nodes/Maxed-Out-99-ComfyUI-MaxedOut
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Restructure Python into domain packages (move-only, schema-verified)

Browse Source 
Split the three monoliths into nodes/ domain modules:
- maxedoutnodes.py -> nodes/{latents,resolution,prompts,masks,media_io}.py
- wan22nodes.py    -> nodes/wan22/{buckets,latent_io,i2v,video_ops}.py
- ltxnodes.py      -> nodes/ltx/{latents,samplers,preview}.py
  (LTX_Image_Scaler_MXD moved from the Wan file to nodes/ltx/latents.py)
- mediacomparers.py -> nodes/comparers.py
- checkpoint_loader_mxd.py + save_checkpoint_mxd.py -> nodes/checkpoints.py
- combine_materials_ffgo_mxd.py -> nodes/ffgo.py
- video_preview_mxd.py -> system/live_preview.py
- model_paths_autoregister_mxd.py -> system/model_paths.py
  (model_storage_config.json still resolved at repo root)

All node mapping keys, display names, categories, inputs, routes, and
events unchanged: scripts/dump_node_schema.py --compare reports 68 nodes
identical to the pre-refactor baseline.

Provably-dead code dropped during the move (verified unreachable):
- _add_mxd_aliases() no-op alias pass (every key already contains MXD)
- unreachable code after return in WAN22_I2V_Image_Scaler_MXD.scale and
  Frames_Select_StartEnd_MXD.main, plus the orphaned _pick_bucket method
- unused BUCKETS_480/BUCKETS_720 module constants
- mediacomparers' unused WEB_DIRECTORY/__all__ declarations

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
Maxed-Out-99
2026-07-10 01:01:30 -07:00
co-authored by Claude Fable 5
parent 064c29c465
commit 2df2c8fd79
26 changed files with 6197 additions and 6029 deletions
Download Patch File Download Diff File Expand all files Collapse all files
__init__.py
+3 -9
View File
@@ -20,18 +20,12 @@ NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}
for _name in (
"maxedoutnodes",
"mediacomparers",
"wan22nodes",
"nodes",
"loraloader_mxd",
"CharacterPrompts",
"ltxnodes",
"video_preview_mxd",
"model_paths_autoregister_mxd",
"combine_materials_ffgo_mxd",
"save_checkpoint_mxd",
"checkpoint_loader_mxd",
"smart_loaders_mxd",
"system.live_preview",
"system.model_paths",
):
_mod = _safe_import(_name)
_class_map, _display_map = _get_mappings(_mod)
checkpoint_loader_mxd.py
-41
View File
@@ -1,41 +0,0 @@
import folder_paths
import comfy.sd
class LoadCheckpointMXD:
DESCRIPTION = (
"Loads a diffusion model checkpoint, same as the core Load Checkpoint node, "
"with the MXD info-icon UI (CivitAI lookup, cached metadata, local notes)."
)
TITLE = "Load Checkpoint MXD"
CATEGORY = "MXD/Loaders"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
}
}
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
FUNCTION = "load_checkpoint"
def load_checkpoint(self, ckpt_name):
ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(
ckpt_path,
output_vae=True,
output_clip=True,
embedding_directory=folder_paths.get_folder_paths("embeddings"),
)
return out[:3]
NODE_CLASS_MAPPINGS = {
"LoadCheckpointMXD": LoadCheckpointMXD,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LoadCheckpointMXD": "Load Checkpoint MXD",
}
ltxnodes.py
-684
View File
@@ -1,684 +0,0 @@
from __future__ import annotations
import os
import re
import base64
import time
import urllib.error
import urllib.request
from fractions import Fraction
from io import BytesIO
from PIL import Image
from threading import Lock, Thread
import torch
import torch.nn.functional as F
import comfy
import comfy.model_management
import comfy.patcher_extension
import comfy.samplers
import comfy.sample
import comfy.utils
import latent_preview
import server
import folder_paths
from comfy_api.latest import VideoFromComponents, VideoComponents
_serv = server.PromptServer.instance
########################################################################################################################
# LTX Video Empty Latent Image
class LTXVideoEmptyLatentMXD:
DESCRIPTION = "Create an LTX Video empty latent batch from connected width/height and frame count."
TITLE = "LTX Empty Latent Video MXD"
CATEGORY = "MXD/Latent"
# All dimensions must be multiples of 32 (LTX 32× spatial compression).
# Lengths must be 8n+1 for LTX's 8× temporal compression.
RESOLUTIONS = {
"16:9 Landscape": None,
"16:9 512×288": (512, 288),
"16:9 768×448": (768, 448),
"16:9 832×480": (832, 480),
"16:9 1024×576": (1024, 576),
"16:9 1280×736": (1280, 736),
"9:16 Portrait": None,
"9:16 288×512": (288, 512),
"9:16 448×768": (448, 768),
"9:16 480×832": (480, 832),
"9:16 576×1024": (576, 1024),
"4:3 Standard": None,
"4:3 512×384": (512, 384),
"4:3 768×576": (768, 576),
"1:1 Square": None,
"1:1 512×512": (512, 512),
"1:1 768×768": (768, 768),
}
def __init__(self):
self.device = comfy.model_management.intermediate_device()
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"length": (
"INT",
{
"default": 97,
"min": 9,
"max": 1025,
"step": 8,
"tooltip": "Number of frames. Must be 8n+1 (e.g. 25, 49, 73, 97, 121, 201).",
},
),
"batch_size": (
"INT",
{
"default": 1,
"min": 1,
"max": 4096,
"tooltip": "Number of latent videos in the batch.",
},
),
},
"optional": {
"width": ("INT", {
"default": 640, "min": 32, "max": 8192, "step": 32,
"tooltip": "Stage 1 width. Connect the LTX Image Scaler stage1_width output for I2V workflows.",
}),
"height": ("INT", {
"default": 384, "min": 32, "max": 8192, "step": 32,
"tooltip": "Stage 1 height. Connect the LTX Image Scaler stage1_height output for I2V workflows.",
}),
},
}
RETURN_TYPES = ("LATENT", "INT")
RETURN_NAMES = ("latent", "length")
FUNCTION = "generate"
def generate(self, length, batch_size=1, width=640, height=384):
# LTX latent: 128 channels, 32× spatial compression, 8× temporal compression
width = max(32, int(width) // 32 * 32)
height = max(32, int(height) // 32 * 32)
length = max(9, 1 + 8 * round((int(length) - 1) / 8))
t = ((length - 1) // 8) + 1
h = height // 32
w = width // 32
latent = torch.zeros([batch_size, 128, t, h, w], device=self.device)
return ({"samples": latent}, length)
########################################################################################################################
# Shared noise helper — equivalent to ComfyUI RandomNoise
class _LTXNoise:
def __init__(self, seed: int):
self.seed = seed
def generate_noise(self, latent: dict) -> torch.Tensor:
samples = latent["samples"]
batch_inds = latent.get("batch_index", None)
return comfy.sample.prepare_noise(samples, self.seed, batch_inds)
########################################################################################################################
# LTX video preview (taeltx TAE decode)
#
# Core ComfyUI has no preview for the LTXAV format used by LTX 2.3, and the
# latent2rgb approximation looks awful for video. This installs a previewer that
# decodes latent frames with the tiny "taeltx" autoencoder for accurate previews.
# The taeltx model is auto-discovered in the vae / vae_approx model folders. If
# it isn't found, it is downloaded to the configured vae model folder.
#
# TAE decode path borrowed from kjnodes / VideoHelperSuite.
_TAELTX_FILENAME = "taeltx2_3.safetensors"
_TAELTX_URL = "https://huggingface.co/Kijai/LTX2.3_comfy/resolve/main/vae/taeltx2_3.safetensors?download=true"
_TAELTX_DOWNLOAD_LOCK = Lock()
def _find_taeltx_path(folder_paths):
for folder in ("vae", "vae_approx"):
try:
names = folder_paths.get_filename_list(folder)
except Exception:
continue
name = next((fn for fn in names if "taeltx" in fn.lower()), None)
if name is not None:
path = folder_paths.get_full_path(folder, name)
if path:
return path
return None
def _download_taeltx(folder_paths):
try:
vae_dirs = folder_paths.get_folder_paths("vae")
except Exception as exc:
print(f"[MXD LTX preview] cannot find ComfyUI vae model folder: {exc}")
return None
if not vae_dirs:
print("[MXD LTX preview] cannot find ComfyUI vae model folder.")
return None
target_dir = vae_dirs[0]
target_path = os.path.join(target_dir, _TAELTX_FILENAME)
partial_path = f"{target_path}.part"
with _TAELTX_DOWNLOAD_LOCK:
if os.path.isfile(target_path):
return target_path
try:
os.makedirs(target_dir, exist_ok=True)
print(f"[MXD LTX preview] downloading {_TAELTX_FILENAME} to {target_path}")
request = urllib.request.Request(_TAELTX_URL, headers={"User-Agent": "ComfyUI-MaxedOut"})
with urllib.request.urlopen(request, timeout=120) as response, open(partial_path, "wb") as out:
while True:
chunk = response.read(1024 * 1024)
if not chunk:
break
out.write(chunk)
if not os.path.isfile(partial_path) or os.path.getsize(partial_path) == 0:
raise RuntimeError("downloaded file is empty")
os.replace(partial_path, target_path)
try:
folder_paths.get_filename_list("vae")
except Exception:
pass
print(f"[MXD LTX preview] downloaded {_TAELTX_FILENAME}")
return target_path
except (OSError, RuntimeError, urllib.error.URLError) as exc:
try:
if os.path.exists(partial_path):
os.remove(partial_path)
except OSError:
pass
print(f"[MXD LTX preview] failed to download {_TAELTX_FILENAME}: {exc}")
return None
def _load_taeltx():
"""Load the taeltx TAE from the vae / vae_approx model folders. Returns a VAE or None."""
try:
import folder_paths
from comfy.sd import VAE
except Exception:
return None
path = _find_taeltx_path(folder_paths)
if not path:
path = _download_taeltx(folder_paths)
if not path:
return None
try:
taeltx = VAE(comfy.utils.load_torch_file(path))
taeltx.first_stage_model.show_progress_bar = False
except Exception as exc:
print(f"[MXD LTX preview] failed to load taeltx ({path}): {exc}")
return None
return taeltx
class _LTXTAEPreviewer:
"""Cycles through LTX video latent frames during sampling, decoding with taeltx."""
def __init__(self, taeltx, rate=8):
self.first_preview = True
self.last_time = 0.0
self.c_index = 0
self.rate = rate
self.taeltx = taeltx
def decode_latent_to_preview_image(self, preview_format, x0):
if x0.ndim == 5:
x0 = x0.movedim(2, 1)
x0 = x0.reshape((-1,) + x0.shape[-3:])
num_images = x0.size(0)
new_time = time.time()
num_previews = int((new_time - self.last_time) * self.rate)
self.last_time += num_previews / self.rate
if num_previews > num_images:
num_previews = num_images
elif num_previews <= 0:
return None
if self.first_preview:
self.first_preview = False
_serv.send_sync(
'MXD_live_preview_start',
{'length': num_images, 'rate': self.rate, 'id': _serv.last_node_id},
)
self.last_time = new_time + 1.0 / self.rate
if self.c_index + num_previews > num_images:
frames = x0.roll(-self.c_index, 0)[:num_previews]
else:
frames = x0[self.c_index:self.c_index + num_previews]
Thread(target=self._send_frames, args=(frames, self.c_index, num_images)).run()
self.c_index = (self.c_index + num_previews) % num_images
return None
def _send_frames(self, image_tensor, ind, leng):
max_size, min_size = 512, 256
image_tensor = self._decode(image_tensor)
if image_tensor.size(1) < min_size or image_tensor.size(2) < min_size:
image_tensor = F.interpolate(
image_tensor.movedim(-1, 0), scale_factor=4, mode='nearest'
).movedim(0, -1)
if image_tensor.size(1) > max_size or image_tensor.size(2) > max_size:
t = image_tensor.movedim(-1, 0)
if t.size(2) < t.size(3):
h = (max_size * t.size(2)) // t.size(3)
t = F.interpolate(t, (h, max_size), mode='nearest')
else:
w = (max_size * t.size(3)) // t.size(2)
t = F.interpolate(t, (max_size, w), mode='nearest')
image_tensor = t.movedim(0, -1)
previews = image_tensor.clamp(0, 1).mul(0xFF).to(device="cpu", dtype=torch.uint8)
node_id = _serv.last_node_id
for preview in previews:
img = Image.fromarray(preview.numpy())
buf = BytesIO()
img.save(buf, format="JPEG", quality=90)
data_url = "data:image/jpeg;base64," + base64.b64encode(buf.getvalue()).decode("ascii")
_serv.send_sync('MXD_live_preview_frame', {'id': node_id, 'index': ind, 'length': leng, 'data': data_url})
# taeltx expands the 8× temporal compression on decode
ind = (ind + 1) % ((leng - 1) * 8 + 1)
def _decode(self, x0):
dev = comfy.model_management.get_torch_device()
dtype = self.taeltx.first_stage_model.decoder[1].weight.dtype
x0 = x0.unsqueeze(0).to(dtype=dtype, device=dev)
return self.taeltx.first_stage_model.decode(x0)[0].permute(1, 2, 3, 0)
def _save_final_ltx_preview(node_id, previewer, x0_v, rate):
"""Decode the full final clip with taeltx and save it as an mp4 to output/live_previews."""
try:
frames = x0_v.movedim(2, 1)
frames = frames.reshape((-1,) + frames.shape[-3:])
frames = previewer._decode(frames).clamp(0, 1).to(device="cpu", dtype=torch.float32)
if frames.ndim != 4 or frames.size(0) == 0:
return
out_dir = os.path.join(folder_paths.get_output_directory(), "live_previews")
os.makedirs(out_dir, exist_ok=True)
safe_id = str(node_id).replace(":", "_").replace("/", "_")
filename = f"{safe_id}_{int(time.time())}.mp4"
path = os.path.join(out_dir, filename)
video = VideoFromComponents(VideoComponents(images=frames, frame_rate=Fraction(max(1, round(rate)))))
video.save_to(path)
print(f"[MXD LTX preview] Saved live preview to {path}")
_serv.send_sync("MXD_live_preview_saved", {
"node_id": node_id, "filename": filename, "subfolder": "live_previews", "type": "output",
})
except Exception as e:
print(f"[MXD LTX preview] Failed to save live preview: {e}")
class _LTXPreviewWrapper:
"""OUTER_SAMPLE wrapper that installs the taeltx video previewer during sampling."""
def __init__(self, taeltx):
self.taeltx = taeltx
def __call__(self, executor, noise, latent_image, sampler, sigmas,
denoise_mask, callback, disable_pbar, seed, latent_shapes):
guider = executor.class_obj
device = comfy.model_management.get_torch_device()
self.taeltx.first_stage_model.to(device)
previewer = _LTXTAEPreviewer(self.taeltx, rate=8)
pbar = comfy.utils.ProgressBar(len(sigmas) - 1)
node_id = _serv.last_node_id
# Strip I2V guide frames appended at the end of the latent before previewing.
num_keyframes = 0
if 'positive' in guider.conds and guider.conds['positive']:
kf = guider.conds['positive'][0].get('keyframe_idxs')
if kf is not None:
num_keyframes = len(torch.unique(kf[0, 0, :, 0]))
def ltx_callback(step, x0, x, total_steps):
x0_v = x0
if x0_v is not None and len(latent_shapes) > 1:
# Audio+video latents are packed into [B, 1, total]; unpack and
# take the video tensor (the 5D one). Audio is a lower-rank entry.
x0_v = next(
(p for p in comfy.utils.unpack_latents(x0, latent_shapes) if p.ndim == 5),
None,
)
if x0_v is not None and x0_v.ndim == 5 and num_keyframes > 0:
x0_v = x0_v[:, :, :-num_keyframes]
preview = (
previewer.decode_latent_to_preview_image("JPEG", x0_v)
if x0_v is not None and x0_v.ndim == 5 else None
)
pbar.update_absolute(step + 1, total_steps, preview)
if step + 1 >= total_steps and x0_v is not None and x0_v.ndim == 5:
_save_final_ltx_preview(node_id, previewer, x0_v, previewer.rate)
if callback is not None:
callback(step, x0, x, total_steps)
try:
return executor(
noise, latent_image, sampler, sigmas, denoise_mask,
ltx_callback, disable_pbar, seed, latent_shapes=latent_shapes,
)
finally:
self.taeltx.first_stage_model.to(comfy.model_management.unet_offload_device())
########################################################################################################################
# LTX KSampler — Stage 1 (T2V / I2V generation at base resolution)
class LTXKSamplerMXD:
DESCRIPTION = (
"LTX-Video Stage 1 sampler for the distilled workflow. Use Distilled 8 Step "
"for the trained schedule, or Custom Sigmas when intentionally testing a "
"manual schedule."
)
TITLE = "LTX Stage 1 Sampler MXD"
CATEGORY = "MXD/Sampling"
MODES = ["Distilled 8 Step", "Custom Sigmas"]
_DISTILLED_SIGMAS = [1.0, 0.99375, 0.9875, 0.98125, 0.975,
0.909375, 0.725, 0.421875, 0.0]
_CUSTOM_SIGMAS_DEFAULT = "1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("MODEL",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"latent_image": ("LATENT",),
"mode": (cls.MODES, {"default": "Distilled 8 Step"}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF, "control_after_generate": True}),
"cfg": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.1}),
"sampler_name": (
["euler_ancestral_cfg_pp", "euler_cfg_pp", "euler"],
{"default": "euler_ancestral_cfg_pp"},
),
"custom_sigmas": (
"STRING",
{
"default": cls._CUSTOM_SIGMAS_DEFAULT,
"multiline": True,
"tooltip": "Only used when mode is Custom Sigmas. Enter comma, space, or newline separated sigma values.",
},
),
"ltx_preview": ("BOOLEAN", {"default": True, "tooltip": "Show LTX video previews during sampling. Downloads the taeltx VAE to your vae model folder if it is missing."}),
},
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("latent",)
FUNCTION = "sample"
OUTPUT_NODE = False
def sample(
self,
model,
positive,
negative,
latent_image,
mode="Distilled 8 Step",
seed=0,
cfg=2.0,
sampler_name="euler_ancestral_cfg_pp",
custom_sigmas=_CUSTOM_SIGMAS_DEFAULT,
ltx_preview=True,
):
sigmas = _select_sigmas(
mode,
{
"Distilled 8 Step": self._DISTILLED_SIGMAS,
},
custom_sigmas,
"LTX Stage 1 Sampler MXD",
)
return _run_sampling(model, positive, negative, latent_image, seed, cfg, sampler_name, sigmas, ltx_preview)
########################################################################################################################
# LTX KSampler 2 — Stage 2 (refinement at 2× resolution with distilled LoRA)
class LTXKSampler2MXD:
DESCRIPTION = (
"LTX-Video Stage 2 refiner for the distilled workflow. Official Refine "
"matches the Lightricks 2.3 two-stage example (start sigma 0.85). "
"Custom Sigmas is for manual testing."
)
TITLE = "LTX Stage 2 Refiner MXD"
CATEGORY = "MXD/Sampling"
# Exact stage-2 refine schedule from the official Lightricks 2.3 two-stage
# workflow (LTX-2.3_T2V_I2V_Two_Stage_Distilled.json, euler_cfg_pp, cfg 1).
# Only the starting sigma (denoise strength) is meant to vary; use Custom
# Sigmas for that.
MODES = ["Official Refine", "Custom Sigmas"]
_OFFICIAL_REFINE_SIGMAS = [0.85, 0.725, 0.4219, 0.0]
_CUSTOM_SIGMAS_DEFAULT = "0.85, 0.725, 0.4219, 0.0"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("MODEL",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"latent_image": ("LATENT",),
"mode": (cls.MODES, {"default": "Official Refine"}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF, "control_after_generate": True}),
"cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1}),
"sampler_name": (
["euler_cfg_pp", "euler_ancestral_cfg_pp", "euler"],
{"default": "euler_cfg_pp"},
),
"custom_sigmas": (
"STRING",
{
"default": cls._CUSTOM_SIGMAS_DEFAULT,
"multiline": True,
"tooltip": "Only used when mode is Custom Sigmas. Enter comma, space, or newline separated sigma values.",
},
),
"ltx_preview": ("BOOLEAN", {"default": True, "tooltip": "Show LTX video previews during sampling. Downloads the taeltx VAE to your vae model folder if it is missing."}),
},
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("latent",)
FUNCTION = "sample"
OUTPUT_NODE = False
def sample(
self,
model,
positive,
negative,
latent_image,
mode="Official Refine",
seed=0,
cfg=1.0,
sampler_name="euler_cfg_pp",
custom_sigmas=_CUSTOM_SIGMAS_DEFAULT,
ltx_preview=True,
):
sigmas = _select_sigmas(
mode,
{
"Official Refine": self._OFFICIAL_REFINE_SIGMAS,
},
custom_sigmas,
"LTX Stage 2 Refiner MXD",
)
return _run_sampling(model, positive, negative, latent_image, seed, cfg, sampler_name, sigmas, ltx_preview)
########################################################################################################################
# Sigma schedule helpers
_SIGMA_RE = re.compile(r"[-+]?(?:\d*\.\d+|\d+\.?)(?:[eE][-+]?\d+)?")
def _select_sigmas(mode, presets, custom_sigmas, node_name):
if mode == "Custom Sigmas":
values = _parse_custom_sigmas(custom_sigmas, node_name)
else:
try:
values = presets[mode]
except KeyError as exc:
allowed = ", ".join([*presets.keys(), "Custom Sigmas"])
raise ValueError(f"{node_name}: unknown mode '{mode}'. Expected one of: {allowed}.") from exc
return torch.tensor(values, dtype=torch.float32)
def _parse_custom_sigmas(custom_sigmas, node_name):
text = str(custom_sigmas or "")
values = [float(match.group(0)) for match in _SIGMA_RE.finditer(text)]
if len(values) < 2:
raise ValueError(f"{node_name}: Custom Sigmas needs at least two sigma values, ending with 0.0.")
for index, (left, right) in enumerate(zip(values, values[1:]), start=1):
if right > left:
raise ValueError(
f"{node_name}: Custom Sigmas must be in descending order. "
f"Value {index + 1} ({right}) is greater than value {index} ({left})."
