Merge d58ad2dd19 into 98bdca4cb2

comfy/comfy_types/node_typing.py

@@ -37,6 +37,8 @@ class IO(StrEnum):
     CONTROL_NET = "CONTROL_NET"
     VAE = "VAE"
     MODEL = "MODEL"
+    LORA_MODEL = "LORA_MODEL"
+    LOSS_MAP = "LOSS_MAP"
     CLIP_VISION = "CLIP_VISION"
     CLIP_VISION_OUTPUT = "CLIP_VISION_OUTPUT"
     STYLE_MODEL = "STYLE_MODEL"

comfy/ldm/cosmos/blocks.py

@@ -797,12 +797,15 @@ class GeneralDITTransformerBlock(nn.Module):
         adaln_lora_B_3D: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         for block in self.blocks:
-            x = block(
+            if self.training:
-                x,
+                x = torch.utils.checkpoint.checkpoint(block, x, emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D, adaln_lora_B_3D, use_reentrant=False)
-                emb_B_D,
+            else:
-                crossattn_emb,
+                x = block(
-                crossattn_mask,
+                    x,
-                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+                    emb_B_D,
-                adaln_lora_B_3D=adaln_lora_B_3D,
+                    crossattn_emb,
-            )
+                    crossattn_mask,
+                    rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+                    adaln_lora_B_3D=adaln_lora_B_3D,
+                )
         return x

comfy/ldm/modules/attention.py

@@ -750,7 +750,7 @@ class BasicTransformerBlock(nn.Module):
             for p in patch:
                 n = p(n, extra_options)
 
-        x += n
+        x = n + x
         if "middle_patch" in transformer_patches:
             patch = transformer_patches["middle_patch"]
             for p in patch:
@@ -790,12 +790,12 @@ class BasicTransformerBlock(nn.Module):
             for p in patch:
                 n = p(n, extra_options)
 
-        x += n
+        x = n + x
         if self.is_res:
             x_skip = x
         x = self.ff(self.norm3(x))
         if self.is_res:
-            x += x_skip
+            x = x_skip + x
 
         return x
 

comfy/model_patcher.py

@@ -17,23 +17,26 @@
 """
 
 from __future__ import annotations
-from typing import Optional, Callable
+
-import torch
+import collections
 import copy
 import inspect
 import logging
-import uuid
-import collections
 import math
+import uuid
+from typing import Callable, Optional
+
+import torch
 
-import comfy.utils
 import comfy.float
-import comfy.model_management
-import comfy.lora
 import comfy.hooks
+import comfy.lora
+import comfy.model_management
 import comfy.patcher_extension
-from comfy.patcher_extension import CallbacksMP, WrappersMP, PatcherInjection
+import comfy.utils
 from comfy.comfy_types import UnetWrapperFunction
+from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP
+
 
 def string_to_seed(data):
     crc = 0xFFFFFFFF

comfy/sd.py

@@ -988,7 +988,28 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffusers or regular format
+def load_diffusion_model_state_dict(sd, model_options={}):
+    """
+    Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats.
+
+    Args:
+        sd (dict): State dictionary containing model weights and configuration
+        model_options (dict, optional): Additional options for model loading. Supports:
+            - dtype: Override model data type
+            - custom_operations: Custom model operations
+            - fp8_optimizations: Enable FP8 optimizations
+
+    Returns:
+        ModelPatcher: A wrapped model instance that handles device management and weight loading.
+        Returns None if the model configuration cannot be detected.
+
+    The function:
+    1. Detects and handles different model formats (regular, diffusers, mmdit)
+    2. Configures model dtype based on parameters and device capabilities
+    3. Handles weight conversion and device placement
+    4. Manages model optimization settings
+    5. Loads weights and returns a device-managed model instance
+    """
     dtype = model_options.get("dtype", None)
 
