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laksjdjf
2023-11-08 19:48:24 +09:00
committed by GitHub
parent 9cae28062e
commit 1a4af0bc29
3 changed files with 661 additions and 7 deletions
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lcm_sampler.py
+2 -1
View File
@@ -1,5 +1,6 @@
from comfy.samplers import *
from comfy.k_diffusion.sampling import generic_step_sampler
import math
def ksampler_lcm(sampler_name, eta, extra_options={}, inpaint_options={}):
class KSAMPLER(Sampler):
@@ -39,7 +40,7 @@ def LCMSampler_outer(eta):
mu = (x - (1 - alpha_cumprod).sqrt() * noise) / alpha_cumprod.sqrt()
if sigma_prev > 0:
# this is not mathematically correct, but eta=1 means lcm_sampler and eta=0 means ddim(euler).
noise_interp = (1 - eta) * noise + eta * noise_sampler(sigma, sigma_prev)
noise_interp = math.sqrt(1 - eta) * noise + math.sqrt(eta) * noise_sampler(sigma, sigma_prev)
mu = alpha_cumprod_prev.sqrt() * mu + (1 - alpha_cumprod_prev).sqrt() * noise_interp
return mu
return LCMSampler_step
lcm_ssd1b_anime.json
+653
View File
@@ -0,0 +1,653 @@
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taesd_decoder.py
+6 -6
View File
@@ -15,16 +15,16 @@ class TAESDDecoder:
@torch.no_grad()
def decode(self, latent):
B = latent['samples'].shape[0]
B = latent.shape[0]
latent = latent.to(model_management.get_torch_device()) * self.scale
x_sample = []
for i in range(0, B, self.max_batch_size):
x_sample.append(self.taesd.decoder((latent['samples'][i:i + self.max_batch_size].to(model_management.get_torch_device()) * self.scale)).detach())
x_sample.append(self.taesd.decoder(latent[i:i + self.max_batch_size]).detach())
x_sample = torch.cat(x_sample, dim=0)
x_sample = x_sample.sub(0.5).mul(2)
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = x_sample.permute(0, 2, 3, 1)
return (x_sample, )
x_sample = x_sample.permute(0, 2, 3, 1).cpu()
return x_sample
class TAESDLoader:
@@ -46,7 +46,7 @@ class TAESDLoader:
RETURN_TYPES = ("VAE",)
FUNCTION = "load"
OUTPUT_IS_LIST = (False,)
CATEGORY = "loader"
CATEGORY = "loaders"
def __init__(self):
self.taesd = None