diff --git a/README.md b/README.md index fa65447..4d68740 100644 --- a/README.md +++ b/README.md @@ -31,6 +31,13 @@
+# V1.7.4 Updates +1) Added a series of advanced Hunyuan LORA mixing tools. +2) Modified Multi Lora Loader to use fast loding. +3) Fix some bugs + +
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No newline at end of file diff --git a/__init__.py b/__init__.py index 49ae6ab..0a6808c 100644 --- a/__init__.py +++ b/__init__.py @@ -6,8 +6,9 @@ NODE_CLASS_MAPPINGS = { "WarpedHunyuanMultiLoraAvgMerge": WarpedHunyuanMultiLoraAvgMerge, "WarpedHunyuanLoraMerge": WarpedHunyuanLoraMerge, "WarpedHunyuanMultiLoraMixer": WarpedHunyuanMultiLoraMixer, - "WarpedHunyuanMultiLoraMixerExt": WarpedHunyuanMultiLoraMixerExt, - "WarpedHunyuanMultiLoraMixerUlt": WarpedHunyuanMultiLoraMixerUlt, + # "WarpedHunyuanMultiLoraMixerExt": WarpedHunyuanMultiLoraMixerExt, + # "WarpedHunyuanMultiLoraMixerUlt": WarpedHunyuanMultiLoraMixerUlt, + # "WarpedHunyuanMultiLoraMixerPlatinum": WarpedHunyuanMultiLoraMixerPlatinum, "WarpedHunyuanLoraAvgMerge": WarpedHunyuanLoraAvgMerge, "WarpedHunyuanLoraBatchMerge": WarpedHunyuanLoraBatchMerge, "WarpedWanLoraMerge": WarpedWanLoraMerge, @@ -38,7 +39,7 @@ NODE_CLASS_MAPPINGS = { # "WarpedHunyuanLoraConvertKeys2": WarpedHunyuanLoraConvertKeys2, "WarpedLoraKeysAndMetadataReader": WarpedLoraKeysAndMetadataReader, "WarpedHunyuanLoraConvert": WarpedHunyuanLoraConvert, - # "WarpedHunyuanLoraConvertToMusubi": WarpedHunyuanLoraConvertToMusubi, + "WarpedHunyuanLoraConvertToMusubi": WarpedHunyuanLoraConvertToMusubi, "WarpedFramepackLoraSelectBatch": WarpedFramepackLoraSelectBatch, "WarpedFramepackMultiLoraSelect": WarpedFramepackMultiLoraSelect, "WarpedFramepackMultiLoraSelectExt": WarpedFramepackMultiLoraSelectExt, @@ -71,6 +72,21 @@ NODE_CLASS_MAPPINGS = { "WarpedLoadImages": WarpedLoadImages, "WarpedSaveImageCaption": WarpedSaveImageCaption, "WarpedModifyCaptionFile": WarpedModifyCaptionFile, + "WarpedAddToCaption": WarpedAddToCaption, + "WarpedPromptConcat": WarpedPromptConcat, + "WarpedPromptConcatExt": WarpedPromptConcatExt, + "WarpedHunyuanVideoToVideo": WarpedHunyuanVideoToVideo, + "WarpedImageLossCalc": WarpedImageLossCalc, + "WarpedUNETLoader": WarpedUNETLoader, + "WarpedUNETConverter": WarpedUNETConverter, + "WarpedHunyuanLoraDoubleBlocksSwap": WarpedHunyuanLoraDoubleBlocksSwap, + "WarpedContinueWorkflowManual": WarpedContinueWorkflowManual, + "WarpedContinueWorkflowAuto": WarpedContinueWorkflowAuto, + "WarpedHunyuanLoraDoubleBlocksModifySegment": WarpedHunyuanLoraDoubleBlocksModifySegment, + "WarpedHunyuanLoraDoubleBlocksModifyMultipleSegments": WarpedHunyuanLoraDoubleBlocksModifyMultipleSegments, + "WarpedHunyuanLoraDoubleBlocksRemoveLinear": WarpedHunyuanLoraDoubleBlocksRemoveLinear, + "WarpedHunyuanLoraDoubleBlocksLayersBlend": WarpedHunyuanLoraDoubleBlocksLayersBlend, + "WarpedLoadHunyuanLoraWeightsByPrefix": WarpedLoadHunyuanLoraWeightsByPrefix, } NODE_DISPLAY_NAME_MAPPINGS = { @@ -78,8 +94,9 @@ NODE_DISPLAY_NAME_MAPPINGS = { "WarpedHunyuanMultiLoraAvgMerge": "Warped Hunyuan Multi Lora Avg Merge", "WarpedHunyuanLoraMerge": "Warped Hunyuan Lora Merge", "WarpedHunyuanMultiLoraMixer": "Warped Hunyuan Multi Lora Mixer", - "WarpedHunyuanMultiLoraMixerExt": "Warped Hunyuan Multi Lora Mixer Ext", - "WarpedHunyuanMultiLoraMixerUlt": "Warped Hunyuan Multi Lora Mixer Ultimate ", + # "WarpedHunyuanMultiLoraMixerExt": "Warped Hunyuan Multi Lora Mixer Ext", + # "WarpedHunyuanMultiLoraMixerUlt": "Warped Hunyuan Multi Lora Mixer Ultimate ", + # "WarpedHunyuanMultiLoraMixerPlatinum": "Warped Hunyuan Multi Lora Mixer Platinum", "WarpedHunyuanLoraAvgMerge": "Warped Hunyuan Lora Avg Merge", "WarpedHunyuanLoraBatchMerge": "Warped Hunyuan Lora Batch Merge", "WarpedWanLoraMerge": "Warped Wan Lora Merge", @@ -110,7 +127,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { # "WarpedHunyuanLoraConvertKeys2": "Warped Hunyuan Lora Convert Keys 2", "WarpedLoraKeysAndMetadataReader": "Warped Lora Keys And Metadata Reader", "WarpedHunyuanLoraConvert": "Warped Hunyuan Lora Convert", - # "WarpedHunyuanLoraConvertToMusubi": "Warped Hunyuan Lora Convert To Musubi", + "WarpedHunyuanLoraConvertToMusubi": "Warped Hunyuan Lora Convert To Musubi", "WarpedFramepackLoraSelectBatch": "Warped Framepack Lora Select Batch", "WarpedFramepackMultiLoraSelect": "Warped Framepack Multi Lora Select", "WarpedFramepackMultiLoraSelectExt": "Warped Framepack Multi Lora Select Ext", @@ -143,6 +160,21 @@ NODE_DISPLAY_NAME_MAPPINGS = { "WarpedLoadImages": "Warped Load Images", "WarpedSaveImageCaption": "Warped Save Image Caption", "WarpedModifyCaptionFile": "Warped Modify Caption File", + "WarpedAddToCaption": "Warped Prefix or Postfix Caption", + "WarpedPromptConcat": "Warped Prompt Concat", + "WarpedPromptConcatExt": "Warped Prompt Concat Ext", + "WarpedHunyuanVideoToVideo": "Warped Hunyuan Video To Video", + "WarpedImageLossCalc": "Warped Image Loss Calc", + "WarpedUNETLoader": "Warped Diffusion Model Loader", + "WarpedUNETConverter": "Warped Diffusion Model Converter", + "WarpedHunyuanLoraDoubleBlocksSwap": "Warped Hunyuan Lora Double Blocks Swap", + "WarpedContinueWorkflowManual": "Warped Continue Workflow Manual", + "WarpedContinueWorkflowAuto": "Warped Continue Workflow Auto", + "WarpedHunyuanLoraDoubleBlocksModifySegment": "Warped Hunyuan Lora Double Blocks Modify Segment", + "WarpedHunyuanLoraDoubleBlocksModifyMultipleSegments": "Warped Hunyuan Lora Double Blocks Modify Multiple Segments", + "WarpedHunyuanLoraDoubleBlocksRemoveLinear": "Warped Hunyuan Lora Double Blocks Remove Linear", + "WarpedHunyuanLoraDoubleBlocksLayersBlend": "Warped Hunyuan Lora Double Blocks Layers Blend", + "WarpedLoadHunyuanLoraWeightsByPrefix": "Warped Load Hunyuan Lora Weights By Prefix", } WEB_DIRECTORY = "./web" diff --git a/nodes.py b/nodes.py index 702b233..3ecb5fd 100644 --- a/nodes.py +++ b/nodes.py @@ -360,36 +360,36 @@ def convert_to_musubi(lora: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: musubi_prefix = "lora_unet" if "lora_A" in key: - new_key = f"{musubi_prefix}_{module_name.replace("_lora_A_weight", ".lora_down.weight")}" + new_key = f"{musubi_prefix}_{module_name.replace('_lora_A_weight', '.lora_down.weight')}" elif "lora_B" in key: - new_key = f"{musubi_prefix}_{module_name.replace("_lora_B_weight", ".lora_up.weight")}" + new_key = f"{musubi_prefix}_{module_name.replace('_lora_B_weight', '.lora_up.weight')}" else: print(f"unexpected key: {key} in diffusion_model LoRA format") continue print("New Key: {}".format(new_key)) - converted_lora[new_key] = weight + converted_lora[new_key] = weight.to(dtype=torch.bfloat16) if has_double_blocks: print("Lora Has Double Blocks.") for block_num in range(20): - converted_lora[f"lora_unet_double_blocks_{block_num}_img_attn_proj.alpha"] = torch.empty([]) - converted_lora[f"lora_unet_double_blocks_{block_num}_img_attn_qkv.alpha"] = torch.empty([]) - converted_lora[f"lora_unet_double_blocks_{block_num}_img_mlp_fc1.alpha"] = torch.empty([]) - converted_lora[f"lora_unet_double_blocks_{block_num}_img_mlp_fc2.alpha"] = torch.empty([]) - converted_lora[f"lora_unet_double_blocks_{block_num}_txt_attn_proj.alpha"] = torch.empty([]) - converted_lora[f"lora_unet_double_blocks_{block_num}_txt_attn_qkv.alpha"] = torch.empty([]) - converted_lora[f"lora_unet_double_blocks_{block_num}_txt_mlp_fc1.alpha"] = torch.empty([]) - converted_lora[f"lora_unet_double_blocks_{block_num}_txt_mlp_fc2.alpha"] = torch.empty([]) + converted_lora[f"lora_unet_double_blocks_{block_num}_img_attn_proj.alpha"] = torch.empty([]).to(dtype=torch.bfloat16) + converted_lora[f"lora_unet_double_blocks_{block_num}_img_attn_qkv.alpha"] = torch.empty([]).to(dtype=torch.bfloat16) + converted_lora[f"lora_unet_double_blocks_{block_num}_img_mlp_fc1.alpha"] = torch.empty([]).to(dtype=torch.bfloat16) + converted_lora[f"lora_unet_double_blocks_{block_num}_img_mlp_fc2.alpha"] = torch.empty([]).to(dtype=torch.bfloat16) + converted_lora[f"lora_unet_double_blocks_{block_num}_txt_attn_proj.alpha"] = torch.empty([]).to(dtype=torch.bfloat16) + converted_lora[f"lora_unet_double_blocks_{block_num}_txt_attn_qkv.alpha"] = torch.empty([]).to(dtype=torch.bfloat16) + converted_lora[f"lora_unet_double_blocks_{block_num}_txt_mlp_fc1.alpha"] = torch.empty([]).to(dtype=torch.bfloat16) + converted_lora[f"lora_unet_double_blocks_{block_num}_txt_mlp_fc2.alpha"] = torch.empty([]).to(dtype=torch.bfloat16) if has_single_blocks: print("Lora Has Single Blocks.") for block_num in range(40): - converted_lora[f"lora_unet_single_blocks_{block_num}_linear1.alpha"] = torch.Tensor([]) - converted_lora[f"lora_unet_single_blocks_{block_num}_linear2.alpha"] = torch.Tensor([]) + converted_lora[f"lora_unet_single_blocks_{block_num}_linear1.alpha"] = torch.Tensor([]).to(dtype=torch.bfloat16) + converted_lora[f"lora_unet_single_blocks_{block_num}_linear2.alpha"] = torch.Tensor([]).to(dtype=torch.bfloat16) return converted_lora @@ -464,7 +464,6 @@ def convert_lora(lora, convert_to="diffusion_model", do_check_for_musuibi=True): for key in temp_lora.keys(): if convert_to in key: - print("HERE 67000") new_lora = temp_lora break @@ -561,8 +560,8 @@ def convert_lora_dimensions(max_dimension, lora): for key in lora.keys(): temp_weights = lora[key] - if (temp_weights.shape[0] == max_dimension) or (temp_weights.shape[1] == max_dimension): - return lora + # if (temp_weights.shape[0] == max_dimension) or (temp_weights.shape[1] == max_dimension): + # return lora if temp_weights.shape[0] < temp_weights.shape[1]: padding = torch.zeros([max_dimension, temp_weights.shape[1]]) @@ -833,14 +832,14 @@ class WarpedHunyuanMultiLoraMerge: padding = torch.zeros([new_lora[key].shape[0], new_lora[key].shape[1]]) padding[:temp_weights.shape[0],:] = temp_weights - temp_weights = padding + temp_weights = padding.to(dtype=torch.bfloat16) elif temp_weights.shape[0] > new_lora[key].shape[0]: temp_weights = temp_weights.clone().detach() new_lora[key] = new_lora[key].clone().detach() padding = torch.zeros([temp_weights.shape[0], temp_weights.shape[1]]) padding[:new_lora[key].shape[0],:] = new_lora[key] - new_lora[key] = padding + new_lora[key] = padding.to(dtype=torch.bfloat16) else: if temp_weights.shape[1] < new_lora[key].shape[1]: temp_weights = temp_weights.clone().detach() @@ -848,21 +847,21 @@ class WarpedHunyuanMultiLoraMerge: padding = torch.zeros([new_lora[key].shape[0], new_lora[key].shape[1]]) padding[:,:temp_weights.shape[1]] = temp_weights - temp_weights = padding + temp_weights = padding.to(dtype=torch.bfloat16) elif temp_weights.shape[1] > new_lora[key].shape[1]: temp_weights = temp_weights.clone().detach() new_lora[key] = new_lora[key].clone().detach() padding = torch.zeros([temp_weights.shape[0], temp_weights.shape[1]]) padding[:,:new_lora[key].shape[1]] = new_lora[key] - new_lora[key] = padding + new_lora[key] = padding.to(dtype=torch.bfloat16) try: - new_lora[key] = torch.add(new_lora[key], temp_weights) + new_lora[key] = torch.add(new_lora[key], temp_weights).to(dtype=torch.bfloat16) except Exception as e: raise(e) else: - new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]) + new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]).to(dtype=torch.bfloat16) if not save_metadata: metadata = None @@ -1164,568 +1163,544 @@ class WarpedHunyuanMultiLoraAvgMerge: return {"ui": {"tags": [save_message]}} +# class WarpedHunyuanMultiLoraMixer: +# @classmethod +# def INPUT_TYPES(s): +# return { +# "required": { +# "save_folder": ("STRING", {"default": get_default_output_folder()}), +# "model_prefix": ("STRING", {"default": "new_model_hy"}), +# "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), +# "num_output": ("INT", {"default": 1, "min": 1, "max": 100}), +# "lora_1": (['None'] + get_lora_list(),), +# "lora_2": (['None'] + get_lora_list(),), +# "lora_3": (['None'] + get_lora_list(),), +# "lora_4": (['None'] + get_lora_list(),), +# "lora_5": (['None'] + get_lora_list(),), +# "lora_6": (['None'] + get_lora_list(),), +# "lora_7": (['None'] + get_lora_list(),), +# "lora_8": (['None'] + get_lora_list(),), +# "save_metadata": ("BOOLEAN", {"default": True}), +# }, +# } +# +# RETURN_TYPES = () +# OUTPUT_NODE = True +# OUTPUT_IS_LIST = (True,) +# FUNCTION = "merge_multiple_loras" +# CATEGORY = "Warped/Hunyuan/Mixers" +# DESCRIPTION = "Load and apply multiple LoRA models with different strengths and block types. Model input is required." +# +# def convert_key_format(self, key: str) -> str: +# """Standardize LoRA key format by removing prefixes.""" +# prefixes = ["diffusion_model.", "transformer."] +# for prefix in prefixes: +# if key.startswith(prefix): +# key = key[len(prefix):] +# break +# return key +# +# def load_lora(self, lora_name: str, strength: float, blocks_type: str) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: +# """Load and filter a single LoRA model.""" +# if not lora_name or strength == 0: +# return {}, {} +# +# # Get the full path to the LoRA file +# lora_path = folder_paths.get_full_path("loras", lora_name) +# if not os.path.exists(lora_path): +# raise ValueError(f"LoRA file not found: {lora_path}") +# +# # Load the LoRA weights +# lora_weights = utils.load_torch_file(lora_path) +# +# return lora_weights +# +# def get_mixtures(self, seed, num_output, lora_keys): +# random.seed(seed) +# mixtures = {} +# +# for i in range(num_output): +# mixtures["{}".format(i + 1)] = {} +# +# for key in lora_keys: +# for mixture_key in mixtures.keys(): +# mixtures[mixture_key][key] = {"single": [], "double": []} +# +# for mixture_key in mixtures.keys(): +# for j in range(40): +# temp_key = "{}".format(random.randint(1, len(lora_keys))) +# mixtures[mixture_key][temp_key]["single"].append(j) +# +# for j in range(20): +# temp_key = "{}".format(random.randint(1, len(lora_keys))) +# mixtures[mixture_key][temp_key]["double"].append(j) +# +# i += 1 +# +# print("\nMixtures\n") +# +# block_metadata = "" +# +# for mixture_key in mixtures.keys(): +# for key in mixtures[mixture_key]: +# print("{} | {}: {}".format(mixture_key, key, mixtures[mixture_key][key])) +# +# if len(block_metadata) > 0: +# block_metadata = "{} | {}".format(block_metadata, "{}: {}: {}".format(mixture_key, key, mixtures[mixture_key][key])) +# else: +# block_metadata = "{}: {}: {}".format(mixture_key, key, mixtures[mixture_key][key]) +# +# print("\n") +# +# return mixtures, block_metadata +# +# def merge_multiple_loras(self, save_folder, model_prefix, seed, num_output, lora_1, lora_2, lora_3, lora_4, lora_5, lora_6, lora_7, lora_8, save_metadata=True): +# print("Save_folder: {}".format(save_folder)) +# os.makedirs(save_folder, exist_ok = True) +# +# temp_loras = {} +# metadata = {"loras": "{} and {} and {} and {} and {} and {} and {} and {}".format(lora_1, lora_2, lora_3, lora_4, lora_5, lora_6, lora_7, lora_8)} +# metadata["seed"] = "{}".format(seed) +# metadata["num_output"] = "{}".format(num_output) +# +# if lora_1 != "None": +# print(lora_1) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_1, 1.0, "all") +# lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") +# temp_loras["1"] = {"lora_weights": lora_weights} +# +# if lora_2 != "None": +# print(lora_2) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_2, 1.0, "all") +# lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") +# temp_loras["2"] = {"lora_weights": lora_weights} +# +# if lora_3 != "None": +# print(lora_3) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_3, 1.0, "all") +# lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") +# temp_loras["3"] = {"lora_weights": lora_weights} +# +# if lora_4 != "None": +# print(lora_4) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_4, 1.0, "all") +# lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") +# temp_loras["4"] = {"lora_weights": lora_weights} +# +# if lora_5 != "None": +# print(lora_5) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_5, 1.0, "all") +# lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") +# temp_loras["5"] = {"lora_weights": lora_weights} +# +# if lora_6 != "None": +# print(lora_6) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_6, 1.0, "all") +# lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") +# temp_loras["6"] = {"lora_weights": lora_weights} +# +# if lora_7 != "None": +# print(lora_7) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_7, 1.0, "all") +# lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") +# temp_loras["7"] = {"lora_weights": lora_weights} +# +# if lora_8 != "None": +# print(lora_8) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_8, 1.0, "all") +# lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") +# temp_loras["8"] = {"lora_weights": lora_weights} +# +# loras = {} +# max_dimension = 0 +# +# for lora_key in temp_loras.keys(): +# # print(lora_key) +# loras[lora_key] = {"lora_weights": {}} +# +# for key in temp_loras[lora_key]["lora_weights"].keys(): +# new_key = key.replace("transformer.", "diffusion_model.") +# loras[lora_key]["lora_weights"][new_key] = temp_loras[lora_key]["lora_weights"][key] +# +# temp_dimension = min(loras[lora_key]["lora_weights"][new_key].shape[0], loras[lora_key]["lora_weights"][new_key].shape[1]) +# +# if temp_dimension > max_dimension: +# max_dimension = temp_dimension +# +# merge_mixtures, block_metadata = self.get_mixtures(seed, num_output, loras.keys()) +# metadata["max_dimension"] = "{}".format(max_dimension) +# +# print("Max Dimension: {}".format(max_dimension)) +# +# # convert the rank/dims for each lora to be mixed +# for lora_key in temp_loras.keys(): +# temp_loras[lora_key]["lora_weights"] = convert_lora_dimensions(max_dimension, temp_loras[lora_key]["lora_weights"]) +# +# save_message = "" +# +# for mixture_key in merge_mixtures: +# new_lora = {} +# output_filename = os.path.join(save_folder, "{}_{:05}.safetensors".format(model_prefix, int(mixture_key))) +# +# metadata["merge_mixture"] = "{}".format(merge_mixtures[mixture_key]) +# # metadata["block_metadata"] = "{}".format(block_metadata[int(mixture_key)]) +# +# for lora_key in loras.keys(): +# mixture_single_blocks = merge_mixtures[mixture_key][lora_key]["single"] +# mixture_double_blocks = merge_mixtures[mixture_key][lora_key]["double"] +# +# for key in loras[lora_key]["lora_weights"].keys(): +# temp_strings = str(key).split('.') +# temp_block_num = int(temp_strings[2]) +# +# if temp_strings[1] == "single_blocks": +# if temp_block_num in mixture_single_blocks: +# new_lora[key] = loras[lora_key]["lora_weights"][key].to(dtype=torch.bfloat16) +# continue +# +# if temp_strings[1] == "double_blocks": +# if temp_block_num in mixture_double_blocks: +# new_lora[key] = loras[lora_key]["lora_weights"][key].to(dtype=torch.bfloat16) +# +# if not save_metadata: +# metadata = None +# +# print("Saving Model To: {}...".format(output_filename)) +# utils.save_torch_file(new_lora, output_filename, metadata=metadata) +# print("Saving Model To: {}...Done.".format(output_filename)) +# +# save_message = "{}\n{}".format(save_message, "Weights Saved To: {}".format(output_filename)) +# +# new_lora = None +# mm.soft_empty_cache() +# gc.collect() +# time.sleep(1) +# +# return {"ui": {"tags": ["save_message"]}} +# +# class WarpedHunyuanMultiLoraMixerExt: +# @classmethod +# def INPUT_TYPES(s): +# return { +# "required": { +# "save_folder": ("STRING", {"default": get_default_output_folder()}), +# "model_prefix": ("STRING", {"default": "new_model_hy"}), +# "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), +# "num_output": ("INT", {"default": 1, "min": 1, "max": 100}), +# "lora_1": (['None'] + get_lora_list(),), +# "strength_1": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "lora_2": (['None'] + get_lora_list(),), +# "strength_2": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "lora_3": (['None'] + get_lora_list(),), +# "strength_3": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "lora_4": (['None'] + get_lora_list(),), +# "strength_4": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "lora_5": (['None'] + get_lora_list(),), +# "strength_5": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "lora_6": (['None'] + get_lora_list(),), +# "strength_6": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "lora_7": (['None'] + get_lora_list(),), +# "strength_7": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "lora_8": (['None'] + get_lora_list(),), +# "strength_8": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "save_metadata": ("BOOLEAN", {"default": True}), +# "convert_to": (["diffusion_model", "transformer"], {"default": "diffusion_model"}), +# "max_dimension": ([32, 64, 128], {"default": 64}), +# }, +# } +# +# RETURN_TYPES = () +# OUTPUT_NODE = True +# OUTPUT_IS_LIST = (True,) +# FUNCTION = "merge_multiple_loras" +# CATEGORY = "Warped/Hunyuan/Mixers" +# DESCRIPTION = "Load and apply multiple LoRA models with different strengths and block types. Model input is required." +# +# def load_lora(self, lora_name: str, strength: float, blocks_type: str) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: +# """Load and filter a single LoRA model.""" +# if not lora_name or strength == 0: +# return {}, {} +# +# # Get the full path to the LoRA file +# lora_path = folder_paths.get_full_path("loras", lora_name) +# if not os.path.exists(lora_path): +# raise ValueError(f"LoRA file not found: {lora_path}") +# +# # Load the LoRA weights +# lora_weights = utils.load_torch_file(lora_path) +# +# lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") +# +# return lora_weights +# +# def get_random_key(self, keys): +# if len(keys) > 0: +# random_key = random.randint(0, len(keys) - 1) +# # print("Keys: {} | Random Key: {}".format(random_key)) +# return keys[random_key] +# +# return -1 +# +# def get_mixtures(self, seed, num_output, lora_keys, block_types): +# random.seed(seed) +# mixtures = {} +# +# for i in range(num_output): +# mixtures["{}".format(i + 1)] = {} +# +# single_block_loras = [] +# double_block_loras = [] +# +# for key in lora_keys: +# for mixture_key in mixtures.keys(): +# mixtures[mixture_key][key] = {"single": [], "double": []} +# +# if block_types[key]["has_single_blocks"]: +# single_block_loras.append(int(key)) +# +# if block_types[key]["has_double_blocks"]: +# double_block_loras.append(int(key)) +# +# for mixture_key in mixtures.keys(): +# if len(single_block_loras) > 0: +# for j in range(40): +# random_key = self.get_random_key(single_block_loras) +# temp_key = "{}".format(random_key) +# mixtures[mixture_key][temp_key]["single"].append(j) +# +# if len(double_block_loras) > 0: +# for j in range(20): +# random_key = self.get_random_key(double_block_loras) +# temp_key = "{}".format(random_key) +# mixtures[mixture_key][temp_key]["double"].append(j) +# +# i += 1 +# +# print("\nMixtures\n") +# +# block_metadata = "" +# +# for mixture_key in mixtures.keys(): +# for key in mixtures[mixture_key]: +# print("{} | {}: {}".format(mixture_key, key, mixtures[mixture_key][key])) +# +# if len(block_metadata) > 0: +# block_metadata = "{} | {}".format(block_metadata, "{}: {}: {}".format(mixture_key, key, mixtures[mixture_key][key])) +# else: +# block_metadata = "{}: {}: {}".format(mixture_key, key, mixtures[mixture_key][key]) +# +# print("\n") +# +# return mixtures, block_metadata +# +# def determine_lora_block_types(self, loras): +# block_types = {} +# +# for lora_key in loras.keys(): +# block_types[lora_key] = { "has_single_blocks": False, "has_double_blocks": False } +# +# for key in loras[lora_key]["lora_weights"].keys(): +# if "single_blocks" in key: +# block_types[lora_key]["has_single_blocks"] = True +# elif "double_blocks" in key: +# block_types[lora_key]["has_double_blocks"] = True +# +# if block_types[lora_key]["has_single_blocks"] and block_types[lora_key]["has_double_blocks"]: +# break +# +# return block_types +# +# def merge_multiple_loras(self, save_folder, model_prefix, seed, num_output, lora_1, strength_1, lora_2, strength_2, lora_3, strength_3, lora_4, strength_4, +# lora_5, strength_5, lora_6, strength_6, lora_7, strength_7, lora_8, strength_8, save_metadata=True, convert_to="diffusion_model", max_dimension=64): +# print("Save_folder: {}".format(save_folder)) +# os.makedirs(save_folder, exist_ok = True) +# +# temp_loras = {} +# metadata = {"loras": "{} and {} and {} and {} and {} and {} and {} and {}".format(lora_1, lora_2, lora_3, lora_4, lora_5, lora_6, lora_7, lora_8)} +# metadata["strengths"] = "{} and {} and {} and {} and {} and {} and {} and {}".format(strength_1, strength_2, strength_3, strength_4, strength_5, strength_6, strength_7, strength_8) +# metadata["seed"] = "{}".format(seed) +# metadata["num_output"] = "{}".format(num_output) +# +# if (lora_1 != "None") and (strength_1 > 0.0): +# print(lora_1) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_1, 1.0, "all") +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# temp_loras["1"] = {"lora_weights": lora_weights, "strength": strength_1} +# +# for key in lora_weights.keys(): +# print("LORA 1: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# break +# +# lora_weights = None +# +# if (lora_2 != "None") and (strength_2 > 0.0): +# print(lora_2) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_2, 1.0, "all") +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# temp_loras["2"] = {"lora_weights": lora_weights, "strength": strength_2} +# +# for key in lora_weights.keys(): +# print("LORA 2: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# break +# +# lora_weights = None +# +# if (lora_3 != "None") and (strength_3 > 0.0): +# print(lora_3) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_3, 1.0, "all") +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# temp_loras["3"] = {"lora_weights": lora_weights, "strength": strength_3} +# +# for key in lora_weights.keys(): +# print("LORA 3: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# break +# +# lora_weights = None +# +# if (lora_4 != "None") and (strength_4 > 0.0): +# print(lora_4) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_4, 1.0, "all") +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# temp_loras["4"] = {"lora_weights": lora_weights, "strength": strength_4} +# +# for key in lora_weights.keys(): +# print("LORA 4: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# break +# +# lora_weights = None +# +# if (lora_5 != "None") and (strength_5 > 0.0): +# print(lora_5) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_5, 1.0, "all") +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# temp_loras["5"] = {"lora_weights": lora_weights, "strength": strength_5} +# +# for key in lora_weights.keys(): +# print("LORA 5: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# break +# +# lora_weights = None +# +# if (lora_6 != "None") and (strength_6 > 0.0): +# print(lora_6) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_6, 1.0, "all") +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# temp_loras["6"] = {"lora_weights": lora_weights, "strength": strength_6} +# +# for key in lora_weights.keys(): +# print("LORA 6: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# break +# +# lora_weights = None +# +# if (lora_7 != "None") and (strength_7 > 0.0): +# print(lora_7) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_7, 1.0, "all") +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# temp_loras["7"] = {"lora_weights": lora_weights, "strength": strength_7} +# +# for key in lora_weights.keys(): +# print("LORA 7: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# break +# +# lora_weights = None +# +# if (lora_8 != "None") and (strength_8 > 0.0): +# print(lora_8) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_8, 1.0, "all") +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# temp_loras["8"] = {"lora_weights": lora_weights, "strength": strength_8} +# +# for key in lora_weights.keys(): +# print("LORA 8: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# break +# +# lora_weights = None +# +# loras = {} +# +# for lora_key in temp_loras.keys(): +# loras[lora_key] = {"lora_weights": {}, "strength": temp_loras[lora_key]["strength"]} +# +# for key in temp_loras[lora_key]["lora_weights"].keys(): +# new_key = key.replace("transformer.", "diffusion_model.") +# loras[lora_key]["lora_weights"][new_key] = temp_loras[lora_key]["lora_weights"][key].to(dtype=torch.bfloat16) +# +# block_types = self.determine_lora_block_types(loras) +# merge_mixtures, block_metadata = self.get_mixtures(seed, num_output, loras.keys(), block_types) +# +# metadata["max_dimension"] = "{}".format(max_dimension) +# metadata["block_types"] = "{}".format(block_types) +# +# print("Max Dimension: {}".format(max_dimension)) +# +# save_message = "" +# +# for mixture_key in merge_mixtures: +# new_lora = {} +# output_filename = os.path.join(save_folder, "{}_{:05}.safetensors".format(model_prefix, int(mixture_key))) +# +# metadata["merge_mixture"] = "{}".format(merge_mixtures[mixture_key]) +# # metadata["block_metadata"] = "{}".format(block_metadata[int(mixture_key)]) +# +# for lora_key in loras.keys(): +# mixture_single_blocks = merge_mixtures[mixture_key][lora_key]["single"] +# mixture_double_blocks = merge_mixtures[mixture_key][lora_key]["double"] +# +# for key in loras[lora_key]["lora_weights"].keys(): +# temp_strings = str(key).split('.') +# temp_block_num = int(temp_strings[2]) +# +# if temp_strings[1] == "single_blocks": +# if temp_block_num in mixture_single_blocks: +# new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]).to(dtype=torch.bfloat16) +# continue +# +# if temp_strings[1] == "double_blocks": +# if temp_block_num in mixture_double_blocks: +# new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]).to(dtype=torch.bfloat16) +# +# if not save_metadata: +# metadata = None +# +# print("Saving Model To: {}...".format(output_filename)) +# utils.save_torch_file(new_lora, output_filename, metadata=metadata) +# print("Saving Model To: {}...Done.".format(output_filename)) +# +# save_message = "{}\n{}".format(save_message, "Weights Saved To: {}".format(output_filename)) +# +# new_lora = None +# mm.soft_empty_cache() +# gc.collect() +# time.sleep(1) +# +# return {"ui": {"tags": ["save_message"]}} + class WarpedHunyuanMultiLoraMixer: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "save_folder": ("STRING", {"default": get_default_output_folder()}), - "model_prefix": ("STRING", {"default": "new_model_hy"}), - "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), - "num_output": ("INT", {"default": 1, "min": 1, "max": 100}), - "lora_1": (['None'] + get_lora_list(),), - "lora_2": (['None'] + get_lora_list(),), - "lora_3": (['None'] + get_lora_list(),), - "lora_4": (['None'] + get_lora_list(),), - "lora_5": (['None'] + get_lora_list(),), - "lora_6": (['None'] + get_lora_list(),), - "lora_7": (['None'] + get_lora_list(),), - "lora_8": (['None'] + get_lora_list(),), - "save_metadata": ("BOOLEAN", {"default": True}), - }, - } - - RETURN_TYPES = () - OUTPUT_NODE = True - OUTPUT_IS_LIST = (True,) - FUNCTION = "merge_multiple_loras" - CATEGORY = "Warped/Hunyuan/Merge" - DESCRIPTION = "Load and apply multiple LoRA models with different strengths and block types. Model input is required." - - def convert_key_format(self, key: str) -> str: - """Standardize LoRA key format by removing prefixes.""" - prefixes = ["diffusion_model.", "transformer."] - for prefix in prefixes: - if key.startswith(prefix): - key = key[len(prefix):] - break - return key - - def load_lora(self, lora_name: str, strength: float, blocks_type: str) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: - """Load and filter a single LoRA model.""" - if not lora_name or strength == 0: - return {}, {} - - # Get the full path to the LoRA file - lora_path = folder_paths.get_full_path("loras", lora_name) - if not os.path.exists(lora_path): - raise ValueError(f"LoRA file not found: {lora_path}") - - # Load the LoRA weights - lora_weights = utils.load_torch_file(lora_path) - - return lora_weights - - def get_mixtures(self, seed, num_output, lora_keys): - random.seed(seed) - mixtures = {} - - for i in range(num_output): - mixtures["{}".format(i + 1)] = {} - - for key in lora_keys: - for mixture_key in mixtures.keys(): - mixtures[mixture_key][key] = {"single": [], "double": []} - - for mixture_key in mixtures.keys(): - for j in range(40): - temp_key = "{}".format(random.randint(1, len(lora_keys))) - mixtures[mixture_key][temp_key]["single"].append(j) - - for j in range(20): - temp_key = "{}".format(random.randint(1, len(lora_keys))) - mixtures[mixture_key][temp_key]["double"].append(j) - - i += 1 - - print("\nMixtures\n") - - block_metadata = "" - - for mixture_key in mixtures.keys(): - for key in mixtures[mixture_key]: - print("{} | {}: {}".format(mixture_key, key, mixtures[mixture_key][key])) - - if len(block_metadata) > 0: - block_metadata = "{} | {}".format(block_metadata, "{}: {}: {}".format(mixture_key, key, mixtures[mixture_key][key])) - else: - block_metadata = "{}: {}: {}".format(mixture_key, key, mixtures[mixture_key][key]) - - print("\n") - - return mixtures, block_metadata - - # def convert_lora_dimensions(self, max_dimension, lora): - # new_lora = {} - # - # for key in lora.keys(): - # temp_weights = lora[key] - # - # if temp_weights.shape[0] < temp_weights.shape[1]: - # if temp_weights.shape[0] < max_dimension: - # padding = torch.zeros([max_dimension, temp_weights.shape[1]]) - # padding[:temp_weights.shape[0],:] = temp_weights - # new_lora[key] = padding - # else: - # new_lora[key] = temp_weights - # else: - # if temp_weights.shape[1] < max_dimension: - # padding = torch.zeros([temp_weights.shape[0], max_dimension]) - # padding[:,:temp_weights.shape[1]] = temp_weights - # new_lora[key] = padding - # else: - # new_lora[key] = temp_weights - # lora = None - # - # return new_lora - - def merge_multiple_loras(self, save_folder, model_prefix, seed, num_output, lora_1, lora_2, lora_3, lora_4, lora_5, lora_6, lora_7, lora_8, save_metadata=True): - print("Save_folder: {}".format(save_folder)) - os.makedirs(save_folder, exist_ok = True) - - temp_loras = {} - metadata = {"loras": "{} and {} and {} and {} and {} and {} and {} and {}".format(lora_1, lora_2, lora_3, lora_4, lora_5, lora_6, lora_7, lora_8)} - metadata["seed"] = "{}".format(seed) - metadata["num_output"] = "{}".format(num_output) - - if lora_1 != "None": - print(lora_1) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_1, 1.0, "all") - lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") - temp_loras["1"] = {"lora_weights": lora_weights} - - if lora_2 != "None": - print(lora_2) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_2, 1.0, "all") - lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") - temp_loras["2"] = {"lora_weights": lora_weights} - - if lora_3 != "None": - print(lora_3) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_3, 1.0, "all") - lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") - temp_loras["3"] = {"lora_weights": lora_weights} - - if lora_4 != "None": - print(lora_4) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_4, 1.0, "all") - lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") - temp_loras["4"] = {"lora_weights": lora_weights} - - if lora_5 != "None": - print(lora_5) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_5, 1.0, "all") - lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") - temp_loras["5"] = {"lora_weights": lora_weights} - - if lora_6 != "None": - print(lora_6) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_6, 1.0, "all") - lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") - temp_loras["6"] = {"lora_weights": lora_weights} - - if lora_7 != "None": - print(lora_7) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_7, 1.0, "all") - lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") - temp_loras["7"] = {"lora_weights": lora_weights} - - if lora_8 != "None": - print(lora_8) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_8, 1.0, "all") - lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") - temp_loras["8"] = {"lora_weights": lora_weights} - - loras = {} - max_dimension = 0 - - for lora_key in temp_loras.keys(): - # print(lora_key) - loras[lora_key] = {"lora_weights": {}} - - for key in temp_loras[lora_key]["lora_weights"].keys(): - new_key = key.replace("transformer.", "diffusion_model.") - loras[lora_key]["lora_weights"][new_key] = temp_loras[lora_key]["lora_weights"][key] - - temp_dimension = min(loras[lora_key]["lora_weights"][new_key].shape[0], loras[lora_key]["lora_weights"][new_key].shape[1]) - - if temp_dimension > max_dimension: - max_dimension = temp_dimension - - merge_mixtures, block_metadata = self.get_mixtures(seed, num_output, loras.keys()) - metadata["max_dimension"] = "{}".format(max_dimension) - - print("Max Dimension: {}".format(max_dimension)) - - # convert the rank/dims for each lora to be mixed - for lora_key in temp_loras.keys(): - temp_loras[lora_key]["lora_weights"] = convert_lora_dimensions(max_dimension, temp_loras[lora_key]["lora_weights"]) - - save_message = "" - - for mixture_key in merge_mixtures: - new_lora = {} - output_filename = os.path.join(save_folder, "{}_{:05}.safetensors".format(model_prefix, int(mixture_key))) - - metadata["merge_mixture"] = "{}".format(merge_mixtures[mixture_key]) - # metadata["block_metadata"] = "{}".format(block_metadata[int(mixture_key)]) - - for lora_key in loras.keys(): - mixture_single_blocks = merge_mixtures[mixture_key][lora_key]["single"] - mixture_double_blocks = merge_mixtures[mixture_key][lora_key]["double"] - - for key in loras[lora_key]["lora_weights"].keys(): - temp_strings = str(key).split('.') - temp_block_num = int(temp_strings[2]) - - if temp_strings[1] == "single_blocks": - if temp_block_num in mixture_single_blocks: - new_lora[key] = loras[lora_key]["lora_weights"][key] - continue - - if temp_strings[1] == "double_blocks": - if temp_block_num in mixture_double_blocks: - new_lora[key] = loras[lora_key]["lora_weights"][key] - - if not save_metadata: - metadata = None - - print("Saving Model To: {}...".format(output_filename)) - utils.save_torch_file(new_lora, output_filename, metadata=metadata) - print("Saving Model To: {}...Done.".format(output_filename)) - - save_message = "{}\n{}".format(save_message, "Weights Saved To: {}".format(output_filename)) - - new_lora = None - mm.soft_empty_cache() - gc.collect() - time.sleep(1) - - return {"ui": {"tags": ["save_message"]}} - -class WarpedHunyuanMultiLoraMixerExt: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "save_folder": ("STRING", {"default": get_default_output_folder()}), - "model_prefix": ("STRING", {"default": "new_model_hy"}), - "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), - "num_output": ("INT", {"default": 1, "min": 1, "max": 100}), - "lora_1": (['None'] + get_lora_list(),), - "strength_1": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), - "lora_2": (['None'] + get_lora_list(),), - "strength_2": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), - "lora_3": (['None'] + get_lora_list(),), - "strength_3": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), - "lora_4": (['None'] + get_lora_list(),), - "strength_4": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), - "lora_5": (['None'] + get_lora_list(),), - "strength_5": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), - "lora_6": (['None'] + get_lora_list(),), - "strength_6": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), - "lora_7": (['None'] + get_lora_list(),), - "strength_7": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), - "lora_8": (['None'] + get_lora_list(),), - "strength_8": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), - "save_metadata": ("BOOLEAN", {"default": True}), - "convert_to": (["diffusion_model", "transformer"], {"default": "diffusion_model"}), - "max_dimension": ([32, 64, 128], {"default": 64}), - }, - } - - RETURN_TYPES = () - OUTPUT_NODE = True - OUTPUT_IS_LIST = (True,) - FUNCTION = "merge_multiple_loras" - CATEGORY = "Warped/Hunyuan/Merge" - DESCRIPTION = "Load and apply multiple LoRA models with different strengths and block types. Model input is required." - - def load_lora(self, lora_name: str, strength: float, blocks_type: str) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: - """Load and filter a single LoRA model.""" - if not lora_name or strength == 0: - return {}, {} - - # Get the full path to the LoRA file - lora_path = folder_paths.get_full_path("loras", lora_name) - if not os.path.exists(lora_path): - raise ValueError(f"LoRA file not found: {lora_path}") - - # Load the LoRA weights - lora_weights = utils.load_torch_file(lora_path) - - lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") - - return lora_weights - - def get_random_key(self, keys): - if len(keys) > 0: - random_key = random.randint(0, len(keys) - 1) - # print("Keys: {} | Random Key: {}".format(random_key)) - return keys[random_key] - - return -1 - - def get_mixtures(self, seed, num_output, lora_keys, block_types): - random.seed(seed) - mixtures = {} - - for i in range(num_output): - mixtures["{}".format(i + 1)] = {} - - single_block_loras = [] - double_block_loras = [] - - for key in lora_keys: - for mixture_key in mixtures.keys(): - mixtures[mixture_key][key] = {"single": [], "double": []} - - if block_types[key]["has_single_blocks"]: - single_block_loras.append(int(key)) - - if block_types[key]["has_double_blocks"]: - double_block_loras.append(int(key)) - - for mixture_key in mixtures.keys(): - if len(single_block_loras) > 0: - for j in range(40): - random_key = self.get_random_key(single_block_loras) - temp_key = "{}".format(random_key) - mixtures[mixture_key][temp_key]["single"].append(j) - - if len(double_block_loras) > 0: - for j in range(20): - random_key = self.get_random_key(double_block_loras) - temp_key = "{}".format(random_key) - mixtures[mixture_key][temp_key]["double"].append(j) - - i += 1 - - print("\nMixtures\n") - - block_metadata = "" - - for mixture_key in mixtures.keys(): - for key in mixtures[mixture_key]: - print("{} | {}: {}".format(mixture_key, key, mixtures[mixture_key][key])) - - if len(block_metadata) > 0: - block_metadata = "{} | {}".format(block_metadata, "{}: {}: {}".format(mixture_key, key, mixtures[mixture_key][key])) - else: - block_metadata = "{}: {}: {}".format(mixture_key, key, mixtures[mixture_key][key]) - - print("\n") - - return mixtures, block_metadata - - def determine_lora_block_types(self, loras): - block_types = {} - - for lora_key in loras.keys(): - block_types[lora_key] = { "has_single_blocks": False, "has_double_blocks": False } - - for key in loras[lora_key]["lora_weights"].keys(): - if "single_blocks" in key: - block_types[lora_key]["has_single_blocks"] = True - elif "double_blocks" in key: - block_types[lora_key]["has_double_blocks"] = True - - if block_types[lora_key]["has_single_blocks"] and block_types[lora_key]["has_double_blocks"]: - break - - return block_types - - def merge_multiple_loras(self, save_folder, model_prefix, seed, num_output, lora_1, strength_1, lora_2, strength_2, lora_3, strength_3, lora_4, strength_4, - lora_5, strength_5, lora_6, strength_6, lora_7, strength_7, lora_8, strength_8, save_metadata=True, convert_to="diffusion_model", max_dimension=64): - print("Save_folder: {}".format(save_folder)) - os.makedirs(save_folder, exist_ok = True) - - temp_loras = {} - metadata = {"loras": "{} and {} and {} and {} and {} and {} and {} and {}".format(lora_1, lora_2, lora_3, lora_4, lora_5, lora_6, lora_7, lora_8)} - metadata["strengths"] = "{} and {} and {} and {} and {} and {} and {} and {}".format(strength_1, strength_2, strength_3, strength_4, strength_5, strength_6, strength_7, strength_8) - metadata["seed"] = "{}".format(seed) - metadata["num_output"] = "{}".format(num_output) - - if (lora_1 != "None") and (strength_1 > 0.0): - print(lora_1) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_1, 1.0, "all") - lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - - temp_loras["1"] = {"lora_weights": lora_weights, "strength": strength_1} - - for key in lora_weights.keys(): - print("LORA 1: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break - - lora_weights = None - - if (lora_2 != "None") and (strength_2 > 0.0): - print(lora_2) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_2, 1.0, "all") - lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - - temp_loras["2"] = {"lora_weights": lora_weights, "strength": strength_2} - - for key in lora_weights.keys(): - print("LORA 2: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break - - lora_weights = None - - if (lora_3 != "None") and (strength_3 > 0.0): - print(lora_3) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_3, 1.0, "all") - lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - - temp_loras["3"] = {"lora_weights": lora_weights, "strength": strength_3} - - for key in lora_weights.keys(): - print("LORA 3: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break - - lora_weights = None - - if (lora_4 != "None") and (strength_4 > 0.0): - print(lora_4) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_4, 1.0, "all") - lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - - temp_loras["4"] = {"lora_weights": lora_weights, "strength": strength_4} - - for key in lora_weights.keys(): - print("LORA 4: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break - - lora_weights = None - - if (lora_5 != "None") and (strength_5 > 0.0): - print(lora_5) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_5, 1.0, "all") - lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - - temp_loras["5"] = {"lora_weights": lora_weights, "strength": strength_5} - - for key in lora_weights.keys(): - print("LORA 5: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break - - lora_weights = None - - if (lora_6 != "None") and (strength_6 > 0.0): - print(lora_6) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_6, 1.0, "all") - lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - - temp_loras["6"] = {"lora_weights": lora_weights, "strength": strength_6} - - for key in lora_weights.keys(): - print("LORA 6: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break - - lora_weights = None - - if (lora_7 != "None") and (strength_7 > 0.0): - print(lora_7) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_7, 1.0, "all") - lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - - temp_loras["7"] = {"lora_weights": lora_weights, "strength": strength_7} - - for key in lora_weights.keys(): - print("LORA 7: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break - - lora_weights = None - - if (lora_8 != "None") and (strength_8 > 0.0): - print(lora_8) - # Load and filter the LoRA weights - lora_weights = self.load_lora(lora_8, 1.0, "all") - lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - - temp_loras["8"] = {"lora_weights": lora_weights, "strength": strength_8} - - for key in lora_weights.keys(): - print("LORA 8: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break - - lora_weights = None - - loras = {} - - for lora_key in temp_loras.keys(): - loras[lora_key] = {"lora_weights": {}, "strength": temp_loras[lora_key]["strength"]} - - for key in temp_loras[lora_key]["lora_weights"].keys(): - new_key = key.replace("transformer.", "diffusion_model.") - loras[lora_key]["lora_weights"][new_key] = temp_loras[lora_key]["lora_weights"][key] - - block_types = self.determine_lora_block_types(loras) - merge_mixtures, block_metadata = self.get_mixtures(seed, num_output, loras.keys(), block_types) - - metadata["max_dimension"] = "{}".format(max_dimension) - metadata["block_types"] = "{}".format(block_types) - - print("Max Dimension: {}".format(max_dimension)) - - save_message = "" - - for mixture_key in merge_mixtures: - new_lora = {} - output_filename = os.path.join(save_folder, "{}_{:05}.safetensors".format(model_prefix, int(mixture_key))) - - metadata["merge_mixture"] = "{}".format(merge_mixtures[mixture_key]) - # metadata["block_metadata"] = "{}".format(block_metadata[int(mixture_key)]) - - for lora_key in loras.keys(): - mixture_single_blocks = merge_mixtures[mixture_key][lora_key]["single"] - mixture_double_blocks = merge_mixtures[mixture_key][lora_key]["double"] - - for key in loras[lora_key]["lora_weights"].keys(): - temp_strings = str(key).split('.') - temp_block_num = int(temp_strings[2]) - - if temp_strings[1] == "single_blocks": - if temp_block_num in mixture_single_blocks: - new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]) - continue - - if temp_strings[1] == "double_blocks": - if temp_block_num in mixture_double_blocks: - new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]) - - if not save_metadata: - metadata = None - - print("Saving Model To: {}...".format(output_filename)) - utils.save_torch_file(new_lora, output_filename, metadata=metadata) - print("Saving Model To: {}...Done.".format(output_filename)) - - save_message = "{}\n{}".format(save_message, "Weights Saved To: {}".format(output_filename)) - - new_lora = None - mm.soft_empty_cache() - gc.collect() - time.sleep(1) - - return {"ui": {"tags": ["save_message"]}} - -class WarpedHunyuanMultiLoraMixerUlt: @classmethod def INPUT_TYPES(s): return { @@ -1760,7 +1735,8 @@ class WarpedHunyuanMultiLoraMixerUlt: "blocks_8": (["all", "double_blocks", "single_blocks"], {"default": "all", "tooltip": "all, single only, or double only block."