zlei6/ComfyUI
1
0
Fork 0
mirror of https://github.com/Comfy-Org/ComfyUI.git synced 2026-08-24 18:10:27 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'master' into alexis/add_output_save_nodes

Browse Source
This commit is contained in:
Alexis Rolland
2026-06-01 18:32:49 -07:00
committed by GitHub
parent 556191e1b0 33799c4a2e
commit cd9b8188dc
279 changed files with 47792 additions and 4927 deletions
Download Patch File Download Diff File Expand all files Collapse all files

123
nodes.py
View File

@@ -1,4 +1,3 @@
from __future__ import annotations
import torch
@@ -69,7 +68,7 @@ class CLIPTextEncode(ComfyNodeABC):
OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",)
FUNCTION = "encode"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
SEARCH_ALIASES = ["text", "prompt", "text prompt", "positive prompt", "negative prompt", "encode text", "text encoder", "encode prompt"]
@@ -88,7 +87,7 @@ class ConditioningCombine:
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "combine"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
SEARCH_ALIASES = ["combine", "merge conditioning", "combine prompts", "merge prompts", "mix prompts", "add prompt"]
def combine(self, conditioning_1, conditioning_2):
@@ -105,7 +104,7 @@ class ConditioningAverage :
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "addWeighted"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):
out = []
@@ -144,7 +143,7 @@ class ConditioningConcat:
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "concat"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
def concat(self, conditioning_to, conditioning_from):
out = []
@@ -177,7 +176,7 @@ class ConditioningSetArea:
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "append"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
def append(self, conditioning, width, height, x, y, strength):
c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8),
@@ -198,7 +197,7 @@ class ConditioningSetAreaPercentage:
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "append"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
def append(self, conditioning, width, height, x, y, strength):
c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x),
@@ -215,7 +214,7 @@ class ConditioningSetAreaStrength:
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "append"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
def append(self, conditioning, strength):
c = node_helpers.conditioning_set_values(conditioning, {"strength": strength})
@@ -235,7 +234,7 @@ class ConditioningSetMask:
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "append"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
def append(self, conditioning, mask, set_cond_area, strength):
set_area_to_bounds = False
@@ -304,7 +303,7 @@ class VAEDecode:
OUTPUT_TOOLTIPS = ("The decoded image.",)
FUNCTION = "decode"
CATEGORY = "latent"
CATEGORY = "model/latent"
DESCRIPTION = "Decodes latent images back into pixel space images."
SEARCH_ALIASES = ["decode", "decode latent", "latent to image", "render latent"]
@@ -358,7 +357,7 @@ class VAEEncode:
RETURN_TYPES = ("LATENT",)
FUNCTION = "encode"
CATEGORY = "latent"
CATEGORY = "model/latent"
SEARCH_ALIASES = ["encode", "encode image", "image to latent"]
def encode(self, vae, pixels):
@@ -390,7 +389,7 @@ class VAEEncodeForInpaint:
RETURN_TYPES = ("LATENT",)
FUNCTION = "encode"
CATEGORY = "latent/inpaint"
CATEGORY = "model/latent/inpaint"
def encode(self, vae, pixels, mask, grow_mask_by=6):
downscale_ratio = vae.spacial_compression_encode()
@@ -439,7 +438,7 @@ class InpaintModelConditioning:
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/inpaint"
CATEGORY = "model/conditioning/inpaint"
def encode(self, positive, negative, pixels, vae, mask, noise_mask=True):
x = (pixels.shape[1] // 8) * 8
@@ -600,7 +599,7 @@ class CheckpointLoaderSimple:
"The VAE model used for encoding and decoding images to and from latent space.")
FUNCTION = "load_checkpoint"
CATEGORY = "loaders"
CATEGORY = "model/loaders"
DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents."
