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Properly disable all progress bars when disable_pbar=True

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comfyanonymous 2023-05-01 15:47:10 -04:00
parent cb3772bbfa
commit d3293c8339
4 changed files with 13 additions and 10 deletions
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8
comfy/extra_samplers/uni_pc.py
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@ -712,7 +712,7 @@ class UniPC:
def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform',
method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
atol=0.0078, rtol=0.05, corrector=False, callback=None atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False
): ):
t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
t_T = self.noise_schedule.T if t_start is None else t_start t_T = self.noise_schedule.T if t_start is None else t_start
@ -723,7 +723,7 @@ class UniPC:
# timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
assert timesteps.shape[0] - 1 == steps assert timesteps.shape[0] - 1 == steps
# with torch.no_grad(): # with torch.no_grad():
for step_index in trange(steps): for step_index in trange(steps, disable=disable_pbar):
if self.noise_mask is not None: if self.noise_mask is not None:
x = x * self.noise_mask + (1. - self.noise_mask) * (self.masked_image * self.noise_schedule.marginal_alpha(timesteps[step_index]) + self.noise * self.noise_schedule.marginal_std(timesteps[step_index])) x = x * self.noise_mask + (1. - self.noise_mask) * (self.masked_image * self.noise_schedule.marginal_alpha(timesteps[step_index]) + self.noise * self.noise_schedule.marginal_std(timesteps[step_index]))
if step_index == 0: if step_index == 0:
@ -835,7 +835,7 @@ def expand_dims(v, dims):
def sample_unipc(model, noise, image, sigmas, sampling_function, max_denoise, extra_args=None, callback=None, disable=None, noise_mask=None, variant='bh1'): def sample_unipc(model, noise, image, sigmas, sampling_function, max_denoise, extra_args=None, callback=None, disable=False, noise_mask=None, variant='bh1'):
to_zero = False to_zero = False
if sigmas[-1] == 0: if sigmas[-1] == 0:
timesteps = torch.nn.functional.interpolate(sigmas[None,None,:-1], size=(len(sigmas),), mode='linear')[0][0] timesteps = torch.nn.functional.interpolate(sigmas[None,None,:-1], size=(len(sigmas),), mode='linear')[0][0]
@ -879,7 +879,7 @@ def sample_unipc(model, noise, image, sigmas, sampling_function, max_denoise, ex
order = min(3, len(timesteps) - 1) order = min(3, len(timesteps) - 1)
uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant) uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant)
x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback) x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable)
if not to_zero: if not to_zero:
x /= ns.marginal_alpha(timesteps[-1]) x /= ns.marginal_alpha(timesteps[-1])
return x return x

8
comfy/ldm/models/diffusion/ddim.py
View File

@ -81,6 +81,7 @@ class DDIMSampler(object):
extra_args=None, extra_args=None,
to_zero=True, to_zero=True,
end_step=None, end_step=None,
disable_pbar=False,
**kwargs **kwargs
): ):
self.make_schedule_timesteps(ddim_timesteps=ddim_timesteps, ddim_eta=eta, verbose=verbose) self.make_schedule_timesteps(ddim_timesteps=ddim_timesteps, ddim_eta=eta, verbose=verbose)
@ -103,7 +104,8 @@ class DDIMSampler(object):
denoise_function=denoise_function, denoise_function=denoise_function,
extra_args=extra_args, extra_args=extra_args,
to_zero=to_zero, to_zero=to_zero,
end_step=end_step end_step=end_step,
disable_pbar=disable_pbar
) )
return samples, intermediates return samples, intermediates
@ -185,7 +187,7 @@ class DDIMSampler(object):
mask=None, x0=None, img_callback=None, log_every_t=100, mask=None, x0=None, img_callback=None, log_every_t=100,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None, unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
ucg_schedule=None, denoise_function=None, extra_args=None, to_zero=True, end_step=None): ucg_schedule=None, denoise_function=None, extra_args=None, to_zero=True, end_step=None, disable_pbar=False):
device = self.model.betas.device device = self.model.betas.device
b = shape[0] b = shape[0]
if x_T is None: if x_T is None:
@ -204,7 +206,7 @@ class DDIMSampler(object):
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
# print(f"Running DDIM Sampling with {total_steps} timesteps") # print(f"Running DDIM Sampling with {total_steps} timesteps")
iterator = tqdm(time_range[:end_step], desc='DDIM Sampler', total=end_step) iterator = tqdm(time_range[:end_step], desc='DDIM Sampler', total=end_step, disable=disable_pbar)
for i, step in enumerate(iterator): for i, step in enumerate(iterator):
index = total_steps - i - 1 index = total_steps - i - 1

4
comfy/sample.py
View File

@ -56,7 +56,7 @@ def cleanup_additional_models(models):
for m in models: for m in models:
m.cleanup() m.cleanup()
def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None): def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False):
device = comfy.model_management.get_torch_device() device = comfy.model_management.get_torch_device()
if noise_mask is not None: if noise_mask is not None:
@ -76,7 +76,7 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback) samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar)
samples = samples.cpu() samples = samples.cpu()
cleanup_additional_models(models) cleanup_additional_models(models)

3
comfy/samplers.py
View File

@ -643,7 +643,8 @@ class KSampler:
extra_args=extra_args, extra_args=extra_args,
mask=noise_mask, mask=noise_mask,
to_zero=sigmas[-1]==0, to_zero=sigmas[-1]==0,
end_step=sigmas.shape[0] - 1) end_step=sigmas.shape[0] - 1,
disable_pbar=disable_pbar)
else: else:
extra_args["denoise_mask"] = denoise_mask extra_args["denoise_mask"] = denoise_mask