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remove dependence on TQDM for sampler progress/interrupt functionality

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AUTOMATIC 2022-10-18 17:23:38 +03:00
parent ec1924ee57
commit cbf15edbf9
2 changed files with 58 additions and 55 deletions
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6
modules/processing.py
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@ -402,12 +402,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
with devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
if state.interrupted or state.skipped:
# if we are interrupted, sample returns just noise
# use the image collected previously in sampler loop
samples_ddim = shared.state.current_latent
samples_ddim = samples_ddim.to(devices.dtype_vae)
x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

107
modules/sd_samplers.py
View File

@ -98,25 +98,8 @@ def store_latent(decoded):
shared.state.current_image = sample_to_image(decoded)
def extended_tdqm(sequence, *args, desc=None, **kwargs):
state.sampling_steps = len(sequence)
state.sampling_step = 0
seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
for x in seq:
if state.interrupted or state.skipped:
break
yield x
state.sampling_step += 1
shared.total_tqdm.update()
ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
class InterruptedException(BaseException):
pass
class VanillaStableDiffusionSampler:
@ -128,14 +111,32 @@ class VanillaStableDiffusionSampler:
self.init_latent = None
self.sampler_noises = None
self.step = 0
self.stop_at = None
self.eta = None
self.default_eta = 0.0
self.config = None
self.last_latent = None
def number_of_needed_noises(self, p):
return 0
def launch_sampling(self, steps, func):
state.sampling_steps = steps
state.sampling_step = 0
try:
return func()
except InterruptedException:
return self.last_latent
def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
if state.interrupted or state.skipped:
raise InterruptedException
if self.stop_at is not None and self.step > self.stop_at:
raise InterruptedException
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
@ -159,11 +160,16 @@ class VanillaStableDiffusionSampler:
res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
if self.mask is not None:
store_latent(self.init_latent * self.mask + self.nmask * res[1])
self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
else:
store_latent(res[1])
self.last_latent = res[1]
store_latent(self.last_latent)
self.step += 1
state.sampling_step = self.step
shared.total_tqdm.update()
return res
def initialize(self, p):
@ -192,7 +198,7 @@ class VanillaStableDiffusionSampler:
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)
samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
return samples
@ -206,9 +212,9 @@ class VanillaStableDiffusionSampler:
# existing code fails with certain step counts, like 9
try:
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, eta=self.eta)
samples_ddim = self.launch_sampling(steps, lambda: 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, eta=self.eta)[0])
except Exception:
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, eta=self.eta)
samples_ddim = self.launch_sampling(steps, lambda: 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, eta=self.eta)[0])
return samples_ddim
@ -223,6 +229,9 @@ class CFGDenoiser(torch.nn.Module):
self.step = 0
def forward(self, x, sigma, uncond, cond, cond_scale):
if state.interrupted or state.skipped:
raise InterruptedException
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
@ -268,25 +277,6 @@ class CFGDenoiser(torch.nn.Module):
return denoised
def extended_trange(sampler, count, *args, **kwargs):
state.sampling_steps = count
state.sampling_step = 0
seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
for x in seq:
if state.interrupted or state.skipped:
break
if sampler.stop_at is not None and x > sampler.stop_at:
break
yield x
state.sampling_step += 1
shared.total_tqdm.update()
class TorchHijack:
def __init__(self, kdiff_sampler):
self.kdiff_sampler = kdiff_sampler
@ -314,9 +304,28 @@ class KDiffusionSampler:
self.eta = None
self.default_eta = 1.0
self.config = None
self.last_latent = None
def callback_state(self, d):
store_latent(d["denoised"])
step = d['i']
latent = d["denoised"]
store_latent(latent)
self.last_latent = latent
if self.stop_at is not None and step > self.stop_at:
raise InterruptedException
state.sampling_step = step
shared.total_tqdm.update()
def launch_sampling(self, steps, func):
state.sampling_steps = steps
state.sampling_step = 0
try:
return func()
except InterruptedException:
return self.last_latent
def number_of_needed_noises(self, p):
return p.steps
@ -339,9 +348,6 @@ class KDiffusionSampler:
self.sampler_noise_index = 0
self.eta = p.eta or opts.eta_ancestral
if hasattr(k_diffusion.sampling, 'trange'):
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)
@ -383,8 +389,9 @@ class KDiffusionSampler:
self.model_wrap_cfg.init_latent = x
return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
return samples
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
steps = steps or p.steps
@ -406,6 +413,8 @@ class KDiffusionSampler:
extra_params_kwargs['n'] = steps
else:
extra_params_kwargs['sigmas'] = sigmas
samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
return samples