)
if abs(values[-1]) > 1e-8:
raise ValueError(f"{node_name}: Custom Sigmas must end with 0.0.")
return values
########################################################################################################################
# Shared sampling logic
def _run_sampling(model, positive, negative, latent_image, seed, cfg, sampler_name, sigmas, ltx_preview=False):
taeltx = _load_taeltx() if ltx_preview else None
if ltx_preview and taeltx is None:
print("[MXD LTX preview] taeltx model not found in vae / vae_approx — skipping preview.")
if taeltx is not None:
model = model.clone()
model.add_wrapper_with_key(
comfy.patcher_extension.WrappersMP.OUTER_SAMPLE,
"ltx_mxd_preview",
_LTXPreviewWrapper(taeltx),
)
guider = comfy.samplers.CFGGuider(model)
guider.set_conds(positive, negative)
guider.set_cfg(cfg)
sampler = comfy.samplers.sampler_object(sampler_name)
latent = latent_image.copy()
latent_samples = latent["samples"]
try:
latent_samples = comfy.sample.fix_empty_latent_channels(
guider.model_patcher, latent_samples,
latent.get("downscale_ratio_spacial", None),
)
except AttributeError:
pass
latent["samples"] = latent_samples
noise_mask = latent.get("noise_mask", None)
noise = _LTXNoise(seed)
if taeltx is not None:
# The preview wrapper owns the progress bar / callback.
callback = None
else:
x0_output = {}
callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = guider.sample(
noise.generate_noise(latent),
latent_samples,
sampler,
sigmas,
denoise_mask=noise_mask,
callback=callback,
disable_pbar=disable_pbar,
seed=seed,
)
samples = samples.to(comfy.model_management.intermediate_device())
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out["samples"] = samples
return (out,)
########################################################################################################################
# LTX Preview — attach the taeltx previewer to any model
class LTXPreviewMXD:
DESCRIPTION = (
"Enables taeltx video previews during sampling for ANY sampler node "
"(SamplerCustomAdvanced, KSampler, etc.), not just the MXD LTX samplers. "
"LTX 2.3 (LTXAV) ships no built-in preview decoder, so core ComfyUI shows "
"nothing; this attaches a wrapper to the model that decodes latent frames "
"with the tiny taeltx autoencoder. Wire it between your model loader and "
"the sampler's model input. Downloads taeltx to your vae folder if missing."
)
TITLE = "LTX Preview MXD"
CATEGORY = "MXD/Sampling"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("MODEL",),
"enabled": ("BOOLEAN", {"default": True, "tooltip": "Turn taeltx previews on/off without unwiring the node."}),
},
}
RETURN_TYPES = ("MODEL",)
RETURN_NAMES = ("model",)
FUNCTION = "apply"
OUTPUT_NODE = False
def apply(self, model, enabled=True):
if not enabled:
return (model,)
taeltx = _load_taeltx()
if taeltx is None:
print("[MXD LTX preview] taeltx model not found in vae / vae_approx — skipping preview.")
return (model,)
model = model.clone()
model.add_wrapper_with_key(
comfy.patcher_extension.WrappersMP.OUTER_SAMPLE,
"ltx_mxd_preview",
_LTXPreviewWrapper(taeltx),
)
return (model,)
########################################################################################################################
NODE_CLASS_MAPPINGS = {
"LTXVideoEmptyLatent_MXD": LTXVideoEmptyLatentMXD,
"LTXKSampler_MXD": LTXKSamplerMXD,
"LTXKSampler2_MXD": LTXKSampler2MXD,
"LTXPreview_MXD": LTXPreviewMXD,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LTXVideoEmptyLatent_MXD": "LTX Empty Latent Video MXD",
"LTXKSampler_MXD": "LTX Stage 1 Sampler MXD",
"LTXKSampler2_MXD": "LTX Stage 2 Refiner MXD",
"LTXPreview_MXD": "LTX Preview MXD",
}
maxedoutnodes.py
-2196
View File
File diff suppressed because it is too large Load Diff
nodes/__init__.py
+40
View File
@@ -0,0 +1,40 @@
import importlib
def _safe_import(module_name: str):
try:
return importlib.import_module(f".{module_name}", __name__)
except Exception as e:
print(f"[ComfyUI-MaxedOut] Failed to import '{module_name}': {e}")
return None
def _get_mappings(mod):
if mod is None:
return {}, {}
class_map = getattr(mod, "NODE_CLASS_MAPPINGS", {}) or {}
display_map = getattr(mod, "NODE_DISPLAY_NAME_MAPPINGS", {}) or {}
return class_map, display_map
NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}
for _name in (
"latents",
"resolution",
"prompts",
"masks",
"media_io",
"comparers",
"checkpoints",
"ffgo",
"wan22",
"ltx",
):
_mod = _safe_import(_name)
_class_map, _display_map = _get_mappings(_mod)
NODE_CLASS_MAPPINGS.update(_class_map)
NODE_DISPLAY_NAME_MAPPINGS.update(_display_map)
__all__ = [
"NODE_CLASS_MAPPINGS",
"NODE_DISPLAY_NAME_MAPPINGS",
]
save_checkpoint_mxd.py → nodes/checkpoints.py
+42
View File
@@ -1,9 +1,49 @@
"""Checkpoint load/save nodes.
Registered nodes:
LoadCheckpointMXD Load Checkpoint MXD (core loader + MXD info-icon UI)
SaveCheckpointMXD Save Checkpoint MXD (core saver with the FakeDevice fix)
Import-time side effect: replaces comfy.diffusers_convert.cat_tensors with a
version that materializes lazily-cast weights first (see comment below).
"""
import torch
import folder_paths
import comfy.sd
import comfy.diffusers_convert
from comfy_extras.nodes_model_merging import save_checkpoint
class LoadCheckpointMXD:
DESCRIPTION = (
"Loads a diffusion model checkpoint, same as the core Load Checkpoint node, "
"with the MXD info-icon UI (CivitAI lookup, cached metadata, local notes)."
)
TITLE = "Load Checkpoint MXD"
CATEGORY = "MXD/Loaders"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
}
}
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
FUNCTION = "load_checkpoint"
def load_checkpoint(self, ckpt_name):
ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(
ckpt_path,
output_vae=True,
output_clip=True,
embedding_directory=folder_paths.get_folder_paths("embeddings"),
)
return out[:3]
# comfy's checkpoint saver builds the CLIP state dict via lazy "casting" params
# (comfy.model_patcher.LazyCastingParam / LazyCastingParamPiece) whose .device
# property returns a fake namedtuple ("FakeDevice") instead of a real torch.device,
@@ -80,9 +120,11 @@ class SaveCheckpointMXD:
NODE_CLASS_MAPPINGS = {
"LoadCheckpointMXD": LoadCheckpointMXD,
"SaveCheckpointMXD": SaveCheckpointMXD,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LoadCheckpointMXD": "Load Checkpoint MXD",
"SaveCheckpointMXD": "Save Checkpoint MXD",
}
mediacomparers.py → nodes/comparers.py
-3
View File
@@ -191,6 +191,3 @@ NODE_DISPLAY_NAME_MAPPINGS = {
MxdImageComparerSave.NAME: "Image Comparer + Save MXD",
MxdVideoComparer.NAME: "Video Comparer MXD",
}
WEB_DIRECTORY = "."
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
combine_materials_ffgo_mxd.py → nodes/ffgo.py
View File
nodes/latents.py
+305
View File
@@ -0,0 +1,305 @@
from __future__ import annotations
import torch, comfy, comfy.model_management
########################################################################################################################
# Flux Empty Latent Image (SD3-compatible)
class FluxEmptyLatentImage:
DESCRIPTION = """Select a Flux resolution and create an empty latent batch."""
TITLE = "Flux Empty Latent Image"
CATEGORY = "MXD/Latent"
RESOLUTIONS = {
"— High Resolutions —": None,
"Square (1:1) 1408x1408": (1408, 1408),
"Standard (4:3) 1664x1216": (1664, 1216),
"Landscape (3:2) 1728x1152": (1728, 1152),
"Widescreen (16:9) 1920x1088": (1920, 1088),
"Ultrawide (21:9) 2176x960": (2176, 960),
"— Standard Resolutions —": None,
"Square (1:1) 1024x1024": (1024, 1024),
"Standard (4:3) 1152x896": (1152, 896),
"Landscape (3:2) 1216x832": (1216, 832),
"Widescreen (16:9) 1344x768": (1344, 768),
"Ultrawide (21:9) 1536x640": (1536, 640),
"— Low Resolutions —": None,
"Square (1:1) 320x320": (320, 320),
"Standard (4:3) 448x320": (448, 320),
"Landscape (3:2) 384x256": (384, 256),
"Widescreen (16:9) 448x256": (448, 256),
"Ultrawide (21:9) 576x256": (576, 256),
}
def __init__(self):
self.device = comfy.model_management.intermediate_device()
@classmethod
def INPUT_TYPES(cls) -> dict:
return {
"required": {
"resolution": (
list(cls.RESOLUTIONS.keys()),
{"default": "Square (1:1) 1024x1024"}
),
"vertical": ("BOOLEAN", {"default": False}),
"batch_size": (
"INT",
{
"default": 1,
"min": 1,
"max": 4096,
"tooltip": "The number of latent images in the batch."
}
)
}
}
RETURN_TYPES = ("LATENT",)
OUTPUT_TOOLTIPS = ("The empty latent image batch.",)
FUNCTION = "generate"
def generate(self, resolution, vertical, batch_size=1) -> tuple:
size = self.RESOLUTIONS.get(resolution)
if size is None:
raise ValueError(f"'{resolution}' is a header or invalid option.")
width, height = size
if vertical:
width, height = height, width
latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device)
return ({"samples": latent},)
########################################################################################################################
# Flux 2 Empty Latent Image (Flux2-compatible)
class Flux2EmptyLatentImage:
DESCRIPTION = """Select a Flux resolution and create an empty Flux 2 latent batch."""
TITLE = "Flux 2 Empty Latent Image"
CATEGORY = "MXD/Latent"
RESOLUTIONS = FluxEmptyLatentImage.RESOLUTIONS
def __init__(self):
self.device = comfy.model_management.intermediate_device()
@classmethod
def INPUT_TYPES(cls) -> dict:
return {
"required": {
"resolution": (
list(cls.RESOLUTIONS.keys()),
{"default": "Square (1:1) 1024x1024"}
),
"vertical": ("BOOLEAN", {"default": False}),
"batch_size": (
"INT",
{
"default": 1,
"min": 1,
"max": 4096,
"tooltip": "The number of latent images in the batch."
}
)
}
}
RETURN_TYPES = ("LATENT",)
OUTPUT_TOOLTIPS = ("The empty Flux 2 latent image batch.",)
FUNCTION = "generate"
def generate(self, resolution, vertical, batch_size=1) -> tuple:
size = self.RESOLUTIONS.get(resolution)
if size is None:
raise ValueError(f"'{resolution}' is a header or invalid option.")
width, height = size
if vertical:
width, height = height, width
latent = torch.zeros([batch_size, 128, height // 16, width // 16], device=self.device)
return ({"samples": latent},)
########################################################################################################################
# Flux Resolution Selector (for feeding into FluxEmptyLatentImage)
class FluxResolutionSelector:
DESCRIPTION = """Pick a Flux resolution string for Flux Empty Latent Image."""
TITLE = "Flux Resolution Selector"
CATEGORY = "MXD/Latent"
@classmethod
def INPUT_TYPES(cls) -> dict:
return {
"required": {
"resolution": (
list(FluxEmptyLatentImage.RESOLUTIONS.keys()), # Include ALL keys including headers
{"default": "Square (1:1) 1024x1024"}
),
}
}
RETURN_TYPES = (list(FluxEmptyLatentImage.RESOLUTIONS.keys()),)
RETURN_NAMES = ("resolution",)
OUTPUT_TOOLTIPS = ("The selected resolution string for FluxEmptyLatentImage.",)
FUNCTION = "select_resolution"
def select_resolution(self, resolution) -> tuple:
return (resolution,)
########################################################################################################################
# Sdxl Empty Latent Image
class SdxlEmptyLatentImage:
DESCRIPTION = """Select an SDXL resolution and create an empty latent batch."""
TITLE = "Sdxl Empty Latent Image (With Resolutions)"
CATEGORY = "MXD/Latent"
# SDXL predefined resolutions (width, height)
RESOLUTIONS = {
"Square (1:1) 1024x1024": (1024, 1024),
"Standard (4:3) 1152x896": (1152, 896),
"Landscape (3:2) 1216x832": (1216, 832),
"Widescreen (16:9) 1344x768": (1344, 768),
"Ultra-Wide (21:9) 1536x640": (1536, 640),
}
def __init__(self):
# Retrieve the intermediate device (usually the GPU) from ComfyUI's model management.
self.device = comfy.model_management.intermediate_device()
@classmethod
def INPUT_TYPES(cls) -> dict:
return {
"required": {
# Dropdown selection for one of the predefined SDXL resolutions.
"resolution": (list(cls.RESOLUTIONS.keys()),),
# Toggle for vertical mode (swaps width and height).
"vertical": ("BOOLEAN", {"default": False}),
# Number of latent images to create in the batch.
"batch_size": (
"INT",
{
"default": 1,
"min": 1,
"max": 4096,
"tooltip": "The number of latent images in the batch."
}
)
}
}
RETURN_TYPES = ("LATENT",)
OUTPUT_TOOLTIPS = ("The empty latent image batch.",)
FUNCTION = "generate"
def generate(self, resolution, vertical, batch_size=1) -> tuple:
# Get the selected resolution tuple (width, height)
width, height = self.RESOLUTIONS[resolution]
# If vertical mode is enabled, swap width and height.
if vertical:
width, height = height, width
# Create an empty latent tensor.
# Typically, the latent space has 4 channels and each spatial dimension is 1/8th of the image.
latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
return ({"samples": latent},)
########################################################################################################################
# Z-Image Turbo Empty Latent Image (SD3-compatible) — Flux-style grouping
class ZImageTurboEmptyLatentImage:
DESCRIPTION = """Select a Z-Image Turbo resolution and create an empty latent batch."""
TITLE = "Z-Image Turbo Empty Latent Image"
CATEGORY = "MXD/Latent"
# Tuned for Z-Image Turbo:
# - Rule of 64: every dimension is a multiple of 64
# - 1MP baseline: 1024x1024 in the standard tier
# - Ceiling: keep presets below 6.5MP
MAX_TOTAL_PIXELS = 6_500_000
MIN_BLOCK = 64
RESOLUTIONS = {
"— High Resolutions —": None,
"Square (1:1) 1536x1536": (1536, 1536),
"Photo (4:3) 1792x1344": (1792, 1344),
"Landscape (3:2) 1920x1280": (1920, 1280),
"Widescreen (16:9) 2048x1152": (2048, 1152),
"Ultrawide (21:9) 2304x1024": (2304, 1024),
"— Standard Resolutions —": None,
"Square (1:1) 1024x1024": (1024, 1024),
"Photo (4:3) 1152x896": (1152, 896),
"Landscape (3:2) 1280x832": (1280, 832),
"Widescreen (16:9) 1344x768": (1344, 768),
"Ultrawide (21:9) 1536x640": (1536, 640),
"— Low Resolutions —": None,
"Square (1:1) 512x512": (512, 512),
"Photo (4:3) 576x448": (576, 448),
"Landscape (3:2) 640x448": (640, 448),
"Widescreen (16:9) 704x384": (704, 384),
"Ultrawide (21:9) 768x320": (768, 320),
}
def __init__(self):
self.device = comfy.model_management.intermediate_device()
@classmethod
def INPUT_TYPES(cls) -> dict:
return {
"required": {
"resolution": (
list(cls.RESOLUTIONS.keys()),
{"default": "Square (1:1) 1024x1024"}
),
"vertical": (
"BOOLEAN",
{"default": False, "tooltip": "Swap width and height."}
),
"batch_size": (
"INT",
{"default": 1, "min": 1, "max": 4096, "tooltip": "Number of latent images in the batch."}
)
}
}
RETURN_TYPES = ("LATENT",)
OUTPUT_TOOLTIPS = ("The empty Z-Image Turbo latent batch.",)
FUNCTION = "generate"
def generate(self, resolution, vertical, batch_size=1) -> tuple:
size = self.RESOLUTIONS.get(resolution)
if size is None:
raise ValueError(f"'{resolution}' is a header or invalid option.")
width, height = size
if vertical:
width, height = height, width
if (width % self.MIN_BLOCK) != 0 or (height % self.MIN_BLOCK) != 0:
raise ValueError(
f"Invalid preset {width}x{height}. Z-Image Turbo requires multiples of {self.MIN_BLOCK}."
)
if (width * height) > self.MAX_TOTAL_PIXELS:
raise ValueError(
f"Invalid preset {width}x{height}. Z-Image Turbo presets must stay at or below {self.MAX_TOTAL_PIXELS:,} pixels."
)
latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device)
return ({"samples": latent},)
########################################################################################################################
NODE_CLASS_MAPPINGS = {
"Flux Empty Latent Image": FluxEmptyLatentImage,
"Flux 2 Empty Latent Image": Flux2EmptyLatentImage,
"Flux Resolution Selector": FluxResolutionSelector,
"Sdxl Empty Latent Image": SdxlEmptyLatentImage,
"ZImageTurboEmptyLatentImage": ZImageTurboEmptyLatentImage,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Flux Empty Latent Image": "Flux Empty Latent Image MXD",
"Flux 2 Empty Latent Image": "Flux 2 Empty Latent Image MXD",
"Flux Resolution Selector": "Flux Resolution Selector MXD",
"Sdxl Empty Latent Image": "SDXL Empty Latent Image MXD",
"ZImageTurboEmptyLatentImage": "ZIT Empty Latent Image MXD",
}
nodes/ltx/__init__.py
+18
View File
@@ -0,0 +1,18 @@
"""LTX Video node package: latent sizing, two-stage samplers, taeltx live preview."""
import importlib
NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}
for _name in (
"latents",
"samplers",
"preview",
):
try:
_mod = importlib.import_module(f".{_name}", __name__)
except Exception as e:
print(f"[ComfyUI-MaxedOut] Failed to import 'nodes.ltx.{_name}': {e}")
continue
NODE_CLASS_MAPPINGS.update(getattr(_mod, "NODE_CLASS_MAPPINGS", {}) or {})
NODE_DISPLAY_NAME_MAPPINGS.update(getattr(_mod, "NODE_DISPLAY_NAME_MAPPINGS", {}) or {})
nodes/ltx/latents.py
+266
View File
@@ -0,0 +1,266 @@
"""LTX Video latent sizing: empty latent generator + two-stage image scaler.
Registered nodes:
LTXVideoEmptyLatent_MXD LTX Empty Latent Video MXD
LTX_Image_Scaler_MXD LTX Video Image Scaler MXD
Official LTX-2.3 rules (Lightricks model card + example workflows):
- Width & height must be divisible by 32; frame count must be 8n+1.
- The distilled two-stage workflow generates Stage 1 low-res, then the
ltx-2.3-spatial-upscaler-x2 doubles it (exactly 2x) for Stage 2.
- The one published two-stage resolution is Stage 1 960x544 -> 1920x1088.
Tiers below are FINAL (Stage 2) sizes; Stage 1 is exactly half. Finals are
kept /64 so Stage 1 stays /32 (the latent constraint). Only the 1080p 16:9
row is officially published by Lightricks; the portrait/square rows and the
720p/576p tiers are /32-aligned siblings at the same pixel budget.
Buckets (FINAL size, all /64) -> Stage 1 (half, all /32):
1080p: 1920x1088 / 1088x1920 / 1408x1408 (Stage 1: 960x544 / 544x960 / 704x704)
720p: 1280x704 / 704x1280 / 960x960 (Stage 1: 640x352 / 352x640 / 480x480)
576p: 1024x576 / 576x1024 / 768x768 (Stage 1: 512x288 / 288x512 / 384x384)
Fit (no pad): proportional resize <= target, /64 aligned.
Crop (no pad): resize-to-cover then center-crop to exact bucket.
Square images map to each tier's square bucket.
"""
from __future__ import annotations
import torch
import comfy.utils
import comfy.model_management
import nodes
from ..wan22.buckets import _is_squareish, _validate_image_batch_4d
########################################################################################################################
# LTX Video Empty Latent Image
class LTXVideoEmptyLatentMXD:
DESCRIPTION = "Create an LTX Video empty latent batch from connected width/height and frame count."
TITLE = "LTX Empty Latent Video MXD"
CATEGORY = "MXD/Latent"
# All dimensions must be multiples of 32 (LTX 32× spatial compression).
# Lengths must be 8n+1 for LTX's 8× temporal compression.
RESOLUTIONS = {
"16:9 Landscape": None,
"16:9 512×288": (512, 288),
"16:9 768×448": (768, 448),
"16:9 832×480": (832, 480),
"16:9 1024×576": (1024, 576),
"16:9 1280×736": (1280, 736),
"9:16 Portrait": None,
"9:16 288×512": (288, 512),
"9:16 448×768": (448, 768),
"9:16 480×832": (480, 832),
"9:16 576×1024": (576, 1024),
"4:3 Standard": None,
"4:3 512×384": (512, 384),
"4:3 768×576": (768, 576),
"1:1 Square": None,
"1:1 512×512": (512, 512),
"1:1 768×768": (768, 768),
}
def __init__(self):
self.device = comfy.model_management.intermediate_device()
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"length": (
"INT",
{
"default": 97,
"min": 9,
"max": 1025,
"step": 8,
"tooltip": "Number of frames. Must be 8n+1 (e.g. 25, 49, 73, 97, 121, 201).",
},
),
"batch_size": (
"INT",
{
"default": 1,
"min": 1,
"max": 4096,
"tooltip": "Number of latent videos in the batch.",
},
),
},
"optional": {
"width": ("INT", {
"default": 640, "min": 32, "max": 8192, "step": 32,
"tooltip": "Stage 1 width. Connect the LTX Image Scaler stage1_width output for I2V workflows.",
}),
"height": ("INT", {
"default": 384, "min": 32, "max": 8192, "step": 32,
"tooltip": "Stage 1 height. Connect the LTX Image Scaler stage1_height output for I2V workflows.",
}),
},
}
RETURN_TYPES = ("LATENT", "INT")
RETURN_NAMES = ("latent", "length")
FUNCTION = "generate"
def generate(self, length, batch_size=1, width=640, height=384):
# LTX latent: 128 channels, 32× spatial compression, 8× temporal compression
width = max(32, int(width) // 32 * 32)
height = max(32, int(height) // 32 * 32)
length = max(9, 1 + 8 * round((int(length) - 1) / 8))
t = ((length - 1) // 8) + 1
h = height // 32
w = width // 32
latent = torch.zeros([batch_size, 128, t, h, w], device=self.device)
return ({"samples": latent}, length)
_LTX_BUCKETS = {
"1080p": {"landscape": (1920, 1088), "portrait": (1088, 1920), "square": (1408, 1408)},
"720p": {"landscape": (1280, 704), "portrait": (704, 1280), "square": (960, 960)},
"576p": {"landscape": (1024, 576), "portrait": (576, 1024), "square": (768, 768)},
}
def _ceil32(x):
x = (int(x) + 31) // 32 * 32
return max(32, x)
def _floor32(x):
x = int(x) // 32 * 32
return max(32, x)
def _floor64(x):
x = int(x) // 64 * 64
return max(64, x)
def _ltx_stage1_dims(final_w, final_h):
"""Return Stage 1 dimensions that upscale exactly to the final size."""
return max(32, int(final_w) // 2), max(32, int(final_h) // 2)
def _ltx_resize_fit_inside(img, out_w, out_h):
"""Resize to fit inside (out_w, out_h), output /64 aligned on both sides."""
_, ih, iw, _ = img.shape
s = min(out_w / iw, out_h / ih)
tw = _floor64(iw * s)
th = _floor64(ih * s)
tw = max(32, min(tw, nodes.MAX_RESOLUTION))
th = max(32, min(th, nodes.MAX_RESOLUTION))
resized = comfy.utils.common_upscale(img.movedim(-1, 1), tw, th, "bilinear", "center").movedim(1, -1)
return resized, tw, th
def _ltx_resize_then_center_crop(img, out_w, out_h):
"""Resize to cover (out_w, out_h) then center-crop to exact /32 target."""
