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Support SD.Next's separate sampler for HiRes-fix. (#58)

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2023-08-03 10:31:39 -07:00
committed by GitHub
parent 639e40970f
commit 5349f00872
1 changed files with 47 additions and 22 deletions
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scripts/dynamic_thresholding.py
+47 -22
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@@ -88,9 +88,12 @@ class Script(scripts.Script):
if not enabled:
return
orig_sampler_name = p.sampler_name
orig_latent_sampler_name = getattr(p, 'latent_sampler', None)
if orig_sampler_name in ["DDIM", "PLMS"]:
raise RuntimeError(f"Cannot use sampler {orig_sampler_name} with Dynamic Thresholding")
if orig_sampler_name == 'UniPC' and p.enable_hr:
if orig_latent_sampler_name in ["DDIM", "PLMS"]:
raise RuntimeError(f"Cannot use secondary sampler {orig_latent_sampler_name} with Dynamic Thresholding")
if 'UniPC' in (orig_sampler_name, orig_latent_sampler_name) and p.enable_hr:
raise RuntimeError(f"UniPC does not support Hires Fix. Auto WebUI silently swaps to DDIM for this, which DynThresh does not support. Please swap to a sampler capable of img2img processing for HR Fix to work.")
mimic_scale = getattr(p, 'dynthres_mimic_scale', mimic_scale)
separate_feature_channels = getattr(p, 'dynthres_separate_feature_channels', separate_feature_channels)
@@ -122,38 +125,60 @@ class Script(scripts.Script):
p.extra_generation_params["Scheduler value"] = sched_val
# Note: the ID number is to protect the edge case of multiple simultaneous runs with different settings
Script.last_id += 1
fixed_sampler_name = f"{orig_sampler_name}_dynthres{Script.last_id}"
# Percentage to portion
threshold_percentile *= 0.01
# Make a placeholder sampler
sampler = sd_samplers.all_samplers_map[orig_sampler_name]
dtData = dynthres_core.DynThresh(mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, experiment_mode, p.steps, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi)
if orig_sampler_name == "UniPC":
def uniPCConstructor(model):
return CustomVanillaSDSampler(dynthres_unipc.CustomUniPCSampler, model, dtData)
newSampler = sd_samplers_common.SamplerData(fixed_sampler_name, uniPCConstructor, sampler.aliases, sampler.options)
else:
def newConstructor(model):
result = sampler.constructor(model)
cfg = CustomCFGDenoiser(result.model_wrap_cfg.inner_model, dtData)
result.model_wrap_cfg = cfg
return result
newSampler = sd_samplers_common.SamplerData(fixed_sampler_name, newConstructor, sampler.aliases, sampler.options)
def make_sampler(orig_sampler_name):
fixed_sampler_name = f"{orig_sampler_name}_dynthres{Script.last_id}"
# Make a placeholder sampler
sampler = sd_samplers.all_samplers_map[orig_sampler_name]
dtData = dynthres_core.DynThresh(mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, experiment_mode, p.steps, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi)
if orig_sampler_name == "UniPC":
def uniPCConstructor(model):
return CustomVanillaSDSampler(dynthres_unipc.CustomUniPCSampler, model, dtData)
newSampler = sd_samplers_common.SamplerData(fixed_sampler_name, uniPCConstructor, sampler.aliases, sampler.options)
else:
def newConstructor(model):
result = sampler.constructor(model)
cfg = CustomCFGDenoiser(result.model_wrap_cfg.inner_model, dtData)
result.model_wrap_cfg = cfg
return result
newSampler = sd_samplers_common.SamplerData(fixed_sampler_name, newConstructor, sampler.aliases, sampler.options)
return fixed_sampler_name, newSampler
# Apply for usage
p.orig_sampler_name = orig_sampler_name
p.sampler_name = fixed_sampler_name
p.fixed_sampler_name = fixed_sampler_name
sd_samplers.all_samplers_map[fixed_sampler_name] = newSampler
p.orig_latent_sampler_name = orig_latent_sampler_name
p.fixed_samplers = []
if orig_latent_sampler_name:
latent_sampler_name, latentSampler = make_sampler(orig_latent_sampler_name)
sd_samplers.all_samplers_map[latent_sampler_name] = latentSampler
p.fixed_samplers.append(latent_sampler_name)
p.latent_sampler = latent_sampler_name
if orig_sampler_name != orig_latent_sampler_name:
p.sampler_name, newSampler = make_sampler(orig_sampler_name)
sd_samplers.all_samplers_map[p.sampler_name] = newSampler
p.fixed_samplers.append(p.sampler_name)
else:
p.sampler_name = p.latent_sampler
if p.sampler is not None:
p.sampler = sd_samplers.create_sampler(fixed_sampler_name, p.sd_model)
p.sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
def postprocess_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi, batch_number, images):
if not enabled or not hasattr(p, 'orig_sampler_name'):
return
p.sampler_name = p.orig_sampler_name
del sd_samplers.all_samplers_map[p.fixed_sampler_name]
if p.orig_latent_sampler_name:
p.latent_sampler = p.orig_latent_sampler_name
for added_sampler in p.fixed_samplers:
del sd_samplers.all_samplers_map[added_sampler]
del p.fixed_samplers
del p.orig_sampler_name
del p.fixed_sampler_name
del p.orig_latent_sampler_name
######################### CompVis Implementation logic #########################