     #Allow loading unets from checkpoint files

comfy_extras/nodes_train.py

@@ -0,0 +1,646 @@
+import datetime
+import json
+import logging
+import math
+import os
+
+import numpy as np
+import safetensors
+import torch
+from PIL import Image, ImageDraw, ImageFont
+from PIL.PngImagePlugin import PngInfo
+
+import comfy.samplers
+import comfy.utils
+import comfy_extras.nodes_custom_sampler
+import folder_paths
+import node_helpers
+from comfy.cli_args import args
+from comfy.comfy_types.node_typing import IO
+
+
+class TrainSampler(comfy.samplers.Sampler):
+
+    def __init__(self, loss_fn, optimizer, loss_callback=None):
+        self.loss_fn = loss_fn
+        self.optimizer = optimizer
+        self.loss_callback = loss_callback
+
+    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
+        self.optimizer.zero_grad()
+        noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas, noise, latent_image, False)
+        latent = model_wrap.inner_model.model_sampling.noise_scaling(
+            torch.zeros_like(sigmas),
+            torch.zeros_like(noise, requires_grad=True),
+            latent_image,
+            False
+        )
+
+        # Ensure model is in training mode and computing gradients
+        denoised = model_wrap(noise, sigmas, **extra_args)
+        try:
+            loss = self.loss_fn(denoised, latent.clone())
+        except RuntimeError as e:
+            if "does not require grad and does not have a grad_fn" in str(e):
+                logging.info("WARNING: This is likely due to the model is loaded in inference mode.")
+        loss.backward()
+        logging.info(f"Current Training Loss: {loss.item():.6f}")
+        if self.loss_callback:
+            self.loss_callback(loss.item())
+
+        self.optimizer.step()
+        # torch.cuda.memory._dump_snapshot("trainn.pickle")
+        # torch.cuda.memory._record_memory_history(enabled=None)
+        return torch.zeros_like(latent_image)
+
+
+class BiasDiff(torch.nn.Module):
+    def __init__(self, bias):
+        super().__init__()
+        self.bias = bias
+
+    def __call__(self, b):
+        return b + self.bias
+
+    def passive_memory_usage(self):
+        return self.bias.nelement() * self.bias.element_size()
+
+    def move_to(self, device):
+        self.to(device=device)
+        return self.passive_memory_usage()
+
+
+class LoraDiff(torch.nn.Module):
+    def __init__(self, lora_down, lora_up):
+        super().__init__()
+        self.lora_down = lora_down
+        self.lora_up = lora_up
+
+    def __call__(self, w):
+        return w + (self.lora_up @ self.lora_down).reshape(w.shape)
+
+    def passive_memory_usage(self):
+        return self.lora_down.nelement() * self.lora_down.element_size() + self.lora_up.nelement() * self.lora_up.element_size()
+
+    def move_to(self, device):
+        self.to(device=device)
+        return self.passive_memory_usage()
+
+
+def load_and_process_images(image_files, input_dir, resize_method="None"):
+    """Utility function to load and process a list of images.
+
+    Args:
+        image_files: List of image filenames
+        input_dir: Base directory containing the images
+        resize_method: How to handle images of different sizes ("None", "Stretch", "Crop", "Pad")
+
+    Returns:
+        torch.Tensor: Batch of processed images
+    """
+    if not image_files:
+        raise ValueError("No valid images found in input")
+
+    output_images = []
+    w, h = None, None
+
+    for file in image_files:
+        image_path = os.path.join(input_dir, file)
+        img = node_helpers.pillow(Image.open, image_path)
+
+        if img.mode == "I":
+            img = img.point(lambda i: i * (1 / 255))
+        img = img.convert("RGB")
+
+        if w is None and h is None:
+            w, h = img.size[0], img.size[1]
+
+        # Resize image to first image
+        if img.size[0] != w or img.size[1] != h:
+            if resize_method == "Stretch":
+                img = img.resize((w, h), Image.Resampling.LANCZOS)
+            elif resize_method == "Crop":
+                img = img.crop((0, 0, w, h))
+            elif resize_method == "Pad":
+                img = img.resize((w, h), Image.Resampling.LANCZOS)
+            elif resize_method == "None":
+                raise ValueError(
+                    "Your input image size does not match the first image in the dataset. Either select a valid resize method or use the same size for all images."
+                )
+
+        img_array = np.array(img).astype(np.float32) / 255.0
+        img_tensor = torch.from_numpy(img_array)[None,]
+        output_images.append(img_tensor)
+
+    return torch.cat(output_images, dim=0)
+
+
