nodes/CosmicLaca-ComfyUI_Primere_Nodes
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V:0.1.5 - Noise seed

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This commit is contained in:
Leslie Perjes
2024-01-09 23:10:56 +01:00
parent fcc4df2011
commit 6bea4967dd
3 changed files with 275 additions and 99 deletions
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Nodes/Dashboard.py
+34 -14
View File
@@ -320,9 +320,9 @@ class PrimereSeed:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"seed": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff}),
},
"required": {
"seed": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff}),
},
}
def seed(self, seed = 0):
@@ -353,9 +353,15 @@ class PrimereFractalLatent:
"modulator": ("FLOAT", {"default": 1.0, "max": 2.0, "min": 0.1, "step": 0.01}),
"modulator_rand_min": ("FLOAT", {"default": 0.8, "max": 2.0, "min": 0.1, "step": 0.01}),
"modulator_rand_max": ("FLOAT", {"default": 1.4, "max": 2.0, "min": 0.1, "step": 0.01}),
"seed": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff, "forceInput": True}),
"noise_seed": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff, "forceInput": True}),
"rand_device": ("BOOLEAN", {"default": False}),
"device": (["cpu", "cuda"],),
"extra_variation": ("BOOLEAN", {"default": False, "label_on": "ON", "label_off": "OFF"}),
# "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
# "variation_increment": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 1.0, "step": 0.01}),
# "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"optional_vae": ("VAE",),
@@ -367,10 +373,28 @@ class PrimereFractalLatent:
FUNCTION = "primere_latent_noise"
CATEGORY = TREE_DASHBOARD
def primere_latent_noise(self, width, height, rand_noise_type, noise_type, rand_alpha_exponent, alpha_exponent, alpha_exp_rand_min, alpha_exp_rand_max, rand_modulator, modulator, modulator_rand_min, modulator_rand_max, seed, rand_device, device, optional_vae = None):
def primere_latent_noise(self, width, height, rand_noise_type, noise_type, rand_alpha_exponent, alpha_exponent, alpha_exp_rand_min, alpha_exp_rand_max, rand_modulator, modulator, modulator_rand_min, modulator_rand_max, noise_seed, seed, rand_device, device, optional_vae = None, extra_variation = False):
if extra_variation == True:
rand_device = True
rand_alpha_exponent = True
rand_modulator = True
rand_noise_type = True
alpha_exp_rand_min = -12.00
alpha_exp_rand_max = 7.00
modulator_rand_min = 0.10
modulator_rand_max = 2.00
noise_seed = seed
if rand_noise_type == True:
pln = PowerLawNoise(device)
noise_type = random.choice(pln.get_noise_types())
if rand_device == True:
device = random.choice(["cpu", "cuda"])
if extra_variation == True and (noise_type == 'white' or noise_type == 'violet'):
alpha_exp_rand_min = 0.00
power_law = PowerLawNoise(device = device)
if rand_alpha_exponent == True:
@@ -379,11 +403,7 @@ class PrimereFractalLatent:
if rand_modulator == True:
modulator = round(random.uniform(modulator_rand_min, modulator_rand_max), 2)
if rand_noise_type == True:
pln = PowerLawNoise(device)
noise_type = random.choice(pln.get_noise_types())
tensors = power_law(1, width, height, scale = 1, alpha = alpha_exponent, modulator = modulator, noise_type = noise_type, seed = seed)
tensors = power_law(1, width, height, scale = 1, alpha = alpha_exponent, modulator = modulator, noise_type = noise_type, seed = noise_seed)
alpha_channel = torch.ones((1, height, width, 1), dtype = tensors.dtype, device = "cpu")
tensors = torch.cat((tensors, alpha_channel), dim = 3)
@@ -628,8 +648,8 @@ class PrimereResolution:
"seed": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff, "forceInput": True}),
"calculate_by_custom": ("BOOLEAN", {"default": False}),
"custom_side_a": ("FLOAT", {"default": 1.6, "min": 1.0, "max": 100.0, "step": 0.1}),
"custom_side_b": ("FLOAT", {"default": 2.8, "min": 1.0, "max": 100.0, "step": 0.1}),
"custom_side_a": ("FLOAT", {"default": 1.6, "min": 1.0, "max": 100.0, "step": 0.05}),
"custom_side_b": ("FLOAT", {"default": 2.8, "min": 1.0, "max": 100.0, "step": 0.05}),
},
"optional": {
