diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
index 27ead0e..66cf745 100644
--- a/ldm/models/diffusion/ddim.py
+++ b/ldm/models/diffusion/ddim.py
@@ -315,7 +315,7 @@ class DDIMSampler(object):
 
     @torch.no_grad()
     def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
-               use_original_steps=False, callback=None):
+               use_original_steps=False, callback=None, img_callback=None):
 
         timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
         timesteps = timesteps[:t_start]
@@ -333,4 +333,5 @@ class DDIMSampler(object):
                                           unconditional_guidance_scale=unconditional_guidance_scale,
                                           unconditional_conditioning=unconditional_conditioning)
             if callback: callback(i)
+            if img_callback: img_callback(x_dec, i)
         return x_dec
\ No newline at end of file
diff --git a/ldm/models/diffusion/dpm_solver/dpm_solver.py b/ldm/models/diffusion/dpm_solver/dpm_solver.py
index 095e5ba..357d092 100644
--- a/ldm/models/diffusion/dpm_solver/dpm_solver.py
+++ b/ldm/models/diffusion/dpm_solver/dpm_solver.py
@@ -876,7 +876,7 @@ class DPM_Solver:
             raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
 
     def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
-                            solver_type='dpm_solver'):
+                            solver_type='dpm_solver', callback=None, img_callback=None):
         """
         The adaptive step size solver based on singlestep DPM-Solver.
         Args:
@@ -931,6 +931,8 @@ class DPM_Solver:
                 s = t
                 x_prev = x_lower
                 lambda_s = ns.marginal_lambda(s)
+                if callback: callback(nfe)
+                if img_callback: img_callback(x, nfe)
             h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
             nfe += order
         print('adaptive solver nfe', nfe)
@@ -939,6 +941,7 @@ class DPM_Solver:
     def sample(self, x, steps=20, 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,
+               callback=None, img_callback=None,
                ):
         """
         Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
@@ -1040,7 +1043,8 @@ class DPM_Solver:
         if method == 'adaptive':
             with torch.no_grad():
                 x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
-                                             solver_type=solver_type)
+                                             solver_type=solver_type,
+                                             callback=callback, img_callback=img_callback)
         elif method == 'multistep':
             assert steps >= order
             timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
@@ -1065,6 +1069,8 @@ class DPM_Solver:
                         step_order = order
                     x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order,
                                                          solver_type=solver_type)
+                    if callback: callback(step)
+                    if img_callback: img_callback(x, step)
                     for i in range(order - 1):
                         t_prev_list[i] = t_prev_list[i + 1]
                         model_prev_list[i] = model_prev_list[i + 1]
@@ -1092,8 +1098,12 @@ class DPM_Solver:
                 r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
                 r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
                 x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
+                if callback: callback(i)
+                if img_callback: img_callback(x, i)
         if denoise_to_zero:
             x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
+            if callback: callback(len(orders)-1)
+            if img_callback: img_callback(x, len(orders)-1)
         return x
 
 
diff --git a/ldm/models/diffusion/dpm_solver/sampler.py b/ldm/models/diffusion/dpm_solver/sampler.py
index 7d137b8..9aa3edb 100644
--- a/ldm/models/diffusion/dpm_solver/sampler.py
+++ b/ldm/models/diffusion/dpm_solver/sampler.py
@@ -82,6 +82,6 @@ class DPMSolverSampler(object):
         )
 
         dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
-        x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
+        x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True, callback=callback, img_callback=img_callback)
 
         return x.to(device), None
\ No newline at end of file