+class LoadImageSetNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": (
+                    [
+                        f
+                        for f in os.listdir(folder_paths.get_input_directory())
+                        if f.endswith((".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".jpe", ".apng", ".tif", ".tiff"))
+                    ],
+                    {"image_upload": True, "allow_batch": True},
+                )
+            },
+            "optional": {
+                "resize_method": (
+                    ["None", "Stretch", "Crop", "Pad"],
+                    {"default": "None"},
+                ),
+            },
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_images"
+    CATEGORY = "loaders"
+    EXPERIMENTAL = True
+    DESCRIPTION = "Loads a batch of images from a directory for training."
+
+    @classmethod
+    def VALIDATE_INPUTS(s, images, resize_method):
+        filenames = images[0] if isinstance(images[0], list) else images
+
+        for image in filenames:
+            if not folder_paths.exists_annotated_filepath(image):
+                return "Invalid image file: {}".format(image)
+        return True
+
+    def load_images(self, input_files, resize_method):
+        input_dir = folder_paths.get_input_directory()
+        valid_extensions = [".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".jpe", ".apng", ".tif", ".tiff"]
+        image_files = [
+            f
+            for f in input_files
+            if any(f.lower().endswith(ext) for ext in valid_extensions)
+        ]
+        output_tensor = load_and_process_images(image_files, input_dir, resize_method)
+        return (output_tensor,)
+
+
+class LoadImageSetFromFolderNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "folder": (folder_paths.get_input_subfolders(), {"tooltip": "The folder to load images from."})
+            },
+            "optional": {
+                "resize_method": (
+                    ["None", "Stretch", "Crop", "Pad"],
+                    {"default": "None"},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_images"
+    CATEGORY = "loaders"
+    EXPERIMENTAL = True
+    DESCRIPTION = "Loads a batch of images from a directory for training."
+
+    def load_images(self, folder, resize_method):
+        sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder)
+        valid_extensions = [".png", ".jpg", ".jpeg", ".webp"]
+        image_files = [
+            f
+            for f in os.listdir(sub_input_dir)
+            if any(f.lower().endswith(ext) for ext in valid_extensions)
+        ]
+        output_tensor = load_and_process_images(image_files, sub_input_dir, resize_method)
+        return (output_tensor,)
+
+
+def draw_loss_graph(loss_map, steps):
+    width, height = 500, 300
+    img = Image.new("RGB", (width, height), "white")
+    draw = ImageDraw.Draw(img)
+
+    min_loss, max_loss = min(loss_map.values()), max(loss_map.values())
+    scaled_loss = [(l - min_loss) / (max_loss - min_loss) for l in loss_map.values()]
+
+    prev_point = (0, height - int(scaled_loss[0] * height))
+    for i, l in enumerate(scaled_loss[1:], start=1):
+        x = int(i / (steps - 1) * width)
+        y = height - int(l * height)
+        draw.line([prev_point, (x, y)], fill="blue", width=2)
+        prev_point = (x, y)
+
+    return img
+
+
+class TrainLoraNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": (IO.MODEL, {"tooltip": "The model to train the LoRA on."}),
+                "vae": (
+                    IO.VAE,
+                    {
+                        "tooltip": "The VAE model to use for encoding images for training."
+                    },
+                ),
+                "positive": (
+                    IO.CONDITIONING,
+                    {"tooltip": "The positive conditioning to use for training."},
+                ),
+                "image": (
+                    IO.IMAGE,
+                    {"tooltip": "The image or image batch to train the LoRA on."},
+                ),
+                "batch_size": (
+                    IO.INT,
+                    {
+                        "default": 1,
+                        "min": 1,
+                        "max": 10000,
+                        "step": 1,
+                        "tooltip": "The batch size to use for training.",
+                    },
+                ),
+                "steps": (
+                    IO.INT,
+                    {
+                        "default": 50,
+                        "min": 1,
+                        "max": 1000,
+                        "tooltip": "The number of steps to train the LoRA for.",
+                    },
+                ),
+                "learning_rate": (