"model_version": ("STRING", {"default": 'BaseModel_1024', "forceInput": True}),
@@ -674,8 +694,8 @@ class PrimereResolutionMultiplier:
"width": ('INT', {"forceInput": True, "default": 512}),
"height": ('INT', {"forceInput": True, "default": 512}),
"use_multiplier": ("BOOLEAN", {"default": True}),
"multiply_sd": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1}),
"multiply_sdxl": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1}),
"multiply_sd": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.02}),
"multiply_sdxl": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.02}),
},
"optional": {
"model_version": ("STRING", {"default": 'BaseModel_1024', "forceInput": True}),
Workflow/Primere_basic_workflow.json
+121 -84
View File
@@ -1,6 +1,6 @@
{
"last_node_id": 17,
"last_link_id": 37,
"last_node_id": 39,
"last_link_id": 139,
"nodes": [
{
"id": 3,
@@ -36,7 +36,7 @@
"Node name for S&R": "PrimereVisualCKPT"
},
"widgets_values": [
"SDXL\\copaxTimelessxlSDXL1_v7.safetensors",
"BestAll\\photon_v1.safetensors",
true,
true
]
@@ -207,9 +207,9 @@
"links": [
17,
18,
19,
20,
34
34,
137
],
"shape": 3,
"slot_index": 0
@@ -219,7 +219,7 @@
"Node name for S&R": "PrimereSeed"
},
"widgets_values": [
-1,
218876056720308,
null,
null,
null
@@ -291,8 +291,8 @@
"name": "VAE",
"type": "VAE",
"links": [
16,
36
36,
134
],
"shape": 3
},
@@ -494,8 +494,8 @@
"name": "WIDTH",
"type": "INT",
"links": [
22,
28
28,
135
],
"shape": 3,
"slot_index": 0
@@ -504,8 +504,8 @@
"name": "HEIGHT",
"type": "INT",
"links": [
23,
29
29,
136
],
"shape": 3,
"slot_index": 1
@@ -519,8 +519,8 @@
768,
true,
"Horizontal",
false,
634167927872988,
true,
729604421095564,
"randomize",
false,
1.6,
@@ -577,7 +577,7 @@
},
"widgets_values": [
"",
817029395929541,
281164421636985,
"randomize"
]
},
@@ -630,21 +630,21 @@
},
"widgets_values": [
"",
467345014983569,
54227952213564,
"randomize"
]
},
{
"id": 11,
"id": 39,
"type": "PrimereLatentNoise",
"pos": [
1755,
368
1760,
447
],
"size": [
332,
471
],
"size": {
"0": 315,
"1": 438
},
"flags": {},
"order": 9,
"mode": 0,
@@ -652,13 +652,12 @@
{
"name": "optional_vae",
"type": "VAE",
"link": 16,
"slot_index": 0
"link": 134
},
{
"name": "width",
"type": "INT",
"link": 22,
"link": 135,
"widget": {
"name": "width"
}
@@ -666,17 +665,17 @@
{
"name": "height",
"type": "INT",
"link": 23,
"link": 136,
"widget": {
"name": "height"
}
},
{
"name": "seed",
"name": "noise_seed",
"type": "INT",
"link": 19,
"link": 137,
"widget": {
"name": "seed"
"name": "noise_seed"
}
}
],
@@ -685,14 +684,16 @@
"name": "LATENTS",
"type": "LATENT",
"links": [
31
138
],
"shape": 3
},
{
"name": "PREVIEWS",
"type": "IMAGE",
"links": null,
"links": [
139
],
"shape": 3
}
],
@@ -702,20 +703,23 @@
"widgets_values": [
512,
512,
true,
false,
"white",
true,
false,
1,
0.5,
1.5,
true,
false,
1,
0.8,
1.4,
102306924483936,
552044321990063,
"randomize",
false,
"cpu",
true,
"cpu"
762455303042557,
"randomize"
]
},
{
@@ -778,19 +782,44 @@
""
]
},
{
"id": 19,
"type": "PreviewImage",
"pos": [
1396,
691
],
"size": {
"0": 334.67486572265625,
"1": 246
},
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 139
}
],
"properties": {
"Node name for S&R": "PreviewImage"
}
},
{
"id": 14,
"type": "PrimereCLIPEncoder",
"pos": [
2110,
95
129
],
"size": {
"0": 330,
"1": 758
},
"flags": {},
"order": 11,
"order": 12,
"mode": 0,
"inputs": [
{
@@ -999,7 +1028,7 @@
"1": 234
},
"flags": {},
"order": 12,
"order": 13,
"mode": 0,
"inputs": [
{
@@ -1023,7 +1052,7 @@
{
"name": "latent_image",
"type": "LATENT",
"link": 31,
"link": 138,
"slot_index": 3
},
{
@@ -1050,7 +1079,7 @@
"Node name for S&R": "KSampler"
},
"widgets_values": [
493544399204151,
1098807145567755,
"randomize",
20,
8,
@@ -1071,7 +1100,7 @@
"1": 46
},
"flags": {},
"order": 13,
"order": 14,
"mode": 0,
"inputs": [
{
@@ -1113,7 +1142,7 @@
"1": 468.0492248535156
},
"flags": {},
"order": 14,
"order": 15,
"mode": 0,
"inputs": [
{
@@ -1248,14 +1277,6 @@
0,
"CHECKPOINT_NAME"
],
[
16,
9,
2,
11,
0,
"VAE"
],
[
17,
10,
@@ -1272,14 +1293,6 @@
1,
"INT"
],
[
19,
10,
0,
11,
3,
"INT"
],
[
20,
10,
@@ -1296,22 +1309,6 @@
1,
"STRING"
