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uhhh button. button for. button for gui sliders allowing post-hoc noi… #190

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5 changes: 4 additions & 1 deletion ...eForge-advanced_model_sampling_backported/advanced_model_sampling/nodes_model_advanced.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@ def INPUT_TYPES(s):

CATEGORY = "advanced/model"

def patch(self, model, sampling, zsnr):
def patch(self, model, sampling, zsnr, patch_timesteps, patch_linear_start, patch_linear_end):
m = model.clone()

sampling_base = ldm_patched.modules.model_sampling.ModelSamplingDiscrete
Expand All @@ -107,6 +107,9 @@ class ModelSamplingAdvanced(sampling_base, sampling_type):

# Create new sampling object
model_sampling = ModelSamplingAdvanced(model.model.model_config)
#if you ever changed the noise schedule in any way, this will recalculate values needed by samplers + schedule generators.
#see model_sampling.py line 27 for related data structures.
model_sampling._register_schedule(timesteps=int(patch_timesteps), linear_start=patch_linear_start, linear_end=patch_linear_end)
if zsnr:
model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))

Expand Down
38 changes: 31 additions & 7 deletions ...e-advanced_model_sampling_backported/scripts/advanced_model_sampling_script_backported.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,10 @@ def __init__(self):
self.continuous_edm_sampling = "v_prediction"
self.continuous_edm_sigma_max = 120.0
self.continuous_edm_sigma_min = 0.002
self.goofysampling_betascale = False #hey this might fix that fixme above! also this is useless unless you're implementing sigma shifting re: simplediffusion *at train time*.
self.hardcodebetas_linear_start = 0.00085 # these
self.hardcodebetas_linear_end = 0.012 # are like,
self.hardcodebetas_timesteps = 1000 # the defaults enumerated elsewhere in model base code and correspond to the sd15->sd3 betas schedule.

sorting_priority = 15

Expand All @@ -36,10 +40,10 @@ def ui(self, *args, **kwargs):
discrete_sampling = gr.Radio(
["eps", "v_prediction", "lcm"],
label="Discrete Sampling Type",
value=self.discrete_sampling
value=self.discrete_sampling
)
discrete_zsnr = gr.Checkbox(label="Zero SNR", value=self.discrete_zsnr)

with gr.Group(visible=False) as continuous_edm_group:
continuous_edm_sampling = gr.Radio(
["v_prediction", "eps"],
Expand All @@ -61,26 +65,42 @@ def ui(self, *args, **kwargs):
value=self.continuous_edm_sigma_min
)

goofysampling_betascale = gr.Checkbox(label="GoofySampling Betascale [x]", value=self.goofysampling_betascale)

with gr.Group(visible=False) as betascale_group:
hardcodebetas_linear_start = gr.Slider(label="betas_linear_start", minimum=0.0, maximum=1.0, step=0.00005, value=self.hardcodebetas_linear_start)
hardcodebetas_linear_end = gr.Slider(label="betas_linear_end", minimum=0.0, maximum=1.0, step=0.00005, value=self.hardcodebetas_linear_end)
hardcodebetas_timesteps = gr.Slider(label="timesteps", minimum=1, maximum=10000.0, step=1, value=self.hardcodebetas_timesteps)

def update_visibility(mode):
return (
gr.Group.update(visible=(mode == "Discrete")),
gr.Group.update(visible=(mode == "Continuous EDM"))
)
def update_goofysampling_visibility(checkbox):
return ( gr.Group.update(visible=checkbox) )

sampling_mode.change(
update_visibility,
inputs=[sampling_mode],
outputs=[discrete_group, continuous_edm_group]
)

goofysampling_betascale.change(
update_goofysampling_visibility,
inputs=goofysampling_betascale,
outputs=[betascale_group]
)

return (enabled, sampling_mode, discrete_sampling, discrete_zsnr,
continuous_edm_sampling, continuous_edm_sigma_max, continuous_edm_sigma_min)
continuous_edm_sampling, continuous_edm_sigma_max, continuous_edm_sigma_min,
goofysampling_betascale, hardcodebetas_linear_start, hardcodebetas_linear_end, hardcodebetas_timesteps)

def process_before_every_sampling(self, p, *args, **kwargs):
if len(args) >= 7:
if len(args) >= 11:
(self.enabled, self.sampling_mode, self.discrete_sampling, self.discrete_zsnr,
self.continuous_edm_sampling, self.continuous_edm_sigma_max,
self.continuous_edm_sigma_min) = args[:7]
self.continuous_edm_sampling, self.continuous_edm_sigma_max, self.continuous_edm_sigma_min,
self.goofysampling_betascale, self.hardcodebetas_linear_start, self.hardcodebetas_linear_end, self.hardcodebetas_timesteps) = args[:11]
else:
logging.warning("Not enough arguments provided to process_before_every_sampling")
return
Expand All @@ -98,7 +118,8 @@ def process_before_every_sampling(self, p, *args, **kwargs):

if self.sampling_mode == "Discrete":
sampler = ModelSamplingDiscrete()
unet = sampler.patch(unet, self.discrete_sampling, self.discrete_zsnr)[0]
unet = sampler.patch(unet, self.discrete_sampling, self.discrete_zsnr,
patch_timesteps=self.hardcodebetas_timesteps, patch_linear_start=self.hardcodebetas_linear_start, patch_linear_end=self.hardcodebetas_linear_end)[0]
elif self.sampling_mode == "Continuous EDM":
sampler = ModelSamplingContinuousEDM()
unet = sampler.patch(unet, self.continuous_edm_sampling,
Expand All @@ -116,6 +137,9 @@ def process_before_every_sampling(self, p, *args, **kwargs):
"continuous_edm_sampling": self.continuous_edm_sampling if self.sampling_mode == "Continuous EDM" else None,
"continuous_edm_sigma_max": self.continuous_edm_sigma_max if self.sampling_mode == "Continuous EDM" else None,
"continuous_edm_sigma_min": self.continuous_edm_sigma_min if self.sampling_mode == "Continuous EDM" else None,
"noise density function override β(0)": self.hardcodebetas_linear_start if self.goofysampling_betascale == True else None,
"noise density function override β(T)": self.hardcodebetas_linear_end if self.goofysampling_betascale == True else None,
"noise density function override T domain": self.hardcodebetas_timesteps if self.goofysampling_betascale == True else None
})

def postprocess(self, p, processed, *args):
Expand Down