+                    IO.FLOAT,
+                    {
+                        "default": 0.0003,
+                        "min": 0.0000001,
+                        "max": 1.0,
+                        "step": 0.00001,
+                        "tooltip": "The learning rate to use for training.",
+                    },
+                ),
+                "rank": (
+                    IO.INT,
+                    {
+                        "default": 8,
+                        "min": 1,
+                        "max": 128,
+                        "tooltip": "The rank of the LoRA layers.",
+                    },
+                ),
+                "optimizer": (
+                    ["Adam", "AdamW", "SGD", "RMSprop"],
+                    {
+                        "default": "Adam",
+                        "tooltip": "The optimizer to use for training.",
+                    },
+                ),
+                "loss_function": (
+                    ["MSE", "L1", "Huber", "SmoothL1"],
+                    {
+                        "default": "MSE",
+                        "tooltip": "The loss function to use for training.",
+                    },
+                ),
+                "seed": (
+                    IO.INT,
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 0xFFFFFFFFFFFFFFFF,
+                        "tooltip": "The seed to use for training (used in generator for LoRA weight initialization and noise sampling)",
+                    },
+                ),
+                "training_dtype": (
+                    ["bf16", "fp32"],
+                    {"default": "bf16", "tooltip": "The dtype to use for training."},
+                ),
+                "existing_lora": (
+                    folder_paths.get_filename_list("loras") + ["[None]"],
+                    {
+                        "default": "[None]",
+                        "tooltip": "The existing LoRA to append to. Set to None for new LoRA.",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = (IO.MODEL, IO.LORA_MODEL, IO.LOSS_MAP, IO.INT)
+    RETURN_NAMES = ("model_with_lora", "lora", "loss", "steps")
+    FUNCTION = "train"
+    CATEGORY = "training"
+    EXPERIMENTAL = True
+
+    def train(
+        self,
+        model,
+        vae,
+        positive,
+        image,
+        batch_size,
+        steps,
+        learning_rate,
+        rank,
+        optimizer,
+        loss_function,
+        seed,
+        training_dtype,
+        existing_lora,
+    ):
+        num_images = image.shape[0]
+        indices = torch.randperm(num_images)[:batch_size]
+        batch_tensor = image[indices]
+
+        # Ensure we're not in inference mode when encoding
+        encoded = vae.encode(batch_tensor)
+        mp = model.clone()
+        dtype = node_helpers.string_to_torch_dtype(training_dtype)
+        mp.set_model_compute_dtype(dtype)
+
+        with torch.inference_mode(False):
+            lora_sd = {}
+            generator = torch.Generator()
+            generator.manual_seed(seed)
+
+            # Load existing LoRA weights if provided
+            existing_weights = {}
+            existing_steps = 0
+            if existing_lora != "[None]":
+                lora_path = folder_paths.get_full_path_or_raise("loras", existing_lora)
+                # Extract steps from filename like "trained_lora_10_steps_20250225_203716"
+                existing_steps = int(existing_lora.split("_steps_")[0].split("_")[-1])
+                if lora_path:
+                    existing_weights = comfy.utils.load_torch_file(lora_path)
+
+            for n, m in mp.model.named_modules():
+                if hasattr(m, "weight_function"):
+                    if m.weight is not None:
+                        key = "{}.weight".format(n)
+                        shape = m.weight.shape
+                        if len(shape) >= 2:
+                            in_dim = math.prod(shape[1:])
+                            out_dim = shape[0]
+
+                            # Check if we have existing weights for this layer
+                            lora_up_key = "{}.lora_up.weight".format(n)
+                            lora_down_key = "{}.lora_down.weight".format(n)
+
+                            if existing_lora != "[None]" and (
+                                    lora_up_key in existing_weights
+                                    and lora_down_key in existing_weights
+                            ):
+                                # Initialize with existing weights
+                                lora_up = torch.nn.Parameter(
+                                        existing_weights[lora_up_key].to(dtype=dtype),
+                                        requires_grad=True,
+                                    )
+                                lora_down = torch.nn.Parameter(
+                                        existing_weights[lora_down_key].to(dtype=dtype),
+                                        requires_grad=True,
+                                    )
+                            else:
+                                if existing_lora != "[None]":
+                                    logging.info(f"Warning: No existing weights found for {lora_up_key} or {lora_down_key}")
+                                # Initialize new weights
+                                lora_down = torch.nn.Parameter(
+                                    torch.zeros(
+                                        (
+                                            rank,
+                                            in_dim,
+                                        ),
+                                        dtype=dtype,
+                                    ),
+                                    requires_grad=True,
+                                )
+                                lora_up = torch.nn.Parameter(
+                                    torch.zeros((out_dim, rank), dtype=dtype),
+                                    requires_grad=True,
+                                )
+                                torch.nn.init.zeros_(lora_up)
+                                torch.nn.init.kaiming_uniform_(
+                                    lora_down, a=math.sqrt(5), generator=generator
+                                )
+
+                            lora_sd[lora_up_key] = lora_up
+                            lora_sd[lora_down_key] = lora_down
+                            mp.add_weight_wrapper(key, LoraDiff(lora_down, lora_up))
+                        else:
+                            diff = torch.nn.Parameter(
+                                torch.zeros(
+                                    m.weight.shape, dtype=dtype, requires_grad=True
+                                )
+                            )
+                            mp.add_weight_wrapper(key, BiasDiff(diff))
+                            lora_sd["{}.diff".format(n)] = diff
+                    if hasattr(m, "bias") and m.bias is not None:
+                        key = "{}.bias".format(n)
+                        bias = torch.nn.Parameter(
+                            torch.zeros(m.bias.shape, dtype=dtype, requires_grad=True)
+                        )
+                        lora_sd["{}.diff_b".format(n)] = bias
+                        mp.add_weight_wrapper(key, BiasDiff(bias))
+
+            if optimizer == "Adam":
+                optimizer = torch.optim.Adam(lora_sd.values(), lr=learning_rate)
+            elif optimizer == "AdamW":
+                optimizer = torch.optim.AdamW(lora_sd.values(), lr=learning_rate)
+            elif optimizer == "SGD":
+                optimizer = torch.optim.SGD(lora_sd.values(), lr=learning_rate)
+            elif optimizer == "RMSprop":
+                optimizer = torch.optim.RMSprop(lora_sd.values(), lr=learning_rate)
+
+            # Setup loss function based on selection
+            if loss_function == "MSE":
+                criterion = torch.nn.MSELoss()
+            elif loss_function == "L1":
+                criterion = torch.nn.L1Loss()
+            elif loss_function == "Huber":
+                criterion = torch.nn.HuberLoss()
+            elif loss_function == "SmoothL1":
+                criterion = torch.nn.SmoothL1Loss()
+
+            # Setup sampler and guider like in test script
+            loss_map = {"loss": []}
+            loss_callback = lambda loss: loss_map["loss"].append(loss)
+            train_sampler = TrainSampler(
+                criterion, optimizer, loss_callback=loss_callback
+            )
+            guider = comfy_extras.nodes_custom_sampler.Guider_Basic(mp)
+            guider.set_conds(positive)  # Set conditioning from input
+            ss = comfy_extras.nodes_custom_sampler.SamplerCustomAdvanced()
+
+            # yoland: this currently resize to the first image in the dataset
+
+            # Training loop
+            for step in range(steps):
+                # Generate random sigma
+                sigma = mp.model.model_sampling.percent_to_sigma(
+                    torch.rand((1,)).item()
+                )
+                sigma = torch.tensor([sigma])
+
+                noise = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(step * 1000 + seed)
+
+                ss.sample(
+                    noise, guider, train_sampler, sigma, {"samples": encoded.clone()}
+                )
+
+            return (mp, lora_sd, loss_map, steps + existing_steps)
