mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-11-12 14:26:42 +08:00
Merge branch 'comfyanonymous:master' into master
This commit is contained in:
RaINi_
GitHub
commit
e9dbf8017a
@ -841,3 +841,107 @@ def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=Non
|
||||
d_prime = w1 * d + w2 * d_2 + w3 * d_3
|
||||
x = x + d_prime * dt
|
||||
return x
|
||||
|
||||
|
||||
#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
|
||||
#under Apache 2 license
|
||||
def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
|
||||
x_next = x
|
||||
|
||||
buffer_model = []
|
||||
for i in trange(len(sigmas) - 1, disable=disable):
|
||||
t_cur = sigmas[i]
|
||||
t_next = sigmas[i + 1]
|
||||
|
||||
x_cur = x_next
|
||||
|
||||
denoised = model(x_cur, t_cur * s_in, **extra_args)
|
||||
if callback is not None:
|
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||||
|
||||
d_cur = (x_cur - denoised) / t_cur
|
||||
|
||||
order = min(max_order, i+1)
|
||||
if order == 1: # First Euler step.
|
||||
x_next = x_cur + (t_next - t_cur) * d_cur
|
||||
elif order == 2: # Use one history point.
|
||||
x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
|
||||
elif order == 3: # Use two history points.
|
||||
x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12
|
||||
elif order == 4: # Use three history points.
|
||||
x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24
|
||||
|
||||
if len(buffer_model) == max_order - 1:
|
||||
for k in range(max_order - 2):
|
||||
buffer_model[k] = buffer_model[k+1]
|
||||
buffer_model[-1] = d_cur
|
||||
else:
|
||||
buffer_model.append(d_cur)
|
||||
|
||||
return x_next
|
||||
|
||||
#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
|
||||
#under Apache 2 license
|
||||
def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
|
||||
x_next = x
|
||||
t_steps = sigmas
|
||||
|
||||
buffer_model = []
|
||||
for i in trange(len(sigmas) - 1, disable=disable):
|
||||
t_cur = sigmas[i]
|
||||
t_next = sigmas[i + 1]
|
||||
|
||||
x_cur = x_next
|
||||
|
||||
denoised = model(x_cur, t_cur * s_in, **extra_args)
|
||||
if callback is not None:
|
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||||
|
||||
d_cur = (x_cur - denoised) / t_cur
|
||||
|
||||
order = min(max_order, i+1)
|
||||
if order == 1: # First Euler step.
|
||||
x_next = x_cur + (t_next - t_cur) * d_cur
|
||||
elif order == 2: # Use one history point.
|
||||
h_n = (t_next - t_cur)
|
||||
h_n_1 = (t_cur - t_steps[i-1])
|
||||
coeff1 = (2 + (h_n / h_n_1)) / 2
|
||||
coeff2 = -(h_n / h_n_1) / 2
|
||||
x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1])
|
||||
elif order == 3: # Use two history points.
|
||||
h_n = (t_next - t_cur)
|
||||
h_n_1 = (t_cur - t_steps[i-1])
|
||||
h_n_2 = (t_steps[i-1] - t_steps[i-2])
|
||||
temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
|
||||
coeff1 = (2 + (h_n / h_n_1)) / 2 + temp
|
||||
coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp
|
||||
coeff3 = temp * h_n_1 / h_n_2
|
||||
x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2])
|
||||
elif order == 4: # Use three history points.
|
||||
h_n = (t_next - t_cur)
|
||||
h_n_1 = (t_cur - t_steps[i-1])
|
||||
h_n_2 = (t_steps[i-1] - t_steps[i-2])
|
||||
h_n_3 = (t_steps[i-2] - t_steps[i-3])
|
||||
temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
|
||||
temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \
|
||||
* (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3))
|
||||
coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2
|
||||
coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2
|
||||
coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2
|
||||
coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2
|
||||
x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3])
|
||||
|
||||
if len(buffer_model) == max_order - 1:
|
||||
for k in range(max_order - 2):
|
||||
buffer_model[k] = buffer_model[k+1]
|
||||
buffer_model[-1] = d_cur.detach()
|
||||
else:
|
||||
buffer_model.append(d_cur.detach())
|
||||
|
||||
return x_next
|
||||
|
||||
@ -218,12 +218,21 @@ def model_lora_keys_clip(model, key_map={}):
|
||||
lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config
|
||||
key_map[lora_key] = k
|
||||
|
||||
for k in sdk: #OneTrainer SD3 lora
|
||||
if k.startswith("t5xxl.transformer.") and k.endswith(".weight"):
|
||||
l_key = k[len("t5xxl.transformer."):-len(".weight")]
|
||||
lora_key = "lora_te3_{}".format(l_key.replace(".", "_"))
|
||||
key_map[lora_key] = k
|
||||
|
||||
k = "clip_g.transformer.text_projection.weight"
|
||||
if k in sdk:
|
||||
key_map["lora_prior_te_text_projection"] = k #cascade lora?
|
||||
# key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too
|
||||
# key_map["lora_te_text_projection"] = k
|
||||
key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora
|
||||
|
||||
k = "clip_l.transformer.text_projection.weight"
|
||||
if k in sdk:
|
||||
key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning
|
||||
|
||||
return key_map
|
||||
|
||||
@ -252,15 +261,17 @@ def model_lora_keys_unet(model, key_map={}):
|
||||
key_map[diffusers_lora_key] = unet_key
|
||||
|
||||
if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
|
||||
for i in range(model.model_config.unet_config.get("depth", 0)):
|
||||
k = "transformer.transformer_blocks.{}.attn.".format(i)
|
||||
qkv = "diffusion_model.joint_blocks.{}.x_block.attn.qkv.weight".format(i)
|
||||
proj = "diffusion_model.joint_blocks.{}.x_block.attn.proj.weight".format(i)
|
||||
if qkv in sd:
|
||||
offset = sd[qkv].shape[0] // 3
|
||||
key_map["{}to_q".format(k)] = (qkv, (0, 0, offset))
|
||||
key_map["{}to_k".format(k)] = (qkv, (0, offset, offset))
|
||||
key_map["{}to_v".format(k)] = (qkv, (0, offset * 2, offset))
|
||||
key_map["{}to_out.0".format(k)] = proj
|
||||
diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
|
||||
for k in diffusers_keys:
|
||||
if k.endswith(".weight"):
|
||||
to = diffusers_keys[k]
|
||||
key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format
|
||||
key_map[key_lora] = to
|
||||
|
||||
key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others?
