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Implement noise augmentation for SD 4X upscale model.

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comfyanonymous
2024-01-03 14:27:11 -05:00
parent ef4f6037cb
commit 8c6493578b
6 changed files with 33 additions and 14 deletions
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6
comfy_extras/nodes_sdupscale.py
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@@ -9,7 +9,7 @@ class SD_4XUpscale_Conditioning:
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}),
# "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}), #TODO
"noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
@@ -18,7 +18,7 @@ class SD_4XUpscale_Conditioning:
CATEGORY = "conditioning/upscale_diffusion"
def encode(self, images, positive, negative, scale_ratio):
def encode(self, images, positive, negative, scale_ratio, noise_augmentation):
width = max(1, round(images.shape[-2] * scale_ratio))
height = max(1, round(images.shape[-3] * scale_ratio))
@@ -30,11 +30,13 @@ class SD_4XUpscale_Conditioning:
for t in positive:
n = [t[0], t[1].copy()]
n[1]['concat_image'] = pixels
n[1]['noise_augmentation'] = noise_augmentation
out_cp.append(n)
for t in negative:
n = [t[0], t[1].copy()]
n[1]['concat_image'] = pixels
n[1]['noise_augmentation'] = noise_augmentation
out_cn.append(n)
latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])