+
+
+class SaveLoRA:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "lora": (
+                    IO.LORA_MODEL,
+                    {
+                        "tooltip": "The LoRA model to save. Do not use the model with LoRA layers."
+                    },
+                ),
+                "prefix": (
+                    "STRING",
+                    {
+                        "default": "trained_lora",
+                        "tooltip": "The prefix to use for the saved LoRA file.",
+                    },
+                ),
+            },
+            "optional": {
+                "steps": (
+                    IO.INT,
+                    {
+                        "forceInput": True,
+                        "tooltip": "Optional: The number of steps to LoRA has been trained for, used to name the saved file.",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    CATEGORY = "loaders"
+    EXPERIMENTAL = True
+    OUTPUT_NODE = True
+
+    def save(self, lora, prefix, steps=None):
+        date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+        if steps is None:
+            output_file = f"models/loras/{prefix}_{date}_lora.safetensors"
+        else:
+            output_file = f"models/loras/{prefix}_{steps}_steps_{date}_lora.safetensors"
+        safetensors.torch.save_file(lora, output_file)
+        return {}
+
+
+class LossGraphNode:
+    def __init__(self):
+        self.output_dir = folder_paths.get_temp_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "loss": (IO.LOSS_MAP, {"default": {}}),
+                "filename_prefix": (IO.STRING, {"default": "loss_graph"}),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "plot_loss"
+    OUTPUT_NODE = True
+    CATEGORY = "training"
+    EXPERIMENTAL = True
+    DESCRIPTION = "Plots the loss graph and saves it to the output directory."
+
+    def plot_loss(self, loss, filename_prefix, prompt=None, extra_pnginfo=None):
+        loss_values = loss["loss"]
+        width, height = 500, 300
+        margin = 40
+
+        img = Image.new(
+            "RGB", (width + margin, height + margin), "white"
+        )  # Extend canvas
+        draw = ImageDraw.Draw(img)
+
+        min_loss, max_loss = min(loss_values), max(loss_values)
+        scaled_loss = [(l - min_loss) / (max_loss - min_loss) for l in loss_values]
+
+        steps = len(loss_values)
+
+        prev_point = (margin, height - int(scaled_loss[0] * height))
+        for i, l in enumerate(scaled_loss[1:], start=1):
+            x = margin + int(i / steps * width)  # Scale X properly
+            y = height - int(l * height)
+            draw.line([prev_point, (x, y)], fill="blue", width=2)
+            prev_point = (x, y)
+
+        draw.line([(margin, 0), (margin, height)], fill="black", width=2)  # Y-axis
+        draw.line(
+            [(margin, height), (width + margin, height)], fill="black", width=2
+        )  # X-axis
+
+        font = None
+        try:
+            font = ImageFont.truetype("arial.ttf", 12)
+        except IOError:
+            font = ImageFont.load_default()
+
+        # Add axis labels
+        draw.text((5, height // 2), "Loss", font=font, fill="black")
+        draw.text((width // 2, height + 10), "Steps", font=font, fill="black")
+
+        # Add min/max loss values
+        draw.text((margin - 30, 0), f"{max_loss:.2f}", font=font, fill="black")
+        draw.text(
+            (margin - 30, height - 10), f"{min_loss:.2f}", font=font, fill="black"
+        )
+
+        metadata = None
+        if not args.disable_metadata:
+            metadata = PngInfo()
+            if prompt is not None:
+                metadata.add_text("prompt", json.dumps(prompt))
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+        date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+        img.save(
+            os.path.join(self.output_dir, f"{filename_prefix}_{date}.png"),
+            pnginfo=metadata,
+        )
+        return {
+            "ui": {
+                "images": [
+                    {
+                        "filename": f"{filename_prefix}_{date}.png",
+                        "subfolder": "",
+                        "type": "temp",
+                    }
+                ]
+            }
+        }
+
+
+NODE_CLASS_MAPPINGS = {
+    "TrainLoraNode": TrainLoraNode,
+    "SaveLoRANode": SaveLoRA,
+    "LoadImageSetFromFolderNode": LoadImageSetFromFolderNode,
+    "LossGraphNode": LossGraphNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "TrainLoraNode": "Train LoRA",
+    "SaveLoRANode": "Save LoRA Weights",
+    "LoadImageSetFromFolderNode": "Load Image Dataset from Folder",
+    "LossGraphNode": "Plot Loss Graph",
+}