|
||||
key_map[key_lora] = to
|
||||
|
||||
key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora
|
||||
key_map[key_lora] = to
|
||||
|
||||
return key_map
|
||||
|
||||
@ -1,7 +1,9 @@
|
||||
import comfy.supported_models
|
||||
import comfy.supported_models_base
|
||||
import comfy.utils
|
||||
import math
|
||||
import logging
|
||||
import torch
|
||||
|
||||
def count_blocks(state_dict_keys, prefix_string):
|
||||
count = 0
|
||||
@ -431,3 +433,38 @@ def model_config_from_diffusers_unet(state_dict):
|
||||
if unet_config is not None:
|
||||
return model_config_from_unet_config(unet_config)
|
||||
return None
|
||||
|
||||
def convert_diffusers_mmdit(state_dict, output_prefix=""):
|
||||
depth = count_blocks(state_dict, 'transformer_blocks.{}.')
|
||||
if depth > 0:
|
||||
out_sd = {}
|
||||
sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth}, output_prefix=output_prefix)
|
||||
for k in sd_map:
|
||||
weight = state_dict.get(k, None)
|
||||
if weight is not None:
|
||||
t = sd_map[k]
|
||||
|
||||
if not isinstance(t, str):
|
||||
if len(t) > 2:
|
||||
fun = t[2]
|
||||
else:
|
||||
fun = lambda a: a
|
||||
offset = t[1]
|
||||
if offset is not None:
|
||||
old_weight = out_sd.get(t[0], None)
|
||||
if old_weight is None:
|
||||
old_weight = torch.empty_like(weight)
|
||||
old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1))
|
||||
|
||||
w = old_weight.narrow(offset[0], offset[1], offset[2])
|
||||
else:
|
||||
old_weight = weight
|
||||
w = weight
|
||||
w[:] = fun(weight)
|
||||
t = t[0]
|
||||
out_sd[t] = old_weight
|
||||
else:
|
||||
out_sd[t] = weight
|
||||
state_dict.pop(k)
|
||||
|
||||
return out_sd
|
||||
|
||||
@ -210,16 +210,19 @@ class ModelPatcher:
|
||||
model_sd = self.model.state_dict()
|
||||
for k in patches:
|
||||
offset = None
|
||||
function = None
|
||||
if isinstance(k, str):
|
||||
key = k
|
||||
else:
|
||||
offset = k[1]
|
||||
key = k[0]
|
||||
if len(k) > 2:
|
||||
function = k[2]
|
||||
|
||||
if key in model_sd:
|
||||
p.add(k)
|
||||
current_patches = self.patches.get(key, [])
|
||||
current_patches.append((strength_patch, patches[k], strength_model, offset))
|
||||
current_patches.append((strength_patch, patches[k], strength_model, offset, function))
|
||||
self.patches[key] = current_patches
|
||||
|
||||
self.patches_uuid = uuid.uuid4()
|
||||
@ -347,6 +350,9 @@ class ModelPatcher:
|
||||
v = p[1]
|
||||
strength_model = p[2]
|
||||
offset = p[3]
|
||||
function = p[4]
|
||||
if function is None:
|
||||
function = lambda a: a
|
||||
|
||||
old_weight = None
|
||||
if offset is not None:
|
||||
@ -371,7 +377,7 @@ class ModelPatcher:
|
||||
if w1.shape != weight.shape:
|
||||
logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
|
||||
else:
|
||||
weight += strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
|
||||
weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype))
|
||||
elif patch_type == "lora": #lora/locon
|
||||
mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
|
||||
mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
|
||||
@ -389,9 +395,9 @@ class ModelPatcher:
|
||||
try:
|
||||
lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
|
||||
if dora_scale is not None:
|
||||
weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
|
||||
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
|
||||
else:
|
||||
weight += ((strength * alpha) * lora_diff).type(weight.dtype)
|
||||
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
|
||||
except Exception as e:
|
||||
logging.error("ERROR {} {} {}".format(patch_type, key, e))
|
||||
elif patch_type == "lokr":
|
||||
@ -435,9 +441,9 @@ class ModelPatcher:
|
||||
try:
|
||||
lora_diff = torch.kron(w1, w2).reshape(weight.shape)
|
||||
if dora_scale is not None:
|
||||
weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
|
||||
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
|
||||
else:
|
||||
weight += ((strength * alpha) * lora_diff).type(weight.dtype)
|
||||
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
|
||||
except Exception as e:
|
||||
logging.error("ERROR {} {} {}".format(patch_type, key, e))
|
||||
elif patch_type == "loha":
|
||||
@ -472,9 +478,9 @@ class ModelPatcher:
|
||||
try:
|
||||
lora_diff = (m1 * m2).reshape(weight.shape)
|
||||
if dora_scale is not None:
|
||||
weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
|
||||
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
|
||||
else:
|
||||
weight += ((strength * alpha) * lora_diff).type(weight.dtype)
|
||||
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
|
||||
except Exception as e:
|
||||
logging.error("ERROR {} {} {}".format(patch_type, key, e))
|
||||
elif patch_type == "glora":
|
||||
@ -493,9 +499,9 @@ class ModelPatcher:
|
||||
try:
|
||||
lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
|
||||
if dora_scale is not None:
|
||||
weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
|
||||
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
|
||||
else:
|
||||
weight += ((strength * alpha) * lora_diff).type(weight.dtype)
|
||||
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
|
||||
except Exception as e:
|
||||
logging.error("ERROR {} {} {}".format(patch_type, key, e))
|
||||
else:
|
||||
|
||||
@ -539,7 +539,8 @@ class Sampler:
|
||||
|
||||
KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
|
||||
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
|
||||
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
|
||||
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
|
||||
"ipndm", "ipndm_v"]
|
||||
|
||||
class KSAMPLER(Sampler):
|
||||
def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
|
||||
|
||||
31
comfy/sd.py
31
comfy/sd.py
@ -298,16 +298,9 @@ class VAE:
|
||||
/ 3.0)
|
||||
return output
|
||||
|
||||
def decode_tiled_1d(self, samples, tile_x=128, overlap=64):
|
||||
output = torch.empty((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
|
||||
|
||||
for j in range(samples.shape[0]):
|
||||
for i in range(0, samples.shape[-1], tile_x - overlap):
|
||||
f = i
|
||||
t = i + tile_x
|
||||
output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] = self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float()
|
||||
|
||||
return output
|
||||
def decode_tiled_1d(self, samples, tile_x=128, overlap=32):
|
||||
decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
|
||||
return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)
|
||||
|
||||
def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
|
||||
steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
|
||||
@ -322,6 +315,10 @@ class VAE:
|
||||
samples /= 3.0
|
||||
return samples
|
||||
|
||||
def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048):
|
||||
encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
|
||||
return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
|
||||
|
||||
def decode(self, samples_in):
|
||||
try:
|
||||
memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
|
||||
@ -365,7 +362,10 @@ class VAE:
|
||||
|
||||
except model_management.OOM_EXCEPTION as e:
|
||||
logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
|
||||
samples = self.encode_tiled_(pixel_samples)
|
||||
if len(pixel_samples.shape) == 3:
|
||||
samples = self.encode_tiled_1d(pixel_samples)
|
||||
else:
|
||||
samples = self.encode_tiled_(pixel_samples)
|
||||
|
||||
return samples
|
||||
|
||||
@ -568,7 +568,14 @@ def load_unet_state_dict(sd): #load unet in diffusers format
|
||||
unet_dtype = model_management.unet_dtype(model_params=parameters)
|
||||
load_device = model_management.get_torch_device()
|
||||
|
||||
if "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
|
||||
if 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3
|
||||
new_sd = model_detection.convert_diffusers_mmdit(sd, "")
|
||||
if new_sd is None:
|
||||
return None
|
||||
model_config = model_detection.model_config_from_unet(new_sd, "")
|
||||
if model_config is None:
|
||||
return None
|
||||
elif "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
|
||||
model_config = model_detection.model_config_from_unet(sd, "")
|
||||
if model_config is None:
|
||||
return None
|
||||
|
||||
@ -9,6 +9,7 @@ from . import model_management
|
||||
import comfy.clip_model
|
||||
import json
|
||||
import logging
|
||||
import numbers
|
||||
|
||||
def gen_empty_tokens(special_tokens, length):
|
||||
start_token = special_tokens.get("start", None)
|
||||
@ -130,10 +131,10 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
|
||||
for x in tokens:
|
||||
tokens_temp = []
|
||||
for y in x:
|
||||
if isinstance(y, int):
|
||||
if isinstance(y, numbers.Integral):
|
||||
if y == token_dict_size: #EOS token
|
||||
y = -1
|
||||
tokens_temp += [y]
|
||||
tokens_temp += [int(y)]
|
||||
else:
|
||||
if y.shape[0] == current_embeds.weight.shape[1]:
|
||||
embedding_weights += [y]
|
||||
|
||||
142
comfy/utils.py
142
comfy/utils.py
@ -6,6 +6,7 @@ import safetensors.torch
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
import logging
|
||||
import itertools
|
||||
|
||||
def load_torch_file(ckpt, safe_load=False, device=None):
|
||||
if device is None:
|
||||
@ -249,6 +250,87 @@ def unet_to_diffusers(unet_config):
|
||||
|
||||
return diffusers_unet_map
|
||||
|
||||
def swap_scale_shift(weight):
|
||||
shift, scale = weight.chunk(2, dim=0)
|
||||
new_weight = torch.cat([scale, shift], dim=0)
|
||||
return new_weight
|
||||
|
||||
MMDIT_MAP_BASIC = {
|
||||
("context_embedder.bias", "context_embedder.bias"),
|
||||
("context_embedder.weight", "context_embedder.weight"),
|
||||
("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
|
||||
("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
|
||||
("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
|
||||
("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
|
||||
("x_embedder.proj.bias", "pos_embed.proj.bias"),
|
||||
("x_embedder.proj.weight", "pos_embed.proj.weight"),
|
||||
("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"),
|
||||
("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"),
|
||||
("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"),
|
||||
("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"),
|
||||
("pos_embed", "pos_embed.pos_embed"),
|
||||
("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
|
||||
("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
|
||||
("final_layer.linear.bias", "proj_out.bias"),
|
||||
("final_layer.linear.weight", "proj_out.weight"),
|
||||
}
|
||||
|
||||
MMDIT_MAP_BLOCK = {
|
||||
("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"),
|
||||
("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"),
|
||||
("context_block.attn.proj.bias", "attn.to_add_out.bias"),
|
||||
("context_block.attn.proj.weight", "attn.to_add_out.weight"),
|
||||
("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"),
|
||||
("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"),
|
||||
("context_block.mlp.fc2.bias", "ff_context.net.2.bias"),
|
||||
("context_block.mlp.fc2.weight", "ff_context.net.2.weight"),
|
