['node'] is None: break else: current_node = data['node'] else: if current_node == 'save_image_websocket_node': images_output = output_images.get(current_node, []) images_output.append(out[8:]) output_images[current_node] = images_output return output_images prompt_text = """ { "3": { "class_type": "KSampler", "inputs": { "cfg": 8, "denoise": 1, "latent_image": [ "5", 0 ], "model": [ "4", 0 ], "negative": [ "7", 0 ], "positive": [ "6", 0 ], "sampler_name": "euler", "scheduler": "normal", "seed": 8566257, "steps": 20 } }, "4": { "class_type": "CheckpointLoaderSimple", "inputs": { "ckpt_name": "v1-5-pruned-emaonly.ckpt" } }, "5": { "class_type": "EmptyLatentImage", "inputs": { "batch_size": 1, "height": 512, "width": 512 } }, "6": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "masterpiece best quality girl" } }, "7": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "bad hands" } }, "8": { "class_type": "VAEDecode", "inputs": { "samples": [ "3", 0 ], "vae": [ "4", 2 ] } }, "save_image_websocket_node": { "class_type": "SaveImageWebsocket", "inputs": { "images": [ "8", 0 ] } } } """ prompt = json.loads(prompt_text) prompt["6"]["inputs"]["text"] = "masterpiece best quality man" prompt["3"]["inputs"]["seed"] = 5 ws = websocket.WebSocket() ws.connect("ws: images = get_images(ws, prompt)
import os import pytest def pytest_addoption(parser): parser.addoption('--output_dir', action="store", default='tests/inference/samples', help='Output directory for generated images') parser.addoption("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)") parser.addoption("--port", type=int, default=8188, help="Set the listen port.") @pytest.fixture(scope="session", autouse=True) def args_pytest(pytestconfig): args = {} args['output_dir'] = pytestconfig.getoption('output_dir') args['listen'] = pytestconfig.getoption('listen') args['port'] = pytestconfig.getoption('port') os.makedirs(args['output_dir'], exist_ok=True) return args def pytest_collection_modifyitems(items): LAST_TESTS = ['test_quality'] last_items = [] for test_name in LAST_TESTS: for item in items.copy(): print(item.module.__name__, item) if item.module.__name__ == test_name: last_items.append(item) items.remove(item) items.extend(last_items)
Additional requirements for running tests: ``` pip install pytest pip install websocket-client==1.6.1 opencv-python==4.6.0.66 scikit-image==0.21.0 ``` Run inference tests: ``` pytest tests/inference ``` Compares images in 2 directories to ensure they are the same 1) Run an inference test to save a directory of "ground truth" images ``` pytest tests/inference --output_dir tests/inference/baseline ``` 2) Make code edits 3) Run inference and quality comparison tests ``` pytest ```

import os import pytest def pytest_addoption(parser): parser.addoption('--baseline_dir', action="store", default='tests/inference/baseline', help='Directory for ground-truth images') parser.addoption('--test_dir', action="store", default='tests/inference/samples', help='Directory for images to test') parser.addoption('--metrics_file', action="store", default='tests/metrics.md', help='Output file for metrics') parser.addoption('--img_output_dir', action="store", default='tests/compare/samples', help='Output directory for diff metric images') @pytest.fixture(scope="session", autouse=True) def args_pytest(pytestconfig): args = {} args['baseline_dir'] = pytestconfig.getoption('baseline_dir') args['test_dir'] = pytestconfig.getoption('test_dir') args['metrics_file'] = pytestconfig.getoption('metrics_file') args['img_output_dir'] = pytestconfig.getoption('img_output_dir') with open(args['metrics_file'], 'a') as f: if os.stat(args['metrics_file']).st_size == 0: f.write("| date | run | file | status | value | \n") f.write("| --- | --- | --- | --- | --- | \n") return args def gather_file_basenames(directory: str): files = [] for file in os.listdir(directory): if file.endswith(".png"): files.append(file) return files def pytest_generate_tests(metafunc): if "baseline_fname" in metafunc.fixturenames: baseline_fnames = gather_file_basenames(metafunc.config.getoption("baseline_dir")) metafunc.parametrize("baseline_fname", baseline_fnames)
