49c3a080b5
Signed-off-by: blob42 <contact@blob42.xyz>
51 lines
3.4 KiB
Python
51 lines
3.4 KiB
Python
import torch
|
|
from modules import sd_samplers_kdiffusion, sd_samplers_common
|
|
|
|
from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
|
|
from ldm_patched.modules.samplers import calculate_sigmas_scheduler
|
|
|
|
|
|
class AlterSampler(sd_samplers_kdiffusion.KDiffusionSampler):
|
|
def __init__(self, sd_model, sampler_name, scheduler_name):
|
|
self.sampler_name = sampler_name
|
|
self.scheduler_name = scheduler_name
|
|
self.unet = sd_model.forge_objects.unet
|
|
self.model = sd_model
|
|
|
|
sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
|
|
super().__init__(sampler_function, sd_model, None)
|
|
|
|
def get_sigmas(self, p, steps):
|
|
if self.scheduler_name == 'turbo':
|
|
timesteps = torch.flip(torch.arange(1, steps + 1) * float(1000.0 / steps) - 1, (0,)).round().long().clip(0, 999)
|
|
sigmas = self.unet.model.model_sampling.sigma(timesteps)
|
|
sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
|
|
else:
|
|
sigmas = calculate_sigmas_scheduler(self.unet.model, self.scheduler_name, steps, is_sdxl=getattr(self.model, "is_sdxl", False))
|
|
return sigmas.to(self.unet.load_device)
|
|
|
|
|
|
def build_constructor(sampler_name, scheduler_name):
|
|
def constructor(m):
|
|
return AlterSampler(m, sampler_name, scheduler_name)
|
|
|
|
return constructor
|
|
|
|
|
|
samplers_data_alter = [
|
|
sd_samplers_common.SamplerData('DDPM', build_constructor(sampler_name='ddpm', scheduler_name='normal'), ['ddpm'], {}),
|
|
sd_samplers_common.SamplerData('DDPM Karras', build_constructor(sampler_name='ddpm', scheduler_name='karras'), ['ddpm_karras'], {}),
|
|
sd_samplers_common.SamplerData('Euler AYS', build_constructor(sampler_name='euler', scheduler_name='ays'), ['euler_ays'], {}),
|
|
sd_samplers_common.SamplerData('Euler A Turbo', build_constructor(sampler_name='euler_ancestral', scheduler_name='turbo'), ['euler_ancestral_turbo'], {}),
|
|
sd_samplers_common.SamplerData('Euler A AYS', build_constructor(sampler_name='euler_ancestral', scheduler_name='ays'), ['euler_ancestral_ays'], {}),
|
|
sd_samplers_common.SamplerData('DPM++ 2M Turbo', build_constructor(sampler_name='dpmpp_2m', scheduler_name='turbo'), ['dpmpp_2m_turbo'], {}),
|
|
sd_samplers_common.SamplerData('DPM++ 2M AYS', build_constructor(sampler_name='dpmpp_2m', scheduler_name='ays'), ['dpmpp_2m_ays'], {}),
|
|
sd_samplers_common.SamplerData('DPM++ 2M SDE Turbo', build_constructor(sampler_name='dpmpp_2m_sde', scheduler_name='turbo'), ['dpmpp_2m_sde_turbo'], {}),
|
|
sd_samplers_common.SamplerData('DPM++ 2M SDE AYS', build_constructor(sampler_name='dpmpp_2m_sde', scheduler_name='ays'), ['dpmpp_2m_sde_ays'], {}),
|
|
sd_samplers_common.SamplerData('LCM Karras', build_constructor(sampler_name='lcm', scheduler_name='karras'), ['lcm_karras'], {}),
|
|
sd_samplers_common.SamplerData('Euler SGMUniform', build_constructor(sampler_name='euler', scheduler_name='sgm_uniform'), ['euler_sgm_uniform'], {}),
|
|
sd_samplers_common.SamplerData('Euler A SGMUniform', build_constructor(sampler_name='euler_ancestral', scheduler_name='sgm_uniform'), ['euler_ancestral_sgm_uniform'], {}),
|
|
sd_samplers_common.SamplerData('DPM++ 2M SGMUniform', build_constructor(sampler_name='dpmpp_2m', scheduler_name='sgm_uniform'), ['dpmpp_2m_sgm_uniform'], {}),
|
|
sd_samplers_common.SamplerData('DPM++ 2M SDE SGMUniform', build_constructor(sampler_name='dpmpp_2m_sde', scheduler_name='sgm_uniform'), ['dpmpp_2m_sde_sgm_uniform'], {}),
|
|
]
|