||||
("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"),
|
||||
("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"),
|
||||
("x_block.attn.proj.bias", "attn.to_out.0.bias"),
|
||||
("x_block.attn.proj.weight", "attn.to_out.0.weight"),
|
||||
("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"),
|
||||
("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
|
||||
("x_block.mlp.fc2.bias", "ff.net.2.bias"),
|
||||
("x_block.mlp.fc2.weight", "ff.net.2.weight"),
|
||||
}
|
||||
|
||||
def mmdit_to_diffusers(mmdit_config, output_prefix=""):
|
||||
key_map = {}
|
||||
|
||||
depth = mmdit_config.get("depth", 0)
|
||||
for i in range(depth):
|
||||
block_from = "transformer_blocks.{}".format(i)
|
||||
block_to = "{}joint_blocks.{}".format(output_prefix, i)
|
||||
|
||||
offset = depth * 64
|
||||
|
||||
for end in ("weight", "bias"):
|
||||
k = "{}.attn.".format(block_from)
|
||||
qkv = "{}.x_block.attn.qkv.{}".format(block_to, end)
|
||||
key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
|
||||
key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
|
||||
key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
|
||||
|
||||
qkv = "{}.context_block.attn.qkv.{}".format(block_to, end)
|
||||
key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset))
|
||||
key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset))
|
||||
key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
|
||||
|
||||
for k in MMDIT_MAP_BLOCK:
|
||||
key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
|
||||
|
||||
map_basic = MMDIT_MAP_BASIC.copy()
|
||||
map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift))
|
||||
map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift))
|
||||
|
||||
for k in map_basic:
|
||||
if len(k) > 2:
|
||||
key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
|
||||
else:
|
||||
key_map[k[1]] = "{}{}".format(output_prefix, k[0])
|
||||
|
||||
return key_map
|
||||
|
||||
def repeat_to_batch_size(tensor, batch_size, dim=0):
|
||||
if tensor.shape[dim] > batch_size:
|
||||
return tensor.narrow(dim, 0, batch_size)
|
||||
@ -425,34 +507,52 @@ def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
|
||||
return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
|
||||
|
||||
@torch.inference_mode()
|
||||
def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
|
||||
output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device)
|
||||
def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
|
||||
dims = len(tile)
|
||||
output = torch.empty([samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:])), device=output_device)
|
||||
|
||||
for b in range(samples.shape[0]):
|
||||
s = samples[b:b+1]
|
||||
out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
|
||||
out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
|
||||
for y in range(0, s.shape[2], tile_y - overlap):
|
||||
for x in range(0, s.shape[3], tile_x - overlap):
|
||||
x = max(0, min(s.shape[-1] - overlap, x))
|
||||
y = max(0, min(s.shape[-2] - overlap, y))
|
||||
s_in = s[:,:,y:y+tile_y,x:x+tile_x]
|
||||
out = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
|
||||
out_div = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
|
||||
|
||||
ps = function(s_in).to(output_device)
|
||||
mask = torch.ones_like(ps)
|
||||
feather = round(overlap * upscale_amount)
|
||||
for t in range(feather):
|
||||
mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
|
||||
mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
|
||||
mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
|
||||
mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
|
||||
out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask
|
||||
out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask
|
||||
if pbar is not None:
|
||||
pbar.update(1)
|
||||
for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))):
|
||||
s_in = s
|
||||
upscaled = []
|
||||
|
||||
for d in range(dims):
|
||||
pos = max(0, min(s.shape[d + 2] - overlap, it[d]))
|
||||
l = min(tile[d], s.shape[d + 2] - pos)
|
||||
s_in = s_in.narrow(d + 2, pos, l)
|
||||
upscaled.append(round(pos * upscale_amount))
|
||||
ps = function(s_in).to(output_device)
|
||||
mask = torch.ones_like(ps)
|
||||
feather = round(overlap * upscale_amount)
|
||||
for t in range(feather):
|
||||
for d in range(2, dims + 2):
|
||||
m = mask.narrow(d, t, 1)
|
||||
m *= ((1.0/feather) * (t + 1))
|
||||
m = mask.narrow(d, mask.shape[d] -1 -t, 1)
|
||||
m *= ((1.0/feather) * (t + 1))
|
||||
|
||||
o = out
|
||||
o_d = out_div
|
||||
for d in range(dims):
|
||||
o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
|
||||
o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
|
||||
|
||||
o += ps * mask
|
||||
o_d += mask
|
||||
|
||||
if pbar is not None:
|
||||
pbar.update(1)
|
||||
|
||||
output[b:b+1] = out/out_div
|
||||
return output
|
||||
|
||||
def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
|
||||
return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels, output_device, pbar)
|
||||
|
||||
PROGRESS_BAR_ENABLED = True
|
||||
def set_progress_bar_enabled(enabled):
|
||||
global PROGRESS_BAR_ENABLED
|
||||
|
||||
369
comfy_extras/nodes_gits.py
Normal file
369
comfy_extras/nodes_gits.py
Normal file
@ -0,0 +1,369 @@
|
||||
# from https://github.com/zju-pi/diff-sampler/tree/main/gits-main
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
def loglinear_interp(t_steps, num_steps):
|
||||
"""
|
||||
Performs log-linear interpolation of a given array of decreasing numbers.