}), "save_metadata": ("BOOLEAN", {"default": True}), "convert_to": (["diffusion_model", "transformer"], {"default": "diffusion_model"}), - "max_dimension": ([32, 64, 128], {"default": 64}), + "max_dimension": ([32, 64, 128], {"default": 128}), + "discard_linear": ("BOOLEAN", {"default": True}), }, } @@ -1768,7 +1744,7 @@ class WarpedHunyuanMultiLoraMixerUlt: OUTPUT_NODE = True OUTPUT_IS_LIST = (True,) FUNCTION = "merge_multiple_loras" - CATEGORY = "Warped/Hunyuan/Merge" + CATEGORY = "Warped/Hunyuan/Mixers" DESCRIPTION = "Load and apply multiple LoRA models with different strengths and block types. Model input is required." def load_lora(self, lora_name: str, strength: float, blocks_type: str) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: @@ -1776,14 +1752,8 @@ class WarpedHunyuanMultiLoraMixerUlt: if not lora_name or strength == 0: return {}, {} - # Get the full path to the LoRA file - lora_path = folder_paths.get_full_path("loras", lora_name) - if not os.path.exists(lora_path): - raise ValueError(f"LoRA file not found: {lora_path}") - - # Load the LoRA weights - lora_weights = utils.load_torch_file(lora_path) - + # # Load the LoRA weights + lora_weights = warped_load_lora_weights(lora_name) lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") return lora_weights @@ -1831,6 +1801,38 @@ class WarpedHunyuanMultiLoraMixerUlt: i += 1 + at_least_one = False + + while not at_least_one: + if len(double_block_loras) > 0: + for key in lora_keys: + lowest = 999 + highest = -1 + lowest_mixture_key = None + highest_mixture_key = None + + for mixture_key in mixtures: + if len(mixtures[mixture_key][key]["double"]) < lowest: + lowest = len(mixtures[mixture_key][key]["double"]) + lowest_mixture_key = mixture_key + + if len(mixtures[mixture_key][key]["double"]) > highest: + highest = len(mixtures[mixture_key][key]["double"]) + highest_mixture_key = mixture_key + + if lowest < 1: + print("low key: {}".format(lowest_mixture_key)) + print("high key: {}".format(highest_mixture_key)) + mixtures[lowest_mixture_key][key]["double"].append(mixtures[highest_mixture_key][key]["double"][0]) + print("low mix: {}".format(mixtures[lowest_mixture_key][key]["double"])) + print("high mixes before: {}".format(mixtures[highest_mixture_key][key]["double"])) + mixtures[highest_mixture_key][key]["double"].pop(0) + print("high mixes after: {}".format(mixtures[highest_mixture_key][key]["double"])) + else: + at_least_one = True + else: + at_least_one = True + print("\nMixtures\n") block_metadata = "" @@ -1866,7 +1868,8 @@ class WarpedHunyuanMultiLoraMixerUlt: return block_types def merge_multiple_loras(self, save_folder, model_prefix, seed, num_output, lora_1, strength_1, blocks_1, lora_2, strength_2, blocks_2, lora_3, strength_3, blocks_3, lora_4, strength_4, blocks_4, - lora_5, strength_5, blocks_5, lora_6, strength_6, blocks_6, lora_7, strength_7, blocks_7, lora_8, strength_8, blocks_8, save_metadata=True, convert_to="diffusion_model", max_dimension=64): + lora_5, strength_5, blocks_5, lora_6, strength_6, blocks_6, lora_7, strength_7, blocks_7, lora_8, strength_8, blocks_8, save_metadata=True, convert_to="diffusion_model", + max_dimension=128, discard_linear=True): print("Save_folder: {}".format(save_folder)) os.makedirs(save_folder, exist_ok = True) @@ -1886,11 +1889,15 @@ class WarpedHunyuanMultiLoraMixerUlt: lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - temp_loras["1"] = {"lora_weights": lora_weights, "strength": strength_1} - + print_it = True for key in lora_weights.keys(): - print("LORA 1: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break + if print_it: + print("LORA 1: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) + print_it = False + + lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) + + temp_loras["1"] = {"lora_weights": lora_weights, "strength": strength_1} lora_weights = None @@ -1904,11 +1911,15 @@ class WarpedHunyuanMultiLoraMixerUlt: lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - temp_loras["2"] = {"lora_weights": lora_weights, "strength": strength_2} - + print_it = True for key in lora_weights.keys(): - print("LORA 2: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break + if print_it: + print("LORA 2: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) + print_it = False + + lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) + + temp_loras["2"] = {"lora_weights": lora_weights, "strength": strength_2} lora_weights = None @@ -1922,11 +1933,15 @@ class WarpedHunyuanMultiLoraMixerUlt: lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - temp_loras["3"] = {"lora_weights": lora_weights, "strength": strength_3} - + print_it = True for key in lora_weights.keys(): - print("LORA 3: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break + if print_it: + print("LORA 3: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) + print_it = False + + lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) + + temp_loras["3"] = {"lora_weights": lora_weights, "strength": strength_3} lora_weights = None @@ -1940,11 +1955,15 @@ class WarpedHunyuanMultiLoraMixerUlt: lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - temp_loras["4"] = {"lora_weights": lora_weights, "strength": strength_4} - + print_it = True for key in lora_weights.keys(): - print("LORA 4: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break + if print_it: + print("LORA 4: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) + print_it = False + + lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) + + temp_loras["4"] = {"lora_weights": lora_weights, "strength": strength_4} lora_weights = None @@ -1958,11 +1977,15 @@ class WarpedHunyuanMultiLoraMixerUlt: lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - temp_loras["5"] = {"lora_weights": lora_weights, "strength": strength_5} - + print_it = True for key in lora_weights.keys(): - print("LORA 5: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break + if print_it: + print("LORA 5: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) + print_it = False + + lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) + + temp_loras["5"] = {"lora_weights": lora_weights, "strength": strength_5} lora_weights = None @@ -1976,11 +1999,15 @@ class WarpedHunyuanMultiLoraMixerUlt: lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - temp_loras["6"] = {"lora_weights": lora_weights, "strength": strength_6} - + print_it = True for key in lora_weights.keys(): - print("LORA 6: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break + if print_it: + print("LORA 6: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) + print_it = False + + lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) + + temp_loras["6"] = {"lora_weights": lora_weights, "strength": strength_6} lora_weights = None @@ -1994,11 +2021,15 @@ class WarpedHunyuanMultiLoraMixerUlt: lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - temp_loras["7"] = {"lora_weights": lora_weights, "strength": strength_7} - + print_it = True for key in lora_weights.keys(): - print("LORA 7: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break + if print_it: + print("LORA 7: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) + print_it = False + + lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) + + temp_loras["7"] = {"lora_weights": lora_weights, "strength": strength_7} lora_weights = None @@ -2012,11 +2043,15 @@ class WarpedHunyuanMultiLoraMixerUlt: lora_weights = convert_lora_dimensions(max_dimension, lora_weights) - temp_loras["8"] = {"lora_weights": lora_weights, "strength": strength_8} - + print_it = True for key in lora_weights.keys(): - print("LORA 8: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) - break + if print_it: + print("LORA 8: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) + print_it = False + + lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) + + temp_loras["8"] = {"lora_weights": lora_weights, "strength": strength_8} lora_weights = None @@ -2027,7 +2062,7 @@ class WarpedHunyuanMultiLoraMixerUlt: for key in temp_loras[lora_key]["lora_weights"].keys(): new_key = key.replace("transformer.", "diffusion_model.") - loras[lora_key]["lora_weights"][new_key] = temp_loras[lora_key]["lora_weights"][key] + loras[lora_key]["lora_weights"][new_key] = temp_loras[lora_key]["lora_weights"][key].to(dtype=torch.bfloat16) block_types = self.determine_lora_block_types(loras) merge_mixtures, block_metadata = self.get_mixtures(seed, num_output, loras.keys(), block_types) @@ -2040,6 +2075,7 @@ class WarpedHunyuanMultiLoraMixerUlt: save_message = "" for mixture_key in merge_mixtures: + time.sleep(1) new_lora = {} output_filename = os.path.join(save_folder, "{}_{:05}.safetensors".format(model_prefix, int(mixture_key))) @@ -2056,16 +2092,23 @@ class WarpedHunyuanMultiLoraMixerUlt: if temp_strings[1] == "single_blocks": if temp_block_num in mixture_single_blocks: - new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]) + new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]).to(dtype=torch.bfloat16) continue if temp_strings[1] == "double_blocks": if temp_block_num in mixture_double_blocks: - new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]) + new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]).to(dtype=torch.bfloat16) if not save_metadata: metadata = None + if discard_linear: + temp_lora = new_lora.copy() + + for temp_key in temp_lora: + if "linear" in temp_key: + del new_lora[temp_key] + print("Saving Model To: {}...".format(output_filename)) utils.save_torch_file(new_lora, output_filename, metadata=metadata) print("Saving Model To: {}...Done.".format(output_filename)) @@ -2079,6 +2122,444 @@ class WarpedHunyuanMultiLoraMixerUlt: return {"ui": {"tags": ["save_message"]}} +# class WarpedHunyuanMultiLoraMixerPlatinum: +# @classmethod +# def INPUT_TYPES(s): +# return { +# "required": { +# "save_folder": ("STRING", {"default": get_default_output_folder()}), +# "model_prefix": ("STRING", {"default": "new_model_hy"}), +# "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), +# "num_output": ("INT", {"default": 1, "min": 1, "max": 100}), +# "lora_1": (['None'] + get_lora_list(),), +# "strength_1": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "blocks_1": (["all", "double_blocks", "single_blocks"], {"default": "all", "tooltip": "all, single only, or double only block."}), +# "lora_2": (['None'] + get_lora_list(),), +# "strength_2": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "blocks_2": (["all", "double_blocks", "single_blocks"], {"default": "all", "tooltip": "all, single only, or double only block."}), +# "lora_3": (['None'] + get_lora_list(),), +# "strength_3": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "blocks_3": (["all", "double_blocks", "single_blocks"], {"default": "all", "tooltip": "all, single only, or double only block."}), +# "lora_4": (['None'] + get_lora_list(),), +# "strength_4": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "blocks_4": (["all", "double_blocks", "single_blocks"], {"default": "all", "tooltip": "all, single only, or double only block."}), +# "lora_5": (['None'] + get_lora_list(),), +# "strength_5": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "blocks_5": (["all", "double_blocks", "single_blocks"], {"default": "all", "tooltip": "all, single only, or double only block."}), +# "lora_6": (['None'] + get_lora_list(),), +# "strength_6": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "blocks_6": (["all", "double_blocks", "single_blocks"], {"default": "all", "tooltip": "all, single only, or double only block."}), +# "lora_7": (['None'] + get_lora_list(),), +# "strength_7": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "blocks_7": (["all", "double_blocks", "single_blocks"], {"default": "all", "tooltip": "all, single only, or double only block."}), +# "lora_8": (['None'] + get_lora_list(),), +# "strength_8": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), +# "blocks_8": (["all", "double_blocks", "single_blocks"], {"default": "all", "tooltip": "all, single only, or double only block."}), +# "save_metadata": ("BOOLEAN", {"default": True}), +# "convert_to": (["diffusion_model", "transformer"], {"default": "diffusion_model"}), +# "max_dimension": ([32, 64, 128], {"default": 128}), +# "remove_linear": ("BOOLEAN", {"default": True}), +# "remove_single_blocks": ("BOOLEAN", {"default": True}), +# }, +# } +# +# RETURN_TYPES = () +# OUTPUT_NODE = True +# OUTPUT_IS_LIST = (True,) +# FUNCTION = "merge_multiple_loras" +# CATEGORY = "Warped/Hunyuan/Mixers" +# DESCRIPTION = "Load and apply multiple LoRA models with different strengths and block types. Model input is required." +# +# def load_lora(self, lora_name: str, strength: float, blocks_type: str) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: +# """Load and filter a single LoRA model.""" +# if not lora_name or strength == 0: +# return {}, {} +# +# # Get the full path to the LoRA file +# lora_path = folder_paths.get_full_path("loras", lora_name) +# if not os.path.exists(lora_path): +# raise ValueError(f"LoRA file not found: {lora_path}") +# +# # Load the LoRA weights +# lora_weights = utils.load_torch_file(lora_path) +# +# lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") +# +# return lora_weights +# +# def get_random_key(self, keys): +# if len(keys) > 0: +# random_key = random.randint(0, len(keys) - 1) +# # print("Keys: {} | Random Key: {}".format(random_key)) +# return keys[random_key] +# +# return -1 +# +# def get_mixtures(self, seed, num_output, lora_keys, block_types): +# random.seed(seed) +# mixtures = {} +# +# for i in range(num_output): +# mixtures["{}".format(i + 1)] = {} +# +# single_block_loras = [] +# double_block_loras = [] +# +# for key in lora_keys: +# for mixture_key in mixtures.keys(): +# mixtures[mixture_key][key] = {"single": [], "double": []} +# +# if block_types[key]["has_single_blocks"]: +# single_block_loras.append(int(key)) +# +# if block_types[key]["has_double_blocks"]: +# double_block_loras.append(int(key)) +# +# for mixture_key in mixtures.keys(): +# if len(single_block_loras) > 0: +# for j in range(40): +# random_key = self.get_random_key(single_block_loras) +# temp_key = "{}".format(random_key) +# mixtures[mixture_key][temp_key]["single"].append(j) +# +# if len(double_block_loras) > 0: +# for j in range(20): +# random_key = self.get_random_key(double_block_loras) +# temp_key = "{}".format(random_key) +# mixtures[mixture_key][temp_key]["double"].append(j) +# +# i += 1 +# +# at_least_one = False +# +# while not at_least_one: +# if len(double_block_loras) > 0: +# for key in lora_keys: +# lowest = 999 +# highest = -1 +# lowest_mixture_key = None +# highest_mixture_key = None +# +# for mixture_key in mixtures: +# if len(mixtures[mixture_key][key]["double"]) < lowest: +# lowest = len(mixtures[mixture_key][key]["double"]) +# lowest_mixture_key = mixture_key +# +# if len(mixtures[mixture_key][key]["double"]) > highest: +# highest = len(mixtures[mixture_key][key]["double"]) +# highest_mixture_key = mixture_key +# +# if lowest < 1: +# print("low key: {}".format(lowest_mixture_key)) +# print("high key: {}".format(highest_mixture_key)) +# mixtures[lowest_mixture_key][key]["double"].append(mixtures[highest_mixture_key][key]["double"][0]) +# print("low mix: {}".format(mixtures[lowest_mixture_key][key]["double"])) +# print("high mixes before: {}".format(mixtures[highest_mixture_key][key]["double"])) +# mixtures[highest_mixture_key][key]["double"].pop(0) +# print("high mixes after: {}".format(mixtures[highest_mixture_key][key]["double"])) +# else: +# at_least_one = True +# else: +# at_least_one = True +# +# print("\nMixtures\n") +# +# block_metadata = "" +# +# for mixture_key in mixtures.keys(): +# for key in mixtures[mixture_key]: +# print("{} | {}: {}".format(mixture_key, key, mixtures[mixture_key][key])) +# +# if len(block_metadata) > 0: +# block_metadata = "{} | {}".format(block_metadata, "{}: {}: {}".format(mixture_key, key, mixtures[mixture_key][key])) +# else: +# block_metadata = "{}: {}: {}".format(mixture_key, key, mixtures[mixture_key][key]) +# +# print("\n") +# +# return mixtures, block_metadata +# +# def determine_lora_block_types(self, loras): +# block_types = {} +# +# for lora_key in loras.keys(): +# block_types[lora_key] = { "has_single_blocks": False, "has_double_blocks": False } +# +# for key in loras[lora_key]["lora_weights"].keys(): +# if "single_blocks" in key: +# block_types[lora_key]["has_single_blocks"] = True +# elif "double_blocks" in key: +# block_types[lora_key]["has_double_blocks"] = True +# +# if block_types[lora_key]["has_single_blocks"] and block_types[lora_key]["has_double_blocks"]: +# break +# +# return block_types +# +# def merge_multiple_loras(self, save_folder, model_prefix, seed, num_output, lora_1, strength_1, blocks_1, lora_2, strength_2, blocks_2, lora_3, strength_3, blocks_3, lora_4, strength_4, blocks_4, +# lora_5, strength_5, blocks_5, lora_6, strength_6, blocks_6, lora_7, strength_7, blocks_7, lora_8, strength_8, blocks_8, save_metadata=True, convert_to="diffusion_model", +# max_dimension=64, remove_linear=True, remove_single_blocks=True): +# print("Save_folder: {}".format(save_folder)) +# os.makedirs(save_folder, exist_ok = True) +# +# temp_loras = {} +# metadata = {"loras": "{} and {} and {} and {} and {} and {} and {} and {}".format(lora_1, lora_2, lora_3, lora_4, lora_5, lora_6, lora_7, lora_8)} +# metadata["strengths"] = "{} and {} and {} and {} and {} and {} and {} and {}".format(strength_1, strength_2, strength_3, strength_4, strength_5, strength_6, strength_7, strength_8) +# metadata["seed"] = "{}".format(seed) +# metadata["num_output"] = "{}".format(num_output) +# +# if (lora_1 != "None") and (strength_1 > 0.0): +# print(lora_1) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_1, 1.0, "all") +# +# if (blocks_1 == "single_blocks") or (blocks_1 == "double_blocks"): +# lora_weights = filter_lora_keys(lora_weights, blocks_1) +# +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# print_it = True +# for key in lora_weights.keys(): +# if print_it: +# print("LORA 1: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# print_it = False +# +# lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) +# +# temp_loras["1"] = {"lora_weights": lora_weights, "strength": strength_1} +# +# lora_weights = None +# +# if (lora_2 != "None") and (strength_2 > 0.0): +# print(lora_2) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_2, 1.0, "all") +# +# if (blocks_2 == "single_blocks") or (blocks_2 == "double_blocks"): +# lora_weights = filter_lora_keys(lora_weights, blocks_2) +# +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# print_it = True +# for key in lora_weights.keys(): +# if print_it: +# print("LORA 2: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# print_it = False +# +# lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) +# +# temp_loras["2"] = {"lora_weights": lora_weights, "strength": strength_2} +# +# lora_weights = None +# +# if (lora_3 != "None") and (strength_3 > 0.0): +# print(lora_3) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_3, 1.0, "all") +# +# if (blocks_3 == "single_blocks") or (blocks_3 == "double_blocks"): +# lora_weights = filter_lora_keys(lora_weights, blocks_3) +# +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# print_it = True +# for key in lora_weights.keys(): +# if print_it: +# print("LORA 3: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# print_it = False +# +# lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) +# +# temp_loras["3"] = {"lora_weights": lora_weights, "strength": strength_3} +# +# lora_weights = None +# +# if (lora_4 != "None") and (strength_4 > 0.0): +# print(lora_4) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_4, 1.0, "all") +# +# if (blocks_4 == "single_blocks") or (blocks_4 == "double_blocks"): +# lora_weights = filter_lora_keys(lora_weights, blocks_4) +# +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# print_it = True +# for key in lora_weights.keys(): +# if print_it: +# print("LORA 4: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# print_it = False +# +# lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) +# +# temp_loras["4"] = {"lora_weights": lora_weights, "strength": strength_4} +# +# lora_weights = None +# +# if (lora_5 != "None") and (strength_5 > 0.0): +# print(lora_5) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_5, 1.0, "all") +# +# if (blocks_5 == "single_blocks") or (blocks_5 == "double_blocks"): +# lora_weights = filter_lora_keys(lora_weights, blocks_5) +# +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# print_it = True +# for key in lora_weights.keys(): +# if print_it: +# print("LORA 5: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# print_it = False +# +# lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) +# +# temp_loras["5"] = {"lora_weights": lora_weights, "strength": strength_5} +# +# lora_weights = None +# +# if (lora_6 != "None") and (strength_6 > 0.0): +# print(lora_6) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_6, 1.0, "all") +# +# if (blocks_6 == "single_blocks") or (blocks_6 == "double_blocks"): +# lora_weights = filter_lora_keys(lora_weights, blocks_6) +# +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# print_it = True +# for key in lora_weights.keys(): +# if print_it: +# print("LORA 6: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# print_it = False +# +# lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) +# +# temp_loras["6"] = {"lora_weights": lora_weights, "strength": strength_6} +# +# lora_weights = None +# +# if (lora_7 != "None") and (strength_7 > 0.0): +# print(lora_7) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_7, 1.0, "all") +# +# if (blocks_7 == "single_blocks") or (blocks_7 == "double_blocks"): +# lora_weights = filter_lora_keys(lora_weights, blocks_7) +# +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# print_it = True +# for key in lora_weights.keys(): +# if print_it: +# print("LORA 7: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# print_it = False +# +# lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) +# +# temp_loras["7"] = {"lora_weights": lora_weights, "strength": strength_7} +# +# lora_weights = None +# +# if (lora_8 != "None") and (strength_8 > 0.0): +# print(lora_8) +# # Load and filter the LoRA weights +# lora_weights = self.load_lora(lora_8, 1.0, "all") +# +# if (blocks_8 == "single_blocks") or (blocks_8 == "double_blocks"): +# lora_weights = filter_lora_keys(lora_weights, blocks_8) +# +# lora_weights = convert_lora_dimensions(max_dimension, lora_weights) +# +# print_it = True +# for key in lora_weights.keys(): +# if print_it: +# print("LORA 8: {} | Sample Shape: {}".format(key, lora_weights[key].shape)) +# print_it = False +# +# lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) +# +# temp_loras["8"] = {"lora_weights": lora_weights, "strength": strength_8} +# +# lora_weights = None +# +# loras = {} +# +# for lora_key in temp_loras.keys(): +# loras[lora_key] = {"lora_weights": {}, "strength": temp_loras[lora_key]["strength"]} +# +# for key in temp_loras[lora_key]["lora_weights"].keys(): +# new_key = key.replace("transformer.", "diffusion_model.") +# loras[lora_key]["lora_weights"][new_key] = temp_loras[lora_key]["lora_weights"][key].to(dtype=torch.bfloat16) +# +# block_types = self.determine_lora_block_types(loras) +# merge_mixtures, block_metadata = self.get_mixtures(seed, num_output, loras.keys(), block_types) +# +# metadata["max_dimension"] = "{}".format(max_dimension) +# metadata["block_types"] = "{}".format(block_types) +# +# print("Max Dimension: {}".format(max_dimension)) +# +# save_message = "" +# +# for mixture_key in merge_mixtures: +# time.sleep(1) +# new_lora = {} +# output_filename = os.path.join(save_folder, "{}_{:05}.safetensors".format(model_prefix, int(mixture_key))) +# +# metadata["merge_mixture"] = "{}".format(merge_mixtures[mixture_key]) +# # metadata["block_metadata"] = "{}".format(block_metadata[int(mixture_key)]) +# +# for lora_key in loras.keys(): +# mixture_single_blocks = merge_mixtures[mixture_key][lora_key]["single"] +# mixture_double_blocks = merge_mixtures[mixture_key][lora_key]["double"] +# +# for key in loras[lora_key]["lora_weights"].keys(): +# temp_strings = str(key).split('.') +# temp_block_num = int(temp_strings[2]) +# +# if temp_strings[1] == "single_blocks": +# if temp_block_num in mixture_single_blocks: +# new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]).to(dtype=torch.bfloat16) +# continue +# +# if temp_strings[1] == "double_blocks": +# if