_, ih, iw, _ = img.shape
s = max(out_w / iw, out_h / ih)
tw = _ceil32(iw * s)
th = _ceil32(ih * s)
tmp = comfy.utils.common_upscale(img.movedim(-1, 1), tw, th, "bilinear", "center").movedim(1, -1)
y0 = max(0, (th - out_h) // 2)
x0 = max(0, (tw - out_w) // 2)
return tmp[:, y0:y0+out_h, x0:x0+out_w, :]
def _ltx_pick_bucket(iw, ih, tier):
"""Pick the landscape / portrait / square bucket for the given tier."""
tier_map = _LTX_BUCKETS[tier]
if _is_squareish(iw, ih):
return tier_map["square"]
return tier_map["landscape"] if iw >= ih else tier_map["portrait"]
def _ltx_scale_image_core(image, tier="1080p", crop_to_fit=True):
"""
Core LTX scaler. Returns (scaled_image, final_w, final_h, stage1_w, stage1_h).
'tier' is the FINAL (Stage 2) size budget; Stage 1 is exactly half.
"""
_, ih, iw, _ = image.shape
bw, bh = _ltx_pick_bucket(iw, ih, tier)
if _is_squareish(iw, ih):
crop_to_fit = False
if crop_to_fit:
out = _ltx_resize_then_center_crop(image, bw, bh)
else:
out, bw, bh = _ltx_resize_fit_inside(image, bw, bh)
final_w = int(out.shape[2])
final_h = int(out.shape[1])
stage1_w, stage1_h = _ltx_stage1_dims(final_w, final_h)
return out, final_w, final_h, stage1_w, stage1_h
class LTX_Image_Scaler_MXD:
"""
MXD Image Scaler for LTX Video (distilled two-stage workflow).
'tier' is the FINAL (Stage 2) size; Stage 1 is exactly half. Finals are /64
so Stage 1 stays /32 (the LTX latent constraint). Wire stage1_width /
stage1_height into the empty latent for the low-res pass; the spatial
upscaler-x2 then doubles it back to the final size.
Tiers (final / Stage 1):
1080p 1920x1088 (official 16:9) / 1088x1920 / 1408x1408 -> half
720p 1280x704 / 704x1280 / 960x960 -> half
576p 1024x576 / 576x1024 / 768x768 -> half
Modes:
Perfect Fit (Crops Edges) resize-to-cover + center-crop to exact bucket.
Closest Fit (No Crop) proportional resize, /64-aligned; may be smaller.
Square images (within +-3% of 1:1) map to the tier's square bucket.
Outputs the scaled image at final size plus the Stage 1 dimensions.
"""
TITLE = "LTX Video Image Scaler MXD"
CATEGORY = "image/processing"
RETURN_TYPES = ("IMAGE", "INT", "INT")
RETURN_NAMES = ("image", "width", "height")
FUNCTION = "scale"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"tier": (["1080p", "720p", "576p"], {"default": "1080p"}),
"crop_to_fit": ("BOOLEAN", {
"default": True,
"label_on": "Crop Edges",
"label_off": "Closest Fit (No Crop)",
}),
}
}
def scale(self, image, tier="1080p", crop_to_fit=True):
image = _validate_image_batch_4d(image, "LTX_Image_Scaler_MXD", "image")
out, _final_w, _final_h, stage1_w, stage1_h = _ltx_scale_image_core(
image, tier=tier, crop_to_fit=crop_to_fit
)
return (out, stage1_w, stage1_h)
NODE_CLASS_MAPPINGS = {
"LTXVideoEmptyLatent_MXD": LTXVideoEmptyLatentMXD,
"LTX_Image_Scaler_MXD": LTX_Image_Scaler_MXD,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LTXVideoEmptyLatent_MXD": "LTX Empty Latent Video MXD",
"LTX_Image_Scaler_MXD": "LTX Video Image Scaler MXD",
}
nodes/ltx/preview.py
+329
View File
@@ -0,0 +1,329 @@
"""LTX live preview via the tiny taeltx autoencoder.
Registered node:
LTXPreview_MXD LTX Preview MXD (attach the previewer to ANY sampler's model)
Core ComfyUI has no preview for the LTXAV format used by LTX 2.3, and the
latent2rgb approximation looks awful for video. This installs a previewer that
decodes latent frames with the tiny "taeltx" autoencoder for accurate previews.
The taeltx model is auto-discovered in the vae / vae_approx model folders. If
it isn't found, it is downloaded to the configured vae model folder.
Sends MXD_live_preview_start / MXD_live_preview_frame / MXD_live_preview_saved
websocket events consumed by web/live_preview_panel_mxd.js. Final clips are
saved to <output>/live_previews.
TAE decode path borrowed from kjnodes / VideoHelperSuite.
"""
from __future__ import annotations
import os
import base64
import time
import urllib.error
import urllib.request
from fractions import Fraction
from io import BytesIO
from PIL import Image
from threading import Lock, Thread
import torch
import torch.nn.functional as F
import comfy
import comfy.model_management
import comfy.patcher_extension
import comfy.utils
import server
import folder_paths as _folder_paths
from comfy_api.latest import VideoFromComponents, VideoComponents
_serv = server.PromptServer.instance
_TAELTX_FILENAME = "taeltx2_3.safetensors"
_TAELTX_URL = "https://huggingface.co/Kijai/LTX2.3_comfy/resolve/main/vae/taeltx2_3.safetensors?download=true"
_TAELTX_DOWNLOAD_LOCK = Lock()
def _find_taeltx_path(folder_paths):
for folder in ("vae", "vae_approx"):
try:
names = folder_paths.get_filename_list(folder)
except Exception:
continue
name = next((fn for fn in names if "taeltx" in fn.lower()), None)
if name is not None:
path = folder_paths.get_full_path(folder, name)
if path:
return path
return None
def _download_taeltx(folder_paths):
try:
vae_dirs = folder_paths.get_folder_paths("vae")
except Exception as exc:
print(f"[MXD LTX preview] cannot find ComfyUI vae model folder: {exc}")
return None
if not vae_dirs:
print("[MXD LTX preview] cannot find ComfyUI vae model folder.")
return None
target_dir = vae_dirs[0]
target_path = os.path.join(target_dir, _TAELTX_FILENAME)
partial_path = f"{target_path}.part"
with _TAELTX_DOWNLOAD_LOCK:
if os.path.isfile(target_path):
return target_path
try:
os.makedirs(target_dir, exist_ok=True)
print(f"[MXD LTX preview] downloading {_TAELTX_FILENAME} to {target_path}")
request = urllib.request.Request(_TAELTX_URL, headers={"User-Agent": "ComfyUI-MaxedOut"})
with urllib.request.urlopen(request, timeout=120) as response, open(partial_path, "wb") as out:
while True:
chunk = response.read(1024 * 1024)
if not chunk:
break
out.write(chunk)
if not os.path.isfile(partial_path) or os.path.getsize(partial_path) == 0:
raise RuntimeError("downloaded file is empty")
os.replace(partial_path, target_path)
try:
folder_paths.get_filename_list("vae")
except Exception:
pass
print(f"[MXD LTX preview] downloaded {_TAELTX_FILENAME}")
return target_path
except (OSError, RuntimeError, urllib.error.URLError) as exc:
try:
if os.path.exists(partial_path):
os.remove(partial_path)
except OSError:
pass
print(f"[MXD LTX preview] failed to download {_TAELTX_FILENAME}: {exc}")
return None
def _load_taeltx():
"""Load the taeltx TAE from the vae / vae_approx model folders. Returns a VAE or None."""
try:
import folder_paths
from comfy.sd import VAE
except Exception:
return None
path = _find_taeltx_path(folder_paths)
if not path:
path = _download_taeltx(folder_paths)
if not path:
return None
try:
taeltx = VAE(comfy.utils.load_torch_file(path))
taeltx.first_stage_model.show_progress_bar = False
except Exception as exc:
print(f"[MXD LTX preview] failed to load taeltx ({path}): {exc}")
return None
return taeltx
class _LTXTAEPreviewer:
"""Cycles through LTX video latent frames during sampling, decoding with taeltx."""
def __init__(self, taeltx, rate=8):
self.first_preview = True
self.last_time = 0.0
self.c_index = 0
self.rate = rate
self.taeltx = taeltx
def decode_latent_to_preview_image(self, preview_format, x0):
if x0.ndim == 5:
x0 = x0.movedim(2, 1)
x0 = x0.reshape((-1,) + x0.shape[-3:])
num_images = x0.size(0)
new_time = time.time()
num_previews = int((new_time - self.last_time) * self.rate)
self.last_time += num_previews / self.rate
if num_previews > num_images:
num_previews = num_images
elif num_previews <= 0:
return None
if self.first_preview:
self.first_preview = False
_serv.send_sync(
'MXD_live_preview_start',
{'length': num_images, 'rate': self.rate, 'id': _serv.last_node_id},
)
self.last_time = new_time + 1.0 / self.rate
if self.c_index + num_previews > num_images:
frames = x0.roll(-self.c_index, 0)[:num_previews]
else:
frames = x0[self.c_index:self.c_index + num_previews]
Thread(target=self._send_frames, args=(frames, self.c_index, num_images)).run()
self.c_index = (self.c_index + num_previews) % num_images
return None
def _send_frames(self, image_tensor, ind, leng):
max_size, min_size = 512, 256
image_tensor = self._decode(image_tensor)
if image_tensor.size(1) < min_size or image_tensor.size(2) < min_size:
image_tensor = F.interpolate(
image_tensor.movedim(-1, 0), scale_factor=4, mode='nearest'
).movedim(0, -1)
if image_tensor.size(1) > max_size or image_tensor.size(2) > max_size:
t = image_tensor.movedim(-1, 0)
if t.size(2) < t.size(3):
h = (max_size * t.size(2)) // t.size(3)
t = F.interpolate(t, (h, max_size), mode='nearest')
else:
w = (max_size * t.size(3)) // t.size(2)
t = F.interpolate(t, (max_size, w), mode='nearest')
image_tensor = t.movedim(0, -1)
previews = image_tensor.clamp(0, 1).mul(0xFF).to(device="cpu", dtype=torch.uint8)
node_id = _serv.last_node_id
for preview in previews:
img = Image.fromarray(preview.numpy())
buf = BytesIO()
img.save(buf, format="JPEG", quality=90)
data_url = "data:image/jpeg;base64," + base64.b64encode(buf.getvalue()).decode("ascii")
_serv.send_sync('MXD_live_preview_frame', {'id': node_id, 'index': ind, 'length': leng, 'data': data_url})
# taeltx expands the 8× temporal compression on decode
ind = (ind + 1) % ((leng - 1) * 8 + 1)
def _decode(self, x0):
dev = comfy.model_management.get_torch_device()
dtype = self.taeltx.first_stage_model.decoder[1].weight.dtype
x0 = x0.unsqueeze(0).to(dtype=dtype, device=dev)
return self.taeltx.first_stage_model.decode(x0)[0].permute(1, 2, 3, 0)
def _save_final_ltx_preview(node_id, previewer, x0_v, rate):
"""Decode the full final clip with taeltx and save it as an mp4 to output/live_previews."""
try:
frames = x0_v.movedim(2, 1)
frames = frames.reshape((-1,) + frames.shape[-3:])
frames = previewer._decode(frames).clamp(0, 1).to(device="cpu", dtype=torch.float32)
if frames.ndim != 4 or frames.size(0) == 0:
return
out_dir = os.path.join(_folder_paths.get_output_directory(), "live_previews")
os.makedirs(out_dir, exist_ok=True)
safe_id = str(node_id).replace(":", "_").replace("/", "_")
filename = f"{safe_id}_{int(time.time())}.mp4"
path = os.path.join(out_dir, filename)
video = VideoFromComponents(VideoComponents(images=frames, frame_rate=Fraction(max(1, round(rate)))))
video.save_to(path)
print(f"[MXD LTX preview] Saved live preview to {path}")
_serv.send_sync("MXD_live_preview_saved", {
"node_id": node_id, "filename": filename, "subfolder": "live_previews", "type": "output",
})
except Exception as e:
print(f"[MXD LTX preview] Failed to save live preview: {e}")
class _LTXPreviewWrapper:
"""OUTER_SAMPLE wrapper that installs the taeltx video previewer during sampling."""
def __init__(self, taeltx):
self.taeltx = taeltx
def __call__(self, executor, noise, latent_image, sampler, sigmas,
denoise_mask, callback, disable_pbar, seed, latent_shapes):
guider = executor.class_obj
device = comfy.model_management.get_torch_device()
self.taeltx.first_stage_model.to(device)
previewer = _LTXTAEPreviewer(self.taeltx, rate=8)
pbar = comfy.utils.ProgressBar(len(sigmas) - 1)
node_id = _serv.last_node_id
# Strip I2V guide frames appended at the end of the latent before previewing.
num_keyframes = 0
if 'positive' in guider.conds and guider.conds['positive']:
kf = guider.conds['positive'][0].get('keyframe_idxs')
if kf is not None:
num_keyframes = len(torch.unique(kf[0, 0, :, 0]))
def ltx_callback(step, x0, x, total_steps):
x0_v = x0
if x0_v is not None and len(latent_shapes) > 1:
# Audio+video latents are packed into [B, 1, total]; unpack and
# take the video tensor (the 5D one). Audio is a lower-rank entry.
x0_v = next(
(p for p in comfy.utils.unpack_latents(x0, latent_shapes) if p.ndim == 5),
None,
)
if x0_v is not None and x0_v.ndim == 5 and num_keyframes > 0:
x0_v = x0_v[:, :, :-num_keyframes]
preview = (
previewer.decode_latent_to_preview_image("JPEG", x0_v)
if x0_v is not None and x0_v.ndim == 5 else None
)
pbar.update_absolute(step + 1, total_steps, preview)
if step + 1 >= total_steps and x0_v is not None and x0_v.ndim == 5:
_save_final_ltx_preview(node_id, previewer, x0_v, previewer.rate)
if callback is not None:
callback(step, x0, x, total_steps)
try:
return executor(
noise, latent_image, sampler, sigmas, denoise_mask,
ltx_callback, disable_pbar, seed, latent_shapes=latent_shapes,
)
finally:
self.taeltx.first_stage_model.to(comfy.model_management.unet_offload_device())
########################################################################################################################
# LTX Preview — attach the taeltx previewer to any model
class LTXPreviewMXD:
DESCRIPTION = (
"Enables taeltx video previews during sampling for ANY sampler node "
"(SamplerCustomAdvanced, KSampler, etc.), not just the MXD LTX samplers. "
"LTX 2.3 (LTXAV) ships no built-in preview decoder, so core ComfyUI shows "
"nothing; this attaches a wrapper to the model that decodes latent frames "
"with the tiny taeltx autoencoder. Wire it between your model loader and "
"the sampler's model input. Downloads taeltx to your vae folder if missing."
)
TITLE = "LTX Preview MXD"
CATEGORY = "MXD/Sampling"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("MODEL",),
"enabled": ("BOOLEAN", {"default": True, "tooltip": "Turn taeltx previews on/off without unwiring the node."}),
},
}
RETURN_TYPES = ("MODEL",)
RETURN_NAMES = ("model",)
FUNCTION = "apply"
OUTPUT_NODE = False
def apply(self, model, enabled=True):
if not enabled:
return (model,)
taeltx = _load_taeltx()
if taeltx is None:
print("[MXD LTX preview] taeltx model not found in vae / vae_approx — skipping preview.")
return (model,)
model = model.clone()
model.add_wrapper_with_key(
comfy.patcher_extension.WrappersMP.OUTER_SAMPLE,
"ltx_mxd_preview",
_LTXPreviewWrapper(taeltx),
)
return (model,)
NODE_CLASS_MAPPINGS = {
"LTXPreview_MXD": LTXPreviewMXD,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LTXPreview_MXD": "LTX Preview MXD",
}
nodes/ltx/samplers.py
+296
View File
@@ -0,0 +1,296 @@
"""LTX two-stage distilled samplers.
Registered nodes:
LTXKSampler_MXD LTX Stage 1 Sampler MXD (distilled 8-step schedule)
LTXKSampler2_MXD LTX Stage 2 Refiner MXD (official refine, start sigma 0.85)
Sigma schedules come from the official Lightricks LTX-2.3 two-stage distilled
workflow (LTX-2.3_T2V_I2V_Two_Stage_Distilled.json). Custom Sigmas mode accepts
a manual descending schedule ending in 0.0 for experimentation.
"""
from __future__ import annotations
import re
import torch
import comfy
import comfy.model_management
import comfy.patcher_extension
import comfy.samplers
import comfy.sample
import comfy.utils
import latent_preview
from .preview import _load_taeltx, _LTXPreviewWrapper
########################################################################################################################
# Shared noise helper — equivalent to ComfyUI RandomNoise
class _LTXNoise:
def __init__(self, seed: int):
self.seed = seed
def generate_noise(self, latent: dict) -> torch.Tensor:
samples = latent["samples"]
batch_inds = latent.get("batch_index", None)
return comfy.sample.prepare_noise(samples, self.seed, batch_inds)
########################################################################################################################
# LTX KSampler — Stage 1 (T2V / I2V generation at base resolution)
class LTXKSamplerMXD:
DESCRIPTION = (
"LTX-Video Stage 1 sampler for the distilled workflow. Use Distilled 8 Step "
"for the trained schedule, or Custom Sigmas when intentionally testing a "
"manual schedule."
)
TITLE = "LTX Stage 1 Sampler MXD"
CATEGORY = "MXD/Sampling"
MODES = ["Distilled 8 Step", "Custom Sigmas"]
_DISTILLED_SIGMAS = [1.0, 0.99375, 0.9875, 0.98125, 0.975,
0.909375, 0.725, 0.421875, 0.0]
_CUSTOM_SIGMAS_DEFAULT = "1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("MODEL",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"latent_image": ("LATENT",),
"mode": (cls.MODES, {"default": "Distilled 8 Step"}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF, "control_after_generate": True}),
"cfg": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.1}),
"sampler_name": (
["euler_ancestral_cfg_pp", "euler_cfg_pp", "euler"],
{"default": "euler_ancestral_cfg_pp"},
),
"custom_sigmas": (
"STRING",
{
"default": cls._CUSTOM_SIGMAS_DEFAULT,
"multiline": True,
"tooltip": "Only used when mode is Custom Sigmas. Enter comma, space, or newline separated sigma values.",
},
),
"ltx_preview": ("BOOLEAN", {"default": True, "tooltip": "Show LTX video previews during sampling. Downloads the taeltx VAE to your vae model folder if it is missing."}),
},
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("latent",)
FUNCTION = "sample"
OUTPUT_NODE = False
def sample(
self,
model,
positive,
negative,
latent_image,
mode="Distilled 8 Step",
seed=0,
cfg=2.0,
sampler_name="euler_ancestral_cfg_pp",
custom_sigmas=_CUSTOM_SIGMAS_DEFAULT,
ltx_preview=True,
):
sigmas = _select_sigmas(
mode,
{
"Distilled 8 Step": self._DISTILLED_SIGMAS,
},
custom_sigmas,
"LTX Stage 1 Sampler MXD",
)
return _run_sampling(model, positive, negative, latent_image, seed, cfg, sampler_name, sigmas, ltx_preview)
########################################################################################################################
# LTX KSampler 2 — Stage 2 (refinement at 2× resolution with distilled LoRA)
class LTXKSampler2MXD:
DESCRIPTION = (
"LTX-Video Stage 2 refiner for the distilled workflow. Official Refine "
"matches the Lightricks 2.3 two-stage example (start sigma 0.85). "
"Custom Sigmas is for manual testing."
)
TITLE = "LTX Stage 2 Refiner MXD"
CATEGORY = "MXD/Sampling"
# Exact stage-2 refine schedule from the official Lightricks 2.3 two-stage
# workflow (LTX-2.3_T2V_I2V_Two_Stage_Distilled.json, euler_cfg_pp, cfg 1).
# Only the starting sigma (denoise strength) is meant to vary; use Custom
# Sigmas for that.
MODES = ["Official Refine", "Custom Sigmas"]
_OFFICIAL_REFINE_SIGMAS = [0.85, 0.725, 0.4219, 0.0]
_CUSTOM_SIGMAS_DEFAULT = "0.85, 0.725, 0.4219, 0.0"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("MODEL",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"latent_image": ("LATENT",),
"mode": (cls.MODES, {"default": "Official Refine"}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF, "control_after_generate": True}),
"cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1}),
"sampler_name": (
["euler_cfg_pp", "euler_ancestral_cfg_pp", "euler"],
{"default": "euler_cfg_pp"},
),
"custom_sigmas": (
"STRING",
{
"default": cls._CUSTOM_SIGMAS_DEFAULT,
"multiline": True,
"tooltip": "Only used when mode is Custom Sigmas. Enter comma, space, or newline separated sigma values.",
},
),
"ltx_preview": ("BOOLEAN", {"default": True, "tooltip": "Show LTX video previews during sampling. Downloads the taeltx VAE to your vae model folder if it is missing."}),
},
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("latent",)
FUNCTION = "sample"
OUTPUT_NODE = False
def sample(
self,
model,
positive,
negative,
latent_image,
mode="Official Refine",
seed=0,
cfg=1.0,
sampler_name="euler_cfg_pp",
custom_sigmas=_CUSTOM_SIGMAS_DEFAULT,
ltx_preview=True,
):
sigmas = _select_sigmas(
mode,
{
"Official Refine": self._OFFICIAL_REFINE_SIGMAS,
},
custom_sigmas,
"LTX Stage 2 Refiner MXD",
)
return _run_sampling(model, positive, negative, latent_image, seed, cfg, sampler_name, sigmas, ltx_preview)
########################################################################################################################
# Sigma schedule helpers
_SIGMA_RE = re.compile(r"[-+]?(?:\d*\.\d+|\d+\.?)(?:[eE][-+]?\d+)?")
def _select_sigmas(mode, presets, custom_sigmas, node_name):
if mode == "Custom Sigmas":
values = _parse_custom_sigmas(custom_sigmas, node_name)
else:
try:
values = presets[mode]
except KeyError as exc:
allowed = ", ".join([*presets.keys(), "Custom Sigmas"])
raise ValueError(f"{node_name}: unknown mode '{mode}'. Expected one of: {allowed}.") from exc
return torch.tensor(values, dtype=torch.float32)
def _parse_custom_sigmas(custom_sigmas, node_name):
text = str(custom_sigmas or "")
values = [float(match.group(0)) for match in _SIGMA_RE.finditer(text)]
if len(values) < 2:
raise ValueError(f"{node_name}: Custom Sigmas needs at least two sigma values, ending with 0.0.")
for index, (left, right) in enumerate(zip(values, values[1:]), start=1):
if right > left:
raise ValueError(
f"{node_name}: Custom Sigmas must be in descending order. "
f"Value {index + 1} ({right}) is greater than value {index} ({left})."