|
||||
"""
|
||||
xs = np.linspace(0, 1, len(t_steps))
|
||||
ys = np.log(t_steps[::-1])
|
||||
|
||||
new_xs = np.linspace(0, 1, num_steps)
|
||||
new_ys = np.interp(new_xs, xs, ys)
|
||||
|
||||
interped_ys = np.exp(new_ys)[::-1].copy()
|
||||
return interped_ys
|
||||
|
||||
NOISE_LEVELS = {
|
||||
0.80: [
|
||||
[14.61464119, 7.49001646, 0.02916753],
|
||||
[14.61464119, 11.54541874, 6.77309084, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.75677586, 2.84484982, 1.78698075, 0.803307, 0.02916753],
|
||||
],
|
||||
0.85: [
|
||||
[14.61464119, 7.49001646, 0.02916753],
|
||||
[14.61464119, 7.49001646, 1.84880662, 0.02916753],
|
||||
[14.61464119, 11.54541874, 6.77309084, 1.56271636, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.11996698, 3.07277966, 1.24153244, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.60512662, 2.6383388, 1.56271636, 0.72133851, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
|
||||
],
|
||||
0.90: [
|
||||
[14.61464119, 6.77309084, 0.02916753],
|
||||
[14.61464119, 7.49001646, 1.56271636, 0.02916753],
|
||||
[14.61464119, 7.49001646, 3.07277966, 0.95350921, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.09240818, 3.07277966, 1.61558151, 0.69515091, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.11996698, 4.86714602, 3.07277966, 1.61558151, 0.69515091, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 2.95596409, 1.61558151, 0.69515091, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
|
||||
[14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.84484982, 1.84880662, 1.08895338, 0.52423614, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.84484982, 1.84880662, 1.08895338, 0.52423614, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.45427561, 3.32507086, 2.45070267, 1.61558151, 0.95350921, 0.45573691, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.45427561, 3.32507086, 2.45070267, 1.61558151, 0.95350921, 0.45573691, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.60512662, 2.95596409, 2.19988537, 1.51179266, 0.89115214, 0.43325692, 0.02916753],
|
||||
],
|
||||
0.95: [
|
||||
[14.61464119, 6.77309084, 0.02916753],
|
||||
[14.61464119, 6.77309084, 1.56271636, 0.02916753],
|
||||
[14.61464119, 7.49001646, 2.84484982, 0.89115214, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.36326075, 0.803307, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.95596409, 1.56271636, 0.64427125, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.95596409, 1.56271636, 0.64427125, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 4.86714602, 3.07277966, 1.91321158, 1.08895338, 0.50118381, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.07277966, 1.91321158, 1.08895338, 0.50118381, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.07277966, 1.91321158, 1.08895338, 0.50118381, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.41535246, 0.803307, 0.38853383, 0.02916753],
|
||||
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.46139455, 2.6383388, 1.84880662, 1.24153244, 0.72133851, 0.34370604, 0.02916753],
|
||||
[14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.46139455, 2.6383388, 1.84880662, 1.24153244, 0.72133851, 0.34370604, 0.02916753],
|
||||
[14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.95596409, 2.19988537, 1.56271636, 1.05362725, 0.64427125, 0.32104823, 0.02916753],
|
||||
[14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 6.44769001, 5.58536053, 4.65472794, 3.60512662, 2.95596409, 2.19988537, 1.56271636, 1.05362725, 0.64427125, 0.32104823, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.65472794, 3.60512662, 2.95596409, 2.19988537, 1.56271636, 1.05362725, 0.64427125, 0.32104823, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.65472794, 3.75677586, 3.07277966, 2.45070267, 1.78698075, 1.24153244, 0.83188516, 0.50118381, 0.22545385, 0.02916753],
|
||||
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.60512662, 2.95596409, 2.36326075, 1.72759056, 1.24153244, 0.83188516, 0.50118381, 0.22545385, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.60512662, 2.95596409, 2.36326075, 1.72759056, 1.24153244, 0.83188516, 0.50118381, 0.22545385, 0.02916753],
|
||||
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.75677586, 3.07277966, 2.45070267, 1.91321158, 1.46270394, 1.05362725, 0.72133851, 0.43325692, 0.19894916, 0.02916753],
|
||||
],
|
||||
1.00: [
|
||||
[14.61464119, 1.56271636, 0.02916753],
|
||||
[14.61464119, 6.77309084, 0.95350921, 0.02916753],
|
||||
[14.61464119, 6.77309084, 2.36326075, 0.803307, 0.02916753],
|
||||
[14.61464119, 7.11996698, 3.07277966, 1.56271636, 0.59516323, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.84484982, 1.41535246, 0.57119018, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.84484982, 1.61558151, 0.86115354, 0.38853383, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.84484982, 1.61558151, 0.86115354, 0.38853383, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 4.86714602, 3.07277966, 1.98035145, 1.24153244, 0.72133851, 0.34370604, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.07277966, 1.98035145, 1.24153244, 0.72133851, 0.34370604, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.27973175, 1.51179266, 0.95350921, 0.54755926, 0.25053367, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.36326075, 1.61558151, 1.08895338, 0.72133851, 0.41087446, 0.17026083, 0.02916753],
|
||||
[14.61464119, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.36326075, 1.61558151, 1.08895338, 0.72133851, 0.41087446, 0.17026083, 0.02916753],
|
||||
[14.61464119, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.60512662, 2.84484982, 2.12350607, 1.56271636, 1.08895338, 0.72133851, 0.41087446, 0.17026083, 0.02916753],
|
||||
[14.61464119, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.60512662, 2.84484982, 2.19988537, 1.61558151, 1.162866, 0.803307, 0.50118381, 0.27464288, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.75677586, 3.07277966, 2.45070267, 1.84880662, 1.36964464, 1.01931262, 0.72133851, 0.45573691, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 5.09240818, 4.26497746, 3.46139455, 2.84484982, 2.19988537, 1.67050016, 1.24153244, 0.92192322, 0.64427125, 0.43325692, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 5.09240818, 4.26497746, 3.60512662, 2.95596409, 2.45070267, 1.91321158, 1.51179266, 1.12534678, 0.83188516, 0.59516323, 0.38853383, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 12.2308979, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 5.09240818, 4.26497746, 3.60512662, 2.95596409, 2.45070267, 1.91321158, 1.51179266, 1.12534678, 0.83188516, 0.59516323, 0.38853383, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 12.2308979, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.26497746, 3.60512662, 2.95596409, 2.45070267, 1.91321158, 1.51179266, 1.12534678, 0.83188516, 0.59516323, 0.38853383, 0.22545385, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.05: [