temp_block_num in mixture_double_blocks: +# new_lora[key] = torch.mul(loras[lora_key]["lora_weights"][key], loras[lora_key]["strength"]).to(dtype=torch.bfloat16) +# +# dummy_lora = new_lora,copy() +# +# for key in dummy_lora: +# if "single_blocks" in key: +# if remove_single_blocks: +# del new_lora[key] +# elif remove_linear: +# if "linear" in key: +# del new_lora[key] +# +# continue +# +# if remove_linear: +# if "linear" in key: +# del new_lora[key] +# +# if not save_metadata: +# metadata = None +# +# print("Saving Model To: {}...".format(output_filename)) +# utils.save_torch_file(new_lora, output_filename, metadata=metadata) +# print("Saving Model To: {}...Done.".format(output_filename)) +# +# save_message = "{}\n{}".format(save_message, "Weights Saved To: {}".format(output_filename)) +# +# new_lora = None +# mm.soft_empty_cache() +# gc.collect() +# time.sleep(1) +# +# return {"ui": {"tags": ["save_message"]}} + class WarpedWanLoraMerge: def __init__(self): self.base_output_dir = get_default_wan_output_folder() @@ -2447,6 +2928,65 @@ class WarpedCreateEmptyLatentBatch: return ({"samples": temp_latent}, batch_size, ) +class WarpedHunyuanVideoToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"video_batch": ("IMAGE", ), + "positive": ("CONDITIONING", ), + "vae": ("VAE", ), + "guidance_type": (["v1 (concat)", "v2 (replace)", "custom"], ) + }, + } + + RETURN_TYPES = ("CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "latent") + FUNCTION = "encode" + + CATEGORY = "Warped/Hunyuan/Conditioning" + + def encode(self, video_batch, positive, vae, guidance_type): + mm.unload_all_models() + + out_latent = {} + + batch_size = 1 + width = video_batch.shape[2] + height = video_batch.shape[1] + length = video_batch.shape[0] + + print("width: {} | height: {} | length: {}".format(width, height, length)) + + video_batch = comfy.utils.common_upscale(video_batch[:length, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + latent = partial_encode_tiled(vae, video_batch) #vae.encode(video_batch) + + print("latents Shape: {}".format(latent.shape)) + + video_tuple = torch.split(video_batch, 1, dim=0) + video_split = [item for item in video_tuple] + + start_image = video_split[0] + + video_tuple = None + video_split = None + + concat_latent_image = vae.encode(start_image) + mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) + mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 + + if guidance_type == "v1 (concat)": + cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask} + elif guidance_type == "v2 (replace)": + cond = {'guiding_frame_index': 0} + latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image + out_latent["noise_mask"] = mask + elif guidance_type == "custom": + cond = {"ref_latent": concat_latent_image} + + positive = node_helpers.conditioning_set_values(positive, cond) + + out_latent["samples"] = latent + return (positive, out_latent) + class WarpedSamplerCustomAdv: @classmethod def INPUT_TYPES(s): @@ -2843,6 +3383,7 @@ class WarpedSamplerCustomAdvLatent: }, "optional": {"scaling_strength": ("FLOAT", {"default": 1.0}), + "is_v2v": ("BOOLEAN", {"default": False}), "output_latents": ("BOOLEAN", {"default": False}), } } @@ -2855,7 +3396,7 @@ class WarpedSamplerCustomAdvLatent: CATEGORY = "Warped/General/Sampling" def sample(self, latent, vae, seed, guider, sampler, sigmas, dec_tile_size, dec_overlap, dec_temporal_size, dec_temporal_overlap, - skip_frames, noise_scale, scaling_strength=1.0, output_latents=False): + skip_frames, noise_scale, scaling_strength=1.0, is_v2v=False, output_latents=False): self.device = mm.get_torch_device() self.offload_device = get_offload_device() mm.unload_all_models() @@ -2873,6 +3414,10 @@ class WarpedSamplerCustomAdvLatent: self.dec_temporal_size = dec_temporal_size self.dec_temporal_overlap = dec_temporal_overlap self.g_output = {} + self.init_mode = "image" + + if is_v2v: + self.init_mode = "video" callback = self.setup_callbacks() disable_pbar = not utils.PROGRESS_BAR_ENABLED @@ -2895,7 +3440,7 @@ class WarpedSamplerCustomAdvLatent: generation_status = "" - noise_latents = self.initialize_frames(latents) + noise_latents = self.initialize_frames(latents, init_mode=self.init_mode) print("-------------------------------------------------------------------------------------------") print("WarpedSamplerCustomAdvLatent: Latents Shape: {} | Noise Latents Shape: {}".format(latents.shape, noise_latents.shape)) @@ -3022,7 +3567,7 @@ class WarpedSamplerCustomAdvLatent: output_images = image if output_images_latents is None: - output_images_latents = torch.zeros([1, 16, 1, self.height, self.width], dtype=torch.float32, device=self.offload_device) + output_images_latents = torch.zeros([1, self.latents_depth, 1, self.height, self.width], dtype=torch.float32, device=self.offload_device) return (output_images, {"samples": output_images_latents}, self.seed, generation_status, valid_output, ) @@ -3095,6 +3640,7 @@ class WarpedSamplerCustomAdvLatent: def initialize_noise(self, frame_count, clear_cache=True): noise_latents_full = torch.zeros([1, self.latents_depth, int(frame_count), self.height, self.width], dtype=torch.float32, device=self.offload_device) + print("WarpedSamplerCustomAdvLatent: Encoded noise_latents_full Shape: {}".format(noise_latents_full.shape)) if Decimal(self.noise_scale).compare(Decimal(0.00)) != 0: @@ -3115,7 +3661,7 @@ class WarpedSamplerCustomAdvLatent: return noise_latents_full - def initialize_frames(self, latents): + def initialize_frames(self, latents, init_mode="image"): if len(latents.shape) < 5: latents = latents.unsqueeze(0) @@ -3123,7 +3669,19 @@ class WarpedSamplerCustomAdvLatent: latents_full = latents.clone().detach() latents_full = latents_full.to(dtype=torch.float32, device=self.offload_device) - noise_latents_full = self.initialize_noise(latents_full.shape[2]) + if init_mode == "image": + noise_latents_full = self.initialize_noise(latents_full.shape[2]) + else: + if Decimal(self.noise_scale).compare(Decimal(0.00)) != 0: + noise_latents_full = warped_prepare_noise(latents_full, self.seed) + print("WarpedSamplerCustomAdvLatent: noise_latents_full Shape: {}".format(noise_latents_full.shape)) + + noise_latents_full = torch.mul(noise_latents_full, self.noise_scale) + + if len(noise_latents_full.shape) < 5: + noise_latents_full.unsqueeze(0) + + noise_latents_full = noise_latents_full.to(dtype=torch.float32, device=self.offload_device) mm.soft_empty_cache() gc.collect() @@ -3195,7 +3753,7 @@ class WarpedSamplerCustomScripted: "negative": ("STRING", {"default": None}), "clip": ("CLIP", ), "preferred_frame_count": ("INT", {"default": 81, "min": 17, "max": nodes.MAX_RESOLUTION, "step": 4}), - "preferred_batch_size": ("INT", {"default": 17, "min": 17, "max": 161, "step": 4}), + "preferred_batch_size": ("INT", {"default": 17, "min": 17, "max": 301, "step": 4}), "use_batch_size": (["next_lowest", "next_highest", "closest", "exact"], {"default": "next_lowest", "tooltip": "Number of frames generated may be impacted by choice."}), "vae": ("VAE", ), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), @@ -3213,7 +3771,7 @@ class WarpedSamplerCustomScripted: "hunyuan_image_interleave": ("INT", {"default": 1, "min": 1, "max": 512, "tooltip": "How much the image influences things vs the text prompt. Higher number means more influence from the text prompt."}), }, "optional": - {"noise_strength": ("FLOAT", {"default": 1.00, "min": 0.10, "max": 1.00, "step": 0.01}), + {"noise_strength": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.00, "step": 0.01}), "blend_frames": ("INT", {"default":0, "min":0, "max": 16, "step": 1}), "t2v_width": ("INT", {"default":480, "min":256, "max": 1280, "step": 16}), "t2v_height": ("INT", {"default":720, "min":256, "max": 1280, "step": 16}), @@ -3222,7 +3780,7 @@ class WarpedSamplerCustomScripted: "start_image": ("IMAGE", ), "flux_guidance": ("FLOAT", {"default": 7, "min": 0.0, "max": 100.0, "step": 0.1}), "use_flux_guidance": ("BOOLEAN", {"default": False}), - "dummy_frames": ("INT", {"default":17, "min":17, "max": 161, "step": 4, "tooltip": "Number of frames to generate in dummy batch."}), + "dummy_frames": ("INT", {"default":17, "min":17, "max": 301, "step": 4, "tooltip": "Number of frames to generate in dummy batch."}), "gen_dummy": ("BOOLEAN", {"default": False, "tooltip": "For t2v or i2v only. Will generate a dummy batch to obtain a starting image for main generation."}), "gen_dummy_only": ("BOOLEAN", {"default": False, "tooltip": "Will generate dummy batch only."}), "use_dummy_image": (["first", "middle", "last", "random", "all"], {"default": "last", "tooltip": "Which dummy batch image to start main generation."}), @@ -3317,10 +3875,15 @@ class WarpedSamplerCustomScripted: print("latent_window_size: {} | batch_count: {} | truncated_frame_count: {} | batch_frame_count: {}".format(self.latent_window_size, self.batch_count, self.truncated_frame_count, self.batch_frame_count)) is_i2v = True + is_v2v = False if start_image is None: start_image = torch.zeros([1, self.t2v_height, self.t2v_width, 3], dtype=torch.float32, device=self.offload_device) is_i2v = False + else: + if start_image.shape[0] > 1: + is_v2v = True + is_i2v = False if self.mode == "Wan 2.2 TI2V": self.latent_depth = 48 @@ -3413,8 +3976,8 @@ class WarpedSamplerCustomScripted: print("start_image Shape: {}".format(start_image.shape)) - if is_i2v: - print("GENERAL HERE 1") + if is_i2v or is_v2v: + # print("GENERAL HERE 1") if len(start_image.shape) < 4: start_image = start_image.unsqueeze(0) @@ -3422,6 +3985,7 @@ class WarpedSamplerCustomScripted: if mode != "Wan 2.2 TI2V": print("Performing clip_vision_encode.") image_embeds = self.clip_vision_encode(start_image.clone().detach()) + print("Performing clip_vision_encode...Done!") if not is_dummy_section: noise_latents = noise[batch_number] @@ -3442,12 +4006,15 @@ class WarpedSamplerCustomScripted: self.use_clip = self.clip.clone() if mode == "Hunyuan": - if is_i2v: + if is_i2v or is_v2v: temp_cond = self.hunyuan_text_encode(self.use_clip, image_embeds, positive_prompt, hunyuan_image_interleave) positive_cond, latents = self.hunyuan_encode(temp_cond, self.vae, start_image.shape[2], start_image.shape[1], ((noise_latents.shape[2] - 1) * 4) + 1, 1, hunyuan_guidance_type, start_image=start_image) if "noise_mask" in latents: - noise_mask = latents["noise_mask"] * scaling_strength + noise_mask = latents["noise_mask"] #* scaling_strength + + if is_v2v: + scaling_strength = vae_scaling_factor latents = latents["samples"] * scaling_strength else: @@ -3462,7 +4029,7 @@ class WarpedSamplerCustomScripted: self.guider.set_conds_single(positive_cond) else: if not is_i2v: - print("HERE WAN 1") + # print("HERE WAN 1") positive_cond, negative_cond = self.wan_text_encode(self.use_clip, positive_text=positive_prompt, negative_text=negative) positive_cond, negative_cond, latents = self.wan_encode(positive_cond, negative_cond, self.vae, start_image.shape[2], start_image.shape[1], ((noise_latents.shape[2] - 1) * 4) + 1, 1, start_image=None, clip_vision_output=None) @@ -3471,7 +4038,7 @@ class WarpedSamplerCustomScripted: latents = latents["samples"] * scaling_strength else: - print("HERE WAN 2") + # print("HERE WAN 2") temp_cond, negative_cond = self.wan_text_encode(self.use_clip, positive_text=positive_prompt, negative_text=negative) positive_cond, negative_cond, latents = self.wan_encode(temp_cond, negative_cond, self.vae, start_image.shape[2], start_image.shape[1], ((noise_latents.shape[2] - 1) * 4) + 1, 1, start_image=start_image, clip_vision_output=image_embeds) @@ -3528,7 +4095,7 @@ class WarpedSamplerCustomScripted: print("WarpedSamplerCustomScripted: Generating {} frames in {} latents...Done.".format(num_frames, latents.shape[2])) - if not is_i2v: + if not is_i2v and not is_v2v: if not self.secondary_model is None: self.guider.model_patcher.model.to(get_offload_device()) @@ -3552,7 +4119,8 @@ class WarpedSamplerCustomScripted: random.seed(self.seed) start_image = dummy_split[random.randrange(0, len(decoded_split) - 1, 1)].clone().detach() - is_i2v = True + if not is_v2v: + is_i2v = True if last_image is None: last_image = start_image.clone().detach() @@ -3770,22 +4338,54 @@ class WarpedSamplerCustomScripted: def setup_latent_noise(self): dummy_noise = None + total_noise_frames = 0 + rnd = torch.Generator("cpu").manual_seed(self.seed) + if self.gen_dummy: - rnd = torch.Generator("cpu").manual_seed(self.seed) - dummy_noise = torch.randn((1, self.latent_depth, self.dummy_latents, self.height, self.width), generator=rnd, device=rnd.device).to(device=rnd.device, dtype=torch.float32) + total_noise_frames += self.dummy_latents + + total_noise_frames += (self.latent_window_size * self.batch_count) - self.batch_count + 1 + + temp_noise = torch.randn((1, self.latent_depth, total_noise_frames, self.height, self.width), generator=rnd, device=rnd.device).to(device=rnd.device, dtype=torch.float32) noise = [] - for i in range(self.batch_count): - rnd = torch.Generator("cpu").manual_seed(self.seed) - temp_noise = torch.randn((1, self.latent_depth, self.latent_window_size, self.height, self.width), generator=rnd, device=rnd.device).to(device=rnd.device, dtype=torch.float32) + latent_batches_tuple = torch.split(temp_noise, self.dummy_latents, dim=2) + latent_batches_split = [item for item in latent_batches_tuple] - noise.append(temp_noise) - temp_noise = None + dummy_noise = latent_batches_split[0].clone().detach() + + temp_noise = None + + i = 0 + + for entry in latent_batches_split: + if i != 0: + if not temp_noise is None: + temp_noise = torch.cat((temp_noise, entry.clone().detach()), 2) + else: + temp_noise = entry.clone().detach() + + i += 1 + + latent_batches_tuple = None + latent_batches_tuple = None + + latent_batches_tuple = torch.split(temp_noise, self.latent_window_size - 1, dim=2) + latent_batches_split = [item for item in latent_batches_tuple] + latent_batches_tuple = None + + for entry in latent_batches_split: + noise.append(entry.clone().detach()) + + noise[len(noise) - 2] = torch.cat((noise[len(noise) - 2], noise[len(noise) - 1]), 2) + del noise[-1] + + latent_batches_split = None if self.verbose_messaging: if not dummy_noise is None: - print("\nsetup_latent_noise: dummy_noise Shape: {}".format(dummy_noise.shape)) + print("setup_latent_noise: Dummy Noise Shape: {}".format(dummy_noise.shape)) i = 0 for entry in noise: @@ -3923,11 +4523,21 @@ class WarpedSamplerCustomScripted: raise ValueError("Unable to find a compatible latent_window_size for number of frames = {} and preferred_batch_size = {}.".format(frame_count, preferred_batch_size)) def clip_vision_encode(self, image, crop="center"): + # image_device = image.get_device() + # image = image.to(device=self.device) + + crop_image = False + if crop != "center": crop_image = False - if crop != "center": - crop_image = False - output = self.clip_vision.encode_image(image, crop=crop_image) - return output + output = self.clip_vision.encode_image(image, crop=crop_image) + + # image = image.to(device=image_device) + + mm.soft_empty_cache() + gc.collect() + time.sleep(0.25) + + return output def encoded_to_decoded_length(self, latent_length): if latent_length <= 0: @@ -3960,7 +4570,7 @@ class WarpedSamplerCustomScripted: if start_image is not None: start_image = comfy.utils.common_upscale(start_image[:length, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - concat_latent_image = vae.encode(start_image) + concat_latent_image = partial_encode_tiled(vae, start_image) # vae.encode(start_image) mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 @@ -4869,7 +5479,7 @@ class WarpedBundleVideoImages: def INPUT_TYPES(s): return {"required": {"video_path": ("STRING", {"default": ""}), - "starting_index": ("INT", {"default": 0}), + "starting_index": ("INT", {"default": 0, "min": 0, "max": 25000000, "step": 1}), "num_frames": ("INT", {"default": 61, "min": 5, "max": 1000001, "step": 4}), "use_gpu": ("BOOLEAN", {"default": True}), }, @@ -4925,7 +5535,12 @@ class WarpedBundleVideoImages: color_coverted = cv2.cvtColor(frameorig, cv2.COLOR_BGR2RGB) video_frames.append(color_coverted) + except: + print("WarpedBundleVideoImages: Exception During Video File Read.") + finally: + cap.release() + try: pil_image = Image.fromarray(video_frames[0]) first_image = pil2tensorSwap(pil_image, device=device) first_image = first_image.to(get_offload_device()) @@ -4949,9 +5564,7 @@ class WarpedBundleVideoImages: temp_image.to(get_offload_device()) temp_image = None except: - print("WarpedBundleVideoImages: Exception During Video File Read.") - finally: - cap.release() + print("WarpedBundleVideoImages: Exception During Video File Processing.") batched_images = batched_images.to(get_offload_device()) @@ -5423,380 +6036,6 @@ class WarpedWanLoadAndEditLoraBlocks(WarpedBaseWanLoraLoader): return (return_model, return_clip,) -# class WarpedImageResize: -# upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] -# @classmethod -# def INPUT_TYPES(self): -# return { -# "required": { -# "image": ("IMAGE",), -# "width": ("INT", { "default": 400, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1, }), -# "height": ("INT", { "default": 720, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1, }), -# "upscale_method": (self.upscale_methods, {"default": "lanczos"}), -# "crop": (["center", "top_left", "top_right", "bottom_left", "bottom_right", "top_center", "bottom_center"], {"default": "center"}), -# "use_gpu": ("BOOLEAN", {"default": False}), -# }, -# } -# -# RETURN_TYPES = ("IMAGE", "IMAGE", "INT", "INT",) -# RETURN_NAMES = ("IMAGE", "scale_orig_image", "width", "height",) -# FUNCTION = "resize" -# CATEGORY = "Warped/General/Image" -# DESCRIPTION = """ -# Resizes the image to the specified width and height. -# Size can be retrieved from the inputs, and the final scale -# is determined in this order of importance: -# - get_image_size -# - width_input and height_input -# - width and height widgets -# -# Keep proportions keeps the aspect ratio of the image, by -# highest dimension. -# """ -# -# def resize(self, image, width, height, upscale_method, crop="center", use_gpu=False): -# print("Original Image Shape: {}".format(image.shape)) -# -# B, H, W, C = image.shape -# self.is_downsize = False -# -# if use_gpu: -# self.device = mm.get_torch_device() -# else: -# self.device = get_offload_device() -# -# scaled_image = image.clone().detach() -# test_image1 = image.clone().detach() -# test_image2 = image.clone().detach() -# -# is_long_side, orig_is_landscape, new_is_landscape, upscale_required = self.determine_side_to_scale(H, W, width, height) -# -# print("Automatic Value Determination: is_long_side: {} | original_is_landscape: {} | new_is_landscape: {} | upscale_required {}".format(is_long_side, orig_is_landscape, new_is_landscape, upscale_required)) -# -# if upscale_required: -# W = int(W / 16) * 16 -# H = int(H / 16) * 16 -# -# print("Modified Width: {} | Height: {}".format(W, H)) -# -# # Scale based on which dimension is smaller in proportion to the desired dimensions -# ratio = max(width / W, height / H) -# temp_width = round(W * ratio) -# temp_height = round(H * ratio) -# -# print("Aspect Ratio Change Required: temp_width: {} | temp_height: {}".format(temp_width, temp_height)) -# -# image = image.movedim(-1,1) -# image = self.upscale(image, temp_width, temp_height, upscale_method, crop) -# image = image.movedim(1,-1) -# -# if orig_is_landscape: -# if is_long_side: -# image = self.scale_to_side(image, width, is_long_side) -# else: -# image = self.scale_to_side(image, height, is_long_side) -# else: -# if is_long_side: -# image = self.scale_to_side(image, height, is_long_side) -# else: -# image = self.scale_to_side(image, width, is_long_side) -# -# scaled_image = image.clone().detach() -# -# image = self.crop(image, width, height, crop) -# -# return(image, scaled_image, image.shape[2], image.shape[1],) -# -# if (width < W) or (height < H): -# # if same orientation -# if (orig_is_landscape and new_is_landscape) or (not orig_is_landscape and not new_is_landscape): -# if is_long_side: -# if not new_is_landscape: -# image = self.scale_to_side(image, height, is_long_side) -# else: -# image = self.scale_to_side(image, width, is_long_side) -# else: -# if new_is_landscape: -# image = self.scale_to_side(image, height, is_long_side) -# else: -# image = self.scale_to_side(image, width, is_long_side) -# # if original is landscape and new is portrait or original is portrait and new is landscape -# else: -# if is_long_side: -# image = self.scale_to_side(image, width, is_long_side) -# else: -# image = self.scale_to_side(image, height, is_long_side) -# -# scaled_image = image.clone().detach() -# -# B, H, W, C = image.shape -# self.is_downsize = True -# -# if self.is_downsize: -# image = self.crop(image, width, height, crop) -# return(image, scaled_image, image.shape[2], image.shape[1],) -# -# if (orig_is_landscape and new_is_landscape) or ((not orig_is_landscape) and (not new_is_landscape)): -# image = image.movedim(-1,1) -# image = self.upscale(image, width, height, upscale_method, crop) -# image = image.movedim(1,-1) -# -# return(image, image, image.shape[2], image.shape[1],) -# -# if orig_is_landscape: -# temp_ratio = round(height // H) -# temp_width = round(W * temp_ratio) -# -# image = image.movedim(-1,1) -# image = self.upscale(image, temp_width, height, upscale_method, "disabled") -# image = image.movedim(1,-1) -# -# scaled_image = image.clone().detach() -# image = self.crop(image, width, height, crop) -# -# return(image, scaled_image, image.shape[2], image.shape[1],) -# -# temp_ratio = round(width // W) -# temp_height = round(H * temp_ratio) -# -# image = image.movedim(-1,1) -# image = self.upscale(image, width, temp_height, upscale_method, "disabled") -# image = image.movedim(1,-1) -# -# scaled_image = image.clone().detach() -# image = self.crop(image, width, height, crop) -# -# return(image, scaled_image, image.shape[2], image.shape[1],) -# -# def determine_side_to_scale(self, original_height, original_width, width, height): -# original_is_landscape = False -# new_is_landscape = False -# -# if original_width > original_height: -# original_is_landscape = True -# -# if width > height: -# new_is_landscape = True -# -# if (original_width == original_height): -# if width <= height: -# original_is_landscape = True -# -# if width < height: -# new_is_landscape = True -# else: -# new_is_landscape = original_is_landscape -# else: -# original_is_landscape = True -# new_is_landscape = True -# -# is_long_side = True -# upscale_required = False -# -# if (not new_is_landscape and original_is_landscape) or (new_is_landscape and not original_is_landscape): -# is_long_side = False -# else: -# if original_is_landscape: -# temp_ratio = round(width // original_width) -# temp_height = round(temp_ratio * height) -# -# if temp_height < height: -# is_long_side = False -# upscale_required = True -# else: -# temp_ratio = round(height // original_height) -# temp_width = round(temp_ratio * width) -# -# if temp_width < width: -# is_long_side = False -# upscale_required = True -# -# return is_long_side, original_is_landscape, new_is_landscape, upscale_required -# -# def crop(self, input_image, width, height, crop_type): -# image_batches_tuple = torch.split(input_image, 1, dim=0) -# image_batches_split = [item for item in image_batches_tuple] -# -# result_images = None -# for image in image_batches_split: -# new_image = tensor2pilSwap(image) -# new_image = new_image[0] -# -# print("Image width: {} height: {} | New width: {} height: {}".format(new_image.width, new_image.height, width, height)) -# -# #(left, upper, right, lower) -# if crop_type == "top_left": -# left = 0 -# upper = 0 -# right = width -# lower = height -# elif crop_type == "top_right": -# left = new_image.width - width -# upper = 0 -# right = new_image.width -# lower = height -# elif crop_type == "top_center": -# left = int(new_image.width // 2) - int(width // 2) -# upper = 0 -# right = (int(new_image.width // 2) - int(width // 2)) + width -# lower = height -# elif crop_type == "bottom_left": -# left = 0 -# upper = new_image.height - height -# right = width -# lower = new_image.height -# elif crop_type == "bottom_right": -# left = new_image.width - width -# upper = new_image.height - height -# right = new_image.width -# lower = new_image.height -# elif crop_type == "bottom_center": -# left = int(new_image.width // 2) - int(width // 2) -# upper = new_image.height - height -# right = (int(new_image.width // 2) - int(width // 2)) + width -# lower = new_image.height -# elif crop_type == "center": -# left = int(new_image.width // 2) - int(width // 2) -# upper = int(new_image.height // 2) - int(height // 2) -# right = (int(new_image.width // 2) - int(width // 2)) + width -# lower = (int(new_image.height // 2) - int(height // 2)) + height -# -# print("Crop Locations: Left: {} | Upper: {} | Right: {} | Lower: {}".format(int(new_image.width // 2) - int(width // 2), 0, width, height)) -# new_image = new_image.crop((left, upper, right, lower)) -# -# newImage = pil2tensorSwap([newImage], device=self.device) -# -# if not result_images is None: -# result_images = torch.cat((result_images, new_image), 0) -# else: -# result_images = new_image -# -# new_image = None -# -# mm.soft_empty_cache() -# gc.collect() -# time.sleep(1) -# -# return result_images -# -# def upscale(self, samples, width, height, upscale_method, crop): -# orig_shape = tuple(samples.shape) -# if len(orig_shape) > 4: -# samples = samples.reshape(samples.shape[0], samples.shape[1], -1, samples.shape[-2], samples.shape[-1]) -# samples = samples.movedim(2, 1) -# samples = samples.reshape(-1, orig_shape[1], orig_shape[-2], orig_shape[-1]) -# -# s = samples -# if crop != "disabed": -# old_width = samples.shape[-1] -# old_height = samples.shape[-2] -# old_aspect = old_width / old_height -# new_aspect = width / height -# x = 0 -# y = 0 -# -# if crop == "center": -# 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.narrow(-2, y, old_height - y * 2).narrow(-1, x, old_width - x * 2) -# elif (s.shape[2] != height) or (s.shape[3] != width): -# if self.is_downsize: -# if len(s.shape) < 4: -# s = s.unsqueeze(0) -# -# print("s.shape: {}".format(s.shape)) -# -# if crop == "top_left": -# if width < height: -# s = s.narrow(-1, 0, width) -# else: -# s = s.narrow(-2, 0, height) -# elif crop == "top_right": -# if width < height: -# s = s.narrow(-1, s.shape[3] - width, width) -# else: -# s = s.narrow(-2, 0, height) -# elif crop == "bottom_left": -# if width < height: -# s = s.narrow(-1, 0, width) -# else: -# s = s.narrow(-2, s.shape[2] - height, height) -# elif crop == "bottom_right": -# if width < height: -# s = s.narrow(-1, s.shape[3] - width, width) -# else: -# s = s.narrow(-2, s.shape[2] - height, height) -# elif crop == "top_center": -# if width < height: -# s = s.narrow(-1, round((s.shape[3] / 2) - (width / 2) - 1), width) -# else: -# s = s.narrow(-1, 0, height) -# elif crop == "bottom_center": -# if width < height: -# s = s.narrow(-1, round((s.shape[3] / 2) - (width / 2) - 1), width) -# print("s.shape 1: {}".format(s.shape)) -# else: -# s = s.narrow(-2, s.shape[2] - height, height) -# print("s.shape 2: {}".format(s.shape)) -# -# if upscale_method == "bislerp": -# out = utils.bislerp(s, width, height) -# elif upscale_method == "lanczos": -# out = utils.lanczos(s, width, height) -# else: -# out = torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) -# -# if len(orig_shape) == 4: -# return out -# -# out = out.reshape((orig_shape[0], -1, orig_shape[1]) + (height, width)) -# return out.movedim(2, 1).reshape(orig_shape[:-2] + (height, width)) -# -# def scale_to_side(self, input_image, length=1024, scale_long_side=True): -# image_batches_tuple = torch.split(input_image, 1, dim=0) -# image_batches_split = [item for item in image_batches_tuple] -# -# result_images = None -# -# for image in image_batches_split: -# img = tensor2pilSwap(image) -# img = img[0] -# -# if scale_long_side: -# if img.height >= img.width: -# newHeight = length -# newWidth = int(float(length / img.height) * img.width) -# else: -# newWidth = length -# newHeight = int(float(length / img.width) * img.height) -# else: -# if img.height <= img.width: -# newHeight = length -# newWidth = int(float(length / img.height) * img.width) -# else: -# newWidth = length -# newHeight = int(float(length / img.width) * img.height) -# -# tempImage = img.resize((newWidth, newHeight), resample=Image.BILINEAR) -# -# newImage = pil2tensorSwap([tempImage], device=self.device) -# -# if not result_images is None: -# result_images = torch.cat((result_images, new_image), 0) -# else: -# result_images = new_image -# -# new_image = None -# -# mm.soft_empty_cache() -# gc.collect() -# time.sleep(1) -# -# return result_images - class WarpedImageScaleToSide: @classmethod def INPUT_TYPES(s): @@ -5863,15 +6102,15 @@ class WarpedImageScaleToSide: else: final_image = newImage - newImage.to(get_offload_device()) + newImage.to(device=get_offload_device()) newImage = None - final_image.to(get_offload_device()) + final_image.to(device=get_offload_device()) mm.soft_empty_cache() gc.collect() time.sleep(1) - return (final_image, final_image.shape[1], final_image.shape[2],) + return (final_image, final_image.shape[2], final_image.shape[1],) def get_base_lora_dirs(): return folder_paths.get_folder_paths("loras") @@ -6280,19 +6519,24 @@ class WarpedHunyuanMultiLoraLoader: if not lora_name or strength == 0: return {}, {} - # Get the full path to the LoRA file - lora_path = folder_paths.get_full_path("loras", lora_name) - if not os.path.exists(lora_path): - raise ValueError(f"LoRA file not found: {lora_path}") + # # Get the full path to the LoRA file + # lora_path = folder_paths.get_full_path("loras", lora_name) + # if not os.path.exists(lora_path): + # raise ValueError(f"LoRA file not found: {lora_path}") # Load the LoRA weights - lora_weights = utils.load_torch_file(lora_path) + # lora_weights = utils.load_torch_file(lora_path) + lora_weights = warped_load_lora_weights(lora_name) lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") + new_weights = {} + + for key in lora_weights: + new_weights[key] = torch.mul(lora_weights[key], strength).to(dtype=torch.bfloat16) # Filter the LoRA weights based on the block type - filtered_lora = filter_lora_keys(lora_weights, blocks_type) + filtered_lora = filter_lora_keys(new_weights, blocks_type) - return lora_weights, filtered_lora + return new_weights, filtered_lora def load_multiple_loras(self, model, **kwargs): """Load and apply multiple LoRA models.""" @@ -6305,14 +6549,16 @@ class WarpedHunyuanMultiLoraLoader: lora_metadata = [] if not temp_lora_name is None and temp_strength != 0: - print("Lora Name: {} | Strength: {} | Block Types: {}".format(temp_lora_name, temp_strength, temp_blocks_type)) + print("\n**** Lora Name: {} | Strength: {} | Block Types: {}\n".format(temp_lora_name, temp_strength, temp_blocks_type)) lora_metadata.append("{}".format("Lora: {} | Strength: {} | Block Types: {}".format(temp_lora_name, temp_strength, temp_blocks_type))) - lora_weights, filtered_lora = self.load_lora(temp_lora_name, 1.0, "all") + lora_weights, filtered_lora = self.load_lora(temp_lora_name, temp_strength, temp_blocks_type) # Apply the LoRA weights to the model if filtered_lora: - model, _ = load_lora_for_models(model, None, filtered_lora, temp_strength, 0) + model, _ = load_lora_for_models(model, None, filtered_lora, 1.0, 0) + else: + model, _ = load_lora_for_models(model, None, lora_weights, 1.0, 0) for i in range(1, 5): temp_lora_name = kwargs.get(f"lora_0{i}") @@ -6320,20 +6566,19 @@ class WarpedHunyuanMultiLoraLoader: temp_blocks_type = kwargs.get(f"blocks_type_0{i}") if temp_lora_name != "None" and temp_strength != 0: - lora_metadata.append("{}".format("Lora: {} | Strength: {} | Block Types: {}".format(temp_lora_name, temp_strength, temp_blocks_type))) - # Load and filter the LoRA weights - lora_weights, filtered_lora = self.load_lora(temp_lora_name, 1.0, "all") + temp_message = "{}".format("Lora: {} | Strength: {} | Block Types: {}".format(temp_lora_name, temp_strength, temp_blocks_type)) + print(temp_message) + lora_metadata.append(temp_message) + + # Load and filter the LoRA weights + lora_weights, filtered_lora = self.load_lora(temp_lora_name, temp_strength, temp_blocks_type) # Apply the LoRA weights to the model if filtered_lora: - model, _ = load_lora_for_models(model, None, filtered_lora, temp_strength, 0) - - # print("\n\nmodel.model:") - # print(model.model) - # print("\n\nmodel:") - # print(model) - # print("\n\n") + model, _ = load_lora_for_models(model, None, filtered_lora, 1.0, 0) + else: + model, _ = load_lora_for_models(model, None, lora_weights, 1.0, 0) return (model, lora_metadata, ) @@ -6530,7 +6775,8 @@ class WarpedHunyuanLoraBatchMerge: raise ValueError(f"LoRA file not found: {lora_path}") # Load the LoRA weights - lora_weights = convert_lora(lora_name, convert_to="diffusion_model") + lora_weights = utils.load_torch_file(lora_path) + lora_weights = convert_lora(lora_weights, convert_to="diffusion_model") filtered_lora = filter_lora_keys(lora_weights, blocks_type) return lora_weights, filtered_lora @@ -6604,7 +6850,7 @@ class WarpedHunyuanLoraBatchMerge: except Exception as e: raise(e) else: - new_lora[key] = torch.mul(loras[lora_key]["filtered_lora"][key], loras[lora_key]["strength"]) + new_lora[key] = torch.mul(temp_loras[lora_key]["filtered_lora"][key], temp_loras[lora_key]["strength"]) if not save_metadata: metadata = None @@ -6619,61 +6865,61 @@ class WarpedHunyuanLoraBatchMerge: return {"ui": {"tags": [save_message]}} -# class WarpedHunyuanLoraConvertToMusubi: -# def __init__(self): -# self.base_output_dir = get_default_output_folder() -# os.makedirs(self.base_output_dir, exist_ok = True) -# -# @classmethod -# def INPUT_TYPES(s): -# return { -# "required": { -# "save_path": ("STRING", {"default": get_default_output_path()}), -# "lora": (get_lora_list(),), -# "save_metadata": ("BOOLEAN", {"default": True}), -# }, -# } -# -# RETURN_TYPES = () -# OUTPUT_NODE = True -# OUTPUT_IS_LIST = (True,) -# FUNCTION = "convert_lora" -# CATEGORY = "Warped/Hunyuan/Lora/experimental" -# DESCRIPTION = "Convert Keys For Hunyuan LORA and Save Modified LORA." -# -# def load_lora(self, lora_name: str) -> Tuple[Dict[str, torch.Tensor],]: -# """Load and filter a single LoRA model.""" -# if not lora_name: -# return {} -# -# # Get the full path to the LoRA file -# lora_path = folder_paths.get_full_path("loras", lora_name) -# if not os.path.exists(lora_path): -# raise ValueError(f"LoRA file not found: {lora_path}") -# -# # Load the LoRA weights -# lora_weights = utils.load_torch_file(lora_path) -# -# return lora_weights -# -# def convert_lora(self, save_path, lora, save_metadata=True): -# metadata = {"original_lora": "{}".format(lora)} -# -# # Load the LoRA weights -# temp_lora = self.load_lora(lora) -# new_lora = convert_to_musubi(temp_lora) -# -# if not save_metadata: -# metadata = None -# -# if len(new_lora) < 1: -# utils.save_torch_file(temp_lora, save_path, metadata=metadata) -# else: -# utils.save_torch_file(new_lora, save_path, metadata=metadata) -# -# save_message = "Weights Saved To: {}".format(save_path) -# -# return {"ui": {"tags": [save_message]}} +class WarpedHunyuanLoraConvertToMusubi: + def __init__(self): + self.base_output_dir = get_default_output_folder() + os.makedirs(self.base_output_dir, exist_ok = True) + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "save_path": ("STRING", {"default": get_default_output_path()}), + "lora": (get_lora_list(),), + "save_metadata": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = () + OUTPUT_NODE = True + OUTPUT_IS_LIST = (True,) + FUNCTION = "convert_lora" + CATEGORY = "Warped/Hunyuan/Lora/Experimental" + DESCRIPTION = "Convert Keys For Hunyuan LORA and Save Modified LORA." + + def load_lora(self, lora_name: str) -> Tuple[Dict[str, torch.Tensor],]: + """Load and filter a single LoRA model.""" + if not lora_name: + return {} + + # Get the full path to the LoRA file + lora_path = folder_paths.get_full_path("loras", lora_name) + if not os.path.exists(lora_path): + raise ValueError(f"LoRA file not found: {lora_path}") + + # Load the LoRA weights + lora_weights = utils.load_torch_file(lora_path) + + return lora_weights + + def convert_lora(self, save_path, lora, save_metadata=True): + metadata = {"original_lora": "{}".format(lora)} + + # Load the LoRA weights + temp_lora = self.load_lora(lora) + new_lora = convert_to_musubi(temp_lora) + + if not save_metadata: + metadata = None + + if len(new_lora) < 1: + utils.save_torch_file(temp_lora, save_path, metadata=metadata) + else: + utils.save_torch_file(new_lora, save_path, metadata=metadata) + + save_message = "Weights Saved To: {}".format(save_path) + + return {"ui": {"tags": [save_message]}} class WarpedHunyuanLoraConvertKeys: def __init__(self): @@ -6942,10 +7188,10 @@ class WarpedHunyuanLoraConvert: # Convert The LORA Weights for key in lora_weights.keys(): - temp_weights = lora_weights[key].clone().detach() + temp_weights = lora_weights[key].clone().detach().to(dtype=torch.bfloat16) if temp_weights.shape[0] < temp_weights.shape[1]: - padding = torch.zeros([target_res, temp_weights.shape[1]]) + padding = torch.zeros([target_res, temp_weights.shape[1]]).to(dtype=torch.bfloat16) # if upscale if temp_weights.shape[0] < target_res: @@ -6956,7 +7202,7 @@ class WarpedHunyuanLoraConvert: padding[:target_res,:] = temp_weights[:target_res,:] new_lora[key] = padding else: - padding = torch.zeros([temp_weights.shape[0], target_res]) + padding = torch.zeros([temp_weights.shape[0], target_res]).to(dtype=torch.bfloat16) # if upscale if temp_weights.shape[1] < target_res: @@ -6999,7 +7245,7 @@ class WarpedFramepackSampler: "shift": ("FLOAT", {"default": 24.0, "min": 0.0, "max": 1000.0, "step": 0.01}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "preferred_frame_count": ("INT", {"default": 301, "min": 33, "max": 1000001, "step": 4, "tooltip": "For I2V and T2V, The total frames in the video. Disreguarded for V2V"}), - "preferred_batch_size": ("INT", {"default": 61, "min": 37, "max": 161, "step": 4, "tooltip": "The preferred number of frames to use for sampling."}), + "preferred_batch_size": ("INT", {"default": 61, "min": 17, "max": 161, "step": 4, "tooltip": "The preferred number of frames to use for sampling."}), "use_batch_size": (["next_lowest", "next_highest", "closest", "exact"], {"default": "next_lowest", "tooltip": "Number of frames generated may be impacted by choice."}), "gpu_memory_preservation": ("FLOAT", {"default": 12.0, "min": 0.0, "max": 128.0, "step": 0.1, "tooltip": "The amount of GPU memory to preserve."}), "sampler": (["unipc_bh1", "unipc_bh2"], @@ -7025,7 +7271,7 @@ class WarpedFramepackSampler: "blend_frames": ("INT", {"default":5, "min":0, "max": 16, "step": 1}), "t2v_width": ("INT", {"default":480, "min":256, "max": 1280, "step": 16}), "t2v_height": ("INT", {"default":720, "min":256, "max": 1280, "step": 16}), - "dummy_frames": ("INT", {"default":17, "min":17, "max": 121, "step": 4, "tooltip": "Number of frames to generate in dummy batch."}), + "dummy_frames": ("INT", {"default":17, "min":17, "max": 161, "step": 4, "tooltip": "Number of frames to generate in dummy batch."}), "gen_dummy": ("BOOLEAN", {"default": False, "tooltip": "For t2v or i2v only. Will generate a dummy batch to obtain a starting image for main generation."}), "gen_dummy_only": ("BOOLEAN", {"default": False, "tooltip": "Will generate dummy batch only."}), "dummy_cache_mode": (["disabled", "use_teacache", "use_magcache"], {"default": "use_magcache", "tooltip": "Whether or not to use magcache or teacache on dummy generation for faster sampling."}), @@ -7612,6 +7858,7 @@ class WarpedFramepackSampler: temp_history_latents = None original_history_latents = None valid_output = False + is_dummy_section = False self.cleanup(unload_models=True, cleanup_models=False, cleanup_cuda=True) @@ -7705,7 +7952,7 @@ class WarpedFramepackSampler: if self.verbose_messaging: print("generated_latents for batch {}: Shape: {}\n".format(latent_padding, generated_latents.shape)) - latent_batches_gend.append(generated_latents.clone().detach() / vae_scaling_factor) + latent_batches_gend.append(generated_latents.clone().detach() / vae_scaling_factor) valid_output = True total_generated_latent_frames += int(generated_latents.shape[2]) @@ -8194,8 +8441,8 @@ class WarpedFramepackSampler: end_latent = self.encode_batched(end_image, self.latent_window_size) end_latent_embedding = self.clip_vision_encode(end_image) end_image = end_image.to(self.offload_device) - next_first_latent = end_latent.clone().detach().to(end_latent) - next_first_embedding = deep_copy(end_latent_embedding) + next_first_latent = end_latent["samples"].clone().detach().to(end_latent["samples"]) + next_first_embedding = copy.deepcopy(end_latent_embedding) if self.verbose_messaging: print("get_video_latent_embeds: first_batch Shape: {}".format(first_batch.shape)) @@ -8222,71 +8469,74 @@ class WarpedFramepackSampler: next_start_four = None next_start_image = None - image_batches_tuple = torch.split(remaining_batches, batch_frame_count - 1, dim=0) - image_batches_split = [item for item in image_batches_tuple] - image_batches_tuple = None - - if self.verbose_messaging: - i = 0 - for entry in image_batches_split: - print("get_video_latent_embeds: batch: {} | split Shape: {}".format(i + 1, image_batches_split[i].shape)) - i += 1 - - print("get_video_latent_embeds Batch: Encoding Start/End Images...") - - batch_number = 1 - for batch in image_batches_split: - batch_key = "{}".format(batch_number) + if remaining_batches.shape[0] > 0: + image_batches_tuple = torch.split(remaining_batches, batch_frame_count - 1, dim=0) + image_batches_split = [item for item in image_batches_tuple] + image_batches_tuple = None if self.verbose_messaging: - print("get_video_latent_embeds: Processing Batch: {}".format(batch_number)) + i = 0 + for entry in image_batches_split: + print("get_video_latent_embeds: batch: {} | split Shape: {}".format(i + 1, image_batches_split[i].shape)) + i += 1 - latent_embeds[batch_key] = {"start_latent": None, "end_latent": None, "start_embedding": None, "end_embedding": None} + print("get_video_latent_embeds Batch: Encoding Start/End Images...") - start_latent = next_first_latent - start_latent_embedding = next_first_embedding + batch_number = 1 + for batch in image_batches_split: + batch_key = "{}".format(batch_number) - end_image = batch[(batch.shape[0] - 1):, :, :, :] - end_image = end_image.to(self.device) - end_latent = self.encode_batched(end_image, self.latent_window_size) - end_latent_embedding = self.clip_vision_encode(end_image) - end_image = end_image.to(self.device) + if self.verbose_messaging: + print("get_video_latent_embeds: Processing Batch: {}".format(batch_number)) - latent_embeds[batch_key]["start_latent"] = (start_latent["samples"] * vae_scaling_factor).to(self.offload_device) - latent_embeds[batch_key]["start_embedding"] = start_latent_embedding - latent_embeds[batch_key]["end_latent"] = (end_latent["samples"] * vae_scaling_factor).to(self.offload_device) - latent_embeds[batch_key]["end_embedding"] = end_latent_embedding + latent_embeds[batch_key] = {"start_latent": None, "end_latent": None, "start_embedding": None, "end_embedding": None} - if batch_number < max(paddings): - next_start_image = batch[(batch.shape[0] - 1):, :, :, :] - next_first_latent = self.encode_batched(next_start_image, self.latent_window_size) - next_first_embedding = copy.deepcopy(end_latent_embedding) - next_start_image = None - else: - next_first_embedding = None - next_start_image = None - next_first_latent = None + start_latent = next_first_latent + start_latent_embedding = next_first_embedding - start_image = None - start_latent = None - start_latent_embedding = None - end_image = None - end_latent = None - end_latent_embedding = None - batch_image_batches_split = None + end_image = batch[(batch.shape[0] - 1):, :, :, :] + end_image = end_image.to(self.device) + end_latent = self.encode_batched(end_image, self.latent_window_size) + # end_latent = end_latent["samples"] + end_latent_embedding = self.clip_vision_encode(end_image) + end_image = end_image.to(self.device) - batch_number += 1 + latent_embeds[batch_key]["start_latent"] = (start_latent * vae_scaling_factor).to(device=self.offload_device) + latent_embeds[batch_key]["start_embedding"] = start_latent_embedding + latent_embeds[batch_key]["end_latent"] = (end_latent["samples"] * vae_scaling_factor).to(device=self.offload_device) + latent_embeds[batch_key]["end_embedding"] = end_latent_embedding - print("get_video_latent_embeds Batch: Encoding Start/End Images...Done") + if batch_number < max(paddings): + next_start_image = batch[(batch.shape[0] - 1):, :, :, :] + next_first_latent = self.encode_batched(next_start_image, self.latent_window_size) + next_first_latent = next_first_latent["samples"] + next_first_embedding = copy.deepcopy(end_latent_embedding) + next_start_image = None + else: + next_first_embedding = None + next_start_image = None + next_first_latent = None - if self.verbose_messaging: - print("get_video_latent_embeds: latent_embeds length: {}".format(len(latent_embeds))) + start_image = None + start_latent = None + start_latent_embedding = None + end_image = None + end_latent = None + end_latent_embedding = None + batch_image_batches_split = None - for batch_key in latent_embeds.keys(): - print("get_video_latent_embeds: Batch {}: start_latent Shape: {}".format(batch_key, latent_embeds[batch_key]["start_latent"].shape)) - print("get_video_latent_embeds: Batch {}: end_latent Shape: {}".format(batch_key, latent_embeds[batch_key]["end_latent"].shape)) - print("get_video_latent_embeds: Batch {}: start_embedding Shape: {}".format(batch_key, latent_embeds[batch_key]["start_embedding"]["last_hidden_state"].shape)) - print("get_video_latent_embeds: Batch {}: end_embedding Shape: {}".format(batch_key, latent_embeds[batch_key]["end_embedding"]["last_hidden_state"].shape)) + batch_number += 1 + + print("get_video_latent_embeds Batch: Encoding Start/End Images...Done") + + if self.verbose_messaging: + print("get_video_latent_embeds: latent_embeds length: {}".format(len(latent_embeds))) + + for batch_key in latent_embeds.keys(): + print("get_video_latent_embeds: Batch {}: start_latent Shape: {}".format(batch_key, latent_embeds[batch_key]["start_latent"].shape)) + print("get_video_latent_embeds: Batch {}: end_latent Shape: {}".format(batch_key, latent_embeds[batch_key]["end_latent"].shape)) + print("get_video_latent_embeds: Batch {}: start_embedding Shape: {}".format(batch_key, latent_embeds[batch_key]["start_embedding"]["last_hidden_state"].shape)) + print("get_video_latent_embeds: Batch {}: end_embedding Shape: {}".format(batch_key, latent_embeds[batch_key]["end_embedding"]["last_hidden_state"].shape)) self.cleanup(unload_models=False, cleanup_models=False, cleanup_cuda=True) @@ -8322,7 +8572,7 @@ class WarpedFramepackSampler: if self.blend_frames < 1: i = 0 for entry in latent_batches: - entry.to(dtype=torch.float32, device=self.device) + entry.to(dtype=torch.bfloat16, device=self.device) if i < (len(latent_batches) - 1): temp_decoded = self.decode_tiled(entry, unload_after=False) @@ -8350,7 +8600,7 @@ class WarpedFramepackSampler: i = 0 for entry in latent_batches: - entry.to(dtype=torch.float32, device=self.device) + entry.to(dtype=torch.bfloat16, device=self.device) if i < (len(latent_batches) - 1): temp_decoded = self.decode_tiled(entry, unload_after=False) @@ -8368,7 +8618,7 @@ class WarpedFramepackSampler: i += 1 - resulting_images = self.assemble_final_result(image_batches) + resulting_images = self.assemble_final_result(temp_decoded_batches) temp_decoded_batches = None self.cleanup(unload_models=False, cleanup_models=False, cleanup_cuda=True) @@ -8574,8 +8824,8 @@ class WarpedFramepackSampler: with torch.no_grad(): i = 0 for entry in image_batches_split: - entry = entry.to(dtype=torch.float32, device=self.device) - entry = torch.cat((next_start_four, entry), 0) + entry = entry.to(dtype=torch.bfloat16, device=self.device) + entry = torch.cat((next_start_four.to(entry), entry), 0) # next_start_image = entry[(entry.shape[0] - 1):, :, :, :] next_start_four = entry[(entry.shape[0] - 4):, :, :, :] @@ -8657,7 +8907,7 @@ class WarpedFramepackLoraSelectBatch: def warped_load_torch_file(ckpt, return_metadata=False): from safetensors.torch import load as safeload - metadata = None + metadata = {} try: f = safeload(ckpt) @@ -8679,6 +8929,29 @@ def warped_load_torch_file(ckpt, return_metadata=False): return (sd, metadata) if return_metadata else sd +def warped_load_lora_weights(lora_name, return_metadata=False): + if (lora_name is None) or (len(lora_name) < 1): + raise ValueError("lora_name cannot be None or Empty.") + + # Get the full path to the LoRA file + lora_path = folder_paths.get_full_path("loras", lora_name) + + if not os.path.exists(lora_path): + raise ValueError(f"LoRA file not found: {lora_path}") + + print("Reading LORA At Path: {}".format(lora_path)) + with open(lora_path, "rb") as file: + lora_weights = file.read() + + print("Loading Lora: {}...".format(lora_name)) + lora_sd, metadata = warped_load_torch_file(lora_weights, return_metadata=True) + print("Loading Lora: {}...Done".format(lora_name)) + + if return_metadata: + return lora_sd, metadata + + return lora_sd + def get_available_devices(): available_devices = ["cpu"] @@ -8700,8 +8973,8 @@ class WarpedDualEncoder(ComfyNodeABC): "negative_text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The negative prompt to be encoded."}), }, } - RETURN_TYPES = (IO.CONDITIONING, IO.CONDITIONING, ) - RETURN_NAMES = ("pos_conditioning", "neg_conditioning", ) + RETURN_TYPES = (IO.CONDITIONING, IO.CONDITIONING, "STRING", "STRING", ) + RETURN_NAMES = ("pos_conditioning", "neg_conditioning", "pos_prompt", "neg_prompt", ) FUNCTION = "process" CATEGORY = "Warped/General/Conditioning" DESCRIPTION = "Encodes both positive and negative prompts." @@ -8724,7 +8997,7 @@ class WarpedDualEncoder(ComfyNodeABC): gc.collect() time.sleep(1) - return (positive_conditioning, negative_conditioning, ) + return (positive_conditioning, negative_conditioning, positive_text, negative_text, ) def encode(self, clip, text): @@ -8743,8 +9016,8 @@ class WarpedSingleEncoder(ComfyNodeABC): "input_text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The prompt to be encoded."}), }, } - RETURN_TYPES = (IO.CONDITIONING, ) - RETURN_NAMES = ("conditioning", ) + RETURN_TYPES = (IO.CONDITIONING, "STRING", ) + RETURN_NAMES = ("conditioning", "prompt") FUNCTION = "process" CATEGORY = "Warped/General/Conditioning" DESCRIPTION = "Encodes a single prompt." @@ -8766,7 +9039,7 @@ class WarpedSingleEncoder(ComfyNodeABC): gc.collect() time.sleep(1) - return (output_conditioning, ) + return (output_conditioning, input_text, ) def encode(self, clip, text): @@ -8857,8 +9130,8 @@ class WarpedDualCLIPLoader: def INPUT_TYPES(self): return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), - "type": (["sdxl", "sd3", "flux", "hunyuan_video"], ), - "device": (get_available_devices(), {"default": "cpu"}), + "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hunyuan_video_15"], ), + "device": (get_available_devices(), {"default": "cuda"}), }, } RETURN_TYPES = ("CLIP", ) @@ -8880,6 +9153,8 @@ class WarpedDualCLIPLoader: clip_type = comfy.sd.CLIPType.FLUX elif type == "hunyuan_video": clip_type = comfy.sd.CLIPType.HUNYUAN_VIDEO + elif type == "hunyuan_video_15": + clip_type = comfy.sd.CLIPType.HUNYUAN_VIDEO_15 model_options = {} if device == "cpu": @@ -9065,6 +9340,9 @@ class WarpedLoadFramePackModel: # Convert Lora To diffusion_model format lora_sd = convert_lora(lora_sd, convert_to="diffusion_model") + # for key in lora_sd: + # print("Lora Key: {}".format(key)) + # If blocks value exist in lora metadata, then select block types if not blocktypes is None: lora_sd = filter_lora_keys(lora_sd, blocktypes) @@ -9295,6 +9573,7 @@ class WarpedNumericalConversion: "int_value": ("INT", {"default": None, "forceInput": True}), "float_value": ("FLOAT", {"default": None, "forceInput": True}), "number_value": ("NUMBER", {"default": None, "forceInput": True}), + "bool_value": ("BOOL", {"default": None, "forceInput": True}), } } @@ -9304,10 +9583,10 @@ class WarpedNumericalConversion: FUNCTION = "int_to_number" CATEGORY = "Warped/General/Utils" - def int_to_number(self, int_value=None, float_value=None, number_value=None): + def int_to_number(self, int_value=None, float_value=None, number_value=None, bool_value=None): value = None - if (int_value == None) and (float_value == None) and (number_value == None): + if (int_value == None) and (float_value == None) and (number_value == None) and (bool_value == None): raise ValueError("WarpedNumericalConversion: All inputs are None. Nothing to convert.") i = 0 @@ -9324,6 +9603,10 @@ class WarpedNumericalConversion: i += 1 value = number_value + if not bool_value is None: + i += 1 + value = bool_value + if i > 1: raise ValueError("WarpedNumericalConversion: More than one type of value inputs simultaneously is not supported.") @@ -9342,7 +9625,8 @@ class WarpedLoraReSave: "required": { "lora_name": (folder_paths.get_filename_list("loras"),), "save_path": ("STRING", {"default": get_default_gen_output_path()}), - "model_name": ("STRING", {"default": ""}), + "trigger_words": ("STRING", {"default": ""}), + "additional_info": ("STRING", {"default": ""}), }, } @@ -9353,7 +9637,7 @@ class WarpedLoraReSave: CATEGORY = "Warped/General/Lora" DESCRIPTION = "Re-saves LORA with new metadata." - def get_metadata(self, lora_path, model_name): + def get_metadata(self, lora_path, trigger_words, additional_info): model_config_path = os.path.join(script_directory, "your_metadata.json") model_config_path = os.path.abspath(model_config_path) @@ -9363,8 +9647,11 @@ class WarpedLoraReSave: with open(model_config_path, "r") as f: metadata = json.load(f) - if len(model_name) > 0: - metadata["model_name"] = model_name + if len(trigger_words) > 0: + metadata["trigger_words"] = trigger_words + + if len(additional_info) > 0: + metadata["additional_info"] = additional_info return metadata except Exception as e: @@ -9373,7 +9660,7 @@ class WarpedLoraReSave: return {"metadata": "No Metadata" } - def load_lora(self, lora_name: str, save_path: str, model_name: str) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: + def load_lora(self, lora_name: str, save_path: str, trigger_words: str, additional_info: str) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: if not lora_name: return {"ui": {"tags": ["Nothing here to Re-save"]}} @@ -9384,7 +9671,10 @@ class WarpedLoraReSave: lora_weights = comfy.utils.load_torch_file(lora_path) - metadata = self.get_metadata(lora_path, model_name) + for key in lora_weights: + lora_weights[key] = lora_weights[key].to(dtype=torch.bfloat16) + + metadata = self.get_metadata(lora_path, trigger_words, additional_info) temp_strings = save_path.split('\\') del temp_strings[len(temp_strings) - 1] @@ -9914,6 +10204,7 @@ class WarpedLoadImages: "index": ("INT", {"default": 0, "min": 0, "max": 150000, "step": 1}), "label": ("STRING", {"default": 'Batch 001', "multiline": False}), "path": ("STRING", {"default": '', "multiline": False}), + "periodic_sleep": ("BOOLEAN", {"default": False}) }, "optional": { "suffix": ("STRING", {"default": '', "multiline": False}) @@ -9927,7 +10218,7 @@ class WarpedLoadImages: CATEGORY = "Warped/General/Image" - def load_batch_images(self, path, index=0, mode="incremental_image", label='Batch 001', suffix=""): + def load_batch_images(self, path, index=0, mode="incremental_image", label='Batch 001', periodic_sleep=False, suffix=""): if not os.path.exists(path): return (None, ) @@ -9935,6 +10226,10 @@ class WarpedLoadImages: self.index = index self.previous_path = path + if periodic_sleep: + if (self.index > 0) and (self.index % 10 == 0): + time.sleep(2) + fl = self.BatchImageLoader(path, label, '*', index) new_paths = fl.image_paths @@ -10123,7 +10418,11 @@ class WarpedModifyCaptionFile: with open(filename, "r") as fp: original = fp.read() - modified = original.replace(find_text, replace_text) + print("\nOriginal Text: {}".format(original)) + + modified = original.replace(find_text.strip(), replace_text.strip()) + + print("Modified Text: {}".format(modified)) with open(filename, "w") as fp: fp.write(modified) @@ -10174,3 +10473,1863 @@ class WarpedModifyCaptionFile: @classmethod def IS_CHANGED(cls, **kwargs): return float("NaN") + +class WarpedAddToCaption: + def __init__(self, index=0): + self.index = index + self.previous_path = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"mode": (["incremental_caption", "random"], {"default": "incremental_caption"}), + "index": ("INT", {"default": 0, "min": 0, "max": 150000, "step": 1}), + "caption_path": ("STRING", {"forceInput": False}), + "ignore_if_contains": ("STRING", {"forceInput": False}), + "add_text": ("STRING", {"forceInput": False}), + "add_mode": (["prefix", "postfix"], {"default": "prefix"}), + }, + } + + RETURN_TYPES = ("STRING", "STRING", "STRING", ) + RETURN_NAMES = ("filename", "original", "modified", ) + OUTPUT_NODE = True + FUNCTION = "load_batch_captions" + + CATEGORY = "Warped/General/Captioning" + + def load_batch_captions(self, mode="incremental_caption", index=0, caption_path="", ignore_if_contains="", add_text="", add_mode="prefix"): + if (len(caption_path) == 0) or (len(add_text) == 0) or (not os.path.exists(caption_path)): + return ("None", "None", "None") + + if caption_path != self.previous_path: + self.index = index + self.previous_path = caption_path + + fl = self.BatchCaptionLoader(caption_path, '*', index) + + retry = True + + try: + filename = fl.caption_paths[self.index] + except: + if retry: + retry = False + self.index = 0 + filename = fl.caption_paths[self.index] + + print("Filename: {}".format(filename)) + + original, modified = self.process_prompt(filename, ignore_if_contains, add_text, add_mode) + + self.index += 1 + + if self.index >= len(fl.caption_paths): + self.index = 0 + + return {"ui": {"string": [filename, original, modified]}, "result": (filename, original, modified)} + + def process_prompt(self, filename, ignore_if_contains, add_text, add_mode): + with open(filename, "r") as fp: + original = fp.read() + + print("\nOriginal Text: {}".format(original)) + + if len(ignore_if_contains) > 0: + if original.find(ignore_if_contains) != -1: + return (original, original) + + if add_mode == "prefix": + modified = "{}, {}".format(add_text, original) + else: + modified = "{}, {}".format(original, add_text) + + print("Modified Text: {}".format(modified)) + + with open(filename, "w") as fp: + fp.write(modified) + + return (original, modified) + + class BatchCaptionLoader: + def __init__(self, directory_path, pattern, index): + self.caption_paths = [] + self.load_captions(directory_path, pattern) + self.caption_paths.sort() + self.index = index + + def load_captions(self, directory_path, pattern): + for file_name in glob.glob(os.path.join(directory_path, pattern), recursive=True): + if file_name.lower().endswith(ALLOWED_CAPTION_EXT): + abs_file_path = os.path.abspath(file_name) + self.caption_paths.append(abs_file_path) + + def get_caption_by_id(self, caption_id): + if caption_id < 0 or caption_id >= len(self.caption_paths): + cstr(f"Invalid caption index `{caption_id}`").error.print() + return + + return self.caption_paths[caption_id] + + def get_next_caption(self): + if self.index >= len(self.caption_paths): + self.index = 0 + + caption_path = self.caption_paths[self.index] + self.index += 1 + + if self.index == len(self.caption_paths): + self.index = 0 + + cstr(f'{cstr.color.YELLOW}{self.label}{cstr.color.END} Index: {self.index}').msg.print() + + return caption_path + + def get_current_caption(self): + if self.index >= len(self.caption_paths): + self.index = 0 + caption_path = self.caption_paths[self.index] + + return caption_path + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + +class WarpedPromptConcat: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"prompt1": ("STRING", {"default": ""}), + "prompt2": ("STRING", {"default": "", "forceInput": True}), + "delimiter": ([",", "."], {"default": ","}), + }, + } + + RETURN_TYPES = ("STRING", ) + RETURN_NAMES = ("prompt", ) + FUNCTION = "do_concat" + + CATEGORY = "Warped/General/Utils" + + def do_concat(self, prompt1="", prompt2="", delimiter=","): + temp_prompt1 = prompt1.strip().strip("\n").strip("\0").strip("\r") + temp_prompt2 = prompt2.strip().strip("\n").strip("\0").strip("\r") + + output_prompt = "{}{} {}".format(temp_prompt1, delimiter, temp_prompt2) + + print("output_prompt: {}".format(output_prompt)) + + return (output_prompt,) + +class WarpedPromptConcatExt: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"prompt1": ("STRING", {"default": ""}), + "prompt2": ("STRING", {"default": "", "forceInput": True}), + "delimiter": ([",", "."], {"default": ","}), + "filter_json_file": ("STRING", {"default": ""}) + }, + } + + RETURN_TYPES = ("STRING", ) + RETURN_NAMES = ("prompt", ) + FUNCTION = "do_concat" + + CATEGORY = "Warped/General/Utils" + + def do_concat(self, prompt1="", prompt2="", delimiter=",", filter_json_file=""): + if len(filter_json_file) < 1: + filter_json_file = None + + temp_prompt1 = prompt1.strip().strip("\n").strip("\0").strip("\r") + temp_prompt2 = prompt2.strip().strip("\n").strip("\0").strip("\r") + + output_prompt = "{}{} {}".format(temp_prompt1, delimiter, temp_prompt2) + + filter_list = [] + if not filter_json_file is None: + if not os.path.exists(filter_json_file): + raise ValueError("{} Not Found!".format(filter_json_file)) + + # try: + with open(filter_json_file) as fp: + filter_list = json.load(fp) + # except: + # print("Error Encountered reading filter_json_file. Returning unfiltered prompt.") + # pass + + for entry in filter_list: + output_prompt = output_prompt.replace(entry["find"], entry["replace"]) + + print("output_prompt: {}".format(output_prompt)) + + return (output_prompt,) + +class WarpedImageLossCalc: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"predicted": ("IMAGE", ), + "target": ("IMAGE", ), + }, + } + + RETURN_TYPES = ("STRING", ) + RETURN_NAMES = ("loss_calc", ) + FUNCTION = "do_calc" + + CATEGORY = "Warped/General/Utils" + + def do_calc(self, predicted, target): + loss = torch.nn.functional.mse_loss(predicted.to(dtype=torch.bfloat16), target.to(dtype=torch.bfloat16), reduction="none") + loss = loss.mean() + loss_calc = "{:5.4f}".format(loss) + + return (loss_calc,) + +class WarpedUNETLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ), + "weight_dtype": (["default", "f16", "bf16", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],) + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_unet" + + CATEGORY = "Warped/General/Loaders" + + def load_unet(self, unet_name, weight_dtype): + model_options = {} + if weight_dtype == "fp8_e4m3fn": + model_options["dtype"] = torch.float8_e4m3fn + elif weight_dtype == "fp8_e4m3fn_fast": + model_options["dtype"] = torch.float8_e4m3fn + model_options["fp8_optimizations"] = True + elif weight_dtype == "fp8_e5m2": + model_options["dtype"] = torch.float8_e5m2 + elif weight_dtype == "bf16": + model_options["dtype"] = torch.bfloat16 + elif weight_dtype == "f16": + model_options["dtype"] = torch.float16 + + unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name) + + print("Reading: {} . . .".format(unet_path)) + with open(unet_path, "rb") as file: + content = file.read() + print("Reading: {} . . . Done!".format(unet_path)) + + chkpt, metadata = warped_load_torch_file(content, return_metadata=True) + + for key in chkpt: + print("Diffusiion Model Key: {} | Shape: {}".format(key, chkpt[key].shape)) + + model = comfy.sd.load_diffusion_model_state_dict(chkpt, model_options=model_options, metadata=metadata) + + if model is None: + logging.error("ERROR UNSUPPORTED DIFFUSION MODEL {}".format(unet_path)) + raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(unet_path, model_detection_error_hint(unet_path, chkpt))) + + # model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options) + return (model,) + +class WarpedUNETConverter: + @classmethod + def INPUT_TYPES(s): + return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ), + "weight_dtype": (["default", "fp16", "bf16", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],) + }} + RETURN_TYPES = () + OUTPUT_NODE = True + OUTPUT_IS_LIST = (True,) + FUNCTION = "convert_model" + + CATEGORY = "Warped/General/Conversion" + + def load_unet(self, unet_name, weight_dtype): + model_options = {} + if weight_dtype == "fp8_e4m3fn": + model_options["dtype"] = torch.float8_e4m3fn + elif weight_dtype == "fp8_e4m3fn_fast": + model_options["dtype"] = torch.float8_e4m3fn + model_options["fp8_optimizations"] = True + elif weight_dtype == "fp8_e5m2": + model_options["dtype"] = torch.float8_e5m2 + elif weight_dtype == "bf16": + model_options["dtype"] = torch.bfloat16 + elif weight_dtype == "fp16": + model_options["dtype"] = torch.float16 + + unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name) + + print("Reading: {} . . .".format(unet_path)) + with open(unet_path, "rb") as file: + content = file.read() + print("Reading: {} . . . Done!".format(unet_path)) + + chkpt, metadata = warped_load_torch_file(content, return_metadata=True) + content = None + new_chkpt = {} + + for key in chkpt: + print("Diffusiion Model Key: {} | Shape: {}".format(key, chkpt[key].shape)) + new_chkpt[key] = chkpt[key].to(dtype=model_options["dtype"]) + + return new_chkpt, metadata, unet_path + + def convert_model(self, unet_name, weight_dtype): + unet_data, metadata, unet_path = self.load_unet(unet_name, weight_dtype) + + metadata = {"original_metadata": "{}".format(metadata)} + + save_path = "{}_{}{}".format(os.path.splitext(unet_path)[0], weight_dtype, os.path.splitext(unet_path)[1]) + + utils.save_torch_file(unet_data, save_path, metadata=metadata) + + unet_data = None + mm.soft_empty_cache() + gc.collect() + time.sleep(1) + + save_message = "Weights Saved To: {}".format(save_path) + + return {"ui": {"tags": [save_message]}} + +class WarpedHunyuanLoraDoubleBlocksSwap: + @classmethod + def INPUT_TYPES(s): + arg_dict = { + "required": { + "lora_model_1": (['None'] + folder_paths.get_filename_list("loras"),), + "lora_model_2": (['None'] + folder_paths.get_filename_list("loras"),), + "mainstrength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}), + "save_path": ("STRING", {"default": get_default_output_path()}), + "save_new_lora": ("BOOLEAN", {"default": False}), + "return_state_only": ("BOOLEAN", {"default": False}), + }, + "optional": { + "model": ("MODEL", {"default": None}), + "state_dictionary": ("WARPEDSTATEDICT", {"default": None}), + "metadata_dict": ("WARPEDMETADICT", {"default": None}), + "metadata_flush": ("BOOLEAN", {"default": False}), + "verbose_messaging": ("BOOLEAN", {"default": False}), + } + } + + for i in range(20): + arg_dict["required"][f"double_blocks.{i}."] = ("BOOLEAN", {"default": False}) + + return arg_dict + + RETURN_TYPES = ("MODEL", "STRING", "WARPEDSTATEDICT", "WARPEDMETADICT",) + RETURN_NAMES = ("model", "metadata", "state_dict", "metadata_dict",) + FUNCTION = "load_lora" + CATEGORY = "Warped/Hunyuan/Mixers" + OUTPUT_NODE = False + DESCRIPTION = "LoRA, single blocks double blocks" + + def load_lora(self, lora_model_1, lora_model_2, mainstrength, save_path, save_new_lora=False, return_state_only=False, model=None, state_dictionary=None, metadata_dict=None, metadata_flush=False, verbose_messaging=False, **kwargs): + if ((lora_model_1 is None) and (state_dictionary)) or (lora_model_2 is None): + ValueError("Both LORA models must be a valid selection, or LORA 1 has to be replaced by optional state dictionary input.") + + from comfy.utils import load_torch_file, save_torch_file + from comfy.sd import load_lora_for_models + from comfy.lora import load_lora + + new_metadata = None + metadata = None + torch.cuda.empty_cache() + gc.collect() + time.sleep(0.25) + + if state_dictionary is None: + lora_1, metadata = warped_load_lora_weights(lora_model_1, return_metadata=True) + else: + lora_1 = state_dictionary + + if not metadata_dict is None: + metadata = metadata_dict + else: + metadata = {} + + if metadata_flush or (metadata is None): + metadata = {} + + new_metadata = metadata.copy() + + lora_2 = warped_load_lora_weights(lora_model_2) + + diffusers_lora_1 = convert_lora(lora_1, convert_to="diffusion_model") + filtered_lora_1 = filter_lora_keys(diffusers_lora_1, "all") + + diffusers_lora_2 = convert_lora(lora_2, convert_to="diffusion_model") + filtered_lora_2 = filter_lora_keys(diffusers_lora_2, "all") + + max_dimension = 0 + + for key in lora_1: + if lora_1[key].shape[0] < lora_1[key].shape[1]: + temp_dimension = lora_1[key].shape[0] + else: + temp_dimension = lora_1[key].shape[1] + + if temp_dimension > max_dimension: + max_dimension = temp_dimension + + break + + for key in lora_2: + if lora_2[key].shape[0] < lora_2[key].shape[1]: + temp_dimension = lora_2[key].shape[0] + else: + temp_dimension = lora_2[key].shape[1] + + if temp_dimension > max_dimension: + max_dimension = temp_dimension + + break + + filtered_lora_1 = convert_lora_dimensions(max_dimension, filtered_lora_1) + filtered_lora_2 = convert_lora_dimensions(max_dimension, filtered_lora_2) + + block_settings = { + **kwargs, + } + + swap_block_settings = {k: v for k, v in block_settings.items() if v is True} + swap_blocks = [] + + for key in swap_block_settings: + if swap_block_settings[key]: + temp_strings = key.split('.') + + if len(temp_strings) > 1: + swap_blocks.append(int(temp_strings[1])) + + for entry in swap_blocks: + temp_search_val = ".{}.".format(entry) + + for key in filtered_lora_1: + if (temp_search_val in key) and (key in filtered_lora_2): + filtered_lora_1[key] = filtered_lora_2[key] + + if len(swap_blocks) > 0: + if (not "modified_loras" in new_metadata) and (not "tune_counter" in new_metadata): + new_metadata = {"modified_loras": "{} and {}".format(lora_model_1, lora_model_2)} + + swap_block_data = "" + + for entry in swap_blocks: + swap_block_data = "{} {}".format(swap_block_data, entry).strip() + + new_metadata["replaced_blocks"] = swap_block_data + else: + new_metadata["modified_loras"] = "{} | {} and {}".format(new_metadata["modified_loras"], lora_model_1, lora_model_2) + + swap_block_data = "" + + for entry in swap_blocks: + swap_block_data = "{} {}".format(swap_block_data, entry).strip() + + if "replaced_blocks" in new_metadata: + new_metadata["replaced_blocks"] = "{} | {}".format(new_metadata["replaced_blocks"], swap_block_data) + else: + new_metadata["replaced_blocks"] = "{}".format(swap_block_data) + + if verbose_messaging: + print("\nLora Metadata: {}".format(new_metadata)) + + if save_new_lora and (len(swap_blocks) > 0): + os.makedirs(os.path.dirname(save_path), exist_ok=True) + + for key in filtered_lora_1: + filtered_lora_1[key] = filtered_lora_1[key].to(dtype=torch.bfloat16) + + print("Saving Model To: {}...".format(save_path)) + save_torch_file(filtered_lora_1, save_path, metadata=new_metadata) + print("Saving Model To: {}...Done.".format(save_path)) + + if (not return_state_only) and (not model is None): + new_model, _ = load_lora_for_models(model, None, filtered_lora_1, mainstrength, 0) + if new_model is not None: + return (new_model, new_metadata, None, None, ) + + return (model, new_metadata, filtered_lora_1, new_metadata, ) + + @classmethod + def IS_CHANGED(s, lora_model_1, lora_model_2, mainstrength, save_path, save_new_lora, return_state_only=False, model=None, state_dictionary=None, metadata_dict=None, metadata_flush=False, verbose_messaging=False, **kwargs): + return f"{lora_model_1}_{lora_model_2}_{mainstrength}_{state_dictionary}_{metadata_dict}" + +def get_base_layer_type(layer_key): + layer_key = layer_key.replace(".weight", "") + + if "single" in layer_key: + layer_key = layer_key.replace("diffusion_model.single_blocks.", "") + else: + layer_key = layer_key.replace("diffusion_model.double_blocks.", "") + + if "img_" in layer_key: + temp_strings = layer_key.split("img_") + layer_key = "img_{}".format(temp_strings[len(temp_strings) - 1]) + else: + temp_strings = layer_key.split("txt_") + layer_key = "txt_{}".format(temp_strings[len(temp_strings) - 1]) + + return layer_key + +class WarpedHunyuanLoraDoubleBlocksLayersBlend: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora_model_1": (['None'] + folder_paths.get_filename_list("loras"),), + "lora_model_2": (['None'] + folder_paths.get_filename_list("loras"),), + "mainstrength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}), + "save_path": ("STRING", {"default": get_default_output_path()}), + "save_new_lora": ("BOOLEAN", {"default": False}), + "return_state_only": ("BOOLEAN", {"default": False}), + "img_attn_proj_lora_A": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "img_attn_proj_lora_B": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "img_attn_qkv_lora_A": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "img_attn_qkv_lora_B": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "img_mlp_fc1_lora_A": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "img_mlp_fc1_lora_B": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "img_mlp_fc2_lora_A": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "img_mlp_fc2_lora_B": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "img_mod_linear_lora_A": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "img_mod_linear_lora_B": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "txt_attn_proj_lora_A": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "txt_attn_proj_lora_B": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "txt_attn_qkv_lora_A": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "txt_attn_qkv_lora_B": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "txt_mlp_fc1_lora_A": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "txt_mlp_fc1_lora_B": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "txt_mlp_fc2_lora_A": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "txt_mlp_fc2_lora_B": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "txt_mod_linear_lora_A": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "txt_mod_linear_lora_B": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "for_all_img_layers": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + "for_all_txt_layers": ("FLOAT", {"default": 0.000, "min": 0.000, "max": 1.000, "step": 0.001}), + }, + "optional": { + "model": ("MODEL", {"default": None}), + "state_dictionary": ("WARPEDSTATEDICT", {"default": None}), + "metadata_dict": ("WARPEDMETADICT", {"default": None}), + "metadata_flush": (["tuner_only", "full", "none"], {"default": "tuner_only"}), + "discard_single_blocks": ("BOOLEAN", {"default": True}), + "verbose_messaging": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("MODEL", "STRING", "WARPEDSTATEDICT", "WARPEDMETADICT",) + RETURN_NAMES = ("model", "metadata", "state_dict", "metadata_dict",) + FUNCTION = "load_lora" + CATEGORY = "Warped/Hunyuan/Mixers" + OUTPUT_NODE = False + DESCRIPTION = "LoRA, single blocks double blocks" + + def load_lora(self, lora_model_1, lora_model_2, mainstrength, save_path, save_new_lora=False, return_state_only=False, model=None, state_dictionary=None, metadata_dict=None, metadata_flush="tuner_only", discard_single_blocks=True, verbose_messaging=False, + img_attn_proj_lora_A=0.00, img_attn_proj_lora_B=0.00, img_attn_qkv_lora_A=0.00, img_attn_qkv_lora_B=0.00, img_mlp_fc1_lora_A=0.00, + img_mlp_fc1_lora_B=0.00, img_mlp_fc2_lora_A=0.00, img_mlp_fc2_lora_B=0.00, img_mod_linear_lora_A=0.00, img_mod_linear_lora_B=0.00, txt_attn_proj_lora_A=0.00, + txt_attn_proj_lora_B=0.00, txt_attn_qkv_lora_A=0.00, txt_attn_qkv_lora_B=0.00, txt_mlp_fc1_lora_A=0.00, txt_mlp_fc1_lora_B=0.00, txt_mlp_fc2_lora_A=0.00, + txt_mlp_fc2_lora_B=0.00, txt_mod_linear_lora_A=0.00, txt_mod_linear_lora_B=0.00, for_all_img_layers=0.00, for_all_txt_layers=0.00): + + if lora_model_1 is None or lora_model_2 is None: + ValueError("Bother LORA models must be valid selections.") + + from comfy.utils import load_torch_file, save_torch_file + from comfy.sd import load_lora_for_models + from comfy.lora import load_lora + + use_for_all_img = False + + if Decimal(for_all_img_layers).compare(Decimal(0.0)) != 0: + use_for_all_img = True + + if use_for_all_img: + print("Using For All Img.") + else: + print("Not Using For All Img.") + + use_for_all_txt = False + + if Decimal(for_all_txt_layers).compare(Decimal(0.0)) != 0: + use_for_all_txt = True + + if use_for_all_txt: + print("Using For All Txt.") + else: + print("Not Using For All Txt.") + + if state_dictionary is None: + lora_1, metadata = warped_load_lora_weights(lora_model_1, return_metadata=True) + print("++++ Metadata: ++++\n{}".format(metadata)) + else: + lora_1 = state_dictionary + + if not metadata_dict is None: + metadata = metadata_dict + else: + metadata = {} + + # print("---- Metadata: ----\n{}".format(metadata)) + + lora_2 = warped_load_lora_weights(lora_model_2) + + diffusers_lora_1 = convert_lora(lora_1, convert_to="diffusion_model") + filtered_lora_1 = filter_lora_keys(diffusers_lora_1, "all") + + diffusers_lora_2 = convert_lora(lora_2, convert_to="diffusion_model") + filtered_lora_2 = filter_lora_keys(diffusers_lora_2, "all") + + max_dimension = 0 + + for key in lora_1: + if lora_1[key].shape[0] < lora_1[key].shape[1]: + temp_dimension = lora_1[key].shape[0] + else: + temp_dimension = lora_1[key].shape[1] + + if temp_dimension > max_dimension: + max_dimension = temp_dimension + + break + + for key in lora_2: + if lora_2[key].shape[0] < lora_2[key].shape[1]: + temp_dimension = lora_2[key].shape[0] + else: + temp_dimension = lora_2[key].shape[1] + + if temp_dimension > max_dimension: + max_dimension = temp_dimension + + break + + filtered_lora_1 = convert_lora_dimensions(max_dimension, filtered_lora_1) + filtered_lora_2 = convert_lora_dimensions(max_dimension, filtered_lora_2) + + tune_data = {} + sub_key = "" + + dummy_lora = filtered_lora_1.copy() + + for key in dummy_lora: + if "single_blocks" in key: + if discard_single_blocks: + del filtered_lora_1[key] + + continue + + do_mod = False + + if not use_for_all_img and "img_" in key: + if "img_attn_proj.lora_A" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - img_attn_proj_lora_A + perc_2 = img_attn_proj_lora_A + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["img_attn_proj.lora_A"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "img_attn_proj.lora_B" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - img_attn_proj_lora_B + perc_2 = img_attn_proj_lora_B + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["img_attn_proj.lora_B"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "img_attn_qkv.lora_A" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - img_attn_qkv_lora_A + perc_2 = img_attn_qkv_lora_A + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["img_attn_qkv.lora_A"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "img_attn_qkv.lora_B" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - img_attn_qkv_lora_B + perc_2 = img_attn_qkv_lora_B + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["img_attn_qkv.lora_B"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "img_mlp.fc1.lora_A" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - img_mlp_fc1_lora_A + perc_2 = img_mlp_fc1_lora_A + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["img_mlp.fc1.lora_A"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "img_mlp.fc1.lora_B" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - img_mlp_fc1_lora_B + perc_2 = img_mlp_fc1_lora_B + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["img_mlp.fc1.lora_B"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "img_mlp.fc2.lora_A" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - img_mlp_fc2_lora_A + perc_2 = img_mlp_fc2_lora_A + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["img_mlp.fc2.lora_A"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "img_mlp.fc2.lora_B" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - img_mlp_fc2_lora_B + perc_2 = img_mlp_fc2_lora_B + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["img_mlp.fc2.lora_B"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "img_mod.linear.lora_A" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - img_mod_linear_lora_A + perc_2 = img_mod_linear_lora_A + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["img_mod.linear.lora_A"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "img_mod.linear.lora_B" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - img_mod_linear_lora_B + perc_2 = img_mod_linear_lora_B + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["img_mod.linear.lora_B"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "img_" in key: + sub_key = key.replace(".weight", "") + sub_key = sub_key.replace("diffusion_model.double_blocks.", "") + + temp_strings = sub_key.split('img_') + sub_key = "img_{}".format(temp_strings[len(temp_strings) - 1]) + + perc_1 = 1.0 - for_all_img_layers + perc_2 = for_all_img_layers + + tune_data[sub_key] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + + if not use_for_all_txt and "txt_" in key: + if "txt_attn_proj.lora_A" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - txt_attn_proj_lora_A + perc_2 = txt_attn_proj_lora_A + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["txt_attn_proj.lora_A"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "txt_attn_proj.lora_B" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - txt_attn_proj_lora_B + perc_2 = txt_attn_proj_lora_B + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["txt_attn_proj.lora_B"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "txt_attn_qkv.lora_A" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - txt_attn_qkv_lora_A + perc_2 = txt_attn_qkv_lora_A + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["txt_attn_qkv.lora_A"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "txt_attn_qkv.lora_B" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - txt_attn_qkv_lora_B + perc_2 = txt_attn_qkv_lora_B + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["txt_attn_qkv.lora_B"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "txt_mlp.fc1.lora_A" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - txt_mlp_fc1_lora_A + perc_2 = txt_mlp_fc1_lora_A + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["txt_mlp.fc1.lora_A"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "txt_mlp.fc1.lora_B" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - txt_mlp_fc1_lora_B + perc_2 = txt_mlp_fc1_lora_B + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["txt_mlp.fc1.lora_B"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "txt_mlp.fc2.lora_A" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - txt_mlp_fc2_lora_A + perc_2 = txt_mlp_fc2_lora_A + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["txt_mlp.fc2.lora_A"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "txt_mlp.fc2.lora_B" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - txt_mlp_fc2_lora_B + perc_2 = txt_mlp_fc2_lora_B + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["txt_mlp.fc2.lora_B"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "txt_mod.linear.lora_A" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - txt_mod_linear_lora_A + perc_2 = txt_mod_linear_lora_A + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["txt_mod.linear.lora_A"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "txt_mod.linear.lora_B" in key: + if key in filtered_lora_2: + perc_1 = 1.0 - txt_mod_linear_lora_B + perc_2 = txt_mod_linear_lora_B + + if Decimal(perc_1).compare(Decimal(1.0)) != 0: + tune_data["txt_mod.linear.lora_B"] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + elif "txt_" in key: + sub_key = key.replace(".weight", "") + sub_key = sub_key.replace("diffusion_model.double_blocks.", "") + + temp_strings = sub_key.split('txt_') + sub_key = "txt_{}".format(temp_strings[len(temp_strings) - 1]) + + perc_1 = 1.0 - for_all_txt_layers + perc_2 = for_all_txt_layers + + tune_data[sub_key] = "perc_1: {} | perc_2: {}".format(perc_1, perc_2) + do_mod = True + + if (key in filtered_lora_1) and (key in filtered_lora_2) and do_mod: + mod_1 = torch.mul(filtered_lora_1[key], perc_1) + mod_2 = torch.mul(filtered_lora_2[key], perc_2) + + new_lora = torch.add(mod_1, mod_2) + else: + continue + + if (metadata_flush == "full") or (metadata is None): + metadata = {} + + print("**** Metadata: ****\n{}".format(metadata)) + + if ((not "tune_counter_1" in metadata) and (not "modified_loras" in metadata)) or (metadata_flush == "full"): + metadata = {"modified_loras": "{} and {}".format(lora_model_1, lora_model_2)} + else: + metadata["modified_loras"] = "{} | {} and {}".format(metadata["modified_loras"], lora_model_1, lora_model_2) + + if len(tune_data.keys()) > 0: + if (not "tune_counter" in metadata) or (not metadata_flush == "none"): + metadata["tune_counter"] = "{}".format(1) + metadata[f'{tune_data}_1'] = "{}".format(tune_data) + else: + tune_counter = int(metadata["tune_counter"]) + 1 + metadata["tune_counter"] = "{}".format(tune_counter) + metadata[f'{tune_data}_{tune_counter}'] = "{}".format(tune_data) + + if verbose_messaging: + print("\nLora Metadata: {}".format(metadata)) + + if save_new_lora: # and (len(swap_blocks) > 0): + os.makedirs(os.path.dirname(save_path), exist_ok=True) + + for key in filtered_lora_1: + filtered_lora_1[key] = filtered_lora_1[key].to(dtype=torch.bfloat16) + + print("Saving Model To: {}...".format(save_path)) + save_torch_file(filtered_lora_1, save_path, metadata=metadata) + print("Saving Model To: {}...Done.".format(save_path)) + + if (not return_state_only) and (not model is None): + new_model, _ = load_lora_for_models(model, None, filtered_lora_1, mainstrength, 0) + if new_model is not None: + return (new_model, metadata, None, None, ) + + return (model, metadata, filtered_lora_1, metadata, ) + + @classmethod + def IS_CHANGED(s, lora_model_1, lora_model_2, mainstrength, save_path, save_new_lora=False, return_state_only=False, model=None, state_dictionary=None, metadata_dict=None, metadata_flush="tuner_only", discard_single_blocks=True, verbose_messaging=False, + img_attn_proj_lora_A=0.00, img_attn_proj_lora_B=0.00, img_attn_qkv_lora_A=0.00, img_attn_qkv_lora_B=0.00, img_mlp_fc1_lora_A=0.00, img_mlp_fc1_lora_B=0.00, img_mlp_fc2_lora_A=0.00, + img_mlp_fc2_lora_B=0.00, img_mod_linear_lora_A=0.00, img_mod_linear_lora_B=0.00, txt_attn_proj_lora_A=0.00, txt_attn_proj_lora_B=0.00, txt_attn_qkv_lora_A=0.00, txt_attn_qkv_lora_B=0.00, txt_mlp_fc1_lora_A=0.00, txt_mlp_fc1_lora_B=0.00, + txt_mlp_fc2_lora_A=0.00, txt_mlp_fc2_lora_B=0.00, txt_mod_linear_lora_A=0.00, txt_mod_linear_lora_B=0.00, for_all_img_layers=0.00, for_all_txt_layers=0.00): + return f"{lora_model_1}_{lora_model_2}_{mainstrength}" + +class WarpedHunyuanLoraDoubleBlocksRemoveLinear: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora_model": (['None'] + folder_paths.get_filename_list("loras"),), + "mainstrength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}), + "save_path": ("STRING", {"default": get_default_output_path()}), + "save_new_lora": ("BOOLEAN", {"default": False}), + "return_state_only": ("BOOLEAN", {"default": False}), + "max_dimension": ([32, 64, 128], {"default": 128}), + }, + "optional": { + "model": ("MODEL", {"default": None}), + "state_dictionary": ("WARPEDSTATEDICT", {"default": None}), + "metadata_dict": ("WARPEDMETADICT", {"default": None}), + "metadata_flush": (["deletes_only", "full", "none"], {"default": "deletes_only"}), + "discard_single_blocks": ("BOOLEAN", {"default": True}), + "verbose_messaging": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("MODEL", "STRING", "WARPEDSTATEDICT", "WARPEDMETADICT",) + RETURN_NAMES = ("model", "metadata", "state_dict", "metadata_dict",) + FUNCTION = "load_lora" + CATEGORY = "Warped/Hunyuan/Mixers" + OUTPUT_NODE = False + DESCRIPTION = "LoRA, single blocks double blocks" + + def load_lora(self, lora_model, mainstrength, save_path, save_new_lora=False, return_state_only=False, max_dimension=128, model=None, state_dictionary=None, metadata_dict=None, metadata_flush="deletes_only", + discard_single_blocks=True, verbose_messaging=False): + + if lora_model is None and state_dictionary is None: + ValueError("Either lora_model or state_dictionary input must be valid selections") + + from comfy.utils import load_torch_file, save_torch_file + from comfy.sd import load_lora_for_models + from comfy.lora import load_lora + + if state_dictionary is None: + lora, metadata = warped_load_lora_weights(lora_model, return_metadata=True) + else: + lora = state_dictionary + + if not metadata_dict is None: + metadata = metadata_dict + else: + metadata = {} + + diffusers_lora = convert_lora(lora, convert_to="diffusion_model") + filtered_lora = filter_lora_keys(diffusers_lora, "all") + filtered_lora = convert_lora_dimensions(max_dimension, filtered_lora) + + tune_data = {} + sub_key = "" + + dummy_lora = filtered_lora.copy() + deleted_keys = {} + + for key in dummy_lora: + if "single_blocks" in key: + if discard_single_blocks: + temp_key = get_base_layer_type(key) + + deleted_keys[temp_key] = "Deleted All" + del filtered_lora[key] + elif "linear" in key: + temp_key = get_base_layer_type(key) + + deleted_keys[temp_key] = "Deleted All" + del filtered_lora[key] + + continue + + if not "linear" in key: + continue + + temp_key = get_base_layer_type(key) + + deleted_keys[temp_key] = "Deleted All" + + del filtered_lora[key] + + if metadata_flush == "full": + metadata = {} + + if ((not "deleted_keys_1" in metadata) and (not "modified_loras" in metadata)) or (metadata_flush == "full"): + metadata = {"modified_loras": "{}".format(lora_model)} + else: + metadata["modified_loras"] = "{} | {}".format(metadata["modified_loras"], lora_model) + + if len(deleted_keys.keys()) > 0: + if (not "deleted_counter" in metadata) or (not metadata_flush == "none"): + metadata["deleted_counter"] = "{}".format(1) + metadata[f'deleted_data_1'] = "{}".format(deleted_keys) + else: + deleted_counter = int(metadata["deleted_counter"]) + 1 + metadata["deleted_counter"] = "{}".format(deleted_counter) + metadata[f'deleted_data_{deleted_counter}'] = "{}".format(deleted_keys) + + if verbose_messaging: + print("\nLora Metadata: {}".format(metadata)) + + if save_new_lora: # and (len(swap_blocks) > 0): + os.makedirs(os.path.dirname(save_path), exist_ok=True) + + for key in filtered_lora: + filtered_lora[key] = filtered_lora[key].to(dtype=torch.bfloat16) + + print(metadata) + + print("Saving Model To: {}...".format(save_path)) + save_torch_file(filtered_lora, save_path, metadata=metadata) + print("Saving Model To: {}...Done.".format(save_path)) + + if (not return_state_only) and (not model is None): + new_model, _ = load_lora_for_models(model, None, filtered_lora, mainstrength, 0) + if new_model is not None: + return (new_model, metadata, None, None, ) + + return (model, metadata, filtered_lora, metadata, ) + + @classmethod + def IS_CHANGED(s, lora_model, mainstrength, save_path, save_new_lora=False, return_state_only=False, max_dimension=128, model=None, state_dictionary=None, metadata_dict=None, metadata_flush="tuner_only", discard_single_blocks=True, verbose_messaging=False): + return f"{lora_model}_{mainstrength}" + +class WarpedHunyuanLoraDoubleBlocksModifySegment: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora_model": (['None'] + folder_paths.get_filename_list("loras"),), + "source_lora": (['None'] + folder_paths.get_filename_list("loras"),), + "mainstrength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}), + "save_path": ("STRING", {"default": get_default_output_path()}), + "save_new_lora": ("BOOLEAN", {"default": False}), + "return_state_only": ("BOOLEAN", {"default": False}), + "segment_number": ("INT", {"default": 2, "min": 1, "max": 127, "step": 1}), + "test_mode": (["zero_all", "perc_all", "by_block_num", "random_noise", "use_source"], {"default": "perc_all"}), + "max_dimension": ([32, 64, 128], {"default": 128}), + }, + "optional": { + "model": ("MODEL", {"default": None}), + "state_dictionary": ("WARPEDSTATEDICT", {"default": None}), + "metadata_dict": ("WARPEDMETADICT", {"default": None}), + "block_number": ([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19], {"default": 0}), + "percentage": ("FLOAT", {"default": 1.000, "min": 0.000, "max": 5.000, "step": 0.001}), + "layer_type": (["all", "img", "txt"], {"default": "all"}), + "discard_single_blocks": ("BOOLEAN", {"default": True}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "verbose_messaging": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("MODEL", "STRING", "WARPEDSTATEDICT", "WARPEDMETADICT",) + RETURN_NAMES = ("model", "metadata", "state_dict", "metadata_dict",) + FUNCTION = "load_lora" + CATEGORY = "Warped/Hunyuan/Mixers/Experimental" + OUTPUT_NODE = False + DESCRIPTION = "LoRA, single blocks double blocks" + + def load_lora(self, lora_model, source_lora, mainstrength, save_path, save_new_lora=False, return_state_only=False, segment_number=0, test_mode="perc_all", max_dimension=128, model=None, state_dictionary=None, metadata_dict=None, + block_number=0, percentage=1.000, layer_type="all", discard_single_blocks=True, seed=0, verbose_messaging=False): + + if lora_model is None and state_dictionary is None: + raise ValueError("Either lora_model or state_dictionary input must be valid selections") + + if segment_number > (max_dimension - 1): + raise ValueError("segment_number cannot be greater than max_dimension - 1.") + + from comfy.utils import save_torch_file + from comfy.sd import load_lora_for_models + from comfy.lora import load_lora + + if state_dictionary is None: + + lora, metadata = warped_load_lora_weights(lora_model, return_metadata=True) + else: + lora = state_dictionary + + if not metadata_dict is None: + metadata = metadata_dict + else: + metadata = {} + + if not "modified_loras" in metadata: + metadata["modified_loras"] = "{} and {}".format(lora_model, source_lora) + else: + metadata["modified_loras"] = "{} | {} and {}".format(metadata["modified_loras"], lora_model, source_lora) + + diffusers_lora = convert_lora(lora, convert_to="diffusion_model") + + if discard_single_blocks: + filtered_lora = filter_lora_keys(diffusers_lora, "double_blocks") + else: + filtered_lora = filter_lora_keys(diffusers_lora, "all") + + filtered_lora = convert_lora_dimensions(max_dimension, filtered_lora) + + source_filtered_lora = None + + if (test_mode == "use_source") and (not source_lora == "None"): + source_lora_path = folder_paths.get_full_path("loras", source_lora) + + if not os.path.exists(source_lora_path): + raise Exception(f"Lora {source_lora} not found at {source_lora_path}") + + temp_source_lora = warped_load_lora_weights(source_lora) + + source_diffusers_lora = convert_lora(temp_source_lora, convert_to="diffusion_model") + source_filtered_lora = filter_lora_keys(source_diffusers_lora, "double_blocks") + source_filtered_lora = convert_lora_dimensions(max_dimension, source_filtered_lora) + + if segment_number > max_dimension: + segment_number = max_dimension + elif segment_number < 0: + segment_number = 0 + + block_filter = "" + + if test_mode == "by_block_num": + block_filter = ".