From 68392684b5d6c8948b7e051690bfb2a935ed31c9 Mon Sep 17 00:00:00 2001 From: kijai <40791699+kijai@users.noreply.github.com> Date: Mon, 10 Nov 2025 19:06:45 +0200 Subject: [PATCH 1/6] Add node to use UniLumos Simply allows fore and background latent inputs for UniLumos relight model, example inputs seem to work: https://github.com/alibaba-damo-academy/Lumos-Custom/tree/main/UniLumos/UniLumos/examples --- nodes.py | 42 ++++++++++++++++++++++++++++++++++++++++++ nodes_sampler.py | 13 +++++++++++++ 2 files changed, 55 insertions(+) diff --git a/nodes.py b/nodes.py index ece8d3e..ca0ddf6 100644 --- a/nodes.py +++ b/nodes.py @@ -1158,6 +1158,46 @@ class WanVideoAnimateEmbeds: } return (image_embeds,) + +# region UniLumos +class WanVideoUniLumosEmbeds: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 832, "min": 64, "max": 8096, "step": 8, "tooltip": "Width of the image to encode"}), + "height": ("INT", {"default": 480, "min": 64, "max": 8096, "step": 8, "tooltip": "Height of the image to encode"}), + "num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}), + }, + "optional": { + "foreground_latents": ("LATENT", {"tooltip": "Video foreground latents"}), + "background_latents": ("LATENT", {"tooltip": "Video background latents"}), + } + } + + RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", ) + RETURN_NAMES = ("image_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, num_frames, width, height, foreground_latents=None, background_latents=None): + target_shape = (16, (num_frames - 1) // VAE_STRIDE[0] + 1, + height // VAE_STRIDE[1], + width // VAE_STRIDE[2]) + + embeds = { + "target_shape": target_shape, + "num_frames": num_frames, + } + if foreground_latents is not None: + embeds["foreground_latents"] = foreground_latents["samples"][0] + else: + embeds["foreground_latents"] = torch.zeros(target_shape[0], target_shape[1], target_shape[2], target_shape[3], device=torch.device("cpu"), dtype=torch.float32) + if background_latents is not None: + embeds["background_latents"] = background_latents["samples"][0] + else: + embeds["background_latents"] = torch.zeros(target_shape[0], target_shape[1], target_shape[2], target_shape[3], device=torch.device("cpu"), dtype=torch.float32) + + return (embeds,) class WanVideoEmptyEmbeds: @classmethod @@ -2206,6 +2246,7 @@ NODE_CLASS_MAPPINGS = { "WanVideoAnimateEmbeds": WanVideoAnimateEmbeds, "WanVideoAddLucyEditLatents": WanVideoAddLucyEditLatents, "WanVideoSchedulerSA_ODE": WanVideoSchedulerSA_ODE, + "WanVideoUniLumosEmbeds": WanVideoUniLumosEmbeds, } NODE_DISPLAY_NAME_MAPPINGS = { @@ -2245,4 +2286,5 @@ NODE_DISPLAY_NAME_MAPPINGS = { "WanVideoAnimateEmbeds": "WanVideo Animate Embeds", "WanVideoAddLucyEditLatents": "WanVideo Add LucyEdit Latents", "WanVideoSchedulerSA_ODE": "WanVideo Scheduler SA-ODE", + "WanVideoUniLumosEmbeds": "WanVideo UniLumos Embeds", } diff --git a/nodes_sampler.py b/nodes_sampler.py index caebe4a..5a549e1 100644 --- a/nodes_sampler.py +++ b/nodes_sampler.py @@ -1137,6 +1137,16 @@ class WanVideoSampler: lynx_embeds["ref_buffer_uncond"] = lynx_ref_buffer_uncond if not math.isclose(cfg[0], 1.0) else None mm.soft_empty_cache() + # UniLumos + foreground_latents = image_embeds.get("foreground_latents", None) + if foreground_latents is not None: + log.info(f"UniLumos foreground latent input shape: {foreground_latents.shape}") + foreground_latents = foreground_latents.to(device, dtype) + background_latents = image_embeds.get("background_latents", None) + if background_latents is not None: + log.info(f"UniLumos background latent input shape: {background_latents.shape}") + background_latents = background_latents.to(device, dtype) + #region model pred def predict_with_cfg(z, cfg_scale, positive_embeds, negative_embeds, timestep, idx, image_cond=None, clip_fea=None, control_latents=None, vace_data=None, unianim_data=None, audio_proj=None, control_camera_latents=None, @@ -1347,6 +1357,9 @@ class WanVideoSampler: z = z * c_in timestep = c_noise + if background_latents is not None or foreground_latents is not None: + z = torch.cat([z, foreground_latents.to(z), background_latents.to(z)], dim=0) + base_params = { 'x': [z], # latent 'y': [image_cond_input] if image_cond_input is not None else None, # image cond From 4576ddb35e3b64ab6a6a38bc26eb8e7c9903ec7c Mon Sep 17 00:00:00 2001 From: kijai <40791699+kijai@users.noreply.github.com> Date: Mon, 10 Nov 2025 19:41:19 +0200 Subject: [PATCH 2/6] Add node to create input for UniLumos --- nodes_utility.py | 97 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 97 insertions(+) diff --git a/nodes_utility.py b/nodes_utility.py index 14f9090..55cd9ce 100644 --- a/nodes_utility.py +++ b/nodes_utility.py @@ -1,6 +1,8 @@ import torch import numpy as np from comfy.utils import common_upscale +from comfy import model_management +from tqdm import tqdm from .utils import log from einops import rearrange @@ -12,6 +14,9 @@ except: VAE_STRIDE = (4, 8, 8) PATCH_SIZE = (1, 2, 2) +main_device = model_management.get_torch_device() +offload_device = model_management.unet_offload_device() + class WanVideoImageResizeToClosest: @classmethod def INPUT_TYPES(s): @@ -660,6 +665,96 @@ class FaceMaskFromPoseKeypoints: cv2.fillPoly(canvas, pts=[outer_contour], color=part_color) return canvas + + +class DrawGaussianNoiseOnImage: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "mask": ("MASK", ), + }, + "optional": { + "device": (["cpu", "gpu"], {"default": "cpu", "tooltip": "Device to use for processing"}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + } + } + + RETURN_TYPES = ("IMAGE", ) + RETURN_NAMES = ("images",) + FUNCTION = "apply" + CATEGORY = "KJNodes/masking" + DESCRIPTION = "Fills the background (masked area) with Gaussian noise sampled using the mean and variance of the subject (unmasked) region." + + def apply(self, image, mask, device="cpu", seed=0): + B, H, W, C = image.shape + BM, HM, WM = mask.shape + + processing_device = main_device if device == "gpu" else torch.device("cpu") + + in_masks = mask.clone().to(processing_device) + in_images = image.clone().to(processing_device) + + # Resize mask to match image dimensions + if HM != H or WM != W: + in_masks = F.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1) + + # Match batch sizes + if B > BM: + in_masks = in_masks.repeat((B + BM - 1) // BM, 1, 1)[:B] + elif BM > B: + in_masks = in_masks[:B] + + output_images = [] + + # Set random seed for reproducibility + generator = torch.Generator(device=processing_device).manual_seed(seed) + + for i in tqdm(range(B), desc="DrawGaussianNoiseOnImage batch"): + curr_mask = in_masks[i] + img_idx = min(i, B - 1) + curr_image = in_images[img_idx] + + # Expand mask to 3 channels + mask_expanded = curr_mask.unsqueeze(-1).expand(-1, -1, 3) + + # Calculate mean and std per channel from the subject region (where mask is 1) + subject_mask = mask_expanded > 0.5 + + # Initialize noise tensor + noise = torch.zeros_like(curr_image) + + for c in range(C): + channel = curr_image[:, :, c] + channel_mask = subject_mask[:, :, c] + + if channel_mask.sum() > 0: + # Get subject pixels + subject_pixels = channel[channel_mask] + + # Calculate statistics + mean = subject_pixels.mean() + std = subject_pixels.std() + + # Generate Gaussian noise for this channel + noise[:, :, c] = torch.normal(mean=mean.item(), std=std.item(), + size=(H, W), generator=generator, + device=processing_device) + + # Clamp noise to valid range + noise = torch.clamp(noise, 0.0, 1.0) + + # Apply: keep subject, fill background with noise + masked_image = curr_image * mask_expanded + noise * (1 - mask_expanded) + output_images.append(masked_image) + + # If no masks were processed, return empty tensor + if not output_images: + return (torch.zeros((0, H, W, 3), dtype=image.dtype),) + + out_rgb = torch.stack(output_images, dim=0).cpu() + + return (out_rgb, ) NODE_CLASS_MAPPINGS = { "WanVideoImageResizeToClosest": WanVideoImageResizeToClosest, @@ -673,6 +768,7 @@ NODE_CLASS_MAPPINGS = { "NormalizeAudioLoudness": NormalizeAudioLoudness, "WanVideoPassImagesFromSamples": WanVideoPassImagesFromSamples, "FaceMaskFromPoseKeypoints": FaceMaskFromPoseKeypoints, + "DrawGaussianNoiseOnImage": DrawGaussianNoiseOnImage, } NODE_DISPLAY_NAME_MAPPINGS = { "WanVideoImageResizeToClosest": "WanVideo Image Resize To Closest", @@ -686,4 +782,5 @@ NODE_DISPLAY_NAME_MAPPINGS = { "NormalizeAudioLoudness": "Normalize Audio Loudness", "WanVideoPassImagesFromSamples": "WanVideo Pass Images From Samples", "FaceMaskFromPoseKeypoints": "Face Mask From Pose Keypoints", + "DrawGaussianNoiseOnImage": "Draw Gaussian Noise On Image", } \ No newline at end of file From de3c9c895a9b6730bfae10816ef955ed2b86b286 Mon Sep 17 00:00:00 2001 From: kijai <40791699+kijai@users.noreply.github.com> Date: Mon, 10 Nov 2025 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"5a2383621a05825d0d0437781afcb8552d9590fd", + "comfy-core": "0.3.26", + "ComfyUI-KJNodes": "a5bd3c86c8ed6b83c55c2d0e7a59515b15a0137f", + "ComfyUI-VideoHelperSuite": "0a75c7958fe320efcb052f1d9f8451fd20c730a8" + }, + "VHS_latentpreview": true, + "VHS_latentpreviewrate": 0, + "VHS_MetadataImage": true, + "VHS_KeepIntermediate": true + }, + "version": 0.4 +} \ No newline at end of file From 47514f678d6b323db622f1033fbe042e674d7112 Mon Sep 17 00:00:00 2001 From: kijai <40791699+kijai@users.noreply.github.com> Date: Tue, 11 Nov 2025 09:46:13 +0200 Subject: [PATCH 4/6] Allow loading original Ovi -models --- nodes_model_loading.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/nodes_model_loading.py b/nodes_model_loading.py index d644770..1fae8ba 100644 --- a/nodes_model_loading.py +++ b/nodes_model_loading.py @@ -1088,6 +1088,14 @@ class WanVideoModelLoader: sd, reader = load_gguf(model_path) gguf_reader.append(reader) + # Ovi + extra_audio_model = False + if any(key.startswith("video_model.") for key in sd.keys()): + sd = {key.replace("video_model.", "", 1).replace("modulation.modulation", "modulation"): value for key, value in sd.items()} + if any(key.startswith("audio_model.") for key in sd.keys()): + extra_audio_model = True + + is_wananimate = "pose_patch_embedding.weight" in sd # rename WanAnimate face fuser block keys to insert into main blocks instead if is_wananimate: @@ -1140,7 +1148,6 @@ class WanVideoModelLoader: raise ValueError("You are attempting to load a VACE module as a WanVideo model, instead you should use the vace_model input and matching T2V base model") # currently this can be VACE, MTV-Crafter, Lynx or Ovi-audio weights - extra_audio_model = False if extra_model is not None: for _model in extra_model: print("Loading extra model: ", _model["path"]) From e01e34da1f6e6a5a353941957ab643a4a605030e Mon Sep 17 00:00:00 2001 From: kijai <40791699+kijai@users.noreply.github.com> Date: Tue, 11 Nov 2025 10:01:06 +0200 Subject: [PATCH 5/6] Update nodes_model_loading.py --- nodes_model_loading.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nodes_model_loading.py b/nodes_model_loading.py index 1fae8ba..6dcfc84 100644 --- a/nodes_model_loading.py +++ b/nodes_model_loading.py @@ -1092,8 +1092,8 @@ class WanVideoModelLoader: extra_audio_model = False if any(key.startswith("video_model.") for key in sd.keys()): sd = {key.replace("video_model.", "", 1).replace("modulation.modulation", "modulation"): value for key, value in sd.items()} - if any(key.startswith("audio_model.") for key in sd.keys()): - extra_audio_model = True + if any(key.startswith("audio_model.") for key in sd.keys()) and any(key.startswith("blocks.") for key in sd.keys()): + extra_audio_model = True is_wananimate = "pose_patch_embedding.weight" in sd From e926f7a069e3efc25bdcc78e1bf65c39ac1a2cd4 Mon Sep 17 00:00:00 2001 From: kijai <40791699+kijai@users.noreply.github.com> Date: Tue, 11 Nov 2025 10:57:46 +0200 Subject: [PATCH 6/6] version bump 1.3.9 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 507faf6..4bcd4d7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "ComfyUI-WanVideoWrapper" description = "ComfyUI wrapper nodes for WanVideo" -version = "1.3.8" +version = "1.3.9" license = {file = "LICENSE"} dependencies = ["accelerate >= 1.2.1", "diffusers >= 0.33.0", "peft >= 0.17.0", "ftfy", "gguf >= 0.17.1", "pyloudnorm"]