|
||||
[14.61464119, 0.95350921, 0.02916753],
|
||||
[14.61464119, 6.77309084, 0.89115214, 0.02916753],
|
||||
[14.61464119, 6.77309084, 2.05039096, 0.72133851, 0.02916753],
|
||||
[14.61464119, 6.77309084, 2.84484982, 1.28281462, 0.52423614, 0.02916753],
|
||||
[14.61464119, 6.77309084, 3.07277966, 1.61558151, 0.803307, 0.34370604, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.84484982, 1.56271636, 0.803307, 0.34370604, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.84484982, 1.61558151, 0.95350921, 0.52423614, 0.22545385, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.07277966, 1.98035145, 1.24153244, 0.74807048, 0.41087446, 0.17026083, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.1956799, 2.27973175, 1.51179266, 0.95350921, 0.59516323, 0.34370604, 0.13792117, 0.02916753],
|
||||
[14.61464119, 7.49001646, 5.09240818, 3.46139455, 2.45070267, 1.61558151, 1.08895338, 0.72133851, 0.45573691, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.09240818, 3.46139455, 2.45070267, 1.61558151, 1.08895338, 0.72133851, 0.45573691, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.36326075, 1.61558151, 1.08895338, 0.72133851, 0.45573691, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.45070267, 1.72759056, 1.24153244, 0.86115354, 0.59516323, 0.38853383, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.65472794, 3.60512662, 2.84484982, 2.19988537, 1.61558151, 1.162866, 0.83188516, 0.59516323, 0.38853383, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.65472794, 3.60512662, 2.84484982, 2.19988537, 1.67050016, 1.28281462, 0.95350921, 0.72133851, 0.52423614, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.65472794, 3.60512662, 2.95596409, 2.36326075, 1.84880662, 1.41535246, 1.08895338, 0.83188516, 0.61951244, 0.45573691, 0.32104823, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.65472794, 3.60512662, 2.95596409, 2.45070267, 1.91321158, 1.51179266, 1.20157266, 0.95350921, 0.74807048, 0.57119018, 0.43325692, 0.29807833, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 8.30717278, 7.11996698, 5.85520077, 4.65472794, 3.60512662, 2.95596409, 2.45070267, 1.91321158, 1.51179266, 1.20157266, 0.95350921, 0.74807048, 0.57119018, 0.43325692, 0.29807833, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 8.30717278, 7.11996698, 5.85520077, 4.65472794, 3.60512662, 2.95596409, 2.45070267, 1.98035145, 1.61558151, 1.32549286, 1.08895338, 0.86115354, 0.69515091, 0.54755926, 0.41087446, 0.29807833, 0.19894916, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.10: [
|
||||
[14.61464119, 0.89115214, 0.02916753],
|
||||
[14.61464119, 2.36326075, 0.72133851, 0.02916753],
|
||||
[14.61464119, 5.85520077, 1.61558151, 0.57119018, 0.02916753],
|
||||
[14.61464119, 6.77309084, 2.45070267, 1.08895338, 0.45573691, 0.02916753],
|
||||
[14.61464119, 6.77309084, 2.95596409, 1.56271636, 0.803307, 0.34370604, 0.02916753],
|
||||
[14.61464119, 6.77309084, 3.07277966, 1.61558151, 0.89115214, 0.4783645, 0.19894916, 0.02916753],
|
||||
[14.61464119, 6.77309084, 3.07277966, 1.84880662, 1.08895338, 0.64427125, 0.34370604, 0.13792117, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.84484982, 1.61558151, 0.95350921, 0.54755926, 0.27464288, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.95596409, 1.91321158, 1.24153244, 0.803307, 0.4783645, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.07277966, 2.05039096, 1.41535246, 0.95350921, 0.64427125, 0.41087446, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.1956799, 2.27973175, 1.61558151, 1.12534678, 0.803307, 0.54755926, 0.36617002, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.32507086, 2.45070267, 1.72759056, 1.24153244, 0.89115214, 0.64427125, 0.45573691, 0.32104823, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 5.09240818, 3.60512662, 2.84484982, 2.05039096, 1.51179266, 1.08895338, 0.803307, 0.59516323, 0.43325692, 0.29807833, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 5.09240818, 3.60512662, 2.84484982, 2.12350607, 1.61558151, 1.24153244, 0.95350921, 0.72133851, 0.54755926, 0.41087446, 0.29807833, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.45070267, 1.84880662, 1.41535246, 1.08895338, 0.83188516, 0.64427125, 0.50118381, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.45070267, 1.91321158, 1.51179266, 1.20157266, 0.95350921, 0.74807048, 0.59516323, 0.45573691, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 5.85520077, 4.45427561, 3.46139455, 2.84484982, 2.19988537, 1.72759056, 1.36964464, 1.08895338, 0.86115354, 0.69515091, 0.54755926, 0.43325692, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.46139455, 2.84484982, 2.19988537, 1.72759056, 1.36964464, 1.08895338, 0.86115354, 0.69515091, 0.54755926, 0.43325692, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.46139455, 2.84484982, 2.19988537, 1.72759056, 1.36964464, 1.08895338, 0.89115214, 0.72133851, 0.59516323, 0.4783645, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.15: [
|
||||
[14.61464119, 0.83188516, 0.02916753],
|
||||
[14.61464119, 1.84880662, 0.59516323, 0.02916753],
|
||||
[14.61464119, 5.85520077, 1.56271636, 0.52423614, 0.02916753],
|
||||
[14.61464119, 5.85520077, 1.91321158, 0.83188516, 0.34370604, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.45070267, 1.24153244, 0.59516323, 0.25053367, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.51179266, 0.803307, 0.41087446, 0.17026083, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.56271636, 0.89115214, 0.50118381, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 6.77309084, 3.07277966, 1.84880662, 1.12534678, 0.72133851, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 6.77309084, 3.07277966, 1.91321158, 1.24153244, 0.803307, 0.52423614, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 2.95596409, 1.91321158, 1.24153244, 0.803307, 0.52423614, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.07277966, 2.05039096, 1.36964464, 0.95350921, 0.69515091, 0.4783645, 0.32104823, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.07277966, 2.12350607, 1.51179266, 1.08895338, 0.803307, 0.59516323, 0.43325692, 0.29807833, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.07277966, 2.12350607, 1.51179266, 1.08895338, 0.803307, 0.59516323, 0.45573691, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.07277966, 2.19988537, 1.61558151, 1.24153244, 0.95350921, 0.74807048, 0.59516323, 0.45573691, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.1956799, 2.45070267, 1.78698075, 1.32549286, 1.01931262, 0.803307, 0.64427125, 0.50118381, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.1956799, 2.45070267, 1.78698075, 1.32549286, 1.01931262, 0.803307, 0.64427125, 0.52423614, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.1956799, 2.45070267, 1.84880662, 1.41535246, 1.12534678, 0.89115214, 0.72133851, 0.59516323, 0.4783645, 0.38853383, 0.32104823, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.1956799, 2.45070267, 1.84880662, 1.41535246, 1.12534678, 0.89115214, 0.72133851, 0.59516323, 0.50118381, 0.41087446, 0.34370604, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.86714602, 3.1956799, 2.45070267, 1.84880662, 1.41535246, 1.12534678, 0.89115214, 0.72133851, 0.59516323, 0.50118381, 0.41087446, 0.34370604, 0.29807833, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.20: [