{}.".format(block_number) + + if not "slice_manipulation_counter" in metadata: + metadata["slice_manipulation_counter"] = "1" + metadata_key = "slice_manipulation_1" + else: + metadata["slice_manipulation_counter"] = "{}".format(int(metadata["slice_manipulation_counter"]) + 1) + metadata_key = f'slice_manipulation_{"{}".format(metadata["slice_manipulation_counter"])}' + + metadata[metadata_key] = {"seed": "{}".format(seed)} + metadata[metadata_key]["lora_model"] = lora_model + metadata[metadata_key]["test_mode"] = test_mode + metadata[metadata_key]["segment_number"] = "{}".format(segment_number) + metadata[metadata_key]["max_dimension"] = "{}".format(max_dimension) + metadata[metadata_key]["block_number"] = "{}".format(block_number) + metadata[metadata_key]["layer_type"] = "{}".format(layer_type) + metadata[metadata_key]["percentage"] = "{}".format(percentage) + metadata[metadata_key]["discard_single_blocks"] = "{}".format(discard_single_blocks) + + if test_mode == "use_source": + metadata[metadata_key]["source_model"] = source_lora + else: + metadata[metadata_key]["source_model"] = None + + metadata[metadata_key] = "{}".format(metadata[metadata_key]) + + for key in filtered_lora: + if "single_blocks" in key: + continue + + if ("layer_type" == "img") and ("txt_" in key): + continue + + if ("layer_type" == "txt") and ("img_" in key): + continue + + if filtered_lora[key].shape[0] < filtered_lora[key].shape[1]: + use_length = filtered_lora[key].shape[0] + test_length = 1 + else: + use_length = int(int(filtered_lora[key].shape[0]) // int(filtered_lora[key].shape[1])) + test_length = int(filtered_lora[key].shape[0]) + + temp_tensor = torch.zeros_like(filtered_lora[key]) + + if (test_mode == "zero_all") or ((len(block_filter) > 0) and (block_filter in key)): + if use_length == test_length: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + elif segment_number == 0: + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + else: + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + else: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + elif segment_number == 0: + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + else: + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + elif test_mode == "perc_all": + temp_perc_tensor = torch.zeros_like(filtered_lora[key]) + + if use_length == test_length: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + + temp_perc_tensor[segment_number - 1:segment_number,:] = filtered_lora[key][segment_number - 1:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + + temp_perc_tensor[:1,:] = filtered_lora[key][:1,:] * percentage + else: + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + + temp_perc_tensor[segment_number:segment_number + 1,:] = filtered_lora[key][segment_number:segment_number + 1,:] * percentage + else: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + + temp_perc_tensor[segment_number - test_length:segment_number,:] = filtered_lora[key][segment_number - test_length:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + + temp_perc_tensor[:test_length,:] = filtered_lora[key][:test_length,:] * percentage + else: + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + + temp_perc_tensor[(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] = filtered_lora[key][(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] * percentage + elif test_mode == "random_noise": + random_noise_latent = warped_prepare_noise(torch.zeros_like(filtered_lora[key]), seed) + temp_perc_tensor = torch.zeros_like(filtered_lora[key]) + + if use_length == test_length: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + + temp_perc_tensor[segment_number - 1:segment_number,:] = random_noise_latent[segment_number - 1:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + + temp_perc_tensor[:1,:] = random_noise_latent[:1,:] * percentage + else: + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + + temp_perc_tensor[segment_number:segment_number + 1,:] = random_noise_latent[segment_number:segment_number + 1,:] * percentage + else: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + + temp_perc_tensor[segment_number - test_length:segment_number,:] = random_noise_latent[segment_number - test_length:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + + temp_perc_tensor[:test_length,:] = random_noise_latent[:test_length,:] * percentage + else: + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + + temp_perc_tensor[(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] = random_noise_latent[(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] * percentage + + temp_tensor = torch.add(temp_tensor, temp_perc_tensor) + elif test_mode == "use_source": + if not source_filtered_lora is None: + if not key in source_filtered_lora: + continue + + temp_perc_tensor = torch.zeros_like(filtered_lora[key]) + + if use_length == test_length: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + temp_perc_tensor[segment_number - 1:segment_number,:] = source_filtered_lora[key][segment_number - 1:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + temp_perc_tensor[:1,:] = source_filtered_lora[key][:1,:] * percentage + else: + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + temp_perc_tensor[segment_number:segment_number + 1,:] = source_filtered_lora[key][segment_number:segment_number + 1,:] * percentage + else: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + temp_perc_tensor[segment_number - test_length:segment_number,:] = source_filtered_lora[key][segment_number - test_length:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + temp_perc_tensor[:test_length,:] = source_filtered_lora[key][:test_length,:] * percentage + else: + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + temp_perc_tensor[(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] = source_filtered_lora[key][(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] * percentage + + temp_tensor = torch.add(temp_tensor, temp_perc_tensor) + else: + print("**** No Source LORA Provided. Unable to make mosification. ****") + + filtered_lora[key] = temp_tensor.to(torch.bfloat16) + + if verbose_messaging: + print("Tester Metadata: {}".format(metadata)) + + if save_new_lora: # and (len(swap_blocks) > 0): + os.makedirs(os.path.dirname(save_path), exist_ok=True) + + for key in filtered_lora: + filtered_lora[key] = filtered_lora[key].to(dtype=torch.bfloat16) + + print("Saving Model To: {}...".format(save_path)) + save_torch_file(filtered_lora, save_path, metadata=metadata) + print("Saving Model To: {}...Done.".format(save_path)) + + if (not return_state_only) and (not model is None): + new_model, _ = load_lora_for_models(model, None, filtered_lora, mainstrength, 0) + if new_model is not None: + return (new_model, metadata, None, None, ) + + return (model, metadata, filtered_lora, metadata, ) + + + @classmethod + def IS_CHANGED(s, lora_model, source_lora, mainstrength, save_path, save_new_lora=False, return_state_only=False, segment_number=0, test_mode="perc_all", max_dimension=128, model=None, state_dictionary=None, metadata_dict=None, block_number=0, percentage=1.000, layer_type="all", discard_single_blocks=True, seed=0, verbose_messaging=False): + return f"{lora_model}_{mainstrength}" + +class WarpedHunyuanLoraDoubleBlocksModifyMultipleSegments: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora_model": (['None'] + folder_paths.get_filename_list("loras"),), + "source_lora": (['None'] + folder_paths.get_filename_list("loras"),), + "mainstrength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}), + "save_path": ("STRING", {"default": get_default_output_path()}), + "save_new_lora": ("BOOLEAN", {"default": False}), + "return_state_only": ("BOOLEAN", {"default": False}), + "segment_numbers": ("STRING", {"default": "2,23"}), + "test_mode": (["zero_all", "perc_all", "by_block_num", "random_noise", "use_source", "add_source", "subtract_source"], {"default": "perc_all"}), + "max_dimension": ([32, 64, 128], {"default": 128}), + }, + "optional": { + "model": ("MODEL", {"default": None}), + "state_dictionary": ("WARPEDSTATEDICT", {"default": None}), + "metadata_dict": ("WARPEDMETADICT", {"default": None}), + "block_number": ([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19], {"default": 0}), + "percentage": ("FLOAT", {"default": 1.000, "min": 0.000, "max": 5.000, "step": 0.001}), + "layer_type": (["all", "img", "txt"], {"default": "all"}), + "discard_single_blocks": ("BOOLEAN", {"default": True}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "verbose_messaging": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("MODEL", "STRING", "WARPEDSTATEDICT", "WARPEDMETADICT",) + RETURN_NAMES = ("model", "metadata", "state_dict", "metadata_dict",) + FUNCTION = "load_lora" + CATEGORY = "Warped/Hunyuan/Mixers/Experimental" + OUTPUT_NODE = False + DESCRIPTION = "LoRA, single blocks double blocks" + + def load_lora(self, lora_model, source_lora, mainstrength, save_path, save_new_lora=False, return_state_only=False, segment_numbers="2,23", test_mode="perc_all", max_dimension=128, model=None, state_dictionary=None, metadata_dict=None, + block_number=0, percentage=1.000, layer_type="all", discard_single_blocks=True, seed=0, verbose_messaging=False): + + if lora_model is None and state_dictionary is None: + raise ValueError("Either lora_model or state_dictionary input must be valid selections") + + if len(segment_numbers) < 1: + raise ValueError("segment_numbers cannot be empty.") + + temp_seg_split = segment_numbers.split(',') + + use_segment_numbers = [] + + for entry in temp_seg_split: + # if range of segments + if "-" in entry.strip(): + temp_seg_split_2 = entry.split("-") + + start = int(temp_seg_split_2[0].strip()) + end = int(temp_seg_split_2[1].strip()) + + if start > end: + raise ValueError("Beginning of segment range cannot be greater than the end of the segment range.") + + for i in range(start, end): + use_segment_numbers.append(i) + + continue + + use_segment_numbers.append(int(entry.strip())) + + for segment_number in use_segment_numbers: + if segment_number > (max_dimension - 1): + raise ValueError("segment_number cannot be greater than max_dimension - 1.") + + if segment_number < 1: + raise ValueError("segment_number cannot be less than 1.") + + from comfy.utils import save_torch_file + from comfy.sd import load_lora_for_models + from comfy.lora import load_lora + + if state_dictionary is None: + + lora, metadata = warped_load_lora_weights(lora_model, return_metadata=True) + else: + lora = state_dictionary + + if not metadata_dict is None: + metadata = metadata_dict + else: + metadata = {} + + if not "modified_loras" in metadata: + metadata["modified_loras"] = "{} and {}".format(lora_model, source_lora) + else: + metadata["modified_loras"] = "{} | {} and {}".format(metadata["modified_loras"], lora_model, source_lora) + + diffusers_lora = convert_lora(lora, convert_to="diffusion_model") + + if discard_single_blocks: + filtered_lora = filter_lora_keys(diffusers_lora, "double_blocks") + else: + filtered_lora = filter_lora_keys(diffusers_lora, "all") + + filtered_lora = convert_lora_dimensions(max_dimension, filtered_lora) + + source_filtered_lora = None + + if (test_mode == "use_source") and (not source_lora == "None"): + source_lora_path = folder_paths.get_full_path("loras", source_lora) + + if not os.path.exists(source_lora_path): + raise Exception(f"Lora {source_lora} not found at {source_lora_path}") + + temp_source_lora = warped_load_lora_weights(source_lora) + + source_diffusers_lora = convert_lora(temp_source_lora, convert_to="diffusion_model") + source_filtered_lora = filter_lora_keys(source_diffusers_lora, "double_blocks") + source_filtered_lora = convert_lora_dimensions(max_dimension, source_filtered_lora) + + block_filter = "" + + if test_mode == "by_block_num": + block_filter = ".{}.".format(block_number) + + if not "slice_manipulation_counter" in metadata: + metadata["slice_manipulation_counter"] = "1" + metadata_key = "slice_manipulation_1" + else: + metadata["slice_manipulation_counter"] = "{}".format(int(metadata["slice_manipulation_counter"]) + 1) + metadata_key = f'slice_manipulation_{"{}".format(metadata["slice_manipulation_counter"])}' + + metadata[metadata_key] = {"seed": "{}".format(seed)} + metadata[metadata_key]["lora_model"] = lora_model + metadata[metadata_key]["test_mode"] = test_mode + metadata[metadata_key]["segment_numbers"] = "{}".format(segment_numbers) + metadata[metadata_key]["max_dimension"] = "{}".format(max_dimension) + metadata[metadata_key]["block_number"] = "{}".format(block_number) + metadata[metadata_key]["layer_type"] = "{}".format(layer_type) + metadata[metadata_key]["percentage"] = "{}".format(percentage) + metadata[metadata_key]["discard_single_blocks"] = "{}".format(discard_single_blocks) + + if test_mode == "use_source": + metadata[metadata_key]["source_model"] = source_lora + else: + metadata[metadata_key]["source_model"] = None + + metadata[metadata_key] = "{}".format(metadata[metadata_key]) + + for key in filtered_lora: + if "single_blocks" in key: + continue + + if ("layer_type" == "img") and ("txt_" in key): + continue + + if ("layer_type" == "txt") and ("img_" in key): + continue + + if filtered_lora[key].shape[0] < filtered_lora[key].shape[1]: + use_length = filtered_lora[key].shape[0] + test_length = 1 + else: + use_length = int(int(filtered_lora[key].shape[0]) // int(filtered_lora[key].shape[1])) + test_length = int(filtered_lora[key].shape[0]) + + temp_tensor = torch.zeros_like(filtered_lora[key]) + + if (test_mode == "zero_all") or ((len(block_filter) > 0) and (block_filter in key)): + for segment_number in use_segment_numbers: + if use_length == test_length: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + elif segment_number == 0: + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + else: + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + else: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + elif segment_number == 0: + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + else: + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + + filtered_lora[key] = temp_tensor.to(torch.bfloat16) + elif test_mode == "perc_all": + temp_perc_tensor = torch.zeros_like(filtered_lora[key]) + + for segment_number in use_segment_numbers: + if use_length == test_length: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + + temp_perc_tensor[segment_number - 1:segment_number,:] = filtered_lora[key][segment_number - 1:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + + temp_perc_tensor[:1,:] = filtered_lora[key][:1,:] * percentage + else: + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + + temp_perc_tensor[segment_number:segment_number + 1,:] = filtered_lora[key][segment_number:segment_number + 1,:] * percentage + else: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + + temp_perc_tensor[segment_number - test_length:segment_number,:] = filtered_lora[key][segment_number - test_length:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + + temp_perc_tensor[:test_length,:] = filtered_lora[key][:test_length,:] * percentage + else: + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + + temp_perc_tensor[(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] = filtered_lora[key][(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] * percentage + + filtered_lora[key] = temp_tensor.to(torch.bfloat16) + elif test_mode == "random_noise": + rnd = torch.manual_seed(seed) + + random_noise_latent = warped_prepare_noise(torch.zeros_like(filtered_lora[key]), seed, generator=rnd) + temp_perc_tensor = torch.zeros_like(filtered_lora[key]) + + for segment_number in use_segment_numbers: + if use_length == test_length: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + + temp_perc_tensor[segment_number - 1:segment_number,:] = random_noise_latent[segment_number - 1:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + + temp_perc_tensor[:1,:] = random_noise_latent[:1,:] * percentage + else: + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + + temp_perc_tensor[segment_number:segment_number + 1,:] = random_noise_latent[segment_number:segment_number + 1,:] * percentage + else: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + + temp_perc_tensor[segment_number - test_length:segment_number,:] = random_noise_latent[segment_number - test_length:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + + temp_perc_tensor[:test_length,:] = random_noise_latent[:test_length,:] * percentage + else: + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + + temp_perc_tensor[(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] = random_noise_latent[(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] * percentage + + temp_tensor = torch.add(temp_tensor, temp_perc_tensor) + filtered_lora[key] = temp_tensor.to(torch.bfloat16) + elif test_mode == "use_source": + if not source_filtered_lora is None: + if not key in source_filtered_lora: + continue + + for segment_number in use_segment_numbers: + temp_perc_tensor = torch.zeros_like(filtered_lora[key]) + + if use_length == test_length: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + temp_perc_tensor[segment_number - 1:segment_number,:] = source_filtered_lora[key][segment_number - 1:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + 1:,:] = filtered_lora[key][segment_number + 1:,:] + temp_perc_tensor[:1,:] = source_filtered_lora[key][:1,:] * percentage + else: + temp_tensor[:segment_number,:] = filtered_lora[key][:segment_number,:] + temp_perc_tensor[segment_number:segment_number + 1,:] = source_filtered_lora[key][segment_number:segment_number + 1,:] * percentage + else: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + temp_perc_tensor[segment_number - test_length:segment_number,:] = source_filtered_lora[key][segment_number - test_length:segment_number,:] * percentage + elif segment_number == 0: + temp_tensor[segment_number + test_length:,:] = filtered_lora[key][segment_number + test_length:,:] + temp_perc_tensor[:test_length,:] = source_filtered_lora[key][:test_length,:] * percentage + else: + temp_tensor[:segment_number * test_length,:] = filtered_lora[key][:segment_number * test_length,:] + temp_perc_tensor[(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] = source_filtered_lora[key][(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] * percentage + + temp_tensor = torch.add(temp_tensor, temp_perc_tensor) + filtered_lora[key] = temp_tensor.to(torch.bfloat16) + elif test_mode == "add_source": + if not source_filtered_lora is None: + if not key in source_filtered_lora: + continue + + for segment_number in use_segment_numbers: + temp_perc_tensor = torch.zeros_like(filtered_lora[key]) + + if use_length == test_length: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_perc_tensor[segment_number - 1:segment_number,:] = source_filtered_lora[key][segment_number - 1:segment_number,:] * percentage + elif segment_number == 0: + temp_perc_tensor[:1,:] = source_filtered_lora[key][:1,:] * percentage + else: + temp_perc_tensor[segment_number:segment_number + 1,:] = source_filtered_lora[key][segment_number:segment_number + 1,:] * percentage + else: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_perc_tensor[segment_number - test_length:segment_number,:] = source_filtered_lora[key][segment_number - test_length:segment_number,:] * percentage + elif segment_number == 0: + temp_perc_tensor[:test_length,:] = source_filtered_lora[key][:test_length,:] * percentage + else: + temp_perc_tensor[(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] = source_filtered_lora[key][(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] * percentage + + temp_tensor = torch.add(filtered_lora[key], temp_perc_tensor) + filtered_lora[key] = temp_tensor.to(torch.bfloat16) + elif test_mode == "subtract_source": + if not source_filtered_lora is None: + if not key in source_filtered_lora: + continue + + for segment_number in use_segment_numbers: + temp_perc_tensor = torch.zeros_like(filtered_lora[key]) + + if use_length == test_length: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_perc_tensor[segment_number - 1:segment_number,:] = source_filtered_lora[key][segment_number - 1:segment_number,:] * percentage + elif segment_number == 0: + temp_perc_tensor[:1,:] = source_filtered_lora[key][:1,:] * percentage + else: + temp_perc_tensor[segment_number:segment_number + 1,:] = source_filtered_lora[key][segment_number:segment_number + 1,:] * percentage + else: + if (not segment_number == 0) and (not segment_number == max_dimension): + temp_perc_tensor[segment_number - test_length:segment_number,:] = source_filtered_lora[key][segment_number - test_length:segment_number,:] * percentage + elif segment_number == 0: + temp_perc_tensor[:test_length,:] = source_filtered_lora[key][:test_length,:] * percentage + else: + temp_perc_tensor[(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] = source_filtered_lora[key][(segment_number * test_length) - test_length:(segment_number * test_length) + test_length,:] * percentage + + temp_tensor = torch.sub(filtered_lora[key], temp_perc_tensor) + filtered_lora[key] = temp_tensor.to(torch.bfloat16) + else: + print("**** No Source LORA Provided. Unable to make mosification. ****") + + if verbose_messaging: + print("Tester Metadata: {}".format(metadata)) + + if save_new_lora: # and (len(swap_blocks) > 0): + os.makedirs(os.path.dirname(save_path), exist_ok=True) + + for key in filtered_lora: + filtered_lora[key] = filtered_lora[key].to(dtype=torch.bfloat16) + + print("Saving Model To: {}...".format(save_path)) + save_torch_file(filtered_lora, save_path, metadata=metadata) + print("Saving Model To: {}...Done.".format(save_path)) + + if (not return_state_only) and (not model is None): + new_model, _ = load_lora_for_models(model, None, filtered_lora, mainstrength, 0) + if new_model is not None: + return (new_model, metadata, None, None, ) + + return (model, metadata, filtered_lora, metadata, ) + + + @classmethod + def IS_CHANGED(s, lora_model, source_lora, mainstrength, save_path, save_new_lora=False, return_state_only=False, segment_numbers="2,23", test_mode="perc_all", max_dimension=128, model=None, state_dictionary=None, metadata_dict=None, block_number=0, percentage=1.000, layer_type="all", discard_single_blocks=True, seed=0, verbose_messaging=False): + return f"{lora_model}_{mainstrength}" + +class WarpedLoadHunyuanLoraWeightsByPrefix: + def __init__(self): + self.index = 0 + self.lora_dir = "" + self.last_prefix = "" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "lora_dir": (get_lora_directories(), ), + "lora_prefix": ("STRING", {"default": '', "multiline": False}), + }, + } + + RETURN_TYPES = ("WARPEDSTATEDICT", "WARPEDMETADICT", ) + RETURN_NAMES = ("state_dict", "metadata_dict", ) + FUNCTION = "load_batch_loras" + + CATEGORY = "Warped/Hunyuan/Lora" + + def load_batch_loras(self, lora_dir, lora_prefix): + self.lora_dir = lora_dir + path = lora_dir + print(path) + + if not os.path.exists(path): + return ("", "", ) + + if not (self.last_prefix == lora_prefix): + self.last_prefix = lora_prefix + self.index = 0 + + retry = False + index = 0 + + try: + filename, full_filename = self.do_the_load(path, lora_prefix, index) + print("WarpedLoadHunyuanLoraWeightsByPrefix: Filename: {} | Full File Path: {}".format(filename, full_filename)) + + lora, metadata = warped_load_lora_weights(filename, return_metadata=True) + + return (lora, metadata,) + except: + self.index = 0 + retry = True + + if retry: + retry = False + filename, full_filename = self.do_the_load(path, lora_prefix, index) + print("WarpedLoadHunyuanLoraWeightsByPrefix: Retrying: Filename: {} | Full File Path: {}".format(filename, full_filename)) + + lora, metadata = warped_load_lora_weights(filename, return_metadata=True) + + return (lora, metadata,) + + return (None, None, ) + + def do_the_load(self, path, prefix, index): + prefix = prefix.strip(' ') + + if (len(prefix) == 1) and (prefix == '*'): + fl = self.BatchLoraLoader(path, '*', index) + else: + prefix = prefix.strip('*') + fl = self.BatchLoraLoader(path, "{}*".format(prefix), index) + + new_paths = fl.lora_paths + + filename = fl.lora_paths[self.index] + + # filename = os.path.join(self.sub_folder, filename) + full_filename = os.path.join(path, filename) + base_dir, lora_name = get_lora_path_parts(full_filename) + + self.index += 1 + + if self.index >= len(fl.lora_paths): + self.index = 0 + + return lora_name, full_filename + + + class BatchLoraLoader: + def __init__(self, directory_path, pattern, index): + self.lora_paths = [] + self.load_loras(directory_path, pattern) + self.lora_paths.sort() + + self.index = index + + def load_loras(self, directory_path, pattern): + for file_name in glob.glob(os.path.join(directory_path, pattern), recursive=True): + temp_strings = file_name.split('\\') + file_name = temp_strings[len(temp_strings) - 1] + + if file_name.lower().endswith("safetensors"): + self.lora_paths.append(file_name) + + def get_lora_by_id(self, lora_id): + if lora_id < 0 or lora_id >= len(self.lora_paths): + cstr(f"WarpedLoadLorasBatchByPrefix: Invalid lora index `{lora_id}`").error.print() + return + + return self.lora_paths[lora_id] + + def get_next_lora(self): + if self.index >= len(self.lora_paths): + self.index = 0 + + lora_path = self.lora_paths[self.index] + self.index += 1 + + if self.index == len(self.lora_paths): + self.index = 0 + + cstr(f'{cstr.color.YELLOW}{cstr.color.END} Index: {self.index}').msg.print() + + return lora_path + + def get_current_lora(self): + if self.index >= len(self.lora_paths): + self.index = 0 + lora_path = self.lora_paths[self.index] + + return lora_path + + @classmethod + def IS_CHANGED(cls, **kwargs): + return float("NaN") + +class WarpedAnyType(str): + """A special type that always compares equal to any value.""" + + def __ne__(self, __value: object) -> bool: + return False + +WARPEDANYTYPE = WarpedAnyType("*") + +class WarpedContinueWorkflowManual: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "any": (WARPEDANYTYPE,), + "continue_workflow": ("BOOLEAN", {"default": True}) + }, + "hidden": { + "id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (WARPEDANYTYPE, ) + RETURN_NAMES = ( "any", ) + FUNCTION = "execute" + CATEGORY = "Warped/General/Utils" + OUTPUT_NODE = True + + def execute(self, any=None, continue_workflow=True, id=None): + if not continue_workflow: + # print(f"Cancelled workflow for {id}") + raise comfy.model_management.InterruptProcessingException() + + return {"result": (any,)} + +class WarpedContinueWorkflowAuto: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "any": (WARPEDANYTYPE,), + "continue_workflow": ("BOOL", {"default": True, "forceInput": True}) + }, + "hidden": { + "id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (WARPEDANYTYPE, ) + RETURN_NAMES = ( "any", ) + FUNCTION = "execute" + CATEGORY = "Warped/General/Utils" + OUTPUT_NODE = True + + def execute(self, any=None, continue_workflow=True, id=None): + if not continue_workflow: + # print(f"Cancelled workflow for {id}") + raise comfy.model_management.InterruptProcessingException() + + return {"result": (any,)} diff --git a/pyproject.toml b/pyproject.toml index 76702be..5ded10b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "ComfyUI-WarpedToolset" description = "A toolset for Hunyuan Video (mainly), with some additional nodes applicable to Framepack Video and WAN Video." -version = "1.7.3" +version = "1.7.4" license = { file = "LICENSE" } [project.urls] @@ -14,3 +14,4 @@ Icon = "" Models = [] +