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import comfy.sd
import comfy.model_sampling
import comfy.latent_formats
import nodes
class InverseCONST:
def calculate_input(self, sigma, noise):
return noise
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_output
def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
return latent_image
def inverse_noise_scaling(self, sigma, latent):
return latent
class InFluxModelSamplingPredNode:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}),
"base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}),
"width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "fluxtapoz"
def patch(self, model, max_shift, base_shift, width, height):
m = model.clone()
x1 = 256
x2 = 4096
mm = (max_shift - base_shift) / (x2 - x1)
b = base_shift - mm * x1
shift = (width * height / (8 * 8 * 2 * 2)) * mm + b
sampling_base = comfy.model_sampling.ModelSamplingFlux
sampling_type = InverseCONST
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(shift=shift)
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class OutCONST:
def calculate_input(self, sigma, noise):
return noise
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_input - model_output * sigma
def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
return latent_image
def inverse_noise_scaling(self, sigma, latent):
return latent / (1.0 - sigma)
class ReverseCONST:
def calculate_input(self, sigma, noise):
return noise
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_output # model_input - model_output * sigma
def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
return latent_image
def inverse_noise_scaling(self, sigma, latent):
return latent / (1.0 - sigma)
class OutFluxModelSamplingPredNode:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}),
"base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}),
"width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"reverse_ode": ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "fluxtapoz"
def patch(self, model, max_shift, base_shift, width, height, reverse_ode=False):
m = model.clone()
x1 = 256
x2 = 4096
mm = (max_shift - base_shift) / (x2 - x1)
b = base_shift - mm * x1
shift = (width * height / (8 * 8 * 2 * 2)) * mm + b
sampling_base = comfy.model_sampling.ModelSamplingFlux
if reverse_ode:
sampling_type = ReverseCONST
else:
sampling_type = OutCONST
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(shift=shift)
m.add_object_patch("model_sampling", model_sampling)
return (m, )
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