diff --git a/webui.py b/webui.py
index 22b701fbb..6c5eafb26 100644
--- a/webui.py
+++ b/webui.py
@@ -35,6 +35,7 @@ import traceback
 from collections import namedtuple
 from contextlib import nullcontext
 import signal
+import tqdm
 
 import k_diffusion.sampling
 from ldm.util import instantiate_from_config
@@ -842,6 +843,7 @@ class StableDiffusionProcessing:
         self.extra_generation_params: dict = extra_generation_params
         self.overlay_images = overlay_images
         self.paste_to = None
+        self.progress_info = ""
 
     def init(self):
         pass
@@ -917,7 +919,6 @@ class CFGDenoiser(nn.Module):
 
         return denoised
 
-
 class KDiffusionSampler:
     def __init__(self, funcname):
         self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model)
@@ -938,12 +939,18 @@ class KDiffusionSampler:
         self.model_wrap_cfg.nmask = p.nmask
         self.model_wrap_cfg.init_latent = p.init_latent
 
+        if hasattr(k_diffusion.sampling, 'trange'):
+            k_diffusion.sampling.trange = lambda *args, **kwargs: tqdm.tqdm(range(*args), desc=p.progress_info, **kwargs)
+
         return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
 
     def sample(self, p: StableDiffusionProcessing, x, conditioning, unconditional_conditioning):
         sigmas = self.model_wrap.get_sigmas(p.steps)
         x = x * sigmas[0]
 
+        if hasattr(k_diffusion.sampling, 'trange'):
+            k_diffusion.sampling.trange = lambda *args, **kwargs: tqdm.tqdm(range(*args), desc=p.progress_info, **kwargs)
+
         samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
         return samples_ddim
 
@@ -1030,6 +1037,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             # we manually generate all input noises because each one should have a specific seed
             x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds)
 
+            p.progress_info = f"Batch {n+1} out of {p.n_iter}"
             samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc)
 
             x_samples_ddim = model.decode_first_stage(samples_ddim)