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add infotext

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add proper support for recalculating conds in k-diffusion samplers
remove support for compvis samplers
This commit is contained in:
AUTOMATIC1111
2023-08-06 17:53:33 +03:00
parent 956e69bf3a
commit 5a0db84b6c
5 changed files with 48 additions and 19 deletions
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24
modules/sd_samplers_kdiffusion.py
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@@ -87,8 +87,9 @@ class CFGDenoiser(torch.nn.Module):
negative prompt.
"""
def __init__(self):
def __init__(self, sampler):
super().__init__()
self.sampler = sampler
self.model_wrap = None
self.mask = None
self.nmask = None
@@ -126,11 +127,17 @@ class CFGDenoiser(torch.nn.Module):
def update_inner_model(self):
self.model_wrap = None
c, uc = self.p.get_conds()
self.sampler.sampler_extra_args['cond'] = c
self.sampler.sampler_extra_args['uncond'] = uc
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
sd_samplers_common.apply_refiner(self)
if sd_samplers_common.apply_refiner(self):
cond = self.sampler.sampler_extra_args['cond']
uncond = self.sampler.sampler_extra_args['uncond']
# at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
# so is_edit_model is set to False to support AND composition.
@@ -282,12 +289,12 @@ class TorchHijack:
class KDiffusionSampler:
def __init__(self, funcname, sd_model):
self.p = None
self.funcname = funcname
self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
self.extra_params = sampler_extra_params.get(funcname, [])
self.model_wrap_cfg = CFGDenoiser()
self.sampler_extra_args = {}
self.model_wrap_cfg = CFGDenoiser(self)
self.model_wrap = self.model_wrap_cfg.inner_model
self.sampler_noises = None
self.stop_at = None
@@ -476,7 +483,7 @@ class KDiffusionSampler:
self.model_wrap_cfg.init_latent = x
self.last_latent = x
extra_args = {
self.sampler_extra_args = {
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
@@ -484,7 +491,7 @@ class KDiffusionSampler:
's_min_uncond': self.s_min_uncond
}
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
@@ -514,13 +521,14 @@ class KDiffusionSampler:
extra_params_kwargs['noise_sampler'] = noise_sampler
self.last_latent = x
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
self.sampler_extra_args = {
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
'cond_scale': p.cfg_scale,
's_min_uncond': self.s_min_uncond
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
}
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True