SEARCH_ALIASES = ["load model", "checkpoint", "model loader", "load checkpoint", "ckpt", "model"]
@@ -646,7 +645,7 @@ class unCLIPCheckpointLoader:
RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION")
FUNCTION = "load_checkpoint"
CATEGORY = "loaders"
CATEGORY = "model/loaders"
def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
@@ -662,7 +661,7 @@ class CLIPSetLastLayer:
RETURN_TYPES = ("CLIP",)
FUNCTION = "set_last_layer"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
def set_last_layer(self, clip, stop_at_clip_layer):
clip = clip.clone()
@@ -691,8 +690,8 @@ class LoraLoader:
OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
FUNCTION = "load_lora"
CATEGORY = "loaders"
DESCRIPTION = "LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
CATEGORY = "model/loaders"
DESCRIPTION = "This LoRA loader is used to modify both diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
SEARCH_ALIASES = ["lora", "load lora", "apply lora", "lora loader", "lora model"]
def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
@@ -701,17 +700,19 @@ class LoraLoader:
lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
lora = None
lora_metadata = None
if self.loaded_lora is not None:
if self.loaded_lora[0] == lora_path:
lora = self.loaded_lora[1]
lora_metadata = self.loaded_lora[2] if len(self.loaded_lora) > 2 else None
else:
self.loaded_lora = None
if lora is None:
lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
self.loaded_lora = (lora_path, lora)
lora, lora_metadata = comfy.utils.load_torch_file(lora_path, safe_load=True, return_metadata=True)
self.loaded_lora = (lora_path, lora, lora_metadata)
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip, lora_metadata=lora_metadata)
return (model_lora, clip_lora)
class LoraLoaderModelOnly(LoraLoader):
@@ -722,6 +723,7 @@ class LoraLoaderModelOnly(LoraLoader):
"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
DESCRIPTION = "This LoRAs loader is used to modify the diffusion model, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
FUNCTION = "load_lora_model_only"
def load_lora_model_only(self, model, lora_name, strength_model):
@@ -788,11 +790,12 @@ class VAELoader:
RETURN_TYPES = ("VAE",)
FUNCTION = "load_vae"
CATEGORY = "loaders"
CATEGORY = "model/loaders"
#TODO: scale factor?
def load_vae(self, vae_name):
metadata = None
vae_path = None
if vae_name == "pixel_space":
sd = {}
sd["pixel_space_vae"] = torch.tensor(1.0)
@@ -811,6 +814,14 @@ class VAELoader:
metadata["tae_latent_channels"] = 128
vae = comfy.sd.VAE(sd=sd, metadata=metadata)
vae.throw_exception_if_invalid()
# Register a reload factory on the patcher so multigpu deepclones
# (Select VAE Device, future MultiGPU VAE work-units) can produce
# per-device clones from the same loader context. Only set when we
# actually have a single backing file -- pixel_space and the
# image TAESDs (composed from separate encoder/decoder files via
# load_taesd) are not addressable by a single vae_path.
if vae_path is not None:
vae.patcher.cached_patcher_init = (comfy.sd.load_vae_patcher, (vae_path, metadata, None))
return (vae,)
class ControlNetLoader:
@@ -821,7 +832,7 @@ class ControlNetLoader:
RETURN_TYPES = ("CONTROL_NET",)
FUNCTION = "load_controlnet"
CATEGORY = "loaders"
CATEGORY = "model/loaders"
SEARCH_ALIASES = ["controlnet", "control net", "cn", "load controlnet", "controlnet loader"]
def load_controlnet(self, control_net_name):
@@ -840,7 +851,7 @@ class DiffControlNetLoader:
RETURN_TYPES = ("CONTROL_NET",)
