CDTuner for ComfyUI
Installation
Copy the code in to a .py file in your ComfyUI/custom_nodes directory, then restart ComfyUI and refresh the ComfyUI page in your browser.
Details
- SaturationTuner modifies the VAE to increase or decrease latent decoding/encoding color saturation. Higher values = more saturation.
- DetailTuner modifies the model to increase or decrease details and contrast in gens. Play around with the values to get a feel for their effect.
- ColorTuner modifies the intermediate latent noise predictions during sampling to adjust their color and contrast.
- LatentColorTuner modifies latent images to adjust their color and contrast.
- Masked versions of the Color and LatentColor tuner nodes allow you to mask off areas to be affected.
Changelog
- 2024-01-11 #1 - Implemented the detail tuning portion of CDTuner (the part that was already implemented by the existing CDTuner node). Reversed the directions of the ColorTuner values so that they make more sense. Added early exit to ColorTuner so that it only gets applied if there is a non-zero value.
- 2024-01-11 #2 - Implemented masked Color and LatentColor tuner nodes. Moved a few calculations around to more sensible positions.
Code
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from comfy.sd import VAE
class SaturationTuner:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"vae": ("VAE",),
"saturation": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
}}
RETURN_TYPES = ("VAE",)
FUNCTION = "patch"
CATEGORY = "custom_node_experiments"
VAEKEYS = [
"decoder.up.1.upsample.conv.weight",
"decoder.up.0.block.0.nin_shortcut.weight",
]
def patch(self, vae, saturation):
sd = vae.get_sd()
for key in self.VAEKEYS:
sd[key] = sd[key].clone() * (1 + saturation * 0.075)
new_vae = VAE(sd=sd)
return (new_vae, )
class DetailTuner:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"detail_1": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.1}),
"detail_2": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.1}),
"contrast_1": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.1}),
}}
RETURN_TYPES = ("MODEL", )
FUNCTION = "patch"
CATEGORY = "custom_node_experiments"
TO_SCALE = [
"diffusion_model.input_blocks.0.0.weight",
"diffusion_model.input_blocks.0.0.bias",
"diffusion_model.out.0.weight",
"diffusion_model.out.0.bias",
]
SCALE_COEF = [
-0.01,
0.02,
-0.01,
0.02,
]
TO_OFFSET = [
"diffusion_model.out.2.bias",
]
OFFSET_COEF = [
[0.02, 0, 0, 0]
]
def patch(self, model, detail_1, detail_2, contrast_1):
if not any((detail_1, detail_2, contrast_1)):
return (model, )
scale_strengths = [detail_1, detail_1, detail_2, detail_2]
offset_strengths = [contrast_1]
m = model.clone()
m.patch_model()
sd = m.model.state_dict()
patches = {}
for i, key in enumerate(self.TO_SCALE):
if scale_strengths[i] != 0:
patches[key] = (sd[key] * self.SCALE_COEF[i] * scale_strengths[i], )
for i, key in enumerate(self.TO_OFFSET):
if offset_strengths[i] != 0:
patches[key] = (torch.tensor(self.OFFSET_COEF[i]) * offset_strengths[i], )
m.unpatch_model()
m.add_patches(patches)
return (m, )
class ColorTuner:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"contrast_2": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
"brightness": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
"cyan_red": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
"magenta_green": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
"yellow_blue": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
}}
RETURN_TYPES = ("MODEL", )
FUNCTION = "patch"
CATEGORY = "custom_node_experiments"
COLORS = [[-1,1/3,2/3],[1,1,0],[0,-1,-1],[1,0,1]]
@staticmethod
def default_cfg(args):
return args["uncond"] + (args["cond"] - args["uncond"]) * args["cond_scale"]
def patch(self, model, contrast_2, brightness, cyan_red, magenta_green, yellow_blue):
# color shifting values
ddratios = [contrast_2, brightness, cyan_red, magenta_green, yellow_blue]
# bypass
if not any(ddratios):
return (model, )
ratios = [x * 20/3 for x in [ddratios[0] * 0.02] + ColorTuner.COLOR_CALC(ddratios[1:])]
m = model.clone()
# we need to have some CFG function to wrap, so we set a default if there isn't one
cfg_func = None
if "sampler_cfg_function" in m.model_options:
cfg_func = m.model_options["sampler_cfg_function"]
else:
cfg_func = ColorTuner.default_cfg
def wrapper(args):
for i, x in enumerate(ratios):
args["cond"][:,i,:,:] = args["cond"][:,i,:,:] + x
args["uncond"][:,i,:,:] = args["uncond"][:,i,:,:] + x
return cfg_func(args)
# set the wrapper
m.set_model_sampler_cfg_function(wrapper)
