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Add noise augmentation to hunyuan image refiner. #9831

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Merged
comfyanonymous merged 1 commit into from
Sep 12, 2025
Merged

Add noise augmentation to hunyuan image refiner. #9831

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4 changes: 4 additions & 0 deletions comfy/model_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1437,6 +1437,7 @@ class HunyuanImage21Refiner(HunyuanImage21):
def concat_cond(self, **kwargs):
noise = kwargs.get("noise", None)
image = kwargs.get("concat_latent_image", None)
noise_augmentation = kwargs.get("noise_augmentation", 0.0)
device = kwargs["device"]

if image is None:
Expand All @@ -1446,6 +1447,9 @@ def concat_cond(self, **kwargs):
image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
image = self.process_latent_in(image)
image = utils.resize_to_batch_size(image, noise.shape[0])
if noise_augmentation > 0:
noise = torch.randn(image.shape, generator=torch.manual_seed(kwargs.get("seed", 0) - 10), dtype=image.dtype, device="cpu").to(image.device)
image = noise_augmentation * noise + (1.0 - noise_augmentation) * image
return image

def extra_conds(self, **kwargs):
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8 changes: 4 additions & 4 deletions comfy_extras/nodes_hunyuan.py
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Expand Up @@ -134,18 +134,18 @@ def INPUT_TYPES(s):
return {"required": {"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent": ("LATENT", ),
"noise_augmentation": ("FLOAT", {"default": 0.10, "min": 0.0, "max": 1.0, "step": 0.01}),
}}

RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")

FUNCTION = "execute"

def execute(self, positive, negative, latent):
def execute(self, positive, negative, latent, noise_augmentation):
latent = latent["samples"]

positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": latent})
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": latent})
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
out_latent = {}
out_latent["samples"] = torch.zeros([latent.shape[0], 32, latent.shape[-3], latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
return (positive, negative, out_latent)
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