FUNCTION = "load_controlnet"
CATEGORY = "loaders"
CATEGORY = "model/loaders"
def load_controlnet(self, model, control_net_name):
controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name)
@@ -860,7 +871,7 @@ class ControlNetApply:
FUNCTION = "apply_controlnet"
DEPRECATED = True
CATEGORY = "conditioning/controlnet"
CATEGORY = "model/conditioning/controlnet"
def apply_controlnet(self, conditioning, control_net, image, strength):
if strength == 0:
@@ -898,7 +909,7 @@ class ControlNetApplyAdvanced:
RETURN_NAMES = ("positive", "negative")
FUNCTION = "apply_controlnet"
CATEGORY = "conditioning/controlnet"
CATEGORY = "model/conditioning/controlnet"
SEARCH_ALIASES = ["controlnet", "apply controlnet", "use controlnet", "control net"]
def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]):
@@ -959,7 +970,7 @@ class CLIPLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox"], ),
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit"], ),
},
"optional": {
"device": (["default", "cpu"], {"advanced": True}),
@@ -969,7 +980,7 @@ class CLIPLoader:
CATEGORY = "advanced/loaders"
DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B"
DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\n pixeldit: gemma 2 2B elm"
def load_clip(self, clip_name, type="stable_diffusion", device="default"):
clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
@@ -1020,7 +1031,7 @@ class CLIPVisionLoader:
RETURN_TYPES = ("CLIP_VISION",)
FUNCTION = "load_clip"
CATEGORY = "loaders"
CATEGORY = "model/loaders"
def load_clip(self, clip_name):
clip_path = folder_paths.get_full_path_or_raise("clip_vision", clip_name)
@@ -1039,7 +1050,7 @@ class CLIPVisionEncode:
RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
FUNCTION = "encode"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
def encode(self, clip_vision, image, crop):
crop_image = True
@@ -1056,7 +1067,7 @@ class StyleModelLoader:
RETURN_TYPES = ("STYLE_MODEL",)
FUNCTION = "load_style_model"
CATEGORY = "loaders"
CATEGORY = "model/loaders"
def load_style_model(self, style_model_name):
style_model_path = folder_paths.get_full_path_or_raise("style_models", style_model_name)
@@ -1078,7 +1089,7 @@ class StyleModelApply:
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "apply_stylemodel"
CATEGORY = "conditioning/style_model"
CATEGORY = "model/conditioning/style_model"
def apply_stylemodel(self, conditioning, style_model, clip_vision_output, strength, strength_type):
cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
@@ -1138,7 +1149,7 @@ class unCLIPConditioning:
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "apply_adm"
CATEGORY = "conditioning"
CATEGORY = "model/conditioning"
def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation):
if strength == 0:
@@ -1155,7 +1166,7 @@ class GLIGENLoader:
RETURN_TYPES = ("GLIGEN",)
FUNCTION = "load_gligen"
CATEGORY = "loaders"
CATEGORY = "model/loaders"
def load_gligen(self, gligen_name):
gligen_path = folder_paths.get_full_path_or_raise("gligen", gligen_name)
@@ -1177,7 +1188,7 @@ class GLIGENTextBoxApply:
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "append"
CATEGORY = "conditioning/gligen"
CATEGORY = "model/conditioning/gligen"
def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y):
c = []
@@ -1207,7 +1218,7 @@ class EmptyLatentImage:
OUTPUT_TOOLTIPS = ("The empty latent image batch.",)
FUNCTION = "generate"
CATEGORY = "latent"
CATEGORY = "model/latent"
DESCRIPTION = "Create a new batch of empty latent images to be denoised via sampling."
