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from ..flux.model import inject_flux
from ..flux.layers import inject_blocks
class ApplyRefFluxNode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "apply"
CATEGORY = "fluxtapoz"
def apply(self, model):
# if hasattr(model.model.diffusion_model, 'is_ref') and model.model.diffusion_model.is_ref:
# return (model,)
inject_flux(model.model.diffusion_model)
inject_blocks(model.model.diffusion_model)
return (model,)
class ConfigureRefFluxNode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"latent": ("LATENT",),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"end_percent": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
"strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001}),
"sigmas": ("SIGMAS",)
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "apply"
CATEGORY = "fluxtapoz"
def apply(self,
model,
latent,
start_percent,
end_percent,
strength,
sigmas):
model = model.clone()
sigma_to_percent = { sigma.item(): idx/len(sigmas) for idx, sigma in enumerate(sigmas)}
sigma_to_step = { sigma.item(): idx for idx, sigma in enumerate(sigmas)}
transformer_options = model.model_options.get('transformer_options', {})
transformer_options = { **transformer_options }
process_latent_in = model.get_model_object("process_latent_in")
transformer_options['REF_OPTIONS'] = {
'ref_latent': process_latent_in(latent['samples']),
'start_percent': start_percent,
'end_percent': end_percent,
'sigma_to_percent': sigma_to_percent,
'sigma_to_step': sigma_to_step,
'strength': strength,
'sigmas': sigmas,
}
model.model_options['transformer_options'] = transformer_options
return (model,)
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