|
||||
[14.61464119, 0.803307, 0.02916753],
|
||||
[14.61464119, 1.56271636, 0.52423614, 0.02916753],
|
||||
[14.61464119, 2.36326075, 0.92192322, 0.36617002, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.24153244, 0.59516323, 0.25053367, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.05039096, 0.95350921, 0.45573691, 0.17026083, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.45070267, 1.24153244, 0.64427125, 0.29807833, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.45070267, 1.36964464, 0.803307, 0.45573691, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.61558151, 0.95350921, 0.59516323, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.67050016, 1.08895338, 0.74807048, 0.50118381, 0.32104823, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.95596409, 1.84880662, 1.24153244, 0.83188516, 0.59516323, 0.41087446, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 3.07277966, 1.98035145, 1.36964464, 0.95350921, 0.69515091, 0.50118381, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 6.77309084, 3.46139455, 2.36326075, 1.56271636, 1.08895338, 0.803307, 0.59516323, 0.45573691, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 6.77309084, 3.46139455, 2.45070267, 1.61558151, 1.162866, 0.86115354, 0.64427125, 0.50118381, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.65472794, 3.07277966, 2.12350607, 1.51179266, 1.08895338, 0.83188516, 0.64427125, 0.50118381, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.65472794, 3.07277966, 2.12350607, 1.51179266, 1.08895338, 0.83188516, 0.64427125, 0.50118381, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.65472794, 3.07277966, 2.12350607, 1.51179266, 1.08895338, 0.83188516, 0.64427125, 0.50118381, 0.41087446, 0.34370604, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.65472794, 3.07277966, 2.19988537, 1.61558151, 1.20157266, 0.92192322, 0.72133851, 0.57119018, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.65472794, 3.07277966, 2.19988537, 1.61558151, 1.24153244, 0.95350921, 0.74807048, 0.59516323, 0.4783645, 0.38853383, 0.32104823, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 7.49001646, 4.65472794, 3.07277966, 2.19988537, 1.61558151, 1.24153244, 0.95350921, 0.74807048, 0.59516323, 0.50118381, 0.41087446, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.25: [
|
||||
[14.61464119, 0.72133851, 0.02916753],
|
||||
[14.61464119, 1.56271636, 0.50118381, 0.02916753],
|
||||
[14.61464119, 2.05039096, 0.803307, 0.32104823, 0.02916753],
|
||||
[14.61464119, 2.36326075, 0.95350921, 0.43325692, 0.17026083, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.24153244, 0.59516323, 0.27464288, 0.09824532, 0.02916753],
|
||||
[14.61464119, 3.07277966, 1.51179266, 0.803307, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.36326075, 1.24153244, 0.72133851, 0.41087446, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.45070267, 1.36964464, 0.83188516, 0.52423614, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.61558151, 0.98595673, 0.64427125, 0.43325692, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.67050016, 1.08895338, 0.74807048, 0.52423614, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.72759056, 1.162866, 0.803307, 0.59516323, 0.45573691, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.95596409, 1.84880662, 1.24153244, 0.86115354, 0.64427125, 0.4783645, 0.36617002, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.95596409, 1.84880662, 1.28281462, 0.92192322, 0.69515091, 0.52423614, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.95596409, 1.91321158, 1.32549286, 0.95350921, 0.72133851, 0.54755926, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.95596409, 1.91321158, 1.32549286, 0.95350921, 0.72133851, 0.57119018, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.95596409, 1.91321158, 1.32549286, 0.95350921, 0.74807048, 0.59516323, 0.4783645, 0.38853383, 0.32104823, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 3.07277966, 2.05039096, 1.41535246, 1.05362725, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 3.07277966, 2.05039096, 1.41535246, 1.05362725, 0.803307, 0.64427125, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 3.07277966, 2.05039096, 1.46270394, 1.08895338, 0.83188516, 0.66947293, 0.54755926, 0.45573691, 0.38853383, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.30: [
|
||||
[14.61464119, 0.72133851, 0.02916753],
|
||||
[14.61464119, 1.24153244, 0.43325692, 0.02916753],
|
||||
[14.61464119, 1.56271636, 0.59516323, 0.22545385, 0.02916753],
|
||||
[14.61464119, 1.84880662, 0.803307, 0.36617002, 0.13792117, 0.02916753],
|
||||
[14.61464119, 2.36326075, 1.01931262, 0.52423614, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.36964464, 0.74807048, 0.41087446, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 3.07277966, 1.56271636, 0.89115214, 0.54755926, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 3.07277966, 1.61558151, 0.95350921, 0.61951244, 0.41087446, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.45070267, 1.36964464, 0.83188516, 0.54755926, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.45070267, 1.41535246, 0.92192322, 0.64427125, 0.45573691, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.6383388, 1.56271636, 1.01931262, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.61558151, 1.05362725, 0.74807048, 0.54755926, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.61558151, 1.08895338, 0.77538133, 0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.59516323, 0.4783645, 0.38853383, 0.32104823, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.72759056, 1.162866, 0.83188516, 0.64427125, 0.50118381, 0.41087446, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.72759056, 1.162866, 0.83188516, 0.64427125, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.78698075, 1.24153244, 0.92192322, 0.72133851, 0.57119018, 0.45573691, 0.38853383, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.84484982, 1.78698075, 1.24153244, 0.92192322, 0.72133851, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.35: [