SEARCH_ALIASES = ["empty", "empty latent", "new latent", "create latent", "blank latent", "blank"]
@@ -1222,18 +1233,20 @@ class LatentFromBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",),
"batch_index": ("INT", {"default": 0, "min": 0, "max": 63}),
"batch_index": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
"length": ("INT", {"default": 1, "min": 1, "max": 64}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "frombatch"
CATEGORY = "latent/batch"
CATEGORY = "model/latent/batch"
def frombatch(self, samples, batch_index, length):
s = samples.copy()
s_in = samples["samples"]
batch_index = min(s_in.shape[0] - 1, batch_index)
if batch_index < 0:
batch_index += s_in.shape[0]
batch_index = max(0, min(s_in.shape[0] - 1, batch_index))
length = min(s_in.shape[0] - batch_index, length)
s["samples"] = s_in[batch_index:batch_index + length].clone()
if "noise_mask" in samples:
@@ -1261,7 +1274,7 @@ class RepeatLatentBatch:
RETURN_TYPES = ("LATENT",)
FUNCTION = "repeat"
CATEGORY = "latent/batch"
CATEGORY = "model/latent/batch"
def repeat(self, samples, amount):
s = samples.copy()
@@ -1293,7 +1306,7 @@ class LatentUpscale:
RETURN_TYPES = ("LATENT",)
FUNCTION = "upscale"
CATEGORY = "latent"
CATEGORY = "model/latent"
def upscale(self, samples, upscale_method, width, height, crop):
if width == 0 and height == 0:
@@ -1326,7 +1339,7 @@ class LatentUpscaleBy:
RETURN_TYPES = ("LATENT",)
FUNCTION = "upscale"
CATEGORY = "latent"
CATEGORY = "model/latent"
def upscale(self, samples, upscale_method, scale_by):
s = samples.copy()
@@ -1344,7 +1357,7 @@ class LatentRotate:
RETURN_TYPES = ("LATENT",)
FUNCTION = "rotate"
CATEGORY = "latent/transform"
CATEGORY = "model/latent/transform"
def rotate(self, samples, rotation):
s = samples.copy()
@@ -1370,7 +1383,7 @@ class LatentFlip:
RETURN_TYPES = ("LATENT",)
FUNCTION = "flip"
CATEGORY = "latent/transform"
CATEGORY = "model/latent/transform"
def flip(self, samples, flip_method):
s = samples.copy()
@@ -1395,7 +1408,7 @@ class LatentComposite:
RETURN_TYPES = ("LATENT",)
FUNCTION = "composite"
CATEGORY = "latent"
CATEGORY = "model/latent"
def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0):
x = x // 8
@@ -1482,7 +1495,7 @@ class LatentCrop:
RETURN_TYPES = ("LATENT",)
FUNCTION = "crop"
CATEGORY = "latent/transform"
CATEGORY = "model/latent/transform"
def crop(self, samples, width, height, x, y):
s = samples.copy()
@@ -1512,7 +1525,7 @@ class SetLatentNoiseMask:
RETURN_TYPES = ("LATENT",)
FUNCTION = "set_mask"
CATEGORY = "latent/inpaint"
CATEGORY = "model/latent/inpaint"
def set_mask(self, samples, mask):
s = samples.copy()
@@ -1521,7 +1534,7 @@ class SetLatentNoiseMask:
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
latent_image = latent["samples"]
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None))
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None), latent.get("downscale_ratio_temporal", None))
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
@@ -1540,6 +1553,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out.pop("downscale_ratio_temporal", None)
out["samples"] = samples
return (out, )
@@ -1565,7 +1579,7 @@ class KSampler:
OUTPUT_TOOLTIPS = ("The denoised latent.",)
FUNCTION = "sample"
CATEGORY = "sampling"
CATEGORY = "model/sampling"
DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image."
SEARCH_ALIASES = ["sampler", "sample", "generate", "denoise", "diffuse", "txt2img", "img2img"]
@@ -1595,7 +1609,7 @@ class KSamplerAdvanced:
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "sampling"
CATEGORY = "model/sampling"
def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
force_full_denoise = True
@@ -1773,7 +1787,7 @@ class LoadImageMask(LoadImage):
}
}
CATEGORY = "mask"
CATEGORY = "image"
RETURN_TYPES = ("MASK",)
FUNCTION = "load_image_mask"
@@ -2385,6 +2399,7 @@ async def init_builtin_extra_nodes():
"nodes_lt_audio.py",
"nodes_lt.py",
"nodes_hooks.py",
"nodes_multigpu.py",
"nodes_load_3d.py",
"nodes_cosmos.py",
"nodes_video.py",
@@ -2407,6 +2422,7 @@ async def init_builtin_extra_nodes():
"nodes_context_windows.py",
"nodes_qwen.py",
"nodes_chroma_radiance.py",
"nodes_pid.py",
"nodes_model_patch.py",
"nodes_easycache.py",
"nodes_audio_encoder.py",
@@ -2438,6 +2454,11 @@ async def init_builtin_extra_nodes():
"nodes_void.py",
"nodes_wandancer.py",
"nodes_hidream_o1.py",
"nodes_save_3d.py",
"nodes_moge.py",
"nodes_mediapipe.py",
"nodes_gaussian_splat.py",
"nodes_triposplat.py"
]
import_failed = []