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Support Multi Image-Caption dataset in lora training node (#8819)
* initial impl of multi img/text dataset * Update nodes_train.py * Support Kohya-ss structure
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Kohaku-Blueleaf
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@ -75,7 +75,7 @@ class BiasDiff(torch.nn.Module):
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return self.passive_memory_usage()
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def load_and_process_images(image_files, input_dir, resize_method="None"):
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def load_and_process_images(image_files, input_dir, resize_method="None", w=None, h=None):
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"""Utility function to load and process a list of images.
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Args:
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@ -90,7 +90,6 @@ def load_and_process_images(image_files, input_dir, resize_method="None"):
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raise ValueError("No valid images found in input")
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output_images = []
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w, h = None, None
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for file in image_files:
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image_path = os.path.join(input_dir, file)
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@ -206,6 +205,103 @@ class LoadImageSetFromFolderNode:
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return (output_tensor,)
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class LoadImageTextSetFromFolderNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"folder": (folder_paths.get_input_subfolders(), {"tooltip": "The folder to load images from."}),
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"clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}),
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},
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"optional": {
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"resize_method": (
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["None", "Stretch", "Crop", "Pad"],
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{"default": "None"},
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),
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"width": (
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IO.INT,
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{
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"default": -1,
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"min": -1,
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"max": 10000,
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"step": 1,
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"tooltip": "The width to resize the images to. -1 means use the original width.",
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},
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),
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"height": (
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IO.INT,
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{
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"default": -1,
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"min": -1,
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"max": 10000,
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"step": 1,
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"tooltip": "The height to resize the images to. -1 means use the original height.",
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},
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)
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},
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}
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RETURN_TYPES = ("IMAGE", IO.CONDITIONING,)
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FUNCTION = "load_images"
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CATEGORY = "loaders"
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EXPERIMENTAL = True
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DESCRIPTION = "Loads a batch of images and caption from a directory for training."
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def load_images(self, folder, clip, resize_method, width=None, height=None):
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if clip is None:
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raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
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logging.info(f"Loading images from folder: {folder}")
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sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder)
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valid_extensions = [".png", ".jpg", ".jpeg", ".webp"]
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image_files = []
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for item in os.listdir(sub_input_dir):
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path = os.path.join(sub_input_dir, item)
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if any(item.lower().endswith(ext) for ext in valid_extensions):
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image_files.append(path)
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elif os.path.isdir(path):
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# Support kohya-ss/sd-scripts folder structure
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repeat = 1
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if item.split("_")[0].isdigit():
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repeat = int(item.split("_")[0])
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image_files.extend([
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os.path.join(path, f) for f in os.listdir(path) if any(f.lower().endswith(ext) for ext in valid_extensions)
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] * repeat)
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caption_file_path = [
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f.replace(os.path.splitext(f)[1], ".txt")
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for f in image_files
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]
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captions = []
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for caption_file in caption_file_path:
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caption_path = os.path.join(sub_input_dir, caption_file)
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if os.path.exists(caption_path):
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with open(caption_path, "r", encoding="utf-8") as f:
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caption = f.read().strip()
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captions.append(caption)
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else:
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captions.append("")
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width = width if width != -1 else None
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height = height if height != -1 else None
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output_tensor = load_and_process_images(image_files, sub_input_dir, resize_method, width, height)
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logging.info(f"Loaded {len(output_tensor)} images from {sub_input_dir}.")
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logging.info(f"Encoding captions from {sub_input_dir}.")
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conditions = []
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empty_cond = clip.encode_from_tokens_scheduled(clip.tokenize(""))
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for text in captions:
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if text == "":
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conditions.append(empty_cond)
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tokens = clip.tokenize(text)
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conditions.extend(clip.encode_from_tokens_scheduled(tokens))
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logging.info(f"Encoded {len(conditions)} captions from {sub_input_dir}.")
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return (output_tensor, conditions)
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def draw_loss_graph(loss_map, steps):
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width, height = 500, 300
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img = Image.new("RGB", (width, height), "white")
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@ -381,6 +477,13 @@ class TrainLoraNode:
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latents = latents["samples"].to(dtype)
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num_images = latents.shape[0]
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logging.info(f"Total Images: {num_images}, Total Captions: {len(positive)}")
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if len(positive) == 1 and num_images > 1:
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positive = positive * num_images
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elif len(positive) != num_images:
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raise ValueError(
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f"Number of positive conditions ({len(positive)}) does not match number of images ({num_images})."
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)
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with torch.inference_mode(False):
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lora_sd = {}
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@ -474,6 +577,7 @@ class TrainLoraNode:
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# setup models
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for m in find_all_highest_child_module_with_forward(mp.model.diffusion_model):
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patch(m)
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mp.model.requires_grad_(False)
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comfy.model_management.load_models_gpu([mp], memory_required=1e20, force_full_load=True)
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# Setup sampler and guider like in test script
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@ -486,7 +590,6 @@ class TrainLoraNode:
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)
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guider = comfy_extras.nodes_custom_sampler.Guider_Basic(mp)
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guider.set_conds(positive) # Set conditioning from input
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ss = comfy_extras.nodes_custom_sampler.SamplerCustomAdvanced()
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# yoland: this currently resize to the first image in the dataset
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@ -495,21 +598,21 @@ class TrainLoraNode:
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try:
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for step in (pbar:=tqdm.trange(steps, desc="Training LoRA", smoothing=0.01, disable=not comfy.utils.PROGRESS_BAR_ENABLED)):
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# Generate random sigma
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sigma = mp.model.model_sampling.percent_to_sigma(
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sigmas = [mp.model.model_sampling.percent_to_sigma(
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torch.rand((1,)).item()
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)
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sigma = torch.tensor([sigma])
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) for _ in range(min(batch_size, num_images))]
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sigmas = torch.tensor(sigmas)
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noise = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(step * 1000 + seed)
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indices = torch.randperm(num_images)[:batch_size]
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ss.sample(
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noise, guider, train_sampler, sigma, {"samples": latents[indices].clone()}
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)
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batch_latent = latents[indices].clone()
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guider.set_conds([positive[i] for i in indices]) # Set conditioning from input
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guider.sample(noise.generate_noise({"samples": batch_latent}), batch_latent, train_sampler, sigmas, seed=noise.seed)
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finally:
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for m in mp.model.modules():
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unpatch(m)
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del ss, train_sampler, optimizer
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del train_sampler, optimizer
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torch.cuda.empty_cache()
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for adapter in all_weight_adapters:
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@ -697,6 +800,7 @@ NODE_CLASS_MAPPINGS = {
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"SaveLoRANode": SaveLoRA,
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"LoraModelLoader": LoraModelLoader,
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"LoadImageSetFromFolderNode": LoadImageSetFromFolderNode,
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"LoadImageTextSetFromFolderNode": LoadImageTextSetFromFolderNode,
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"LossGraphNode": LossGraphNode,
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}
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@ -705,5 +809,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"SaveLoRANode": "Save LoRA Weights",
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"LoraModelLoader": "Load LoRA Model",
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"LoadImageSetFromFolderNode": "Load Image Dataset from Folder",
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"LoadImageTextSetFromFolderNode": "Load Image and Text Dataset from Folder",
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"LossGraphNode": "Plot Loss Graph",
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}
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