execution.py

@@ -1,23 +1,34 @@
-import sys
 import copy
-import logging
-import threading
 import heapq
+import inspect
+import logging
+import sys
+import threading
 import time
 import traceback
 from enum import Enum
-import inspect
 from typing import List, Literal, NamedTuple, Optional
 
 import torch
-import nodes
 
 import comfy.model_management
-from comfy_execution.graph import get_input_info, ExecutionList, DynamicPrompt, ExecutionBlocker
+import nodes
-from comfy_execution.graph_utils import is_link, GraphBuilder
+from comfy_execution.caching import (
-from comfy_execution.caching import HierarchicalCache, LRUCache, CacheKeySetInputSignature, CacheKeySetID
+    CacheKeySetID,
+    CacheKeySetInputSignature,
+    HierarchicalCache,
+    LRUCache,
+)
+from comfy_execution.graph import (
+    DynamicPrompt,
+    ExecutionBlocker,
+    ExecutionList,
+    get_input_info,
+)
+from comfy_execution.graph_utils import GraphBuilder, is_link
 from comfy_execution.validation import validate_node_input
 
+
 class ExecutionResult(Enum):
     SUCCESS = 0
     FAILURE = 1

folder_paths.py

@@ -272,6 +272,9 @@ def filter_files_extensions(files: Collection[str], extensions: Collection[str])
 
 
 def get_full_path(folder_name: str, filename: str) -> str | None:
+    """
+    Get the full path of a file in a folder, has to be a file
+    """
     global folder_names_and_paths
     folder_name = map_legacy(folder_name)
     if folder_name not in folder_names_and_paths:
@@ -289,6 +292,9 @@ def get_full_path(folder_name: str, filename: str) -> str | None:
 
 
 def get_full_path_or_raise(folder_name: str, filename: str) -> str:
+    """
+    Get the full path of a file in a folder, has to be a file
+    """
     full_path = get_full_path(folder_name, filename)
     if full_path is None:
         raise FileNotFoundError(f"Model in folder '{folder_name}' with filename '{filename}' not found.")
@@ -390,3 +396,26 @@ def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, im
         os.makedirs(full_output_folder, exist_ok=True)
         counter = 1
     return full_output_folder, filename, counter, subfolder, filename_prefix
+
+def get_input_subfolders() -> list[str]:
+    """Returns a list of all subfolder paths in the input directory, recursively.
+
+    Returns:
+        List of folder paths relative to the input directory, excluding the root directory
+    """
+    input_dir = get_input_directory()
+    folders = []
+
+    try:
+        if not os.path.exists(input_dir):
+            return []
+
+        for root, dirs, _ in os.walk(input_dir):
+            rel_path = os.path.relpath(root, input_dir)
+            if rel_path != ".":  # Only include non-root directories
+                # Normalize path separators to forward slashes
+                folders.append(rel_path.replace(os.sep, '/'))
+
+        return sorted(folders)
+    except FileNotFoundError:
+        return []

nodes.py

@@ -2240,6 +2240,7 @@ def init_builtin_extra_nodes():
         "nodes_model_downscale.py",
         "nodes_images.py",
         "nodes_video_model.py",
+        "nodes_train.py",
         "nodes_sag.py",
         "nodes_perpneg.py",
         "nodes_stable3d.py",

tests-unit/folder_paths_test/misc_test.py

@@ -0,0 +1,51 @@
+import pytest
+import os
+import tempfile
+from folder_paths import get_input_subfolders, set_input_directory
+
+@pytest.fixture(scope="module")
+def mock_folder_structure():
+    with tempfile.TemporaryDirectory() as temp_dir:
+        # Create a nested folder structure
+        folders = [
+            "folder1",
+            "folder1/subfolder1",
+            "folder1/subfolder2",
+            "folder2",
+            "folder2/deep",
+            "folder2/deep/nested",
+            "empty_folder"
+        ]
+
+        # Create the folders
+        for folder in folders:
+            os.makedirs(os.path.join(temp_dir, folder))
+
+        # Add some files to test they're not included
+        with open(os.path.join(temp_dir, "root_file.txt"), "w") as f:
+            f.write("test")
+        with open(os.path.join(temp_dir, "folder1", "test.txt"), "w") as f:
+            f.write("test")
+
+        set_input_directory(temp_dir)
+        yield temp_dir
+
+
+def test_gets_all_folders(mock_folder_structure):
+    folders = get_input_subfolders()
+    expected = ["folder1", "folder1/subfolder1", "folder1/subfolder2",
+                "folder2", "folder2/deep", "folder2/deep/nested", "empty_folder"]
+    assert sorted(folders) == sorted(expected)
+
+
+def test_handles_nonexistent_input_directory():
+    with tempfile.TemporaryDirectory() as temp_dir:
+        nonexistent = os.path.join(temp_dir, "nonexistent")
+        set_input_directory(nonexistent)
+        assert get_input_subfolders() == []
+
+
+def test_empty_input_directory():
+    with tempfile.TemporaryDirectory() as temp_dir:
+        set_input_directory(temp_dir)
+        assert get_input_subfolders() == []  # Empty since we don't include root