nodes/ltdrdata-ComfyUI-Inspire-Pack
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rename: variant_seed -> variation_seed

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Dr.Lt.Data
2023-11-07 17:18:57 +09:00
parent b1ae2c6176
commit ec1ec2c8fc
2 changed files with 31 additions and 31 deletions
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__init__.py
+1 -1
View File
@@ -7,7 +7,7 @@
import importlib
print(f"### Loading: ComfyUI-Inspire-Pack (V0.35)")
print(f"### Loading: ComfyUI-Inspire-Pack (V0.35.1)")
node_list = [
"lora_block_weight",
a1111_compat.py
+30 -30
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@@ -4,7 +4,7 @@ import numpy as np
import latent_preview
def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incremental_seed_mode="comfy", variant_seed=None, variant_strength=None):
def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incremental_seed_mode="comfy", variation_seed=None, variation_strength=None):
"""
creates random noise given a latent image and a seed.
optional arg skip can be used to skip and discard x number of noise generations for a given seed
@@ -13,29 +13,29 @@ def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incre
latent_size = latent_image.size()
latent_size_1batch = [1, latent_size[1], latent_size[2], latent_size[3]]
if variant_strength > 0 or incremental_seed_mode.startswith("variant str inc"):
if variation_strength > 0 or incremental_seed_mode.startswith("variation str inc"):
if noise_device == "cpu":
variant_generator = torch.manual_seed(variant_seed)
variation_generator = torch.manual_seed(variation_seed)
else:
torch.cuda.manual_seed(variant_seed)
variant_generator = None
torch.cuda.manual_seed(variation_seed)
variation_generator = None
variant_latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout,
generator=variant_generator, device=noise_device)
variation_latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout,
generator=variation_generator, device=noise_device)
else:
variant_latent = None
variation_latent = None
def apply_variant(input_latent, strength_up=None):
if variant_latent is None:
def apply_variation(input_latent, strength_up=None):
if variation_latent is None:
return input_latent
else:
strength = variant_strength
strength = variation_strength
if strength_up is not None:
strength += strength_up
variant_noise = variant_latent.expand(input_latent.size()[0], -1, -1, -1)
result = (1 - strength) * input_latent + strength * variant_noise
variation_noise = variation_latent.expand(input_latent.size()[0], -1, -1, -1)
result = (1 - strength) * input_latent + strength * variation_noise
return result
# method: incremental seed batch noise
@@ -53,7 +53,7 @@ def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incre
latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout,
generator=generator, device=noise_device)
latent = apply_variant(latent)
latent = apply_variation(latent)
if latents is None:
latents = latent
@@ -62,8 +62,8 @@ def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incre
return latents
# method: incremental variant batch noise
elif noise_inds is None and incremental_seed_mode.startswith("variant str inc"):
# method: incremental variation batch noise
elif noise_inds is None and incremental_seed_mode.startswith("variation str inc"):
batch_cnt = latent_size[0]
latents = None
@@ -78,7 +78,7 @@ def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incre
generator=generator, device=noise_device)
step = float(incremental_seed_mode[16:])
latent = apply_variant(latent, step*i)
latent = apply_variation(latent, step*i)
if latents is None:
latents = latent
@@ -97,7 +97,7 @@ def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incre
if noise_inds is None:
latents = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout,
generator=generator, device=noise_device)
latents = apply_variant(latents)
latents = apply_variation(latents)
return latents
unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
@@ -114,7 +114,7 @@ def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incre
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
noise_mode="CPU", disable_noise=False, start_step=None, last_step=None, force_full_denoise=False,
incremental_seed_mode="comfy", variant_seed=None, variant_strength=None):
incremental_seed_mode="comfy", variation_seed=None, variation_strength=None):
device = comfy.model_management.get_torch_device()
noise_device = "cpu" if noise_mode == "CPU" else device
latent_image = latent["samples"]
@@ -123,7 +123,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device=noise_device)
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = prepare_noise(latent_image, seed, batch_inds, noise_device, incremental_seed_mode, variant_seed=variant_seed, variant_strength=variant_strength)
noise = prepare_noise(latent_image, seed, batch_inds, noise_device, incremental_seed_mode, variation_seed=variation_seed, variation_strength=variation_strength)
noise_mask = None
if "noise_mask" in latent:
@@ -165,9 +165,9 @@ class KSampler_inspire:
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"noise_mode": (["GPU(=A1111)", "CPU"],),
"batch_seed_mode": (["incremental", "comfy", "variant str inc:0.01", "variant str inc:0.05"],),
"variant_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variant_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
@@ -176,8 +176,8 @@ class KSampler_inspire:
CATEGORY = "InspirePack/a1111_compat"
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, batch_seed_mode="comfy", variant_seed=None, variant_strength=None):
return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, incremental_seed_mode=batch_seed_mode, variant_seed=variant_seed, variant_strength=variant_strength)
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, batch_seed_mode="comfy", variation_seed=None, variation_strength=None):
return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, incremental_seed_mode=batch_seed_mode, variation_seed=variation_seed, variation_strength=variation_strength)
class KSamplerAdvanced_inspire:
@@ -198,9 +198,9 @@ class KSamplerAdvanced_inspire:
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"noise_mode": (["GPU(=A1111)", "CPU"],),
"return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
"batch_seed_mode": (["incremental", "comfy", "variant str inc:0.01", "variant str inc:0.05"],),
"variant_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variant_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
@@ -209,7 +209,7 @@ class KSamplerAdvanced_inspire:
CATEGORY = "InspirePack/a1111_compat"
def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, noise_mode, return_with_leftover_noise, denoise=1.0, batch_seed_mode="comfy", variant_seed=None, variant_strength=None):
def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, noise_mode, return_with_leftover_noise, denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None):
force_full_denoise = True
if return_with_leftover_noise:
@@ -223,7 +223,7 @@ class KSamplerAdvanced_inspire:
return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step,
force_full_denoise=force_full_denoise, noise_mode=noise_mode, incremental_seed_mode=batch_seed_mode,
variant_seed=variant_seed, variant_strength=variant_strength)
variation_seed=variation_seed, variation_strength=variation_strength)
NODE_CLASS_MAPPINGS = {