)
if abs(values[-1]) > 1e-8:
raise ValueError(f"{node_name}: Custom Sigmas must end with 0.0.")
return values
########################################################################################################################
# Shared sampling logic
def _run_sampling(model, positive, negative, latent_image, seed, cfg, sampler_name, sigmas, ltx_preview=False):
taeltx = _load_taeltx() if ltx_preview else None
if ltx_preview and taeltx is None:
print("[MXD LTX preview] taeltx model not found in vae / vae_approx — skipping preview.")
if taeltx is not None:
model = model.clone()
model.add_wrapper_with_key(
comfy.patcher_extension.WrappersMP.OUTER_SAMPLE,
"ltx_mxd_preview",
_LTXPreviewWrapper(taeltx),
)
guider = comfy.samplers.CFGGuider(model)
guider.set_conds(positive, negative)
guider.set_cfg(cfg)
sampler = comfy.samplers.sampler_object(sampler_name)
latent = latent_image.copy()
latent_samples = latent["samples"]
try:
latent_samples = comfy.sample.fix_empty_latent_channels(
guider.model_patcher, latent_samples,
latent.get("downscale_ratio_spacial", None),
)
except AttributeError:
pass
latent["samples"] = latent_samples
noise_mask = latent.get("noise_mask", None)
noise = _LTXNoise(seed)
if taeltx is not None:
# The preview wrapper owns the progress bar / callback.
callback = None
else:
x0_output = {}
callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = guider.sample(
noise.generate_noise(latent),
latent_samples,
sampler,
sigmas,
denoise_mask=noise_mask,
callback=callback,
disable_pbar=disable_pbar,
seed=seed,
)
samples = samples.to(comfy.model_management.intermediate_device())
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out["samples"] = samples
return (out,)
NODE_CLASS_MAPPINGS = {
"LTXKSampler_MXD": LTXKSamplerMXD,
"LTXKSampler2_MXD": LTXKSampler2MXD,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LTXKSampler_MXD": "LTX Stage 1 Sampler MXD",
"LTXKSampler2_MXD": "LTX Stage 2 Refiner MXD",
}
nodes/masks.py
+563
View File
@@ -0,0 +1,563 @@
from __future__ import annotations
import torch, comfy, comfy.utils, folder_paths, random
import torch.nn.functional as F
import numpy as np
from PIL import Image, ImageColor
from nodes import SaveImage
########################################################################################################################
class LatentHalfMasks:
DESCRIPTION = """Split a latent into left and right half masks."""
TITLE = "Latent Half Masks"
CATEGORY = "MXD/Latent"
RETURN_TYPES = ("MASK", "MASK")
RETURN_NAMES = ("mask_left", "mask_right")
OUTPUT_TOOLTIPS = (
"Mask covering the left half of the latent.",
"Mask covering the right half of the latent.",
)
FUNCTION = "make_masks"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"latent": ("LATENT",),
}
}
RETURN_TYPES = ("MASK", "MASK")
RETURN_NAMES = ("mask_left", "mask_right")
FUNCTION = "make_masks"
CATEGORY = "MXD/latent"
def make_masks(self, latent):
# Infer width/height from latent (assumes 8x scale)
samples = latent.get("samples", None)
if samples is None or not isinstance(samples, torch.Tensor):
raise ValueError("LatentHalfMasks: invalid latent or missing 'samples' tensor.")
h_lat, w_lat = samples.shape[-2], samples.shape[-1]
w, h = int(w_lat * 8), int(h_lat * 8)
# Always vertical, center split, no feather, no swap
split_px = w // 2
left = torch.zeros((h, w), dtype=torch.float32)
right = torch.zeros((h, w), dtype=torch.float32)
left[:, :split_px] = 1.0
right[:, split_px:] = 1.0
return left, right
########################################################################################################################
# Get Latent Size
class GetLatentSizeMXD:
DESCRIPTION = """Get image width/height from a latent."""
TITLE = "Get Latent Size"
CATEGORY = "MXD/Latent"
RETURN_TYPES = ("INT", "INT")
RETURN_NAMES = ("width", "height")
OUTPUT_TOOLTIPS = ("Latent-derived image width in pixels.", "Latent-derived image height in pixels.")
FUNCTION = "get_size"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"latent": ("LATENT",),
}
}
def get_size(self, latent):
if isinstance(latent, dict):
width = latent.get("width")
height = latent.get("height")
if width is not None and height is not None:
try:
return (int(width), int(height))
except Exception:
pass
samples = latent.get("samples")
else:
samples = None
if samples is None or not isinstance(samples, torch.Tensor):
raise ValueError("GetLatentSizeMXD: invalid latent or missing 'samples' tensor.")
channels = samples.shape[1] if samples.dim() >= 2 else 0
scale = 16 if channels >= 64 else 8
h_lat, w_lat = samples.shape[-2], samples.shape[-1]
return (int(w_lat * scale), int(h_lat * scale))
########################################################################################################################
# --- Helper function to find the bounding box of a mask ---
def get_bounding_box(mask_tensor):
"""
Finds the bounding box of a non-zero region in a mask tensor.
The mask is expected to be a 2D tensor (H, W).
Returns a tuple (x_min, y_min, x_max, y_max) or None if the mask is empty.
"""
# Get non-zero coordinates from the mask
non_zero_coords = torch.nonzero(mask_tensor, as_tuple=False)
# If the mask is empty, there is no bounding box
if non_zero_coords.numel() == 0:
return None
# Find the min and max coordinates for y (dim 0) and x (dim 1)
min_y = non_zero_coords[:, 0].min().item()
max_y = non_zero_coords[:, 0].max().item()
min_x = non_zero_coords[:, 1].min().item()
max_x = non_zero_coords[:, 1].max().item()
# The bounding box for PIL needs (left, upper, right, lower).
# We add +1 to the max values because the upper bound is exclusive.
return (min_x, min_y, max_x + 1, max_y + 1)
# --- Tensor to PIL and PIL to Tensor conversion helpers ---
def tensor_to_pil(tensor):
"""Converts a torch tensor (B, H, W, C) to a list of PIL Images."""
if tensor is None:
return []
# Handle different tensor dimensions
if tensor.dim() == 4: # Batch of images
images = []
for i in range(tensor.shape[0]):
img_np = 255. * tensor[i].cpu().numpy()
images.append(Image.fromarray(np.clip(img_np, 0, 255).astype(np.uint8)))
return images
elif tensor.dim() == 3: # Single image
img_np = 255. * tensor.cpu().numpy()
return [Image.fromarray(np.clip(img_np, 0, 255).astype(np.uint8))]
else:
raise ValueError(f"Unsupported tensor dimension: {tensor.dim()}")
def pil_to_tensor(pil_images):
"""Converts a list of PIL Images back to a torch tensor (B, H, W, C)."""
if not isinstance(pil_images, list):
pil_images = [pil_images]
tensors = []
for img in pil_images:
# Convert to RGB, then to a numpy array, normalize, and create a tensor
img_np = np.array(img.convert("RGB")).astype(np.float32) / 255.0
tensors.append(torch.from_numpy(img_np).unsqueeze(0))
# Stack all tensors into a single batch tensor
return torch.cat(tensors, dim=0)
# --------------------------------------------------------------------
# ✨ The Main Node Class ✨
# --------------------------------------------------------------------
class PlaceImageByMask:
Description = """Place an overlay image inside the mask bounds on a base image."""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"base_image": ("IMAGE",),
"mask": ("MASK",),
"overlay_image": ("IMAGE",),
},
"optional": {
"maintain_aspect_ratio": ("BOOLEAN", {"default": True}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "place_image"
CATEGORY = "MXD/Image"
def place_image(self, base_image, overlay_image, mask, maintain_aspect_ratio=True):
# Convert input tensors to lists of PIL Images
base_pils = tensor_to_pil(base_image)
overlay_pils = tensor_to_pil(overlay_image)
processed_images = []
# Process each image in the batch
for i, base_pil in enumerate(base_pils):
# Work with an RGBA version of the base image for clean pasting
composited_image = base_pil.convert("RGBA")
# Select the corresponding overlay and mask for the current base image
# Clamping the index prevents errors if batch sizes are mismatched
overlay_pil = overlay_pils[min(i, len(overlay_pils) - 1)].convert("RGBA")
current_mask = mask[min(i, mask.shape[0] - 1)]
# Find the bounding box from the mask
bbox = get_bounding_box(current_mask)
# If no mask is found, just use the original base image and skip to the next
if not bbox:
raise ValueError("The base image must be masked where you want the overlay to appear.")
x_min, y_min, x_max, y_max = bbox
box_width = x_max - x_min
box_height = y_max - y_min
# If the bounding box has no area, skip to the next image
if box_width <= 0 or box_height <= 0:
processed_images.append(base_pil)
continue
# --- Resize the overlay image using the specified method ---
if maintain_aspect_ratio:
# Resize to fit *within* the box, preserving aspect ratio (like a thumbnail)
resized_overlay = overlay_pil.copy()
resized_overlay.thumbnail((box_width, box_height), Image.Resampling.LANCZOS)
# Calculate position to center the resized overlay within the bounding box
paste_x = x_min + (box_width - resized_overlay.width) // 2
paste_y = y_min + (box_height - resized_overlay.height) // 2
paste_pos = (paste_x, paste_y)
else:
# As originally requested: stretch to fill the bounding box exactly
resized_overlay = overlay_pil.resize((box_width, box_height), resample=Image.Resampling.LANCZOS)
paste_pos = (x_min, y_min)
# --- Paste the resized overlay onto the base image ---
# The alpha channel of the overlay itself is used as the mask for pasting.
# This ensures transparent areas of the overlay are handled correctly.
composited_image.paste(resized_overlay, paste_pos, resized_overlay)
processed_images.append(composited_image)
# Convert the list of processed PIL images back to a single batch tensor for output
output_tensor = pil_to_tensor(processed_images)
return (output_tensor,)
######################################################################################################################################
class CropImageByMask:
DESCRIPTION = """Crop images to the mask bounds when a mask is provided."""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE", ),
},
"optional": {
"mask": ("MASK", ),
}
}
RETURN_TYPES = ("IMAGE", )
RETURN_NAMES = ("image", )
FUNCTION = "crop"
CATEGORY = "MXD/image"
def crop(self, image, mask=None):
# If no mask is provided or the mask is completely empty, return the original image
if mask is None or not torch.any(mask > 0):
return (image, )
B, H, W, C = image.shape
mask = mask.round()
# Find bounding box for each batch
crops = []
for b in range(B):
current_mask = mask[min(b, mask.shape[0]-1)]
# Check if the mask for this specific image is empty.
if not torch.any(current_mask > 0):
# If a specific mask in a batch is empty, we can't crop.
# To prevent errors with torch.cat later due to different sizes,
# we'll skip cropping for the whole batch and return the original.
# This ensures the output is always a valid tensor.
print("Warning: An empty mask was found in a batch. Returning original images.")
return (image, )
# Get coordinates of non-zero elements
rows = torch.any(current_mask > 0, dim=1)
cols = torch.any(current_mask > 0, dim=0)
# Find boundaries
y_min, y_max = torch.where(rows)[0][[0, -1]]
x_min, x_max = torch.where(cols)[0][[0, -1]]
# Crop image
crop = image[b:b+1, y_min:y_max+1, x_min:x_max+1, :]
crops.append(crop)
# Note: This will raise an error if the crops have different sizes.
# The original code had this limitation.
cropped_images = torch.cat(crops, dim=0)
return (cropped_images, )
########################################################################################################################
class SmartCropByMaskMXD:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", ),
"mask": ("MASK", ),
},
}
RETURN_TYPES = ("IMAGE", )
RETURN_NAMES = ("image", )
FUNCTION = "crop"
CATEGORY = "image/transform"
DESCRIPTION = "Slides a square crop window horizontally + vertically to center on subject mask."
def crop(self, image, mask):
B, H, W, C = image.shape
mask = mask.round()
crops = []
for b in range(B):
mask_b = mask[min(b, mask.shape[0]-1)]
# Get non-zero rows and columns
rows = torch.any(mask_b > 0, dim=1)
cols = torch.any(mask_b > 0, dim=0)
# Default to center
center_x = W // 2
center_y = H // 2
# Update center_x from mask if possible
if torch.any(cols):
x_min, x_max = torch.where(cols)[0][[0, -1]]
center_x = (x_min + x_max) // 2
# Update center_y from mask if possible
if torch.any(rows):
y_min, y_max = torch.where(rows)[0][[0, -1]]
center_y = (y_min + y_max) // 2
# Compute square crop box
side = min(H, W)
half = side // 2
left = max(0, center_x - half)
right = min(W, left + side)
left = right - side # clamp again
top = max(0, center_y - half)
bottom = min(H, top + side)
top = bottom - side # clamp again
# Final crop: safe slicing
crop = image[b:b+1, top:bottom, left:right, :]
crops.append(crop)
return (torch.cat(crops, dim=0), )
########################################################################################################################
class BboxDetectorCombinedBatchMXD:
DESCRIPTION = "Run an Impact Pack BBOX_DETECTOR combined mask over each image in a batch."
CATEGORY = "MXD/Detector"
RETURN_TYPES = ("MASK",)
RETURN_NAMES = ("mask",)
FUNCTION = "detect"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"bbox_detector": ("BBOX_DETECTOR",),
"images": ("IMAGE",),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 4, "min": -512, "max": 512, "step": 1}),
}
}
def detect(self, bbox_detector, images, threshold=0.5, dilation=4):
if images.ndim == 3:
images = images.unsqueeze(0)
if images.ndim != 4:
raise ValueError(f"[BboxDetectorCombinedBatchMXD] Expected IMAGE tensor [B,H,W,C], got shape {tuple(images.shape)}")
masks = []
frame_count, height, width, _ = images.shape
pbar = comfy.utils.ProgressBar(frame_count)
for i in range(frame_count):
frame = images[i:i + 1]
mask = bbox_detector.detect_combined(frame, threshold, dilation)
if mask is None:
mask = torch.zeros((height, width), dtype=torch.float32, device="cpu")
elif torch.is_tensor(mask):
mask = mask.detach().to(dtype=torch.float32, device="cpu")
else:
mask = torch.as_tensor(mask, dtype=torch.float32, device="cpu")
if mask.ndim == 3 and mask.shape[0] == 1:
mask = mask.squeeze(0)
if mask.ndim != 2:
raise ValueError(f"[BboxDetectorCombinedBatchMXD] Detector returned unexpected mask shape {tuple(mask.shape)} for frame {i}.")
masks.append(mask.unsqueeze(0))
pbar.update(1)
return (torch.cat(masks, dim=0),)
########################################################################################################################
def _parse_mxd_mask_color(color_string):
if color_string is None:
return [255, 255, 255]
text = str(color_string).strip()
color = [255, 255, 255]
if "," in text:
try:
values = [float(channel.strip()) for channel in text.split(",")]
if all(0.0 <= value <= 1.0 for value in values):
color = [int(value * 255) for value in values]
else:
color = [int(value) for value in values]
except Exception:
color = [255, 255, 255]
else:
try:
color = list(ImageColor.getrgb(text))
except Exception:
try:
value = float(text)
value = int(value * 255) if 0.0 <= value <= 1.0 else int(value)
color = [value, value, value]
except Exception:
color = [255, 255, 255]
color = np.clip(color, 0, 255).astype(np.int32).tolist()
if len(color) < 3:
color = (color + [color[-1] if color else 255] * 3)[:3]
return color[:4]
def _mxd_image_batch(image):
if image is None:
return None
if image.ndim == 3:
image = image.unsqueeze(0)
if image.ndim != 4:
raise ValueError(f"[ImageAndMaskPreviewMXD] Expected IMAGE tensor [B,H,W,C], got shape {tuple(image.shape)}")
return image.to(dtype=torch.float32)
def _mxd_mask_batch(mask, height=None, width=None, batch_size=None, device=None):
if mask is None:
return None
if mask.ndim == 2:
mask = mask.unsqueeze(0)
elif mask.ndim == 4 and mask.shape[-1] == 1:
mask = mask[..., 0]
elif mask.ndim == 4 and mask.shape[1] == 1:
mask = mask[:, 0]
if mask.ndim != 3:
raise ValueError(f"[ImageAndMaskPreviewMXD] Expected MASK tensor [B,H,W], got shape {tuple(mask.shape)}")
mask = mask.to(dtype=torch.float32, device=device if device is not None else mask.device).clamp(0.0, 1.0)
if height is not None and width is not None and (mask.shape[-2] != height or mask.shape[-1] != width):
mask = F.interpolate(mask.unsqueeze(1), size=(height, width), mode="bilinear", align_corners=False).squeeze(1)
if batch_size is not None:
mask = comfy.utils.repeat_to_batch_size(mask, batch_size)
return mask
class ImageAndMaskPreviewMXD(SaveImage):
DESCRIPTION = """Return an image with a mask composited over it without creating a node preview."""
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("composite",)
FUNCTION = "execute"
CATEGORY = "MXD/Image"
OUTPUT_NODE = False
def __init__(self):
self.output_dir = folder_paths.get_temp_directory()
self.type = "temp"
self.prefix_append = "_temp_" + "".join(random.choice("abcdefghijklmnopqrstupvxyz") for _ in range(5))
self.compress_level = 4
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask_opacity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"mask_color": ("STRING", {"default": "255, 255, 255", "tooltip": "RGB/RGBA CSV, hex, or color name."}),
"pass_through": ("BOOLEAN", {"default": True, "tooltip": "Legacy option. This node now always returns the composite without creating a preview."}),
},
"optional": {
"image": ("IMAGE",),
"mask": ("MASK",),
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
def _build_composite(self, image=None, mask=None, mask_opacity=1.0, mask_color="255, 255, 255"):
image = _mxd_image_batch(image)
if image is None and mask is None:
raise ValueError("[ImageAndMaskPreviewMXD] Connect an image, a mask, or both.")
if image is None:
mask = _mxd_mask_batch(mask)
return mask.unsqueeze(-1).expand(-1, -1, -1, 3).contiguous()
if image.shape[-1] == 1:
image = image.expand(-1, -1, -1, 3).clone()
elif image.shape[-1] >= 3:
image = image[..., :3].clone()
else:
raise ValueError(f"[ImageAndMaskPreviewMXD] Expected IMAGE tensor with 1 or more channels, got shape {tuple(image.shape)}")
if mask is None:
return image
batch_size, height, width, channels = image.shape
mask = _mxd_mask_batch(mask, height, width, batch_size, image.device)
color = _parse_mxd_mask_color(mask_color)
alpha = mask.mul(float(mask_opacity)).clamp(0.0, 1.0)
if len(color) == 4:
alpha = alpha * (color[3] / 255.0)
rgb = torch.tensor(color[:3], dtype=image.dtype, device=image.device).view(1, 1, 1, channels) / 255.0
alpha = alpha.unsqueeze(-1)
return (image * (1.0 - alpha) + rgb * alpha).clamp(0.0, 1.0)
def execute(self, mask_opacity, mask_color, pass_through, filename_prefix="ComfyUI", image=None, mask=None, prompt=None, extra_pnginfo=None):
composite = self._build_composite(image=image, mask=mask, mask_opacity=mask_opacity, mask_color=mask_color)
return (composite,)
########################################################################################################################
NODE_CLASS_MAPPINGS = {
"LatentHalfMasks": LatentHalfMasks,
"Get Latent Size": GetLatentSizeMXD,
"Place Image By Mask": PlaceImageByMask,
"Crop Image By Mask": CropImageByMask,
"SmartCropByMaskMXD": SmartCropByMaskMXD,
"BboxDetectorCombinedBatchMXD": BboxDetectorCombinedBatchMXD,
"ImageAndMaskPreviewMXD": ImageAndMaskPreviewMXD,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LatentHalfMasks": "Latent to L/R Masks MXD",
"Get Latent Size": "Get Latent Size MXD",
"Place Image By Mask": "Place Image by Mask MXD",
"Crop Image By Mask": "Crop Image by Mask MXD",
"SmartCropByMaskMXD": "Smart Crop by Mask MXD",
"BboxDetectorCombinedBatchMXD": "BBOX Detector Combined Batch MXD",
"ImageAndMaskPreviewMXD": "Image and Mask Preview MXD",
}
nodes/media_io.py
+864
View File
@@ -0,0 +1,864 @@
from __future__ import annotations
import torch, os, folder_paths, node_helpers, json, hashlib, re
import numpy as np
from PIL import Image, ImageOps, ImageSequence
from nodes import PreviewImage, SaveImage
try:
from comfy_api.input_impl import VideoFromFile
HAVE_COMFY_API_VIDEO = True
except Exception as _e:
VideoFromFile = None
HAVE_COMFY_API_VIDEO = False
print(f"[ComfyUI-MaxedOut] comfy_api video I/O not available in media_io: {_e}")
########################################################################################################################
# ---------- Helpers (copied from latent loader style) ----------
def _safe_json_loads(s):
if s is None:
return None
if isinstance(s, bytes):
try:
s = s.decode("utf-8", "ignore")
except Exception:
return None
if not isinstance(s, str):
return None
try:
return json.loads(s)
except Exception:
try:
return json.loads(json.loads(s))
except Exception:
return None
def _extract_params_from_prompt_json(prompt_json: dict):
"""
Returns (positive, negative) from saved Comfy prompt graph.
"""
pos = ""
neg = ""
if not isinstance(prompt_json, dict):
return pos, neg
# unwrap if saved as {"prompt": {...}}
graph = prompt_json.get("prompt", prompt_json)
if not isinstance(graph, dict):
return pos, neg
# try to find KSampler/KSamplerAdvanced node
ks = None
for _, v in graph.items():
if "KSampler" in v.get("class_type", ""):
ks = v
break
if not ks:
return pos, neg
kin = ks.get("inputs", {})
def _as_node_id(x):
return str(x[0]) if isinstance(x, (list, tuple)) and x else None
def _text_from_clip(node_id):
n = graph.get(str(node_id), {})
if n.get("class_type") == "CLIPTextEncode":
return str(n.get("inputs", {}).get("text", "")).strip()
return ""
pos = _text_from_clip(_as_node_id(kin.get("positive")))
neg = _text_from_clip(_as_node_id(kin.get("negative")))
return pos, neg
def _strip_counter(name: str) -> str:
# Only strip the trailing pattern we generate when saving: "_<5digits>_"
# Preserve numeric-only base names like "96".
stem, _ = os.path.splitext(name)
m = re.match(r"^(.*?)(?:_\d{5}_)$", stem)
return m.group(1) if m else stem
# ---------- Node ----------
def _indent_paths(paths):
indented = []
for path in paths:
if not path:
indented.append("")
continue
clean_path = path.lstrip("  ")
depth = clean_path.count("/")
indent = " " * (depth * 4)
indented.append(indent + clean_path)
return indented
def _scan_subdir_mtimes(root: str, subdirs: set, branch_latest: dict, exts: tuple = None):
"""
Walk `root`, adding every subfolder's relative path to `subdirs` and
bubbling the mtime of its most recently modified file up to every
ancestor branch (including "" for the root) in `branch_latest`.
When `exts` is given, a folder (and its ancestors) is only added if it
directly or recursively contains at least one file matching `exts` --
so folders with no relevant content don't show up as pickable at all.
"""
try:
for dirpath, dirnames, filenames in os.walk(root):
# Exclude hidden folders (e.g. .git, .github) and __pycache__
dirnames[:] = [d for d in dirnames if not d.startswith('.') and d != '__pycache__']
rel_path = os.path.relpath(dirpath, root)
rel_path = "" if rel_path == "." else rel_path.replace(os.path.sep, "/")
latest = 0.0
has_match = exts is None
for f in filenames:
if exts and not f.lower().endswith(exts):
continue
has_match = True
try:
m = os.path.getmtime(os.path.join(dirpath, f))
except OSError:
continue
if m > latest:
latest = m
if not has_match:
continue
if rel_path:
subdirs.add(rel_path)
parts = [p for p in rel_path.split("/") if p]
for i in range(len(parts) + 1):
branch = "/".join(parts[:i])
if latest > branch_latest.get(branch, -1.0):
branch_latest[branch] = latest
if i > 0:
subdirs.add(branch)
except OSError:
pass
def _list_image_batch_subdirs(root: str, exts: tuple = None):
"""
Recursive subfolders under `root`, newest first. Each folder is ordered by
the mtime of the most recently modified file anywhere inside it (so a
folder that just received a new file jumps back to the top). '' = the
root itself, always first. If `exts` is given, only folders that
directly or recursively contain a matching file are included.
"""
subdirs = set()
branch_latest = {}
_scan_subdir_mtimes(root, subdirs, branch_latest, exts)
ordered = sorted(subdirs, key=lambda d: (-branch_latest.get(d, -1.0), d.lower()))
return [""] + ordered
def _list_image_batch_subdirs_union(output_root: str, input_root: str, exts: tuple = None):
"""
Union of recursive subfolders from both roots, newest first. A folder
present under both roots is ranked by whichever side has the more
recent file, so it doesn't matter which source the user has selected.
"""
subdirs = set()
branch_latest = {}
_scan_subdir_mtimes(output_root, subdirs, branch_latest, exts)
_scan_subdir_mtimes(input_root, subdirs, branch_latest, exts)
ordered = sorted(subdirs, key=lambda d: (-branch_latest.get(d, -1.0), d.lower()))
return [""] + ordered
IMAGE_BATCH_EXTS = (".png", ".jpg", ".jpeg", ".webp")
VIDEO_BATCH_EXTS = (".mp4",)
def _sort_paths_newest_first(paths):
"""Sort file paths by mtime desc (newest first), stable by normalized path."""
def _mtime(path):
try:
return os.path.getmtime(path)
except OSError:
return 0.0
return sorted(paths, key=lambda p: (-_mtime(p), p.replace("\\", "/").lower()))
def _list_files_recursive(root: str, exts: tuple):
"""Recursively list files under `root` matching `exts`, newest first, as relpaths."""
try:
files = []
for dirpath, dirnames, filenames in os.walk(root):
dirnames[:] = [d for d in dirnames if not d.startswith('.') and d != '__pycache__']
for f in filenames:
if f.lower().endswith(exts):
files.append(os.path.join(dirpath, f))
files = _sort_paths_newest_first(files)
return [os.path.relpath(f, root).replace(os.sep, "/") for f in files]
except OSError:
return []
def _list_files_recursive_union(output_root: str, input_root: str, exts: tuple):
"""
Union of recursive files from both roots, newest first. A relative path
present under both roots is ranked by whichever side's file is more
recent, so it doesn't matter which source the user has selected.
"""
mtimes = {}
def scan(root):
try:
for dirpath, dirnames, filenames in os.walk(root):
dirnames[:] = [d for d in dirnames if not d.startswith('.') and d != '__pycache__']
for f in filenames:
if not f.lower().endswith(exts):
continue
full = os.path.join(dirpath, f)
rel = os.path.relpath(full, root).replace(os.sep, "/")
try:
m = os.path.getmtime(full)
except OSError:
m = 0.0
if m > mtimes.get(rel, -1.0):
mtimes[rel] = m
except OSError:
pass
scan(output_root)
scan(input_root)
return sorted(mtimes, key=lambda p: (-mtimes[p], p.lower())) or [""]
# Server routes so the frontend can swap folder/file dropdowns between
# inputs/outputs without reloading the page.
try:
from server import PromptServer as _MXD_PromptServer
from aiohttp import web as _mxd_web
@_MXD_PromptServer.instance.routes.get("/mxd/image_batch/folders")
async def _mxd_list_image_batch_folders(request):
return _mxd_web.json_response({
"outputs": _indent_paths(_list_image_batch_subdirs(folder_paths.get_output_directory())),
"inputs": _indent_paths(_list_image_batch_subdirs(folder_paths.get_input_directory())),
})
@_MXD_PromptServer.instance.routes.get("/mxd/video_batch/folders")
async def _mxd_list_video_batch_folders(request):
return _mxd_web.json_response({
"outputs": _indent_paths(_list_image_batch_subdirs(folder_paths.get_output_directory(), VIDEO_BATCH_EXTS)),
"inputs": _indent_paths(_list_image_batch_subdirs(folder_paths.get_input_directory(), VIDEO_BATCH_EXTS)),
})
@_MXD_PromptServer.instance.routes.get("/mxd/single_loader/files")
async def _mxd_list_single_loader_files(request):
kind = request.query.get("kind", "image")
exts = VIDEO_BATCH_EXTS if kind == "video" else IMAGE_BATCH_EXTS
return _mxd_web.json_response({
"outputs": _list_files_recursive(folder_paths.get_output_directory(), exts),
"inputs": _list_files_recursive(folder_paths.get_input_directory(), exts),
})
except Exception as _e:
print(f"[LoadImageBatchMXD] Could not register folders route: {_e}")
class LoadImageBatchMXD:
DESCRIPTION = """Load images from an inputs or outputs folder, make masks from alpha, and read prompts."""
TITLE = "Load Image Batch (Inputs/Outputs + Prompts)"
CATEGORY = "MXD/Image"
RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING")
RETURN_NAMES = ("IMAGE", "MASK", "positive", "negative")
OUTPUT_IS_LIST = (True, True, True, True)
FUNCTION = "load_batch"
@classmethod
def INPUT_TYPES(cls):
# Provide the union of inputs + outputs subfolders so any saved value
# validates regardless of which source it belongs to. The frontend
# filters the visible list down to the selected source on the fly.
union = _indent_paths(_list_image_batch_subdirs_union(
folder_paths.get_output_directory(), folder_paths.get_input_directory()
))
return {
"required": {
"source": (("outputs", "inputs"), {"default": "outputs"}),
"folder": (tuple(union), {"default": ""}),
}
}
def _extract_prompts(self, image: Image.Image):
pos, neg = "", ""
try:
raw = image.info.get("prompt")
if raw:
prompt_json = _safe_json_loads(raw)
if prompt_json:
pos, neg = _extract_params_from_prompt_json(prompt_json)
else:
pos = raw
except Exception as e:
print(f"[LoadImageBatchMXD] Prompt parse failed: {e}")
return pos, neg
def load_batch(self, folder: str, source: str = "outputs"):
folder = folder.lstrip("  ")
root = (
folder_paths.get_input_directory()
if source == "inputs"
else folder_paths.get_output_directory()
)
folder_path = os.path.normpath(os.path.join(root, folder)) if folder else root
if not os.path.isdir(folder_path):
raise FileNotFoundError(f"No such folder: {folder_path}")
valid_exts = IMAGE_BATCH_EXTS
# Recursively find all matching files
files = []
for dirpath, dirnames, filenames in os.walk(folder_path):
dirnames.sort()
for f in sorted(filenames):
if f.lower().endswith(valid_exts):
files.append(os.path.join(dirpath, f))
if not files:
raise FileNotFoundError(f"No valid images found in folder '{folder_path}' (including subfolders)")
images, masks, positives, negatives, prefixes = [], [], [], [], []
for path in files:
i = Image.open(path)
i = ImageOps.exif_transpose(i)
pos, neg = self._extract_prompts(i)
positives.append(pos)
negatives.append(neg)
rgb = i.convert("RGB")
arr = np.array(rgb).astype(np.float32) / 255.0
img_t = torch.from_numpy(arr)[None, ...]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask_t = 1.0 - torch.from_numpy(mask).unsqueeze(0)
else:
h, w = arr.shape[:2]
mask_t = torch.zeros((1, h, w), dtype=torch.float32)
images.append(img_t)
masks.append(mask_t)
return (images, masks, positives, negatives)
class LoadVideoBatchMXD:
DESCRIPTION = """Load videos from an inputs or outputs folder as a batch."""
TITLE = "Load Video Batch (Inputs/Outputs)"
CATEGORY = "MXD/Video"
RETURN_TYPES = ("VIDEO",)
RETURN_NAMES = ("VIDEO",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "load_batch"
@classmethod
def INPUT_TYPES(cls):
# Same union-of-sources pattern as LoadImageBatchMXD; reuses that
# node's folder listing helper, filtered to folders that actually
# contain a video so empty/irrelevant folders don't show up.
union = _indent_paths(_list_image_batch_subdirs_union(
folder_paths.get_output_directory(), folder_paths.get_input_directory(), VIDEO_BATCH_EXTS
))
return {
"required": {
"source": (("outputs", "inputs"), {"default": "outputs"}),
"folder": (tuple(union), {"default": ""}),
}
}
def load_batch(self, folder: str, source: str = "outputs"):
if not HAVE_COMFY_API_VIDEO:
raise RuntimeError(
"[LoadVideoBatchMXD] Video output requires a newer ComfyUI core with "
"comfy_api.latest / comfy_api.input_impl support. Please update ComfyUI."
)
folder = folder.lstrip("  ")
root = (
folder_paths.get_input_directory()
if source == "inputs"
else folder_paths.get_output_directory()
)
folder_path = os.path.normpath(os.path.join(root, folder)) if folder else root
if not os.path.isdir(folder_path):
raise FileNotFoundError(f"No such folder: {folder_path}")
valid_exts = VIDEO_BATCH_EXTS
# Recursively find all matching files
files = []
for dirpath, dirnames, filenames in os.walk(folder_path):
dirnames.sort()
for f in sorted(filenames):
if f.lower().endswith(valid_exts):
files.append(os.path.join(dirpath, f))
if not files:
raise FileNotFoundError(f"No valid videos found in folder '{folder_path}' (including subfolders)")
videos = [VideoFromFile(path) for path in files]
return (videos,)
class LoadImageFromFolderMXD:
DESCRIPTION = (
"Load a single image from any inputs/outputs subfolder. Turn on run_folder "
"to auto-queue every image in that same folder, one after another."
)
TITLE = "Load Image (From Folder) MXD"
CATEGORY = "MXD/Image"
RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING", "STRING")
RETURN_NAMES = ("IMAGE", "MASK", "positive", "negative", "filename")
FUNCTION = "load_image"
@classmethod
def INPUT_TYPES(cls):
# Union of both sources so any saved value validates regardless of which
# source it belongs to; the frontend narrows the visible list to the
# selected source on the fly (mirrors LoadImageBatchMXD's folder picker).
union = _list_files_recursive_union(
folder_paths.get_output_directory(), folder_paths.get_input_directory(), IMAGE_BATCH_EXTS
)
return {
"required": {
"source": (("outputs", "inputs"), {"default": "outputs"}),
"image": (tuple(union), ),
"run_folder": ("BOOLEAN", {
"default": False,
"tooltip": "When enabled, hitting Queue Prompt auto-queues every image in this file's folder, one after another, instead of just the selected file.",
}),
}
}
def _extract_prompts(self, image: Image.Image):
pos, neg = "", ""
try:
raw = image.info.get("prompt")
if raw:
prompt_json = _safe_json_loads(raw)
if prompt_json:
pos, neg = _extract_params_from_prompt_json(prompt_json)
else:
pos = raw
except Exception as e:
print(f"[LoadImageFromFolderMXD] Prompt parse failed: {e}")
return pos, neg
def load_image(self, image: str, source: str = "outputs", run_folder: bool = False):
image = image.lstrip("  ")
root = (
folder_paths.get_input_directory()
if source == "inputs"
else folder_paths.get_output_directory()
)
path = os.path.normpath(os.path.join(root, image)) if image else None
if not path or not os.path.isfile(path):
raise FileNotFoundError(f"No such image: {path}")
i = Image.open(path)
i = ImageOps.exif_transpose(i)
pos, neg = self._extract_prompts(i)
rgb = i.convert("RGB")
arr = np.array(rgb).astype(np.float32) / 255.0
img_t = torch.from_numpy(arr)[None, ...]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask_t = 1.0 - torch.from_numpy(mask).unsqueeze(0)
else:
h, w = arr.shape[:2]
mask_t = torch.zeros((1, h, w), dtype=torch.float32)
return (img_t, mask_t, pos, neg, os.path.basename(path))
@classmethod
def IS_CHANGED(cls, image, source="outputs", run_folder=False):
root = (
folder_paths.get_input_directory()
if source == "inputs"
else folder_paths.get_output_directory()
)
path = os.path.normpath(os.path.join(root, image.lstrip("  "))) if image else None
if not path or not os.path.isfile(path):
return ""
m = hashlib.sha256()
with open(path, "rb") as f:
m.update(f.read())
return m.digest().hex()
class LoadVideoFromFolderMXD:
DESCRIPTION = (
"Load a single video from any inputs/outputs subfolder. Turn on run_folder "
"to auto-queue every video in that same folder, one after another."
)
TITLE = "Load Video (From Folder) MXD"
CATEGORY = "MXD/Video"
RETURN_TYPES = ("VIDEO", "STRING")
RETURN_NAMES = ("VIDEO", "filename")
FUNCTION = "load_video"
@classmethod
def INPUT_TYPES(cls):
union = _list_files_recursive_union(
folder_paths.get_output_directory(), folder_paths.get_input_directory(), VIDEO_BATCH_EXTS
)
return {
"required": {
"source": (("outputs", "inputs"), {"default": "outputs"}),
"video": (tuple(union), ),
"run_folder": ("BOOLEAN", {
"default": False,
"tooltip": "When enabled, hitting Queue Prompt auto-queues every video in this file's folder, one after another, instead of just the selected file.",
}),
}
}
def load_video(self, video: str, source: str = "outputs", run_folder: bool = False):
if not HAVE_COMFY_API_VIDEO:
raise RuntimeError(
"[LoadVideoFromFolderMXD] Video output requires a newer ComfyUI core with "
"comfy_api.latest / comfy_api.input_impl support. Please update ComfyUI."
)
video = video.lstrip("  ")
root = (
folder_paths.get_input_directory()
if source == "inputs"
else folder_paths.get_output_directory()
)
path = os.path.normpath(os.path.join(root, video)) if video else None
if not path or not os.path.isfile(path):
raise FileNotFoundError(f"No such video: {path}")
return (VideoFromFile(path), os.path.basename(path))
@classmethod
def IS_CHANGED(cls, video, source="outputs", run_folder=False):
root = (
folder_paths.get_input_directory()
if source == "inputs"
else folder_paths.get_output_directory()
)
path = os.path.normpath(os.path.join(root, video.lstrip("  "))) if video else None
if not path or not os.path.isfile(path):
return ""
try:
return str(os.path.getmtime(path))
except OSError:
return ""
class LoadImageWithPromptsMXD:
DESCRIPTION = """Load one input image, create a mask from alpha, and read prompts if present."""
CATEGORY = "image"
RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING")
RETURN_NAMES = ("IMAGE", "MASK", "positive", "negative")
FUNCTION = "load_image"
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
files = folder_paths.filter_files_content_types(files, ["image"])
files = _sort_paths_newest_first([os.path.join(input_dir, f) for f in files])
files = [os.path.basename(f) for f in files]
return {"required": {"image": (files, {"image_upload": True})}}
def _extract_prompts(self, img: Image.Image):
pos, neg = "", ""
raw = img.info.get("prompt")
if raw:
prompt_json = _safe_json_loads(raw)
if prompt_json:
pos, neg = _extract_params_from_prompt_json(prompt_json)
else:
pos = raw
return pos, neg
def load_image(self, image):
image_path = folder_paths.get_annotated_filepath(image)
img = node_helpers.pillow(Image.open, image_path)
output_images, output_masks = [], []
pos, neg = "", ""
w, h = None, None
excluded_formats = ['MPO']
for i in ImageSequence.Iterator(img):
i = node_helpers.pillow(ImageOps.exif_transpose, i)
if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255))
frame = i.convert("RGB")
if len(output_images) == 0:
w, h = frame.size
# extract prompts only once (from first frame)
pos, neg = self._extract_prompts(i)
if frame.size != (w, h):
continue
arr = np.array(frame).astype(np.float32) / 255.0
tensor_img = torch.from_numpy(arr)[None, ...]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
elif i.mode == 'P' and 'transparency' in i.info:
mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((1, 64, 64), dtype=torch.float32, device="cpu")
output_images.append(tensor_img)
output_masks.append(mask.unsqueeze(0))
if len(output_images) > 1 and img.format not in excluded_formats:
output_image = torch.cat(output_images, dim=0)
output_mask = torch.cat(output_masks, dim=0)
else:
output_image = output_images[0]
output_mask = output_masks[0]
return (output_image, output_mask, pos, neg)
@classmethod
def IS_CHANGED(s, image):
image_path = folder_paths.get_annotated_filepath(image)
m = hashlib.sha256()
with open(image_path, 'rb') as f:
m.update(f.read())
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, image):
if not folder_paths.exists_annotated_filepath(image):
return f"Invalid image file: {image}"
return True
########################################################################################################################
class SaveImage_MXD:
TITLE = "Save Image MXD"
CATEGORY = "MXD/Image"
OUTPUT_NODE = True
FUNCTION = "save"
DESCRIPTION = """Save images to the output folder or preview them."""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE", {"tooltip": "Images to preview and/or save."}),
"filename_prefix": ("STRING", {
"default": "ComfyUI",
"tooltip": "File name prefix. Tip: you can use a subfolder like 'tests/my_run'."
}),
"mode": ([
"Save + Preview",
"Save Only",
"Preview only"
], {
"default": "Save + Preview",
"tooltip": "Choose whether to write files to disk, only preview, or save quietly."
}),
},
"optional": {
"embed_workflow": ("BOOLEAN", {
"default": True,
"tooltip": "Embed workflow metadata when saving PNG previews/files."
}),
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ()
OUTPUT_TOOLTIPS = ("Saves and/or previews the images.",)
@staticmethod
def _filtered_extra_pnginfo(extra_pnginfo, embed_workflow):
if embed_workflow or not isinstance(extra_pnginfo, dict):
return extra_pnginfo
filtered = {k: v for k, v in extra_pnginfo.items() if str(k).lower() != "workflow"}
return filtered or None
def save(self, images, filename_prefix, mode, embed_workflow=True, prompt=None, extra_pnginfo=None):
if embed_workflow:
save_prompt = prompt
save_extra_pnginfo = self._filtered_extra_pnginfo(extra_pnginfo, True)
else:
# Core SaveImage embeds the hidden `prompt` graph too.
# Drop both to truly disable workflow reconstruction from saved files.
save_prompt = None
save_extra_pnginfo = None
if mode.startswith("Preview"):
return PreviewImage().save_images(images, filename_prefix, save_prompt, save_extra_pnginfo)
result = SaveImage().save_images(images, filename_prefix, save_prompt, save_extra_pnginfo)
if mode == "Save Only" and isinstance(result, dict):
# Strip UI previews so nothing shows up in the ComfyUI viewer.
return {k: v for k, v in result.items() if k != "ui"}
return result
########################################################################################################################
class ExtractWorkflowFromImageMXD:
TITLE = "Extract Workflow From Image MXD"
CATEGORY = "MXD/Image"
OUTPUT_NODE = True
FUNCTION = "extract_and_save"
DESCRIPTION = """Save workflow metadata to a JSON file from a wired image execution context."""
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
self.prefix_append = ""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", {"tooltip": "Any connected image. Used to trigger extraction/save."}),
"filename_prefix": ("STRING", {
"default": "workflow/ComfyUI",
"tooltip": "Output JSON prefix. You can include subfolders, e.g. 'workflow/my_run'.",
}),
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("json_path",)
OUTPUT_TOOLTIPS = ("Relative path to the saved JSON file in outputs.",)
@staticmethod
def _decode_json_candidate(value):
if value is None:
return None
if isinstance(value, (dict, list)):
return value
if isinstance(value, bytes):
for enc in ("utf-8", "utf-16", "latin-1"):
try:
value = value.decode(enc)
break
except Exception:
continue
if isinstance(value, bytes):
value = value.decode("utf-8", "ignore")
if not isinstance(value, str):
return None
raw = value.strip()
if not raw:
return None
if raw.lower().startswith("workflow:"):
raw = raw.split(":", 1)[1].strip()
parsed = _safe_json_loads(raw)
if isinstance(parsed, (dict, list)):
return parsed
return None
def _extract_workflow_from_context(self, prompt=None, extra_pnginfo=None):
if isinstance(extra_pnginfo, dict):
for key in ("workflow", "Workflow"):
parsed = self._decode_json_candidate(extra_pnginfo.get(key))
if parsed is not None:
return parsed
parsed_extra = self._decode_json_candidate(extra_pnginfo)
if isinstance(parsed_extra, dict):
for key in ("workflow", "Workflow"):
parsed = self._decode_json_candidate(parsed_extra.get(key))
if parsed is not None:
return parsed
if prompt is not None:
parsed_prompt = self._decode_json_candidate(prompt)
if parsed_prompt is not None:
return {"prompt": parsed_prompt}
if isinstance(prompt, dict):
return {"prompt": prompt}
return None
def extract_and_save(self, image, filename_prefix="workflow/ComfyUI", prompt=None, extra_pnginfo=None):
workflow = self._extract_workflow_from_context(prompt, extra_pnginfo)
if workflow is None:
raise ValueError(
"No workflow metadata is available in this execution context. "
"Connect generated images from the current run, or ensure workflow metadata is present."
)
filename_prefix += self.prefix_append
height = image[0].shape[0]
width = image[0].shape[1]
full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(
filename_prefix, self.output_dir, width, height
)
os.makedirs(full_output_folder, exist_ok=True)
file = f"{filename}_{counter:05}_.json"
save_path = os.path.join(full_output_folder, file)
with open(save_path, "w", encoding="utf-8", newline="\n") as f:
json.dump(workflow, f, ensure_ascii=False, indent=2)
rel = os.path.join(subfolder, file) if subfolder else file
rel = rel.replace("\\", "/")
return {
"ui": {"text": [f"Saved workflow JSON: {rel}"]},
"result": (rel,),
}
########################################################################################################################
NODE_CLASS_MAPPINGS = {
"Load Image Batch MXD": LoadImageBatchMXD,
"Load Video Batch MXD": LoadVideoBatchMXD,
"LoadImageFromFolderMXD": LoadImageFromFolderMXD,
"LoadVideoFromFolderMXD": LoadVideoFromFolderMXD,
"LoadImageWithPromptsMXD": LoadImageWithPromptsMXD,
"Save Image MXD": SaveImage_MXD,
"Extract Workflow From Image MXD": ExtractWorkflowFromImageMXD,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Load Image Batch MXD": "Load Image Batch (Inputs/Outputs) MXD",
"Load Video Batch MXD": "Load Video Batch (Inputs/Outputs) MXD",
"LoadImageFromFolderMXD": "Load Image (From Folder) MXD",
"LoadVideoFromFolderMXD": "Load Video (From Folder) MXD",
"LoadImageWithPromptsMXD": "Load Image MXD",
"Save Image MXD": "Save Image MXD",
"Extract Workflow From Image MXD": "Extract Workflow From Image MXD",
}
nodes/prompts.py
+217
View File
@@ -0,0 +1,217 @@
from __future__ import annotations
import torch, comfy, math, node_helpers, comfy.model_management, comfy.utils
from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
try:
from comfy_api.latest import io
HAVE_COMFY_API = True
except Exception as _e:
io = None
HAVE_COMFY_API = False
print(f"[ComfyUI-MaxedOut] comfy_api not available in prompts: {_e}")
########################################################################################################################
# Prompt with Guidance (Flux)
class PromptWithGuidance(ComfyNodeABC):
DESCRIPTION = """Encode text and apply Flux guidance in one node."""
