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Add conditioning additives to KSampler Cycle

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This commit is contained in:
Jordan Thompson
2023-06-23 23:45:30 -07:00
parent 2a35e30aa8
commit 7d95bb3617
1 changed files with 49 additions and 2 deletions
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WAS_Node_Suite.py
+49 -2
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@@ -8437,6 +8437,14 @@ class WAS_KSampler_Cycle:
},
"optional": {
"upscale_model": ("UPSCALE_MODEL",),
"pos_additive": ("CONDITIONING",),
"neg_additive": ("CONDITIONING",),
"pos_add_strength": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 1.0, "step": 0.01}),
"pos_add_strength_scaling": (["enable", "disable"],),
"pos_add_strength_cutoff": ("FLOAT", {"default": 2.0, "min": 0.01, "max": 10.0, "step": 0.01}),
"neg_add_strength": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 1.0, "step": 0.01}),
"neg_add_strength_scaling": (["enable", "disable"],),
"neg_add_strength_cutoff": ("FLOAT", {"default": 2.0, "min": 0.01, "max": 10.0, "step": 0.01}),
}
}
@@ -8446,11 +8454,17 @@ class WAS_KSampler_Cycle:
CATEGORY = "WAS Suite/Sampling"
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, tiled_vae, upscale_factor, upscale_steps, starting_denoise, cycle_denoise, scale_denoise, scale_sampling, vae, upscale_model=None):
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, tiled_vae, upscale_factor,
upscale_steps, starting_denoise, cycle_denoise, scale_denoise, scale_sampling, vae, pos_additive=None, pos_add_strength=None,
pos_add_strength_scaling=None, pos_add_strength_cutoff=None, neg_additive=None, neg_add_strength=None,
neg_add_strength_scaling=None, neg_add_strength_cutoff=None, upscale_model=None):
division_factor = upscale_steps if steps >= upscale_steps else steps
current_upscale_factor = upscale_factor ** (1 / (division_factor - 1))
tiled_vae = (tiled_vae == "enable")
scale_denoise = (scale_denoise == "enable")
pos_add_strength_scaling = (pos_add_strength_scaling == "enable")
neg_add_strength_scaling = (neg_add_strength_scaling == "enable")
WTools = WAS_Tools_Class()
@@ -8459,6 +8473,40 @@ class WAS_KSampler_Cycle:
( round(cycle_denoise * (2 ** (-(i-1))), 2) if i > 0 else cycle_denoise )
if i > 0 else starting_denoise
)
if pos_additive:
pos_strength = (
( round(pos_add_strength * (2 ** (i-1)), 2)
if i > 0
else pos_add_strength )
if pos_add_strength_scaling
else pos_add_strength
)
pos_strength = (
pos_add_strength_cutoff
if pos_strength > pos_add_strength_cutoff
else pos_strength
)
comb = nodes.ConditioningAverage()
positive = comb.addWeighted(pos_additive, positive, pos_strength)[0]
if neg_additive:
neg_strength = (
( round(neg_add_strength * (2 ** (i-1)), 2)
if i > 0
else neg_add_strength )
if neg_add_strength_scaling
else neg_add_strength
)
neg_strength = (
neg_add_strength_cutoff
if neg_strength > neg_add_strength_cutoff
else neg_strength
)
comb = nodes.ConditioningAverage()
negative = comb.addWeighted(neg_additive, negative, neg_strength)[0]
if i != 0:
latent_image = latent_image_result
@@ -8499,7 +8547,6 @@ class WAS_KSampler_Cycle:
upscaled_tensors = upscaler.upscale(upscale_model, tensors)
tensor_images = []
for tensor in upscaled_tensors[0]:
print(tensor.shape)
tensor_images.append(pil2tensor(tensor2pil(tensor).resize((new_width, new_height), Image.Resampling(resample_filters[scale_sampling]))))
tensor_images = torch.cat(tensor_images, dim=0)
if tiled_vae: