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emergency fix for #1199

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AUTOMATIC 2022-09-28 10:49:07 +03:00
parent 15f333a266
commit 2ab64ec81a
1 changed files with 13 additions and 12 deletions
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25
modules/sd_samplers.py
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@ -3,6 +3,7 @@ import numpy as np
import torch import torch
import tqdm import tqdm
from PIL import Image from PIL import Image
import inspect
import k_diffusion.sampling import k_diffusion.sampling
import ldm.models.diffusion.ddim import ldm.models.diffusion.ddim
@ -38,11 +39,11 @@ samplers = [
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS'] samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
sampler_extra_params = { sampler_extra_params = {
'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'], 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_euler_ancestral':['eta'], 'sample_euler_ancestral': ['eta'],
'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'], 'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'], 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_dpm_2_ancestral':['eta'], 'sample_dpm_2_ancestral': ['eta'],
} }
def setup_img2img_steps(p, steps=None): def setup_img2img_steps(p, steps=None):
@ -231,7 +232,7 @@ class KDiffusionSampler:
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization) self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
self.funcname = funcname self.funcname = funcname
self.func = getattr(k_diffusion.sampling, self.funcname) self.func = getattr(k_diffusion.sampling, self.funcname)
self.extra_params = sampler_extra_params.get(funcname,[]) self.extra_params = sampler_extra_params.get(funcname, [])
self.model_wrap_cfg = CFGDenoiser(self.model_wrap) self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
self.sampler_noises = None self.sampler_noises = None
self.sampler_noise_index = 0 self.sampler_noise_index = 0
@ -278,9 +279,9 @@ class KDiffusionSampler:
k_diffusion.sampling.torch = TorchHijack(self) k_diffusion.sampling.torch = TorchHijack(self)
extra_params_kwargs = {} extra_params_kwargs = {}
for val in self.extra_params: for param_name in self.extra_params:
if hasattr(p,val): if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
extra_params_kwargs[val] = getattr(p,val) extra_params_kwargs[param_name] = getattr(p, param_name)
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
@ -300,9 +301,9 @@ class KDiffusionSampler:
k_diffusion.sampling.torch = TorchHijack(self) k_diffusion.sampling.torch = TorchHijack(self)
extra_params_kwargs = {} extra_params_kwargs = {}
for val in self.extra_params: for param_name in self.extra_params:
if hasattr(p,val): if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
extra_params_kwargs[val] = getattr(p,val) extra_params_kwargs[param_name] = getattr(p, param_name)
samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)