@classmethod
def INPUT_TYPES(cls) -> InputTypeDict:
return {
"required": {
"text": (IO.STRING, {"multiline": True, "dynamicPrompts": True}),
"clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}),
"guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1})
}
}
RETURN_TYPES = (IO.CONDITIONING,)
FUNCTION = "encode_and_guide"
CATEGORY = "MXD/conditioning"
def encode_and_guide(self, text, clip, guidance):
if clip is None:
raise RuntimeError("CLIP model is None. Your checkpoint may not contain a text encoder.")
tokens = clip.tokenize(text)
conditioning = clip.encode_from_tokens_scheduled(tokens)
conditioning = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
return (conditioning,)
########################################################################################################################
if HAVE_COMFY_API:
class QwenImageEditSingleMXD(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="QwenImageEditSingleMXD",
display_name="Qwen Image Edit + Latent MXD",
category="MXD/conditioning",
description="Encode prompt/image and output a matching empty latent.",
inputs=[
io.Clip.Input("clip"),
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
io.Vae.Input("vae", optional=True),
io.Image.Input("image", optional=True),
io.Int.Input("batch_size", default=1, min=1, max=4096),
],
outputs=[
io.Conditioning.Output(),
io.Latent.Output(), # New Output
],
)
@classmethod
def execute(cls, clip, prompt, vae=None, image=None, batch_size=1) -> io.NodeOutput:
ref_latents = []
images_vl = []
llama_template = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
image_prompt = ""
# Default fallback size if no image is provided (1024x1024)
final_width, final_height = 1024, 1024
if image is not None:
samples = image.movedim(-1, 1)
# --- VISION SCALING (384px area) ---
total_vl = int(384 * 384)
scale_vl = math.sqrt(total_vl / (samples.shape[3] * samples.shape[2]))
width_vl = round(samples.shape[3] * scale_vl)
height_vl = round(samples.shape[2] * scale_vl)
s_vl = comfy.utils.common_upscale(samples, width_vl, height_vl, "area", "disabled")
images_vl.append(s_vl.movedim(1, -1))
# --- LATENT/VAE SCALING (1024px area) ---
total_lat = int(1024 * 1024)
scale_lat = math.sqrt(total_lat / (samples.shape[3] * samples.shape[2]))
# Calculate final dimensions to be multiples of 8
final_width = round(samples.shape[3] * scale_lat / 8.0) * 8
final_height = round(samples.shape[2] * scale_lat / 8.0) * 8
if vae is not None:
s_lat = comfy.utils.common_upscale(samples, final_width, final_height, "area", "disabled")
ref_latents.append(vae.encode(s_lat.movedim(1, -1)[:, :, :, :3]))
image_prompt += "Picture 1: <|vision_start|><|image_pad|><|vision_end|>"
# 1. Generate the Empty Latent (SD3 Style: 16 channels, 1/8th resolution)
# This replaces the need for the separate EmptySD3LatentImage node
latent_tensor = torch.zeros(
[batch_size, 16, final_height // 8, final_width // 8],
device=comfy.model_management.intermediate_device()
)
latent_output = {"samples": latent_tensor}
# 2. Process Conditioning
tokens = clip.tokenize(image_prompt + prompt, images=images_vl, llama_template=llama_template)
conditioning = clip.encode_from_tokens_scheduled(tokens)
if len(ref_latents) > 0:
conditioning = node_helpers.conditioning_set_values(
conditioning,
{"reference_latents": ref_latents},
append=True,
)
return io.NodeOutput(conditioning, latent_output)
########################################################################################################################
class QwenImageEditTripleMXD(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="QwenImageEditTripleMXD",
display_name="Qwen Image Edit Prompt MXD (Triple)",
category="advanced/conditioning",
inputs=[
io.Clip.Input("clip"),
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
io.Vae.Input("vae", optional=True),
io.Image.Input("image1", optional=True),
io.Image.Input("image2", optional=True),
io.Image.Input("image3", optional=True),
io.Int.Input("batch_size", default=1, min=1, max=4096),
],
outputs=[
io.Conditioning.Output(),
io.Latent.Output(),
],
)
@classmethod
def execute(cls, clip, prompt, vae=None, image1=None, image2=None, image3=None, batch_size=1) -> io.NodeOutput:
ref_latents = []
images = [image1, image2, image3]
images_vl = []
llama_template = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
image_prompt = ""
# Default fallback
latent_width = 1024
latent_height = 1024
for i, image in enumerate(images):
if image is not None:
samples = image.movedim(-1, 1)
# 1. VL Model Scaling (LLM Vision)
total_vl = int(384 * 384)
scale_by_vl = math.sqrt(total_vl / (samples.shape[3] * samples.shape[2]))
width_vl = round(samples.shape[3] * scale_by_vl)
height_vl = round(samples.shape[2] * scale_by_vl)
s_vl = comfy.utils.common_upscale(samples, width_vl, height_vl, "area", "disabled")
images_vl.append(s_vl.movedim(1, -1))
# 2. VAE Scaling (Synchronized to 16-step for SD3 compatibility)
if vae is not None:
total_ref = int(1024 * 1024)
scale_by_ref = math.sqrt(total_ref / (samples.shape[3] * samples.shape[2]))
# Pixels as multiple of 16 ensures Latent (Pixels/8) is always even
width_ref = round(samples.shape[3] * scale_by_ref / 16.0) * 16
height_ref = round(samples.shape[2] * scale_by_ref / 16.0) * 16
if i == 0:
latent_width = width_ref
latent_height = height_ref
s_ref = comfy.utils.common_upscale(samples, width_ref, height_ref, "area", "disabled")
ref_latents.append(vae.encode(s_ref.movedim(1, -1)[:, :, :, :3]))
image_prompt += "Picture {}: <|vision_start|><|image_pad|><|vision_end|>".format(i + 1)
# Process tokens and conditioning
tokens = clip.tokenize(image_prompt + prompt, images=images_vl, llama_template=llama_template)
conditioning = clip.encode_from_tokens_scheduled(tokens)
if len(ref_latents) > 0:
conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": ref_latents}, append=True)
# Create Output Latent
latent = torch.zeros([batch_size, 16, latent_height // 8, latent_width // 8], device=comfy.model_management.intermediate_device())
# FIXED: Return outputs positionally to match the schema defined above
# Output 1: Conditioning, Output 2: Latent Dictionary
return io.NodeOutput(conditioning, {"samples": latent})
########################################################################################################################
NODE_CLASS_MAPPINGS = {
"Prompt With Guidance (Flux)": PromptWithGuidance,
}
if HAVE_COMFY_API:
NODE_CLASS_MAPPINGS.update({
"QwenImageEditSingleMXD": QwenImageEditSingleMXD,
"QwenImageEditTripleMXD": QwenImageEditTripleMXD,
})
NODE_DISPLAY_NAME_MAPPINGS = {
"Prompt With Guidance (Flux)": "Prompt with Flux Guidance MXD",
}
if HAVE_COMFY_API:
NODE_DISPLAY_NAME_MAPPINGS.update({
"QwenImageEditSingleMXD": "Qwen Image Edit + Latent MXD",
"QwenImageEditTripleMXD": "Qwen Image Edit Prompt MXD (Triple)",
})
nodes/resolution.py
+288
View File
@@ -0,0 +1,288 @@
from __future__ import annotations
import math, comfy, comfy.utils, torch
from .latents import SdxlEmptyLatentImage
########################################################################################################################
# Image Scale To Total Pixels (SDXL Safe)
class SDXLImageScaleToTotalPixelsSafe:
DESCRIPTION = """Scale to a target megapixel count and keep aspect ratio. Skips SDXL-safe sizes."""
upscale_methods = ["bilinear", "bicubic", "lanczos", "nearest-exact", "area"]
# SDXL-safe resolutions (width, height) – store one orientation only,
# the code will check both (w, h) and (h, w)
SDXL_SAFE_RESOLUTIONS = [
(1024, 1024),
(1152, 896),
(1216, 832),
(1344, 768),
(1536, 640),
]
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"upscale_method": (cls.upscale_methods, {"default": "bilinear"}),
"total_megapixels": (
"FLOAT",
{
"default": 1.0,
"min": 0.01,
"max": 128.0,
"step": 0.01,
"tooltip": "Set the total megapixels (e.g., 1.0 = 1 MP)",
},
),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "MXD/Upscaling"
def upscale(self, image, upscale_method, total_megapixels):
if upscale_method in ["nearest-exact", "area"]:
raise Exception(
f"❌ '{upscale_method}' gives poor results.\n\n"
f"👉 Go to the Scale SDXL Image MXD node and switch to another like 'lanczos'.\n\n"
f"Node may be hidden behind KSampler."
)
b, h, w, c = image.shape
# Skip scaling if the image already matches an SDXL-safe resolution
if (w, h) in self.SDXL_SAFE_RESOLUTIONS or (h, w) in self.SDXL_SAFE_RESOLUTIONS:
return (image,)
# ComfyUI-native megapixel math
samples = image.movedim(-1, 1)
orig_h, orig_w = samples.shape[2], samples.shape[3]
target_pixels = int(round(total_megapixels * 1024 * 1024))
scale_by = math.sqrt(target_pixels / (orig_w * orig_h))
new_w = max(1, round(orig_w * scale_by))
new_h = max(1, round(orig_h * scale_by))
scaled = comfy.utils.common_upscale(samples, new_w, new_h, upscale_method, "disabled")
scaled = scaled.movedim(1, -1)
return (scaled,)
########################################################################################################################
# Flux Image Scale To Total Pixels (Flux Safe)
class FluxImageScaleToTotalPixelsSafe:
DESCRIPTION = """Scale to a target megapixel count and keep aspect ratio. Skips Flux-safe sizes."""
upscale_methods = ["bilinear", "bicubic", "lanczos", "nearest-exact", "area"]
# Flux-safe resolutions (width, height) – stored in one orientation only
FLUX_SAFE_RESOLUTIONS = [
(1408, 1408),
(1728, 1152),
(1664, 1216),
(1920, 1088),
(2176, 960),
(1024, 1024),
(1216, 832),
(1152, 896),
(1344, 768),
(1536, 640),
(320, 320),
(384, 256),
(448, 320),
(448, 256),
(576, 256),
]
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"upscale_method": (cls.upscale_methods, {"default": "bilinear"}),
"total_megapixels": (
"FLOAT",
{
"default": 1.0,
"min": 0.01,
"max": 128.0,
"step": 0.01,
"tooltip": "Set the total megapixels (e.g., 1.0 = 1 MP)",
},
),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "MXD/Upscaling"
def upscale(self, image, upscale_method, total_megapixels):
if upscale_method in ["nearest-exact", "area"]:
raise Exception(
f"❌ '{upscale_method}' gives poor results.\n\n"
f"👉 Go to the Scale Flux Image MXD node and switch to another like 'lanczos'.\n\n"
f"Node may be hidden behind KSampler."
)
b, h, w, c = image.shape
# Skip scaling if image matches any Flux-safe resolution
if (w, h) in self.FLUX_SAFE_RESOLUTIONS or (h, w) in self.FLUX_SAFE_RESOLUTIONS:
return (image,)
samples = image.movedim(-1, 1)
orig_h, orig_w = samples.shape[2], samples.shape[3]
target_pixels = int(round(total_megapixels * 1024 * 1024))
scale_by = math.sqrt(target_pixels / (orig_w * orig_h))
new_w = max(1, round(orig_w * scale_by))
new_h = max(1, round(orig_h * scale_by))
scaled = comfy.utils.common_upscale(samples, new_w, new_h, upscale_method, "disabled")
scaled = scaled.movedim(1, -1)
return (scaled,)
########################################################################################################################
class FluxResolutionMatcher:
DESCRIPTION = """Match the closest Flux resolution and orientation for the input image."""
CATEGORY = "MXD/Latent"
FUNCTION = "match_resolution"
RETURN_NAMES = ("resolution", "vertical")
# Full set kept for compatibility (enum list must match FluxEmptyLatentImage)
RESOLUTIONS = {
"— High Resolutions —": None,
"Square (1:1) 1408x1408": (1408, 1408),
"Standard (4:3) 1664x1216": (1664, 1216),
"Landscape (3:2) 1728x1152": (1728, 1152),
"Widescreen (16:9) 1920x1088": (1920, 1088),
"Ultrawide (21:9) 2176x960": (2176, 960),
"— Standard Resolutions —": None,
"Square (1:1) 1024x1024": (1024, 1024),
"Standard (4:3) 1152x896": (1152, 896),
"Landscape (3:2) 1216x832": (1216, 832),
"Widescreen (16:9) 1344x768": (1344, 768),
"Ultrawide (21:9) 1536x640": (1536, 640),
"— Low Resolutions —": None,
"Square (1:1) 320x320": (320, 320),
"Standard (4:3) 448x320": (448, 320),
"Landscape (3:2) 384x256": (384, 256),
"Widescreen (16:9) 448x256": (448, 256),
"Ultrawide (21:9) 576x256": (576, 256),
}
# Keep same enum type so it connects to FluxEmptyLatentImage
RETURN_TYPES = (list(RESOLUTIONS.keys()), "BOOLEAN")
# Precompute aspect ratio groups (only for standard resolutions)
ASPECT_RATIO_GROUPS = {}
for res_str, dims in RESOLUTIONS.items():
if dims is None:
continue
# ✅ Skip high and low groups for logic
if "High" in res_str or "Low" in res_str:
continue
group_name = " ".join(res_str.split(' ')[:-1])
if group_name not in ASPECT_RATIO_GROUPS:
w, h = dims
ratio = w / h
ASPECT_RATIO_GROUPS[group_name] = {'ratio': ratio, 'resolutions': []}
ASPECT_RATIO_GROUPS[group_name]['resolutions'].append(res_str)
@classmethod
def INPUT_TYPES(cls):
return {"required": {"image": ("IMAGE",)}}
def match_resolution(self, image: torch.Tensor):
if image.dim() < 4 or image.shape[1] < 1 or image.shape[2] < 1:
print("Warning: Invalid image tensor received. Falling back to default resolution.")
return ("Square (1:1) 1024x1024", False)
_batch, height, width, _channels = image.shape
is_vertical = height > width
img_aspect_ratio = (height / width) if is_vertical else (width / height)
img_area = height * width
best_ar_group_name = min(
self.ASPECT_RATIO_GROUPS.keys(),
key=lambda name: abs(img_aspect_ratio - self.ASPECT_RATIO_GROUPS[name]['ratio'])
)
candidate_res_strings = self.ASPECT_RATIO_GROUPS[best_ar_group_name]['resolutions']
best_res_string = min(
candidate_res_strings,
key=lambda res_str: abs(img_area - (self.RESOLUTIONS[res_str][0] * self.RESOLUTIONS[res_str][1]))
)
return (best_res_string, is_vertical)
########################################################################################################################
class SDXLResolutionMatcher:
DESCRIPTION = """Match the closest SDXL resolution and orientation for the input image."""
CATEGORY = "MXD/Latent"
FUNCTION = "match_resolution"
RETURN_NAMES = ("resolution", "vertical")
# Use the exact same enum list as SdxlEmptyLatentImage
RESOLUTIONS = SdxlEmptyLatentImage.RESOLUTIONS
RETURN_TYPES = (list(RESOLUTIONS.keys()), "BOOLEAN")
ASPECT_RATIO_GROUPS = {}
for res_str, dims in RESOLUTIONS.items():
if dims is None:
continue
group_name = " ".join(res_str.split(" ")[:-1])
if group_name not in ASPECT_RATIO_GROUPS:
w, h = dims
ratio = w / h
ASPECT_RATIO_GROUPS[group_name] = {"ratio": ratio, "resolutions": []}
ASPECT_RATIO_GROUPS[group_name]["resolutions"].append(res_str)
@classmethod
def INPUT_TYPES(cls):
return {"required": {"image": ("IMAGE",)}}
def match_resolution(self, image: torch.Tensor):
if image.dim() < 4 or image.shape[1] < 1 or image.shape[2] < 1:
print("Warning: Invalid image tensor received. Falling back to default resolution.")
return ("Square (1:1) 1024x1024", False)
_batch, height, width, _channels = image.shape
is_vertical = height > width
img_aspect_ratio = (height / width) if is_vertical else (width / height)
img_area = height * width
best_ar_group_name = min(
self.ASPECT_RATIO_GROUPS.keys(),
key=lambda name: abs(img_aspect_ratio - self.ASPECT_RATIO_GROUPS[name]["ratio"])
)
candidate_res_strings = self.ASPECT_RATIO_GROUPS[best_ar_group_name]["resolutions"]
best_res_string = min(
candidate_res_strings,
key=lambda res_str: abs(img_area - (self.RESOLUTIONS[res_str][0] * self.RESOLUTIONS[res_str][1]))
)
return (best_res_string, is_vertical)
########################################################################################################################
NODE_CLASS_MAPPINGS = {
"Image Scale To Total Pixels (SDXL Safe)": SDXLImageScaleToTotalPixelsSafe,
"Flux Image Scale To Total Pixels (Flux Safe)": FluxImageScaleToTotalPixelsSafe,
"FluxResolutionMatcher": FluxResolutionMatcher,
"SDXLResolutionMatcher": SDXLResolutionMatcher,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Image Scale To Total Pixels (SDXL Safe)": "Scale SDXL Image MXD",
"Flux Image Scale To Total Pixels (Flux Safe)": "Scale Flux Image MXD",
"FluxResolutionMatcher": "Flux Resolution Matcher MXD",
"SDXLResolutionMatcher": "SDXL Resolution Matcher MXD",
}
nodes/wan22/__init__.py
+19
View File
@@ -0,0 +1,19 @@
"""WAN 2.2 node package: buckets/scalers, latent save-load, I2V conditioning, video ops."""
import importlib
NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}
for _name in (
"buckets",
"latent_io",
"i2v",
"video_ops",
):
try:
_mod = importlib.import_module(f".{_name}", __name__)
except Exception as e:
print(f"[ComfyUI-MaxedOut] Failed to import 'nodes.wan22.{_name}': {e}")
continue
NODE_CLASS_MAPPINGS.update(getattr(_mod, "NODE_CLASS_MAPPINGS", {}) or {})
NODE_DISPLAY_NAME_MAPPINGS.update(getattr(_mod, "NODE_DISPLAY_NAME_MAPPINGS", {}) or {})
nodes/wan22/buckets.py
+594
View File
@@ -0,0 +1,594 @@
"""WAN 2.2 resolution buckets: empty latents, image scaler, resolution matcher, outpaint pad.
Registered nodes:
Wan2_2EmptyLatentImageMXD Wan 2.2 Empty Latent Image MXD
wan22EmptyHunyuanLatentVideoMXD WAN2.2 Empty Latent Video MXD
WAN22_I2V_Image_Scaler_MXD Image Scaler Wan 2.2 I2V MXD
WAN22_I2V_Match_Resolution_MXD Match Resolution Wan 2.2 I2V MXD
PadImageForOutpaintingMXD Pad Image for Outpainting MXD
Canonical WAN 2.2 buckets: 480p tier 832x480 / 480x832 / 624x624, 720p tier
1280x720 / 720x1280 / 1024x1024. All scaling keeps dimensions 16-aligned.
"""
from __future__ import annotations
from typing import Tuple
import torch
import comfy.utils
import comfy.model_management
import nodes
# ---- Canonical WAN 2.2 buckets ----
SQUARE_TOL = 0.03 # exact-ish square passthrough tolerance
AUTO_SQUARE_MAX_AR = 1.25 # Auto may crop to square when the source is within 25% of 1:1.
def _ar(w, h):
return w / max(1, h)
def _safe_hw(w, h):
w = max(16, min(w, nodes.MAX_RESOLUTION))
h = max(16, min(h, nodes.MAX_RESOLUTION))
return w, h
def _floor16(x):
x = int(x) // 16 * 16
return max(16, x)
def _ceil16(x):
x = (int(x) + 15) // 16 * 16
return max(16, x)
def _is_squareish(w, h, tol=SQUARE_TOL):
r = _ar(w, h)
return abs(r - 1.0) <= tol
def _is_auto_square_candidate(w, h):
r = _ar(w, h)
return max(r, 1.0 / max(r, 1e-9)) <= AUTO_SQUARE_MAX_AR
def _wan22_tier_from_area(iw, ih):
area = iw * ih
area_480 = 832 * 480
area_720 = 1280 * 720
return "480p" if abs(area - area_480) / area_480 <= abs(area - area_720) / area_720 else "720p"
def _wan22_square_bucket(tier, iw=None, ih=None):
if tier == "720p":
return (1024, 1024)
if tier == "480p":
return (624, 624)
return (1024, 1024) if _wan22_tier_from_area(iw, ih) == "720p" else (624, 624)
def _wan22_oriented_bucket(tier, orientation, iw=None, ih=None):
if tier == "Auto":
tier = _wan22_tier_from_area(iw, ih)
if orientation == "Tall":
return (480, 832) if tier == "480p" else (720, 1280)
if orientation == "Wide":
return (832, 480) if tier == "480p" else (1280, 720)
return _wan22_square_bucket(tier, iw, ih)
def _closest_bucket(img_w, img_h, bucket_list, cover=False):
"""
Pick the best (bw,bh) from bucket_list for this image.
Uses scale closeness + AR diff to rank.
"""
in_ar = _ar(img_w, img_h)
best, best_key = None, (float("inf"), 0.0)
for bw, bh in bucket_list:
s = max(bw/img_w, bh/img_h) if cover else min(bw/img_w, bh/img_h)
ar_diff = abs(_ar(bw, bh) - in_ar)
key = (abs(1.0 - s), ar_diff)
if key < best_key:
best_key, best = key, (bw, bh)
return best
def _resize_then_center_crop(img, out_w, out_h):
"""
Resize to cover target (ensures >= target on both sides after ceil16),
then center-crop. No padding.
"""
t, ih, iw, c = img.shape
s = max(out_w / iw, out_h / ih)
tw = _ceil16(iw * s)
th = _ceil16(ih * s)
tmp = comfy.utils.common_upscale(img.movedim(-1, 1), tw, th, "bilinear", "center").movedim(1, -1)
y0 = max(0, (th - out_h) // 2)
x0 = max(0, (tw - out_w) // 2)
return tmp[:, y0:y0+out_h, x0:x0+out_w, :]
def _resize_fit_inside(img, out_w, out_h):
"""
Resize to fit inside target (ensures <= target on both sides via floor16),
and return the resized tensor only. No padding.