],
[
22,
13,
0,
11,
1,
"INT"
],
[
23,
13,
1,
11,
2,
"INT"
],
[
24,
9,
@@ -1368,14 +1365,6 @@
0,
"MODEL"
],
[
31,
11,
0,
15,
3,
"LATENT"
],
[
32,
14,
@@ -1423,6 +1412,54 @@
17,
0,
"IMAGE"
],
[
134,
9,
2,
39,
0,
"VAE"
],
[
135,
13,
0,
39,
1,
"INT"
],
[
136,
13,
1,
39,
2,
"INT"
],
[
137,
10,
0,
39,
3,
"INT"
],
[
138,
39,
0,
15,
3,
"LATENT"
],
[
139,
39,
1,
19,
0,
"IMAGE"
]
],
"groups": [],
components/utility.py
+120 -1
View File
@@ -458,5 +458,124 @@ def img_resizer(image: torch.Tensor, width: int, height: int, interpolation_mode
image = image.permute(0, 3, 1, 2)
image = F.resize(image, (height, width), interpolation=interpolation_mode, antialias=True)
image = image.permute(0, 2, 3, 1)
return image
return image
def apply_variation_noise(latent_image, noise_device, variation_seed, variation_strength, mask=None):
latent_size = latent_image.size()
latent_size_1batch = [1, latent_size[1], latent_size[2], latent_size[3]]
if noise_device == "cpu":
variation_generator = torch.manual_seed(variation_seed)
else:
torch.cuda.manual_seed(variation_seed)
variation_generator = None
variation_latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, generator=variation_generator, device=noise_device)
variation_noise = variation_latent.expand(latent_image.size()[0], -1, -1, -1)
if variation_strength == 0:
return latent_image
elif mask is None:
result = (1 - variation_strength) * latent_image + variation_strength * variation_noise
else:
# this seems precision is not enough when variation_strength is 0.0
result = (mask == 1).float() * ((1 - variation_strength) * latent_image + variation_strength * variation_noise * mask) + (mask == 0).float() * latent_image
return result
def prepare_noise(latent_image, seed, noise_inds=None, noise_device="cpu", incremental_seed_mode="comfy", variation_seed=None, variation_strength=None):
latent_size = latent_image.size()
latent_size_1batch = [1, latent_size[1], latent_size[2], latent_size[3]]
if variation_strength is not None and variation_strength > 0 or incremental_seed_mode.startswith("variation str inc"):
if noise_device == "cpu":
variation_generator = torch.manual_seed(variation_seed)
else:
torch.cuda.manual_seed(variation_seed)
variation_generator = None
variation_latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, generator=variation_generator, device=noise_device)
else:
variation_latent = None
def apply_variation(input_latent, strength_up=None):
if variation_latent is None:
return input_latent
else:
strength = variation_strength
if strength_up is not None:
strength += strength_up
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
if noise_inds is None and incremental_seed_mode == "incremental":
batch_cnt = latent_size[0]
latents = None
for i in range(batch_cnt):
if noise_device == "cpu":
generator = torch.manual_seed(seed+i)
else:
torch.cuda.manual_seed(seed+i)
generator = None
latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device=noise_device)
latent = apply_variation(latent)
if latents is None:
latents = latent
else:
latents = torch.cat((latents, latent), dim=0)
return latents
# 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
for i in range(batch_cnt):
if noise_device == "cpu":
generator = torch.manual_seed(seed)
else:
torch.cuda.manual_seed(seed)
generator = None
latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device=noise_device)
step = float(incremental_seed_mode[18:])
latent = apply_variation(latent, step*i)
if latents is None:
latents = latent
else:
latents = torch.cat((latents, latent), dim=0)
return latents
# method: comfy batch noise
if noise_device == "cpu":
generator = torch.manual_seed(seed)
else:
torch.cuda.manual_seed(seed)
generator = None
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_variation(latents)
return latents
unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
noises = []
for i in range(unique_inds[-1] + 1):
noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device=noise_device)
if i in unique_inds:
noises.append(noise)
noises = [noises[i] for i in inverse]
noises = torch.cat(noises, axis=0)
return noises