|
||||
[14.61464119, 0.69515091, 0.02916753],
|
||||
[14.61464119, 0.95350921, 0.34370604, 0.02916753],
|
||||
[14.61464119, 1.56271636, 0.57119018, 0.19894916, 0.02916753],
|
||||
[14.61464119, 1.61558151, 0.69515091, 0.29807833, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.84880662, 0.83188516, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.162866, 0.64427125, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.36964464, 0.803307, 0.50118381, 0.32104823, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.41535246, 0.83188516, 0.54755926, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.56271636, 0.95350921, 0.64427125, 0.45573691, 0.32104823, 0.22545385, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.56271636, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 3.07277966, 1.61558151, 1.01931262, 0.72133851, 0.52423614, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 3.07277966, 1.61558151, 1.01931262, 0.72133851, 0.52423614, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 3.07277966, 1.61558151, 1.05362725, 0.74807048, 0.54755926, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.4783645, 0.38853383, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.45070267, 1.51179266, 1.01931262, 0.74807048, 0.57119018, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.40: [
|
||||
[14.61464119, 0.59516323, 0.02916753],
|
||||
[14.61464119, 0.95350921, 0.34370604, 0.02916753],
|
||||
[14.61464119, 1.08895338, 0.43325692, 0.13792117, 0.02916753],
|
||||
[14.61464119, 1.56271636, 0.64427125, 0.27464288, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.61558151, 0.803307, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.05039096, 0.95350921, 0.54755926, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.24153244, 0.72133851, 0.43325692, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.28281462, 0.803307, 0.52423614, 0.36617002, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.41535246, 0.86115354, 0.59516323, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.4783645, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.72133851, 0.54755926, 0.43325692, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.61558151, 1.05362725, 0.74807048, 0.57119018, 0.45573691, 0.38853383, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.43325692, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.45: [
|
||||
[14.61464119, 0.59516323, 0.02916753],
|
||||
[14.61464119, 0.803307, 0.25053367, 0.02916753],
|
||||
[14.61464119, 0.95350921, 0.34370604, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.91321158, 0.95350921, 0.57119018, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.19988537, 1.08895338, 0.64427125, 0.41087446, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.28281462, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.72133851, 0.54755926, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
],
|
||||
1.50: [
|
||||
[14.61464119, 0.54755926, 0.02916753],
|
||||
[14.61464119, 0.803307, 0.25053367, 0.02916753],
|
||||
[14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
[14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
|
||||
],
|
||||
}
|
||||
|
||||
class GITSScheduler:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required":
|
||||
{"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}),
|
||||
"steps": ("INT", {"default": 10, "min": 2, "max": 1000}),
|
||||
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
||||
}
|
||||
}
|
||||
RETURN_TYPES = ("SIGMAS",)
|
||||
CATEGORY = "sampling/custom_sampling/schedulers"
|
||||
|
||||
FUNCTION = "get_sigmas"
|
||||
|
||||
def get_sigmas(self, coeff, steps, denoise):
|
||||
total_steps = steps
|
||||
if denoise < 1.0:
|
||||
if denoise <= 0.0:
|
||||
return (torch.FloatTensor([]),)
|
||||
total_steps = round(steps * denoise)
|
||||
|
||||
if steps <= 20:
|
||||
sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:]
|
||||
else:
|
||||
sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:]
|
||||
sigmas = loglinear_interp(sigmas, steps + 1)
|
||||
|
||||
sigmas = sigmas[-(total_steps + 1):]
|
||||
sigmas[-1] = 0
|
||||
return (torch.FloatTensor(sigmas), )
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"GITSScheduler": GITSScheduler,
|
||||
}
|
||||
@ -52,9 +52,32 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
arg_dict = { "model1": ("MODEL",),
|
||||
"model2": ("MODEL",)}
|
||||
|
||||
argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
|
||||
|
||||
arg_dict["pos_embed."] = argument
|
||||
arg_dict["x_embedder."] = argument
|
||||
arg_dict["context_embedder."] = argument
|
||||
arg_dict["y_embedder."] = argument
|
||||
arg_dict["t_embedder."] = argument
|
||||
|
||||
for i in range(24):
|
||||
arg_dict["joint_blocks.{}.".format(i)] = argument
|
||||
|
||||
arg_dict["final_layer."] = argument
|
||||
|
||||
return {"required": arg_dict}
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"ModelMergeSD1": ModelMergeSD1,
|
||||
"ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
|
||||
"ModelMergeSDXL": ModelMergeSDXL,
|
||||
"ModelMergeSD3_2B": ModelMergeSD3_2B,
|
||||
}
|
||||
|
||||
1
nodes.py
1
nodes.py
@ -1991,6 +1991,7 @@ def init_custom_nodes():
|
||||
"nodes_webcam.py",
|
||||
"nodes_audio.py",
|
||||
"nodes_sd3.py",
|
||||
"nodes_gits.py",
|
||||
]
|
||||
|
||||
import_failed = []
|
||||
|
||||
@ -15,3 +15,4 @@ psutil
|
||||
#non essential dependencies:
|
||||
kornia>=0.7.1
|
||||
spandrel
|
||||
soundfile
|
||||
|
||||
15
server.py
15
server.py
@ -527,9 +527,22 @@ class PromptServer():
|
||||
self.prompt_queue.delete_history_item(id_to_delete)
|
||||
|
||||
return web.Response(status=200)
|
||||
|
||||
|
||||
def add_routes(self):
|
||||
self.user_manager.add_routes(self.routes)
|
||||
|
||||
# Prefix every route with /api for easier matching for delegation.
|
||||
# This is very useful for frontend dev server, which need to forward
|
||||
# everything except serving of static files.
|
||||
# Currently both the old endpoints without prefix and new endpoints with
|
||||
# prefix are supported.
|
||||
api_routes = web.RouteTableDef()
|
||||
for route in self.routes:
|
||||
# Custom nodes might add extra static routes. Only process non-static
|
||||
# routes to add /api prefix.
|
||||
if isinstance(route, web.RouteDef):
|
||||
api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs)
|
||||
self.app.add_routes(api_routes)
|
||||
self.app.add_routes(self.routes)
|
||||
|
||||
for name, dir in nodes.EXTENSION_WEB_DIRS.items():
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user