"""
t, ih, iw, c = img.shape
s = min(out_w / iw, out_h / ih)
tw = _floor16(iw * s)
th = _floor16(ih * s)
tw, th = _safe_hw(tw, th)
resized = comfy.utils.common_upscale(img.movedim(-1, 1), tw, th, "bilinear", "center").movedim(1, -1)
return resized, tw, th
def _validate_image_batch_4d(image, node_name, input_name):
if image is None:
raise ValueError(f"[{node_name}] '{input_name}' is required.")
if not torch.is_tensor(image):
raise TypeError(f"[{node_name}] '{input_name}' must be an IMAGE torch tensor, got {type(image).__name__}.")
if image.ndim != 4:
raise ValueError(f"[{node_name}] '{input_name}' must have shape [T,H,W,C], got {tuple(image.shape)}.")
if image.shape[0] <= 0:
raise ValueError(f"[{node_name}] '{input_name}' contains zero images/frames.")
if image.shape[1] <= 0 or image.shape[2] <= 0 or image.shape[3] <= 0:
raise ValueError(f"[{node_name}] '{input_name}' has invalid dimensions {tuple(image.shape)}.")
return image
def _resize_to_explicit_resolution(img, out_w, out_h, match_mode="crop_to_match"):
"""
Resize IMAGE batch to an explicit resolution.
- crop_to_match: cover + center crop (exact output)
- fit_inside_only: preserve AR, no crop (may be smaller)
- stretch_exact: force exact output (distorts AR)
"""
out_w = int(out_w)
out_h = int(out_h)
if out_w <= 0 or out_h <= 0:
raise ValueError(f"Invalid target resolution {out_w}x{out_h}.")
if match_mode == "crop_to_match":
return _resize_then_center_crop(img, out_w, out_h)
if match_mode == "fit_inside_only":
_, ih, iw, _ = img.shape
s = min(out_w / max(1, iw), out_h / max(1, ih))
tw = max(1, min(out_w, int(iw * s)))
th = max(1, min(out_h, int(ih * s)))
return comfy.utils.common_upscale(img.movedim(-1, 1), tw, th, "bilinear", "center").movedim(1, -1)
if match_mode == "stretch_exact":
return comfy.utils.common_upscale(img.movedim(-1, 1), out_w, out_h, "bilinear", "center").movedim(1, -1)
raise ValueError(
f"Invalid match_mode '{match_mode}'. Expected one of: crop_to_match, fit_inside_only, stretch_exact."
)
_WAN22_VALID_RES = {
(832, 480), (480, 832),
(1280, 720), (720, 1280),
(624, 624), (1024, 1024),
}
def _wan22_is_valid_dim(w, h):
return (w, h) in _WAN22_VALID_RES
def _wan22_pick_bucket(iw, ih, tier, crop_to_fit, aspect_mode="Auto"):
if tier == "Safe Auto":
tier = "Auto"
if aspect_mode in ("Tall", "Wide", "Square"):
return _wan22_oriented_bucket(tier, aspect_mode, iw, ih)
is_squareish = _is_squareish(iw, ih)
is_landscape = iw >= ih
# --- Square handling ---
if is_squareish or (crop_to_fit and _is_auto_square_candidate(iw, ih)):
return _wan22_square_bucket(tier, iw, ih)
# --- Explicit tiers ---
if tier == "480p":
return _closest_bucket(iw, ih, [(832, 480)] if is_landscape else [(480, 832)], cover=crop_to_fit)
if tier == "720p":
return _closest_bucket(iw, ih, [(1280, 720)] if is_landscape else [(720, 1280)], cover=crop_to_fit)
# --- Auto tier logic ---
buckets_480 = [(832, 480)] if is_landscape else [(480, 832)]
buckets_720 = [(1280, 720)] if is_landscape else [(720, 1280)]
iw_ih = iw * ih
area_480, area_720 = 832 * 480, 1280 * 720
scale_to_480 = abs(iw_ih - area_480) / area_480
scale_to_720 = abs(iw_ih - area_720) / area_720
# prefer minimal scaling
if iw <= 832 and ih <= 480:
return _closest_bucket(iw, ih, buckets_480, cover=crop_to_fit)
return _closest_bucket(iw, ih, buckets_480 if scale_to_480 <= scale_to_720 else buckets_720, cover=crop_to_fit)
def _wan22_scale_image_core(image, tier="Auto", crop_to_fit=False, aspect_mode="Auto"):
"""
Shared WAN 2.2 scaler core.
Returns (scaled_image, out_w, out_h, did_passthrough).
"""
_, ih, iw, _ = image.shape
# --- Safe Auto logic ---
if tier == "Safe Auto":
# passthrough if already WAN-safe
if _wan22_is_valid_dim(iw, ih):
return image, iw, ih, True
area = iw * ih
area_480, area_720 = 832 * 480, 1280 * 720
min_area, max_area = int(area_480 * 0.5), int(area_720 * 1.8)
if area < min_area or area > max_area:
size_label = "small" if area < min_area else "large"
raise ValueError(
f"[WAN22_I2V_Image_Scaler_MXD] Input resolution {iw}x{ih} is too {size_label} for WAN 2.2 video buckets.\n"
"WAN 2.2 works best around:\n"
" - 480p tier ~= 832x480 (or 480x832)\n"
" - 720p tier ~= 1280x720 (or 720x1280)\n"
" - Squares: 624x624 or 1024x1024\n\n"
"Please use a source closer to 480p/720p, or first process it "
"through your WAN 2.2 workflow. This ensures extend runs without mismatch."
)
# fallback to Auto scaling
tier = "Auto"
# --- Normal path (Auto / 480p / 720p) ---
bw, bh = _wan22_pick_bucket(iw, ih, tier, crop_to_fit, aspect_mode=aspect_mode)
if crop_to_fit:
bw, bh = _safe_hw(_ceil16(bw), _ceil16(bh))
out = _resize_then_center_crop(image, bw, bh)
else:
bw, bh = _safe_hw(_floor16(bw), _floor16(bh))
out, _, _ = _resize_fit_inside(image, bw, bh)
return out, int(out.shape[2]), int(out.shape[1]), False
# ---------- Empty latent image generator (for video nodes) ----------
class Wan2_2EmptyLatentImageMXD:
"""
Utility node for WAN 2.2 workflows.
Generates an empty latent tensor at common video-friendly resolutions.
"""
DESCRIPTION = """Create an empty WAN 2.2 latent at a preset resolution."""
TITLE = "WAN2.2 Empty Latent Image"
CATEGORY = "WAN2.2/Latent"
RESOLUTIONS = {
"— 720p —": None,
"Widescreen (16:9) 1280×720": (1280, 720),
"— 480p —": None,
"Widescreen (16:9) 832×480": (832, 480),
"Square (1:1) 624×624": (624, 624),
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
@classmethod
def INPUT_TYPES(cls):
options = list(cls.RESOLUTIONS.keys())
return {
"required": {
"resolution": (
options,
{"default": "Square (1:1) 960×960", "tooltip": "Select target resolution preset."}
),
"vertical": (
"BOOLEAN",
{"default": False, "label_on": "Vertical", "label_off": "Landscape",
"tooltip": "Swap width/height for vertical orientation."}
),
"batch_size": (
"INT",
{"default": 1, "min": 1, "max": 4096, "tooltip": "Number of latents to generate."}
),
}
}
def generate(self, resolution, vertical, batch_size):
size = self.RESOLUTIONS.get(resolution)
if size is None:
raise ValueError(f"'{resolution}' is a header or invalid option.")
w, h = size
if vertical:
w, h = h, w
# Safety: ensure divisible by 8
if (w % 8) or (h % 8):
raise ValueError(f"Resolution must be divisible by 8. Got {w}x{h}.")
# WAN video length always t=1
t = 1
latent = torch.zeros(
[batch_size, 16, t, h // 8, w // 8],
device=comfy.model_management.intermediate_device()
)
return ({"samples": latent},)
# ---------- Empty latent video generator with presets (for video nodes) ----------
class wan22EmptyHunyuanLatentVideoMXD:
"""
Exactly like core EmptyHunyuanLatentVideo, but width/height are replaced
with valid WAN 2.2 resolution presets and a vertical toggle.
"""
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
CATEGORY = "latent/video"
RESOLUTIONS = {
"— 720p —": None,
"Widescreen (16:9) 1280×720": (1280, 720),
"Square (1:1) 1024×1024": (1024, 1024),
"— 480p —": None,
"Widescreen (16:9) 832×480": (832, 480),
"Square (1:1) 624×624": (624, 624),
}
@classmethod
def INPUT_TYPES(cls):
options = list(cls.RESOLUTIONS.keys())
return {
"required": {
"resolution": (
options,
{"default": "Widescreen (16:9) 832×480"}
),
"vertical": (
"BOOLEAN",
{"default": False, "label_on": "Vertical", "label_off": "Landscape"}
),
"length": (
"INT",
{"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}
),
"batch_size": (
"INT",
{"default": 1, "min": 1, "max": 4096}
),
}
}
def generate(self, resolution, vertical, length, batch_size):
size = self.RESOLUTIONS.get(resolution)
if size is None:
raise ValueError(f"'{resolution}' is not a selectable resolution.")
w, h = size
if vertical:
w, h = h, w
# identical to core behavior:
t = ((length - 1) // 4) + 1
latent = torch.zeros(
[batch_size, 16, t, h // 8, w // 8],
device=comfy.model_management.intermediate_device()
)
return ({"samples": latent},)
class WAN22_I2V_Image_Scaler_MXD:
"""
MXD Image Scaler for WAN 2.2 (NO PADDING)
- Modes: Auto / 480p / 720p (legacy "Safe Auto" still accepted)
- Fit (no pad): proportional resize ≤ target; returns resized dims.
- Crop (no pad): resize-to-cover then center-crop to exact target.
- Square handling:
* Auto & 480p: ~square → 624×624
* 720p: ~square -> 1024x1024
- “Safe Auto”:
* If input is already a valid WAN 2.2 bucket, passthrough.
* If input is far outside 480p–720p range, error early.
* Otherwise, same logic as Auto.
* Perfect for video-extend workflows.
"""
TITLE = "Image Bucket Scaler MXD (No Pad)"
CATEGORY = "image/processing"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "scale"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"tier": (["Auto", "480p", "720p"], {"default": "Auto"}),
"crop_to_fit": ("BOOLEAN", {
"default": True,
"label_on": "Perfect Fit (Crops Edges)",
"label_off": "Closest Fit (No Crop)"
}),
"aspect_mode": (["Auto", "Tall", "Wide", "Square"], {
"default": "Auto",
"tooltip": "Auto picks wide/tall/square from the source. Use Square/Tall/Wide to force the target bucket shape."
}),
}
}
def scale(self, image, tier="Auto", crop_to_fit=False, aspect_mode="Auto"):
# Keep legacy "Safe Auto" values from old workflows working, but expose only one Auto in UI.
internal_tier = "Safe Auto" if tier == "Auto" else tier
out, _, _, _ = _wan22_scale_image_core(
image,
tier=internal_tier,
crop_to_fit=crop_to_fit,
aspect_mode=aspect_mode,
)
return (out,)
class WAN22_I2V_Match_Resolution_MXD:
"""
Match a second image (or image batch) to a reference image resolution for WAN 2.2
first/last-frame workflows.
"""
TITLE = "WAN 2.2 I2V Match Resolution"
CATEGORY = "image/processing"
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("matched_image",)
FUNCTION = "match_resolution"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"reference_image": ("IMAGE", {
"tooltip": "Reference size source (usually the first image after WAN bucket scaling)."
}),
"image_to_match": ("IMAGE", {
"tooltip": "Image or batch to resize using the reference image resolution."
}),
"match_mode": (["crop_to_match", "fit_inside_only", "stretch_exact"], {
"default": "crop_to_match",
"tooltip": "crop_to_match = exact size via cover+center crop; fit_inside_only = no crop, may be smaller; stretch_exact = exact size with distortion."
}),
"enforce_wan_bucket": ("BOOLEAN", {
"default": False,
"label_on": "Validate WAN Bucket",
"label_off": "No WAN Validation",
"tooltip": "If enabled, reference_image must already be a WAN 2.2 bucket size."
}),
}
}
def match_resolution(self, reference_image, image_to_match, match_mode="crop_to_match", enforce_wan_bucket=False):
node_name = "WAN22_I2V_Match_Resolution_MXD"
reference_image = _validate_image_batch_4d(reference_image, node_name, "reference_image")
image_to_match = _validate_image_batch_4d(image_to_match, node_name, "image_to_match")
_, ref_h, ref_w, _ = reference_image.shape
if enforce_wan_bucket and not _wan22_is_valid_dim(ref_w, ref_h):
raise ValueError(
f"[{node_name}] Reference image resolution {ref_w}x{ref_h} is not a valid WAN 2.2 bucket.\n"
"Valid WAN 2.2 buckets are:\n"
" - 832x480 / 480x832\n"
" - 1280x720 / 720x1280\n"
" - 624x624 / 1024x1024\n\n"
"Recommended workflow:\n"
" 1. Scale the first image with 'Image Scaler Wan 2.2 I2V MXD'\n"
" 2. Use this node to match the second image to the scaled first image"
)
matched = _resize_to_explicit_resolution(
image_to_match,
out_w=ref_w,
out_h=ref_h,
match_mode=match_mode,
)
return (matched,)
class PadImageForOutpaintingMXD:
SEARCH_ALIASES = ["extend canvas", "expand image", "outpaint pad"]
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "expand_image"
CATEGORY = "image/transform"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"left": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 2}),
"top": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 2}),
"right": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 2}),
"bottom": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 2}),
"round_to": (["None", "2", "8", "16", "32", "64"], {"default": "16"}),
}
}
@staticmethod
def _nearest_multiple(value: int, multiple: int, padded: bool) -> int:
if multiple <= 1 or value % multiple == 0:
return value
lower = (value // multiple) * multiple
upper = lower + multiple
if lower <= 0:
return upper
if not padded:
return lower
return lower if value - lower <= upper - value else upper
@staticmethod
def _axis_plan(size: int, before: int, after: int, multiple: int) -> Tuple[int, int, int, int, int]:
target = size + before + after
if multiple > 1:
target = PadImageForOutpaintingMXD._nearest_multiple(target, multiple, before + after > 0)
delta = target - (size + before + after)
if delta < 0:
remove = -delta
from_after = min(after, remove)
after -= from_after
remove -= from_after
from_before = min(before, remove)
before -= from_before
remove -= from_before
crop_before = remove // 2
crop_after = remove - crop_before
else:
crop_before = 0
crop_after = 0
if before > 0 and after > 0:
add_before = delta // 2
before += add_before
after += delta - add_before
elif before > 0:
before += delta
else:
after += delta
final_size = size - crop_before - crop_after + before + after
if final_size <= 0:
raise ValueError("[PadImageForOutpaintingMXD] Rounding removed the full image on one axis.")
return before, after, crop_before, crop_after, final_size
def expand_image(self, image, left, top, right, bottom, round_to="16"):
image = _validate_image_batch_4d(image, "PadImageForOutpaintingMXD", "image")
batch, height, width, channels = image.size()
multiple = 1 if round_to == "None" else int(round_to)
left, right, crop_left, crop_right, final_width = self._axis_plan(width, left, right, multiple)
top, bottom, crop_top, crop_bottom, final_height = self._axis_plan(height, top, bottom, multiple)
cropped = image[:, crop_top:height - crop_bottom, crop_left:width - crop_right, :]
crop_height = cropped.shape[1]
crop_width = cropped.shape[2]
new_image = torch.full(
(batch, final_height, final_width, channels),
0.5,
dtype=image.dtype,
device=image.device,
)
new_image[:, top:top + crop_height, left:left + crop_width, :] = cropped
mask = torch.ones(
(final_height, final_width),
dtype=torch.float32,
device=image.device,
)
mask[top:top + crop_height, left:left + crop_width] = 0.0
return (new_image, mask.unsqueeze(0))
NODE_CLASS_MAPPINGS = {
"Wan2_2EmptyLatentImageMXD": Wan2_2EmptyLatentImageMXD,
"wan22EmptyHunyuanLatentVideoMXD": wan22EmptyHunyuanLatentVideoMXD,
"WAN22_I2V_Image_Scaler_MXD": WAN22_I2V_Image_Scaler_MXD,
"WAN22_I2V_Match_Resolution_MXD": WAN22_I2V_Match_Resolution_MXD,
"PadImageForOutpaintingMXD": PadImageForOutpaintingMXD,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Wan2_2EmptyLatentImageMXD": "Wan 2.2 Empty Latent Image MXD",
"wan22EmptyHunyuanLatentVideoMXD": "WAN2.2 Empty Latent Video MXD",
"WAN22_I2V_Image_Scaler_MXD": "Image Scaler Wan 2.2 I2V MXD",
"WAN22_I2V_Match_Resolution_MXD": "Match Resolution Wan 2.2 I2V MXD",
"PadImageForOutpaintingMXD": "Pad Image for Outpainting MXD",
}
nodes/wan22/i2v.py
+298
View File
@@ -0,0 +1,298 @@
"""WAN 2.2 image-to-video conditioning nodes (all require comfy_api; skipped when absent).
Registered nodes (only when HAVE_COMFY_API):
Wan22ImageToVideoMXD Wan 2.2 Image to Video MXD
WAN22_I2V_Video_Prep_MXD WAN 2.2 Video Prep I2V MXD
Wan22FirstLastImageToVideoMXD Wan 2.2 I2V First & Last Frame MXD
These expect pre-sized inputs (use the buckets.py scaler upstream); they do no
scaling or CLIP-vision of their own.
"""
from __future__ import annotations
import torch
import comfy.model_management
import node_helpers, nodes
# Comfy API
try:
from comfy_api.latest import io
from comfy_api.input_impl import VideoFromComponents
from comfy_api.util import VideoComponents
HAVE_COMFY_API = True
except Exception as _e:
io = None
VideoFromComponents = None
VideoComponents = None
HAVE_COMFY_API = False
print(f"[ComfyUI-MaxedOut] comfy_api not available in wan22.i2v: {_e}")
from .buckets import _wan22_scale_image_core
def _resample_video_frames_to_fps(frames, in_fps, out_fps):
"""
Resample a frame sequence to a target FPS using nearest-frame selection.
Preserves clip duration approximately by dropping/duplicating frames,
instead of only changing FPS metadata (which changes playback speed).
Returns (frames_out, fps_out, changed).
"""
if frames is None or frames.ndim != 4:
raise ValueError("Expected frame tensor with shape [T,H,W,C].")
if in_fps is None:
raise ValueError("Input video FPS is missing; cannot force FPS safely.")
in_fps = float(in_fps)
out_fps = float(out_fps)
if in_fps <= 0:
raise ValueError(f"Invalid input FPS: {in_fps}")
if out_fps <= 0:
raise ValueError(f"Invalid target FPS: {out_fps}")
if frames.shape[0] <= 1:
return frames, float(out_fps), False
if abs(in_fps - out_fps) < 1e-6:
return frames, float(out_fps), False
n_in = int(frames.shape[0])
# Match the first/last frame span, then pick nearest frames on that timeline.
n_out = max(1, int(round(((n_in - 1) * out_fps) / in_fps)) + 1)
if n_out == n_in:
# Frame count may stay the same for near-equal FPS; metadata still becomes exact.
return frames, float(out_fps), False
idx = torch.linspace(0, n_in - 1, steps=n_out, device=frames.device)
idx = idx.round().to(dtype=torch.long)
out = frames.index_select(0, idx)
return out, float(out_fps), True
# ---------- WAN 2.2 Image to Video (no scaling; expects pre-sized input) ----------
if HAVE_COMFY_API:
class Wan22ImageToVideoMXD(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="Wan22ImageToVideoMXD",
display_name="WAN 2.2 Image to Video MXD",
category="conditioning/video_models",
description="WAN 2.2 image to video without scaling or CLIP vision.",
inputs=[
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Vae.Input("vae"),
io.Int.Input("length", default=81, min=1, max=16384, step=4),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Image.Input("start_image", optional=False),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
@classmethod
def execute(cls, positive, negative, vae, length, batch_size, start_image) -> io.NodeOutput:
if start_image is None:
raise ValueError("start_image must be provided (already pre-sized).")
frames_in, ih, iw, ch = start_image.shape
frames_used = min(frames_in, length)
t = ((length - 1) // 4) + 1
latent = torch.zeros(
[batch_size, 16, t, ih // 8, iw // 8],
device=comfy.model_management.intermediate_device()
)
# create placeholder image tensor
image = torch.ones(
(length, ih, iw, ch),
device=start_image.device,
dtype=start_image.dtype
) * 0.5
image[:frames_used] = start_image[:frames_used]
# encode using VAE
concat_latent_image = vae.encode(image[:, :, :, :3])
# mask zeros out the frames used
mask = torch.ones(
(1, 1, t, concat_latent_image.shape[-2], concat_latent_image.shape[-1]),
device=image.device,
dtype=image.dtype
)
mask[:, :, :((frames_used - 1) // 4) + 1] = 0.0
positive = node_helpers.conditioning_set_values(
positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}
)
negative = node_helpers.conditioning_set_values(
negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask}
)
out_latent = {"samples": latent}
return io.NodeOutput(positive, negative, out_latent)
class WAN22_I2V_Video_Prep_MXD:
"""
Prepare a source video for iterative WAN 2.2 extension:
- scale entire video using WAN bucket logic
- output the scaled frame batch directly
- keep default workflow simple for common use
"""
CATEGORY = "MXD/video"
FUNCTION = "prepare"
RETURN_TYPES = ("VIDEO", "IMAGE", "FLOAT")
RETURN_NAMES = ("scaled_video", "images", "fps")
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"video": ("VIDEO",),
"tier": (["Auto", "480p", "720p"], {"default": "Auto"}),
"crop_to_fit": ("BOOLEAN", {
"default": True,
"label_on": "Perfect Fit (Crops Edges)",
"label_off": "Closest Fit (No Crop)"
}),
"force_fps": ("BOOLEAN", {
"default": False,
"label_on": "Force FPS",
"label_off": "Keep Source FPS",
"tooltip": "When enabled, resample frames (drop/duplicate) and set exact target fps."
}),
"target_fps": ("INT", {
"default": 16,
"min": 1,
"max": 1000,
"step": 1,
"tooltip": "Used when Force FPS is enabled. Output video fps will be set exactly to this value."
}),
"aspect_mode": (["Auto", "Tall", "Wide", "Square"], {
"default": "Auto",
"tooltip": "Auto picks wide/tall/square from the source. Use Square/Tall/Wide to force the target bucket shape."