import datetime import numpy as np import os from PIL import Image import pytest from pytest import fixture from typing import Tuple, List from cv2 import imread, cvtColor, COLOR_BGR2RGB from skimage.metrics import structural_similarity as ssim """ This test suite compares images in 2 directories by file name The directories are specified by the command line arguments --baseline_dir and --test_dir """ def ssim_score(img0: np.ndarray, img1: np.ndarray) -> Tuple[float, np.ndarray]: score, diff = ssim(img0, img1, channel_axis=-1, full=True) diff = (diff * 255).astype("uint8") return score, diff METRICS = {"ssim": ssim_score} METRICS_PASS_THRESHOLD = {"ssim": 0.95} class TestCompareImageMetrics: @fixture(scope="class") def test_file_names(self, args_pytest): test_dir = args_pytest['test_dir'] fnames = self.gather_file_basenames(test_dir) yield fnames del fnames @fixture(scope="class", autouse=True) def teardown(self, args_pytest): yield baseline_dir = args_pytest['baseline_dir'] test_dir = args_pytest['test_dir'] img_output_dir = args_pytest['img_output_dir'] metrics_file = args_pytest['metrics_file'] grid_dir = os.path.join(img_output_dir, "grid") os.makedirs(grid_dir, exist_ok=True) for metric_dir in METRICS.keys(): metric_path = os.path.join(img_output_dir, metric_dir) for file in os.listdir(metric_path): if file.endswith(".png"): score = self.lookup_score_from_fname(file, metrics_file) image_file_list = [] image_file_list.append([ os.path.join(baseline_dir, file), os.path.join(test_dir, file), os.path.join(metric_path, file) ]) image_list = [[Image.open(file) for file in files] for files in image_file_list] grid = self.image_grid(image_list) grid.save(os.path.join(grid_dir, f"{metric_dir}_{score:.3f}_{file}")) @fixture() def fname(self, baseline_fname): yield baseline_fname del baseline_fname def test_directories_not_empty(self, args_pytest): baseline_dir = args_pytest['baseline_dir'] test_dir = args_pytest['test_dir'] assert len(os.listdir(baseline_dir)) != 0, f"Baseline directory {baseline_dir} is empty" assert len(os.listdir(test_dir)) != 0, f"Test directory {test_dir} is empty" def test_dir_has_all_matching_metadata(self, fname, test_file_names, args_pytest): baseline_file_path = os.path.join(args_pytest['baseline_dir'], fname) file_paths = [os.path.join(args_pytest['test_dir'], f) for f in test_file_names] file_match = self.find_file_match(baseline_file_path, file_paths) assert file_match is not None, f"Could not find a file in {args_pytest['test_dir']} with matching metadata to {baseline_file_path}" @pytest.mark.parametrize("metric", METRICS.keys()) def test_pipeline_compare( self, args_pytest, fname, test_file_names, metric, ): baseline_dir = args_pytest['baseline_dir'] test_dir = args_pytest['test_dir'] metrics_output_file = args_pytest['metrics_file'] img_output_dir = args_pytest['img_output_dir'] baseline_file_path = os.path.join(baseline_dir, fname) file_paths = [os.path.join(test_dir, f) for f in test_file_names] test_file = self.find_file_match(baseline_file_path, file_paths) sample_baseline = self.read_img(baseline_file_path) sample_secondary = self.read_img(test_file) score, metric_img = METRICS[metric](sample_baseline, sample_secondary) metric_status = score > METRICS_PASS_THRESHOLD[metric] with open(metrics_output_file, 'a') as f: run_info = os.path.splitext(fname)[0] metric_status_str = "PASS ✅" if metric_status else "FAIL ❌" date_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") f.write(f"| {date_str} | {run_info} | {metric} | {metric_status_str} | {score} | \n") metric_img_dir = os.path.join(img_output_dir, metric) os.makedirs(metric_img_dir, exist_ok=True) output_filename = f'{fname}' Image.fromarray(metric_img).save(os.path.join(metric_img_dir, output_filename)) assert score > METRICS_PASS_THRESHOLD[metric] def read_img(self, filename: str) -> np.ndarray: cvImg = imread(filename) cvImg = cvtColor(cvImg, COLOR_BGR2RGB) return cvImg def image_grid(self, img_list: list[list[Image.Image]]): rows = len(img_list) cols = len(img_list[0]) w, h = img_list[0][0].size grid = Image.new('RGB', size=(cols*w, rows*h)) for i, row in enumerate(img_list): for j, img in enumerate(row): grid.paste(img, box=(j*w, i*h)) return grid def lookup_score_from_fname(self, fname: str, metrics_output_file: str ) -> float: fname_basestr = os.path.splitext(fname)[0] with open(metrics_output_file, 'r') as f: for line in f: if fname_basestr in line: score = float(line.split('|')[5]) return score raise ValueError(f"Could not find score for {fname} in {metrics_output_file}") def gather_file_basenames(self, directory: str): files = [] for file in os.listdir(directory): if file.endswith(".png"): files.append(file) return files def read_file_prompt(self, fname:str) -> str: img = Image.open(fname) img.load() return img.info['prompt'] def find_file_match(self, baseline_file: str, file_paths: List[str]): baseline_prompt = self.read_file_prompt(baseline_file) if baseline_prompt is None or baseline_prompt == "": return None basename = os.path.basename(baseline_file) file_path_basenames = [os.path.basename(f) for f in file_paths] if basename in file_path_basenames: match_index = file_path_basenames.index(basename) file_paths.insert(0, file_paths.pop(match_index)) for f in file_paths: test_file_prompt = self.read_file_prompt(f) if baseline_prompt == test_file_prompt: return f
from copy import deepcopy from io import BytesIO from urllib import request import numpy import os from PIL import Image import pytest from pytest import fixture import time import torch from typing import Union import json import subprocess import websocket import uuid import urllib.request import urllib.parse from comfy.samplers import KSampler """ These tests generate and save images through a range of parameters """ class ComfyGraph: def __init__(self, graph: dict, sampler_nodes: list[str], ): self.graph = graph self.sampler_nodes = sampler_nodes def set_prompt(self, prompt, negative_prompt=None): for node in self.sampler_nodes: prompt_node = self.graph[node]['inputs']['positive'][0] self.graph[prompt_node]['inputs']['text'] = prompt if negative_prompt: negative_prompt_node = self.graph[node]['inputs']['negative'][0] self.graph[negative_prompt_node]['inputs']['text'] = negative_prompt def set_sampler_name(self, sampler_name:str, ): for node in self.sampler_nodes: self.graph[node]['inputs']['sampler_name'] = sampler_name def set_scheduler(self, scheduler:str): for node in self.sampler_nodes: self.graph[node]['inputs']['scheduler'] = scheduler def set_filename_prefix(self, prefix:str): for node in self.graph: if self.graph[node]['class_type'] == 'SaveImage': self.graph[node]['inputs']['filename_prefix'] = prefix class ComfyClient: def connect(self, listen:str = '127.0.0.1', port:Union[str,int] = 8188, client_id: str = str(uuid.uuid4()) ): self.client_id = client_id self.server_address = f"{listen}:{port}" ws = websocket.WebSocket() ws.connect("ws: self.ws = ws def queue_prompt(self, prompt): p = {"prompt": prompt, "client_id": self.client_id} data = json.dumps(p).encode('utf-8') req = urllib.request.Request("http: return json.loads(urllib.request.urlopen(req).read()) def get_image(self, filename, subfolder, folder_type): data = {"filename": filename, "subfolder": subfolder, "type": folder_type} url_values = urllib.parse.urlencode(data) with urllib.request.urlopen("http: return response.read() def get_history(self, prompt_id): with urllib.request.urlopen("http: return json.loads(response.read()) def get_images(self, graph, save=True): prompt = graph if not save: prompt_str = json.dumps(prompt) prompt_str = prompt_str.replace('SaveImage', 'PreviewImage') prompt = json.loads(prompt_str) prompt_id = self.queue_prompt(prompt)['prompt_id'] output_images = {} while True: out = self.ws.recv() if isinstance(out, str): message = json.loads(out) if message['type'] == 'executing': data = message['data'] if data['node'] is None and data['prompt_id'] == prompt_id: break else: continue history = self.get_history(prompt_id)[prompt_id] for o in history['outputs']: for node_id in history['outputs']: node_output = history['outputs'][node_id] if 'images' in node_output: images_output = [] for image in node_output['images']: image_data = self.get_image(image['filename'], image['subfolder'], image['type']) images_output.append(image_data) output_images[node_id] = images_output return output_images default_graph_file = 'tests/inference/graphs/default_graph_sdxl1_0.json' with open(default_graph_file, 'r') as file: default_graph = json.loads(file.read()) DEFAULT_COMFY_GRAPH = ComfyGraph(graph=default_graph, sampler_nodes=['10','14']) DEFAULT_COMFY_GRAPH_ID = os.path.splitext(os.path.basename(default_graph_file))[0] comfy_graph_list = [DEFAULT_COMFY_GRAPH] comfy_graph_ids = [DEFAULT_COMFY_GRAPH_ID] prompt_list = [ 'a painting of a cat', ] sampler_list = KSampler.SAMPLERS scheduler_list = KSampler.SCHEDULERS @pytest.mark.inference @pytest.mark.parametrize("sampler", sampler_list) @pytest.mark.parametrize("scheduler", scheduler_list) @pytest.mark.parametrize("prompt", prompt_list) class TestInference: @fixture(scope="class", autouse=True) def _server(self, args_pytest): p = subprocess.Popen([ 'python','main.py', '--output-directory', args_pytest["output_dir"], '--listen', args_pytest["listen"], '--port', str(args_pytest["port"]), ]) yield p.kill() torch.cuda.empty_cache() def start_client(self, listen:str, port:int): comfy_client = ComfyClient() n_tries = 5 for i in range(n_tries): time.sleep(4) try: comfy_client.connect(listen=listen, port=port) except ConnectionRefusedError as e: print(e) print(f"({i+1}/{n_tries}) Retrying...") else: break return comfy_client @fixture(scope="class", params=comfy_graph_list, ids=comfy_graph_ids, autouse=True) def _client_graph(self, request, args_pytest, _server) -> (ComfyClient, ComfyGraph): comfy_graph = request.param comfy_client = self.start_client(args_pytest["listen"], args_pytest["port"]) comfy_client.get_images(graph=comfy_graph.graph, save=False) yield comfy_client, comfy_graph del comfy_client del comfy_graph torch.cuda.empty_cache() @fixture def client(self, _client_graph): client = _client_graph[0] yield client @fixture def comfy_graph(self, _client_graph): graph = deepcopy(_client_graph[1]) yield graph def test_comfy( self, client, comfy_graph, sampler, scheduler, prompt, request ): test_info = request.node.name comfy_graph.set_filename_prefix(test_info) comfy_graph.set_sampler_name(sampler) comfy_graph.set_scheduler(scheduler) comfy_graph.set_prompt(prompt) images = client.get_images(comfy_graph.graph) assert len(images) != 0, "No images generated" for images_output in images.values(): for image_data in images_output: pil_image = Image.open(BytesIO(image_data)) assert numpy.array(pil_image).any() != 0, "Image is blank"

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