nodes/remingtonspaz-ComfyUI-ReferenceChain
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- Added negative conditioning inputs and outputs

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This commit is contained in:
Satrio Dewantono
2026-02-01 10:36:37 -08:00
parent 1a33ae1ba1
commit 15c6a4fc2d
1 changed files with 33 additions and 13 deletions
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nodes.py
+33 -13
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@@ -28,6 +28,7 @@ class ReferenceChainConditioning:
return {
"required": {
"conditioning": ("CONDITIONING",),
"neg_conditioning": ("CONDITIONING",),
"vae": ("VAE",),
"upscale_method": (cls.upscale_methods, {"default": "lanczos"}),
"scale_megapixels": ("FLOAT", {
@@ -44,8 +45,8 @@ class ReferenceChainConditioning:
}
}
RETURN_TYPES = ("CONDITIONING", "IMAGE")
RETURN_NAMES = ("conditioning", "first_image_scaled")
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "IMAGE")
RETURN_NAMES = ("conditioning", "neg_conditioning", "first_image_scaled")
FUNCTION = "process"
CATEGORY = "conditioning"
@@ -105,12 +106,13 @@ class ReferenceChainConditioning:
s = s.movedim(1, -1)
return s
def process(self, conditioning, vae, upscale_method, scale_megapixels, images):
def process(self, conditioning, neg_conditioning, vae, upscale_method, scale_megapixels, images):
"""
Process multiple images and chain them as reference latents.
Args:
conditioning: Input conditioning to append reference latents to
neg_conditioning: Input negative conditioning to append reference latents to
vae: VAE model for encoding images to latents
upscale_method: Method for scaling images
scale_megapixels: Target megapixels for image scaling
@@ -135,10 +137,11 @@ class ReferenceChainConditioning:
if not images_list:
# Return empty 1x1 black image as placeholder
empty_image = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
return (conditioning, empty_image)
return (conditioning, neg_conditioning, empty_image)
# Process each image
result_conditioning = conditioning
result_neg_conditioning = neg_conditioning
first_scaled_image = None
for image_filename in images_list:
@@ -169,15 +172,22 @@ class ReferenceChainConditioning:
{"reference_latents": [latent_samples]},
append=True
)
# Append to negative conditioning as well
result_neg_conditioning = node_helpers.conditioning_set_values(
result_neg_conditioning,
{"reference_latents": [latent_samples]},
append=True
)
# Ensure we have a first image (fallback to empty if somehow none processed)
if first_scaled_image is None:
first_scaled_image = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
return (result_conditioning, first_scaled_image)
return (result_conditioning, result_neg_conditioning, first_scaled_image)
@classmethod
def IS_CHANGED(cls, conditioning, vae, upscale_method, scale_megapixels, images):
def IS_CHANGED(cls, conditioning, neg_conditioning, vae, upscale_method, scale_megapixels, images):
"""Check if the node needs to be re-executed based on image changes."""
try:
images_list = json.loads(images)
@@ -203,7 +213,7 @@ class ReferenceChainConditioning:
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(cls, conditioning, vae, upscale_method, scale_megapixels, images):
def VALIDATE_INPUTS(cls, conditioning, neg_conditioning, vae, upscale_method, scale_megapixels, images):
"""Validate that all specified images exist."""
try:
images_list = json.loads(images)
@@ -239,6 +249,7 @@ class ReferenceChainConditioningBase64:
return {
"required": {
"conditioning": ("CONDITIONING",),
"neg_conditioning": ("CONDITIONING",),
"vae": ("VAE",),
"upscale_method": (cls.upscale_methods, {"default": "lanczos"}),
"scale_megapixels": ("FLOAT", {
@@ -254,8 +265,8 @@ class ReferenceChainConditioningBase64:
}
}
RETURN_TYPES = ("CONDITIONING", "IMAGE")
RETURN_NAMES = ("conditioning", "first_image_scaled")
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "IMAGE")
RETURN_NAMES = ("conditioning", "neg_conditioning", "first_image_scaled")
FUNCTION = "process"
CATEGORY = "conditioning"
@@ -304,12 +315,13 @@ class ReferenceChainConditioningBase64:
s = s.movedim(1, -1)
return s
def process(self, conditioning, vae, upscale_method, scale_megapixels, images_base64):
def process(self, conditioning, neg_conditioning, vae, upscale_method, scale_megapixels, images_base64):
"""
Process multiple base64 images and chain them as reference latents.
Args:
conditioning: Input conditioning to append reference latents to
neg_conditioning: Input negative conditioning to append reference latents to
vae: VAE model for encoding images to latents
upscale_method: Method for scaling images
scale_megapixels: Target megapixels for image scaling
@@ -334,10 +346,11 @@ class ReferenceChainConditioningBase64:
# If no images, return conditioning unchanged with empty image
if not images_list:
empty_image = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
return (conditioning, empty_image)
return (conditioning, neg_conditioning, empty_image)
# Process each image
result_conditioning = conditioning
result_neg_conditioning = neg_conditioning
first_scaled_image = None
for i, base64_data in enumerate(images_list):
@@ -361,6 +374,13 @@ class ReferenceChainConditioningBase64:
{"reference_latents": [latent_samples]},
append=True
)
# Append to negative conditioning as well
result_neg_conditioning = node_helpers.conditioning_set_values(
result_neg_conditioning,
{"reference_latents": [latent_samples]},
append=True
)
except Exception as e:
print(f"[ReferenceChainConditioningBase64] Warning: Failed to process image {i}: {e}")
continue
@@ -369,10 +389,10 @@ class ReferenceChainConditioningBase64:
if first_scaled_image is None:
first_scaled_image = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
return (result_conditioning, first_scaled_image)
return (result_conditioning, result_neg_conditioning, first_scaled_image)
@classmethod
def IS_CHANGED(cls, conditioning, vae, upscale_method, scale_megapixels, images_base64):
def IS_CHANGED(cls, conditioning, neg_conditioning, vae, upscale_method, scale_megapixels, images_base64):
"""Check if the node needs to be re-executed based on input changes."""
# Hash the base64 data directly
m = hashlib.sha256()