return (m, )
@classmethod
def COLOR_CALC(cls, colors):
outs = [[y * colors[i] * 0.02 for y in x] for i, x in enumerate(cls.COLORS)]
return [sum(x) for x in zip(*outs)]
class ColorTunerMasked:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"contrast_2": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
"brightness": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
"cyan_red": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
"magenta_green": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
"yellow_blue": ("FLOAT", {"default": 0.0, "min": -20.0, "max": 20.0, "step": 0.1}),
"mask": ("MASK", ),
}}
RETURN_TYPES = ("MODEL", )
FUNCTION = "patch"
CATEGORY = "custom_node_experiments"
COLORS = [[-1,1/3,2/3],[1,1,0],[0,-1,-1],[1,0,1]]
@staticmethod
def default_cfg(args):
return args["uncond"] + (args["cond"] - args["uncond"]) * args["cond_scale"]
def patch(self, model, contrast_2, brightness, cyan_red, magenta_green, yellow_blue, mask):
# color shifting values
ddratios = [contrast_2, brightness, cyan_red, magenta_green, yellow_blue]
# bypass
if not any(ddratios):
return (model, )
ratios = [x * 20/3 for x in [ddratios[0] * 0.02] + ColorTuner.COLOR_CALC(ddratios[1:])]
m = model.clone()
# we need to have some CFG function to wrap, so we set a default if there isn't one
cfg_func = None
if "sampler_cfg_function" in m.model_options:
cfg_func = m.model_options["sampler_cfg_function"]
else:
cfg_func = ColorTuner.default_cfg
mask = mask.reshape((mask.shape[0], 1, mask.shape[1], mask.shape[2]))
def wrapper(args):
# masking has to be done in the wrapper as we need the cond/uncond shape
l_mask = torch.nn.functional.interpolate(mask, size=args["cond"].shape[2:], mode="bilinear")
l_mask = l_mask.to(device=args["cond"].device)
for i, x in enumerate((x * l_mask for x in ratios)):
args["cond"][:,i,:,:] = args["cond"][:,i,:,:] + x
args["uncond"][:,i,:,:] = args["uncond"][:,i,:,:] + x
return cfg_func(args)
# set the wrapper
m.set_model_sampler_cfg_function(wrapper)
return (m, )
class LatentColorTuner:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latent": ("LATENT",),
"contrast": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
"brightness": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
"cyan_red": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
"magenta_green": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
"yellow_blue": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
}}
RETURN_TYPES = ("LATENT", )
FUNCTION = "patch"
CATEGORY = "custom_node_experiments"
def patch(self, latent, contrast, brightness, cyan_red, magenta_green, yellow_blue):
# color shifting values
ddratios = [contrast, brightness, cyan_red, magenta_green, yellow_blue]
if not any(ddratios):
return (latent, )
ratios = [x * 20/3 for x in [ddratios[0] * 0.02] + ColorTuner.COLOR_CALC(ddratios[1:])]
l = latent.copy()
l["samples"] = l["samples"].clone()
for i, x in enumerate(ratios):
l["samples"][:,i,:,:] = l["samples"][:,i,:,:] - x
return (l, )
class LatentColorTunerMasked:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT",),
"contrast": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
"brightness": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
"cyan_red": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
"magenta_green": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
"yellow_blue": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
"mask": ("MASK", ),
},
}
RETURN_TYPES = ("LATENT", )
FUNCTION = "patch"
CATEGORY = "custom_node_experiments"
def patch(self, latent, contrast, brightness, cyan_red, magenta_green, yellow_blue, mask):
# color shifting values
ddratios = [contrast, brightness, cyan_red, magenta_green, yellow_blue]
if not any(ddratios):
return (latent, )
l = latent.copy()
l["samples"] = l["samples"].clone()
mask = mask.reshape((mask.shape[0], 1, mask.shape[1], mask.shape[2]))
mask = torch.nn.functional.interpolate(mask, size=l["samples"].shape[2:], mode="bilinear")
mask = mask.to(device=l["samples"].device)
ratios = [x * 20/3 * mask for x in [ddratios[0] * 0.02] + ColorTuner.COLOR_CALC(ddratios[1:])]
for i, x in enumerate(ratios):
l["samples"][:,i,:,:] = l["samples"][:,i,:,:] - x
return (l, )
NODE_CLASS_MAPPINGS = {
"SaturationTuner": SaturationTuner,
"ColorTuner": ColorTuner,
"ColorTunerMasked": ColorTunerMasked,
"DetailTuner": DetailTuner,
"LatentColorTuner": LatentColorTuner,
"LatentColorTunerMasked": LatentColorTunerMasked,
}
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