Properly disable all progress bars when disable_pbar=True

comfy/extra_samplers/uni_pc.py

@@ -712,7 +712,7 @@ class UniPC:
 
     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',
-        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_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)
             assert timesteps.shape[0] - 1 == steps
             # 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:
                     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:
@@ -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
         if sigmas[-1] == 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)
         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:
             x /= ns.marginal_alpha(timesteps[-1])
         return x

comfy/ldm/models/diffusion/ddim.py

@@ -81,6 +81,7 @@ class DDIMSampler(object):
                       extra_args=None,
                       to_zero=True,
                       end_step=None,
+                      disable_pbar=False,
                       **kwargs
                       ):
         self.make_schedule_timesteps(ddim_timesteps=ddim_timesteps, ddim_eta=eta, verbose=verbose)
@@ -103,7 +104,8 @@ class DDIMSampler(object):
                                                     denoise_function=denoise_function,
                                                     extra_args=extra_args,
                                                     to_zero=to_zero,
-                                                    end_step=end_step
+                                                    end_step=end_step,
+                                                    disable_pbar=disable_pbar
                                                     )
         return samples, intermediates
 
@@ -185,7 +187,7 @@ class DDIMSampler(object):
                       mask=None, x0=None, img_callback=None, log_every_t=100,
                       temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=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
         b = shape[0]
         if x_T is None:
@@ -204,7 +206,7 @@ class DDIMSampler(object):
         total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
         # 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):
             index = total_steps - i - 1

comfy/sample.py

@@ -56,7 +56,7 @@ def cleanup_additional_models(models):
     for m in models:
         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()
 
     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)
 
-    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()
 
     cleanup_additional_models(models)

comfy/samplers.py

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