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added highres fix feature

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AUTOMATIC
2022-09-19 16:42:56 +03:00
parent 8a32a71ca3
commit 6d7ca54a1a
5 changed files with 121 additions and 38 deletions
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55
modules/sd_samplers.py
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@@ -38,9 +38,9 @@ samplers = [
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
def setup_img2img_steps(p):
if opts.img2img_fix_steps:
steps = int(p.steps / min(p.denoising_strength, 0.999))
def setup_img2img_steps(p, steps=None):
if opts.img2img_fix_steps or steps is not None:
steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
t_enc = p.steps - 1
else:
steps = p.steps
@@ -115,8 +115,8 @@ class VanillaStableDiffusionSampler:
self.step += 1
return res
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
steps, t_enc = setup_img2img_steps(p)
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
steps, t_enc = setup_img2img_steps(p, steps)
# existing code fails with cetain step counts, like 9
try:
@@ -127,16 +127,16 @@ class VanillaStableDiffusionSampler:
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
self.sampler.p_sample_ddim = self.p_sample_ddim_hook
self.mask = p.mask
self.nmask = p.nmask
self.init_latent = p.init_latent
self.mask = p.mask if hasattr(p, 'mask') else None
self.nmask = p.nmask if hasattr(p, 'nmask') else None
self.init_latent = x
self.step = 0
samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
return samples
def sample(self, p, x, conditioning, unconditional_conditioning):
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
for fieldname in ['p_sample_ddim', 'p_sample_plms']:
if hasattr(self.sampler, fieldname):
setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
@@ -145,11 +145,13 @@ class VanillaStableDiffusionSampler:
self.init_latent = None
self.step = 0
steps = steps or p.steps
# existing code fails with cetin step counts, like 9
try:
samples_ddim, _ = self.sampler.sample(S=p.steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
except Exception:
samples_ddim, _ = self.sampler.sample(S=p.steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
return samples_ddim
@@ -186,7 +188,7 @@ class CFGDenoiser(torch.nn.Module):
return denoised
def extended_trange(count, *args, **kwargs):
def extended_trange(sampler, count, *args, **kwargs):
state.sampling_steps = count
state.sampling_step = 0
@@ -194,6 +196,9 @@ def extended_trange(count, *args, **kwargs):
if state.interrupted:
break
if sampler.stop_at is not None and x > sampler.stop_at:
break
yield x
state.sampling_step += 1
@@ -222,6 +227,7 @@ class KDiffusionSampler:
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
self.sampler_noises = None
self.sampler_noise_index = 0
self.stop_at = None
def callback_state(self, d):
store_latent(d["denoised"])
@@ -240,8 +246,8 @@ class KDiffusionSampler:
self.sampler_noise_index += 1
return res
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
steps, t_enc = setup_img2img_steps(p)
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
steps, t_enc = setup_img2img_steps(p, steps)
sigmas = self.model_wrap.get_sigmas(steps)
@@ -251,33 +257,36 @@ class KDiffusionSampler:
sigma_sched = sigmas[steps - t_enc - 1:]
self.model_wrap_cfg.mask = p.mask
self.model_wrap_cfg.nmask = p.nmask
self.model_wrap_cfg.init_latent = p.init_latent
self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
self.model_wrap_cfg.init_latent = x
self.model_wrap.step = 0
self.sampler_noise_index = 0
if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
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)
def sample(self, p, x, conditioning, unconditional_conditioning):
sigmas = self.model_wrap.get_sigmas(p.steps)
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
steps = steps or p.steps
sigmas = self.model_wrap.get_sigmas(steps)
x = x * sigmas[0]
self.model_wrap_cfg.step = 0
self.sampler_noise_index = 0
if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
samples_ddim = 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)
return samples_ddim
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)
return samples