}),
},
}
def prepare(self, video, tier="Auto", crop_to_fit=True, force_fps=False, target_fps=16, aspect_mode="Auto"):
comp = video.get_components()
if isinstance(comp.images, list):
if len(comp.images) == 0:
raise ValueError("[WAN22_I2V_Video_Prep_MXD] Input video has zero frames.")
frames = torch.stack(comp.images)
else:
frames = comp.images
if frames is None:
raise ValueError("[WAN22_I2V_Video_Prep_MXD] Input video has no frames.")
if frames.ndim == 3:
frames = frames.unsqueeze(0)
if frames.ndim != 4:
raise ValueError(f"[WAN22_I2V_Video_Prep_MXD] Unexpected frame tensor shape: {tuple(frames.shape)}")
if frames.shape[0] <= 0:
raise ValueError("[WAN22_I2V_Video_Prep_MXD] Input video has zero frames.")
out_frame_rate = float(comp.frame_rate) if comp.frame_rate is not None else None
if force_fps:
frames, out_frame_rate, _ = _resample_video_frames_to_fps(
frames, comp.frame_rate, target_fps
)
# "Auto" in video prep uses the safer extend-friendly behavior.
# Keep accepting legacy "Safe Auto" values from older saved workflows.
internal_tier = "Safe Auto" if tier == "Auto" else tier
scaled_frames, _, _, _ = _wan22_scale_image_core(
frames,
tier=internal_tier,
crop_to_fit=crop_to_fit,
aspect_mode=aspect_mode,
)
scaled_video = VideoFromComponents(
VideoComponents(
images=scaled_frames,
audio=comp.audio,
frame_rate=out_frame_rate,
)
)
fps = float(out_frame_rate) if out_frame_rate is not None else 0.0
return (scaled_video, scaled_frames, fps)
class Wan22FirstLastImageToVideoMXD(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="Wan22FirstLastImageToVideoMXD",
display_name="WAN 2.2 First & Last I2V MXD",
category="conditioning/video_models",
inputs=[
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Vae.Input("vae"),
io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Image.Input("start_image", optional=True),
io.Image.Input("end_image", optional=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
@classmethod
def execute(cls, positive, negative, vae, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput:
spacial_scale = vae.spacial_compression_encode()
# Assume incoming images are already pre-sized by upstream nodes.
height, width = start_image.shape[1], start_image.shape[2] if start_image is not None else (vae.latent_channels * spacial_scale, vae.latent_channels * spacial_scale)
latent = torch.zeros(
[batch_size, vae.latent_channels, ((length - 1) // 4) + 1, height // spacial_scale, width // spacial_scale],
device=comfy.model_management.intermediate_device()
)
image = torch.ones((length, height, width, 3)) * 0.5
mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1]))
if start_image is not None:
image[:start_image.shape[0]] = start_image
mask[:, :, :start_image.shape[0] + 3] = 0.0
if end_image is not None:
image[-end_image.shape[0]:] = end_image
mask[:, :, -end_image.shape[0]:] = 0.0
concat_latent_image = vae.encode(image[:, :, :, :3])
mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2)
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
out_latent = {"samples": latent}
return io.NodeOutput(positive, negative, out_latent)
NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}
if HAVE_COMFY_API:
NODE_CLASS_MAPPINGS.update({
"Wan22ImageToVideoMXD": Wan22ImageToVideoMXD,
"WAN22_I2V_Video_Prep_MXD": WAN22_I2V_Video_Prep_MXD,
"Wan22FirstLastImageToVideoMXD": Wan22FirstLastImageToVideoMXD,
})
NODE_DISPLAY_NAME_MAPPINGS.update({
"Wan22ImageToVideoMXD": "Wan 2.2 Image to Video MXD",
"WAN22_I2V_Video_Prep_MXD": "WAN 2.2 Video Prep I2V MXD",
"Wan22FirstLastImageToVideoMXD": "Wan 2.2 I2V First & Last Frame MXD",
})
nodes/wan22/latent_io.py
+1457
View File
File diff suppressed because it is too large Load Diff
nodes/wan22/video_ops.py
+588
View File
@@ -0,0 +1,588 @@
"""Video frame utilities and video I/O nodes.
Registered nodes (always):
Frames_Select_StartEnd_MXD Select Frames MXD
Frames_Remove_From_Start_MXD Remove Frames From Start MXD
GroupVideoFramesMXD Group Video Frames MXD
Registered nodes (only when HAVE_COMFY_API):
CombineVideos_MXD Combine Videos MXD
LoadVideoMXD Load Video MXD
SaveVideoMXD Save Video MXD (merges a prior stage's workflow
into the embedded metadata via latent_io helpers)
PreviewVideoMXD Preview Video MXD
Route: GET /mxd/videos/input (video-only file list for LoadVideoMXD's combo).
"""
from __future__ import annotations
import os
import torch
import folder_paths
import comfy.model_management
from comfy.cli_args import args
# Comfy API
try:
from comfy_api.latest import io, ui
from comfy_api.input import VideoInput
from comfy_api.input_impl import VideoFromFile, VideoFromComponents
from comfy_api.util import VideoComponents, VideoContainer, VideoCodec
HAVE_COMFY_API = True
except Exception as _e:
io = None
ui = None
VideoInput = None
VideoFromFile = None
VideoFromComponents = None
VideoComponents = None
VideoContainer = None
VideoCodec = None
HAVE_COMFY_API = False
print(f"[ComfyUI-MaxedOut] comfy_api not available in wan22.video_ops: {_e}")
from server import PromptServer
from aiohttp import web
from .latent_io import _merge_prior_workflow_into_current
VIDEO_EXTS = {".mp4", ".mov", ".mkv", ".webm", ".avi"}
routes = PromptServer.instance.routes
@routes.get("/mxd/videos/input")
async def mxd_list_input_videos(request):
"""
Return a JSON list of *video* files under the input folder (relative paths),
sorted by last modified time (newest first) so the combo's 'first' entry
is always the latest render.
"""
input_dir = folder_paths.get_input_directory()
entries = []
for root, _, filenames in os.walk(input_dir):
for name in filenames:
ext = os.path.splitext(name)[1].lower()
if ext in VIDEO_EXTS:
full = os.path.join(root, name)
rel = os.path.relpath(full, input_dir).replace("\\", "/")
try:
mtime = os.path.getmtime(full)
except OSError:
mtime = 0
entries.append((mtime, rel))
# Sort newest -> oldest, to match Comfy's internal behavior
entries.sort(key=lambda x: x[0], reverse=True)
files = [rel for _, rel in entries]
return web.json_response(files)
def _select_frames_start_end(frames, count=1, offset=1, mode="end"):
total = int(frames.shape[0])
if total <= 0:
raise ValueError("No frames available for selection.")
# Clamp offset and count
offset = max(1, min(offset, total))
count = max(1, min(count, total - offset + 1))
if mode == "start":
start_idx = offset - 1
end_idx = start_idx + count
selected = frames[start_idx:end_idx].clone()
elif mode == "end":
start_idx = max(0, total - offset - count + 1)
end_idx = start_idx + count
selected = frames[start_idx:end_idx].clone()
else:
raise ValueError(f"Invalid mode '{mode}'. Expected 'start' or 'end'.")
return selected
# ---------- MXD Frames Select Start/End (from start or end of sequence) ----------
class Frames_Select_StartEnd_MXD:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"frames": ("IMAGE",),
"count": ("INT", {
"default": 1,
"min": 1,
"max": 10000,
"tooltip": "Number of frames to select"
}),
"offset": ("INT", {
"default": 1,
"min": 1,
"max": 10000,
"tooltip": "How far into the video to start selection (from start or end)"
}),
"mode": (["start", "end"], {
"default": "end",
"tooltip": "Select frames from the start or end of the sequence"
}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "main"
CATEGORY = "MXD/images"
def main(self, frames=None, count=1, offset=1, mode="end"):
selected = _select_frames_start_end(frames, count=count, offset=offset, mode=mode)
return (selected,)
# ---------- MXD Frames Remove From Start ----------
class Frames_Remove_From_Start_MXD:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"frames": ("IMAGE",),
"count": ("INT", {
"default": 10,
"min": 1,
"max": 10000,
"tooltip": "Number of frames to remove from the start"
}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "main"
CATEGORY = "MXD/images"
def main(self, frames=None, count=10):
# Skip the first `count` frames instead of keeping them
frames_after = frames[count:].clone()
return (frames_after,)
class GroupVideoFramesMXD:
CATEGORY = "MXD/Video"
TITLE = "Group Video Frames (MXD)"
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE_GROUPS",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "group_frames"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"frames": ("IMAGE",),
"group_size": ("INT", {"default": 81, "min": 1, "max": 5000, "step": 1}),
}
}
def group_frames(self, frames, group_size):
import math, torch
all_frames = list(frames)
total = len(all_frames)
num_groups = math.ceil(total / group_size)
grouped_tensors = []
for i in range(num_groups):
start = i * group_size
end = min(start + group_size, total)
group = all_frames[start:end]
clean = []
for f in group:
# drop redundant singleton batch dim if present
if f.ndim == 4 and f.shape[0] == 1:
f = f.squeeze(0) # (H,W,C)
# ensure shape (H,W,C)
if f.ndim != 3:
print(f"[GroupVideoFramesMXD] weird frame shape {f.shape}")
continue
clean.append(f)
# stack back to (N,H,W,C)
if len(clean) == 0:
continue
stacked = torch.stack(clean, dim=0)
grouped_tensors.append(stacked)
print(f"[GroupVideoFramesMXD] Split {total} frames into {len(grouped_tensors)} groups of up to {group_size}.")
return (grouped_tensors,)
if HAVE_COMFY_API:
class CombineVideos_MXD:
"""
Combine two VIDEO inputs end-to-end (sequentially).
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"front_video": ("VIDEO", {"tooltip": "The first video (plays first)"}),
"back_video": ("VIDEO", {"tooltip": "The second video (plays after the first)"}),
},
}
RETURN_TYPES = ("VIDEO",)
RETURN_NAMES = ("video",)
FUNCTION = "combine"
CATEGORY = "MXD/video"
def combine(self, front_video, back_video):
comp_a = front_video.get_components()
comp_b = back_video.get_components()
# Check frame rate consistency
if comp_a.frame_rate != comp_b.frame_rate:
raise ValueError(f"FPS mismatch: {comp_a.frame_rate} vs {comp_b.frame_rate}")
# Concatenate frame tensors along batch/time dimension (dim=0)
frames_a = torch.stack(comp_a.images) if isinstance(comp_a.images, list) else comp_a.images
frames_b = torch.stack(comp_b.images) if isinstance(comp_b.images, list) else comp_b.images
if frames_a.shape[1] != frames_b.shape[1] or frames_a.shape[2] != frames_b.shape[2]:
raise ValueError(
"Resolution mismatch in CombineVideos_MXD: "
f"front_video={frames_a.shape[2]}x{frames_a.shape[1]}, "
f"back_video={frames_b.shape[2]}x{frames_b.shape[1]}. "
"Use 'WAN 2.2 Video Prep I2V MXD' before WAN generation so scaled base video and generated clip match."
)
combined_images = torch.cat([frames_a, frames_b], dim=0)
# Combine audio sequentially
combined_audio = None
if comp_a.audio is not None or comp_b.audio is not None:
def _extract_audio(audio_obj):
if audio_obj is None:
return None, None, None, None
if torch.is_tensor(audio_obj):
return audio_obj, None, "tensor", None
if isinstance(audio_obj, dict):
wave_key = "waveform" if "waveform" in audio_obj else ("samples" if "samples" in audio_obj else None)
if wave_key is None or not torch.is_tensor(audio_obj.get(wave_key)):
raise TypeError(f"Unsupported audio dict format. Keys: {list(audio_obj.keys())}")
return audio_obj[wave_key], audio_obj.get("sample_rate"), "dict", wave_key
waveform = getattr(audio_obj, "waveform", None)
sample_rate = getattr(audio_obj, "sample_rate", None)
if torch.is_tensor(waveform):
return waveform, sample_rate, "object", None
raise TypeError(f"Unsupported audio payload type: {type(audio_obj).__name__}")
wave_a, sr_a, kind_a, wave_key_a = _extract_audio(comp_a.audio)
wave_b, sr_b, kind_b, wave_key_b = _extract_audio(comp_b.audio)
rank_a = wave_a.ndim if wave_a is not None else None
rank_b = wave_b.ndim if wave_b is not None else None
def _to_bct(w):
if w is None:
return None
if w.ndim == 1:
return w.unsqueeze(0).unsqueeze(0) # [1,1,T]
if w.ndim == 2:
return w.unsqueeze(0) # [1,C,T]
if w.ndim == 3:
return w # [B,C,T]
raise ValueError(f"Unsupported audio tensor rank: {w.ndim}")
wave_a = _to_bct(wave_a)
wave_b = _to_bct(wave_b)
if wave_a is None and wave_b is not None:
wave_a = torch.zeros((wave_b.shape[0], wave_b.shape[1], 0), dtype=wave_b.dtype, device=wave_b.device)
if wave_b is None and wave_a is not None:
wave_b = torch.zeros((wave_a.shape[0], wave_a.shape[1], 0), dtype=wave_a.dtype, device=wave_a.device)
if wave_a is not None and wave_b is not None:
if wave_a.shape[0] != wave_b.shape[0]:
if wave_a.shape[0] == 1:
wave_a = wave_a.expand(wave_b.shape[0], -1, -1)
elif wave_b.shape[0] == 1:
wave_b = wave_b.expand(wave_a.shape[0], -1, -1)
else:
raise ValueError(f"Audio batch mismatch: {wave_a.shape[0]} vs {wave_b.shape[0]}")
if wave_a.shape[1] != wave_b.shape[1]:
if wave_a.shape[1] == 1:
wave_a = wave_a.expand(-1, wave_b.shape[1], -1)
elif wave_b.shape[1] == 1:
wave_b = wave_b.expand(-1, wave_a.shape[1], -1)
else:
raise ValueError(f"Audio channel mismatch: {wave_a.shape[1]} vs {wave_b.shape[1]}")
if sr_a is not None and sr_b is not None and sr_a != sr_b:
raise ValueError(f"Audio sample-rate mismatch: {sr_a} vs {sr_b}")
combined_wave = torch.cat([wave_a, wave_b], dim=2)
out_sr = sr_a if sr_a is not None else sr_b
target_rank = rank_a if rank_a is not None else rank_b
if target_rank == 1 and combined_wave.shape[0] == 1 and combined_wave.shape[1] == 1:
combined_wave = combined_wave.squeeze(0).squeeze(0)
elif target_rank == 2 and combined_wave.shape[0] == 1:
combined_wave = combined_wave.squeeze(0)
out_kind = kind_a if kind_a is not None else kind_b
if out_kind == "dict":
out_key = wave_key_a if kind_a == "dict" else wave_key_b
combined_audio = {out_key or "waveform": combined_wave}
if out_sr is not None:
combined_audio["sample_rate"] = out_sr
else:
combined_audio = combined_wave
combined_video = VideoFromComponents(
VideoComponents(
images=combined_images,
audio=combined_audio,
frame_rate=comp_a.frame_rate,
)
)
return (combined_video,)
# ---------- Load Video MXD (video-only picker with refresh) ----------
class LoadVideoMXD:
"""Load a video from /input with a refresh button (videos only)."""
CATEGORY = "image/video"
FUNCTION = "load"
RETURN_TYPES = ("VIDEO", "STRING")
RETURN_NAMES = ("video", "video_path")
TITLE = "Load Video MXD"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"file": ("COMBO", {
# Only allow video uploads in the picker
"video_upload": True,
# Custom route that returns ONLY videos in /input
"remote": {
"route": "/mxd/videos/input",
"refresh_button": True,
"control_after_refresh": "first",
},
}),
}
}
# --- helpers --------------------------------------------------------------
@staticmethod
def _resolve_video_path(file: str) -> str:
"""
Try to resolve `file` in a backwards-compatible way:
1. If it's an annotated path, let folder_paths handle it.
2. Otherwise treat it as relative to the input directory.
"""
# 1) Try annotated style (old workflows / uploads)
try:
return folder_paths.get_annotated_filepath(file)
except Exception:
pass
# 2) Fall back to /input relative
base = folder_paths.get_input_directory()
candidate = os.path.join(base, file)
if os.path.isfile(candidate):
return candidate
# If all else fails, just return what we got (will error later)
return candidate
@staticmethod
def _is_video_file(path: str) -> bool:
_, ext = os.path.splitext(path)
return ext.lower() in VIDEO_EXTS
# --- main function --------------------------------------------------------
def load(self, file: str):
video_path = self._resolve_video_path(file)
if not os.path.isfile(video_path):
raise FileNotFoundError(f"[LoadVideoMXD] File not found: {video_path}")
if not self._is_video_file(video_path):
raise ValueError(f"[LoadVideoMXD] Not a video file: {video_path}")
print(f"[LoadVideoMXD] Loaded exactly: {video_path}")
return (VideoFromFile(video_path), video_path)
# --- nice-to-haves --------------------------------------------------------
@classmethod
def IS_CHANGED(cls, file: str):
try:
p = cls._resolve_video_path(file)
return os.path.getmtime(p)
except Exception:
return 0
@classmethod
def VALIDATE_INPUTS(cls, file: str):
# First, try the annotated path (for backwards compat)
if folder_paths.exists_annotated_filepath(file):
resolved = folder_paths.get_annotated_filepath(file)
if not cls._is_video_file(resolved):
return f"This node only accepts video files ({', '.join(sorted(VIDEO_EXTS))})."
return True
# Then, try treating it as /input-relative
base = folder_paths.get_input_directory()
candidate = os.path.join(base, file)
if os.path.isfile(candidate):
if not cls._is_video_file(candidate):
return f"This node only accepts video files ({', '.join(sorted(VIDEO_EXTS))})."
return True
return f"Invalid video file: {file}"
# ---------- Save Video MXD ----------
class SaveVideoMXD(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="SaveVideoMXD",
display_name="Save Video MXD",
category="image/video",
description="Saves the input video to your ComfyUI output directory.",
inputs=[
io.Video.Input("video", tooltip="The video to save."),
io.String.Input("filename_prefix", default="video/ComfyUI", tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."),
io.Combo.Input("format", options=VideoContainer.as_input(), default="auto", tooltip="The format to save the video as."),
io.Combo.Input("codec", options=VideoCodec.as_input(), default="auto", tooltip="The codec to use for the video."),
io.Boolean.Input(
"embed_workflow",
default=True,
label_on="embed",
label_off="skip",
tooltip="When high_workflow is connected, merge it into this video's embedded workflow "
"so dragging the final video into ComfyUI shows both the high-noise stage and "
"this stage together.",
),
io.String.Input(
"high_workflow",
optional=True,
force_input=True,
tooltip="Connect a Load Latent node's 'high_workflow' output here to carry the "
"high-noise stage's workflow into this video's metadata.",
),
],
hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
is_output_node=True,
)
@classmethod
def execute(cls, video: VideoInput, filename_prefix: str, format: str, codec: str,
embed_workflow: bool = True, high_workflow: str = "") -> io.NodeOutput:
width, height = video.get_dimensions()
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
filename_prefix,
folder_paths.get_output_directory(),
width,
height
)
saved_metadata = None
if not args.disable_metadata:
metadata = {}
if cls.hidden.extra_pnginfo is not None:
metadata.update(cls.hidden.extra_pnginfo)
if cls.hidden.prompt is not None:
metadata["prompt"] = cls.hidden.prompt
if embed_workflow and high_workflow:
current_workflow = metadata.get("workflow")
merged_workflow = _merge_prior_workflow_into_current(high_workflow, current_workflow)
if merged_workflow is not current_workflow:
metadata["workflow"] = merged_workflow
if len(metadata) > 0:
saved_metadata = metadata
file = f"{filename}_{counter:05}_.{VideoContainer.get_extension(format)}"
video.save_to(
os.path.join(full_output_folder, file),
format=VideoContainer(format),
codec=codec,
metadata=saved_metadata
)
return io.NodeOutput(ui=ui.PreviewVideo([ui.SavedResult(file, subfolder, io.FolderType.output)]))
class PreviewVideoMXD(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="PreviewVideoMXD",
display_name="Preview Video MXD",
category="image/video",
description="Preview a video without saving output (optional pass-through).",
inputs=[
io.Video.Input("input_video", tooltip="Video to preview."),
],
outputs=[
io.Video.Output("output_video", tooltip="Passes the same video forward."),
],
# Allow this node to run even when output_video is not connected.
is_output_node=True,
)
@classmethod
def execute(cls, input_video: VideoInput):
# Save a temporary H264 file so ComfyUI has something to preview
out_dir = os.path.join(folder_paths.get_output_directory(), "previews")
os.makedirs(out_dir, exist_ok=True)
preview_path = os.path.join(out_dir, "preview_temp.mp4")
input_video.save_to(preview_path, format="mp4", codec="h264")
# Return the raw video object (not a tuple)
return io.NodeOutput(
input_video,
ui=ui.PreviewVideo([
ui.SavedResult("preview_temp.mp4", "previews", io.FolderType.output)
])
)
NODE_CLASS_MAPPINGS = {
"Frames_Remove_From_Start_MXD": Frames_Remove_From_Start_MXD,
"GroupVideoFramesMXD": GroupVideoFramesMXD,
"Frames_Select_StartEnd_MXD": Frames_Select_StartEnd_MXD,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Frames_Remove_From_Start_MXD": "Remove Frames From Start MXD",
"GroupVideoFramesMXD": "Group Video Frames MXD",
"Frames_Select_StartEnd_MXD": "Select Frames MXD",
}
if HAVE_COMFY_API:
NODE_CLASS_MAPPINGS.update({
"CombineVideos_MXD": CombineVideos_MXD,
"LoadVideoMXD": LoadVideoMXD,
"SaveVideoMXD": SaveVideoMXD,
"PreviewVideoMXD": PreviewVideoMXD,
})
NODE_DISPLAY_NAME_MAPPINGS.update({
"CombineVideos_MXD": "Combine Videos MXD",
"LoadVideoMXD": "Load Video MXD",
"SaveVideoMXD": "Save Video MXD",
"PreviewVideoMXD": "Preview Video MXD",
})
system/__init__.py
+5
View File
@@ -0,0 +1,5 @@
"""Import-time side-effect modules (no nodes registered here).
live_preview.py monkeypatches latent_preview for streaming video previews
model_paths.py registers the user's external model storage folders
"""
video_preview_mxd.py → system/live_preview.py
View File
model_paths_autoregister_mxd.py → system/model_paths.py
+5 -3
View File
@@ -3,7 +3,7 @@ with folder_paths, the same way ComfyUI/models/<type> works.
The root is resolved in this order (first hit wins):
1. MAXEDOUT_MODEL_STORAGE environment variable
2. model_storage_config.json next to this file (gitignored -- copy
2. model_storage_config.json at the repo root (gitignored -- copy
model_storage_config.json.example to create your own, it never gets
committed)
3. The "MXD > Model Storage > Root Folder" setting in the ComfyUI
@@ -23,8 +23,10 @@ try:
except ImportError:
folder_paths = None
_THIS_DIR = os.path.dirname(os.path.abspath(__file__))
_CONFIG_PATH = os.path.join(_THIS_DIR, "model_storage_config.json")
# The config lives at the REPO ROOT (one level above this system/ package),
# where users have always placed it — keep that path stable across refactors.
_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
_CONFIG_PATH = os.path.join(_REPO_ROOT, "model_storage_config.json")
def _root_from_env():
wan22nodes.py
-3093
View File
File diff suppressed because it is too large Load Diff