Fix logspam and live previews

modules/models/diffusion/uni_pc/sampler.py

@@ -19,9 +19,10 @@ class UniPCSampler(object):
                 attr = attr.to(torch.device("cuda"))
         setattr(self, name, attr)
 
-    def set_hooks(self, before, after):
-        self.before_sample = before
-        self.after_sample = after
+    def set_hooks(self, before_sample, after_sample, after_update):
+        self.before_sample = before_sample
+        self.after_sample = after_sample
+        self.after_update = after_update
 
     @torch.no_grad()
     def sample(self,
@@ -50,9 +51,17 @@ class UniPCSampler(object):
                ):
         if conditioning is not None:
             if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                ctmp = conditioning[list(conditioning.keys())[0]]
+                while isinstance(ctmp, list): ctmp = ctmp[0]
+                cbs = ctmp.shape[0]
                 if cbs != batch_size:
                     print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+            elif isinstance(conditioning, list):
+                for ctmp in conditioning:
+                    if ctmp.shape[0] != batch_size:
+                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
             else:
                 if conditioning.shape[0] != batch_size:
                     print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
@@ -60,6 +69,7 @@ class UniPCSampler(object):
         # sampling
         C, H, W = shape
         size = (batch_size, C, H, W)
+        print(f'Data shape for UniPC sampling is {size}, eta {eta}')
 
         device = self.model.betas.device
         if x_T is None:
@@ -79,7 +89,7 @@ class UniPCSampler(object):
             guidance_scale=unconditional_guidance_scale,
         )
 
-        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample)
+        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
         x = uni_pc.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=3, lower_order_final=True)
 
         return x.to(device), None

modules/models/diffusion/uni_pc/uni_pc.py

@@ -378,7 +378,8 @@ class UniPC:
         condition=None,
         unconditional_condition=None,
         before_sample=None,
-        after_sample=None
+        after_sample=None,
+        after_update=None
     ):
         """Construct a UniPC.
 
@@ -394,6 +395,7 @@ class UniPC:
         self.unconditional_condition = unconditional_condition
         self.before_sample = before_sample
         self.after_sample = after_sample
+        self.after_update = after_update
 
     def dynamic_thresholding_fn(self, x0, t=None):
         """
@@ -434,15 +436,6 @@ class UniPC:
         noise = self.noise_prediction_fn(x, t)
         dims = x.dim()
         alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        from pprint import pp
-        print("X:")
-        pp(x)
-        print("sigma_t:")
-        pp(sigma_t)
-        print("noise:")
-        pp(noise)
-        print("alpha_t:")
-        pp(alpha_t)
         x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
         if self.thresholding:
             p = 0.995   # A hyperparameter in the paper of "Imagen" [1].
@@ -524,7 +517,7 @@ class UniPC:
             return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
 
     def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):
-        print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
+        #print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
         ns = self.noise_schedule
         assert order <= len(model_prev_list)
 
@@ -568,7 +561,7 @@ class UniPC:
             A_p = C_inv_p
 
         if use_corrector:
-            print('using corrector')
+            #print('using corrector')
             C_inv = torch.linalg.inv(C)
             A_c = C_inv
 
@@ -627,7 +620,7 @@ class UniPC:
         return x_t, model_t
 
     def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True):
-        print(f'using unified predictor-corrector with order {order} (solver type: B(h))')
+        #print(f'using unified predictor-corrector with order {order} (solver type: B(h))')
         ns = self.noise_schedule
         assert order <= len(model_prev_list)
         dims = x.dim()
@@ -695,7 +688,7 @@ class UniPC:
             D1s = None
 
         if use_corrector:
-            print('using corrector')
+            #print('using corrector')
             # for order 1, we use a simplified version
             if order == 1:
                 rhos_c = torch.tensor([0.5], device=b.device)
@@ -755,8 +748,9 @@ class UniPC:
         t_T = self.noise_schedule.T if t_start is None else t_start
         device = x.device
         if method == 'multistep':
-            assert steps >= order
+            assert steps >= order, "UniPC order must be < sampling steps"
             timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
+            print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps")
             assert timesteps.shape[0] - 1 == steps
             with torch.no_grad():
                 vec_t = timesteps[0].expand((x.shape[0]))
@@ -768,6 +762,8 @@ class UniPC:
                     x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
                     if model_x is None:
                         model_x = self.model_fn(x, vec_t)
+                    if self.after_update is not None:
+                        self.after_update(x, model_x)
                     model_prev_list.append(model_x)
                     t_prev_list.append(vec_t)
                 for step in range(order, steps + 1):
@@ -776,13 +772,15 @@ class UniPC:
                         step_order = min(order, steps + 1 - step)
                     else:
                         step_order = order
-                    print('this step order:', step_order)
+                    #print('this step order:', step_order)
                     if step == steps:
-                        print('do not run corrector at the last step')
+                        #print('do not run corrector at the last step')
                         use_corrector = False
                     else:
                         use_corrector = True
                     x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
+                    if self.after_update is not None:
+                        self.after_update(x, model_x)
                     for i in range(order - 1):
                         t_prev_list[i] = t_prev_list[i + 1]
                         model_prev_list[i] = model_prev_list[i + 1]

modules/sd_samplers_compvis.py

@@ -103,16 +103,11 @@ class VanillaStableDiffusionSampler:
 
         return x, ts, cond, unconditional_conditioning
 
-    def after_sample(self, x, ts, cond, uncond, res):
-        if self.is_unipc:
-            # unipc model_fn returns (pred_x0)
-            # p_sample_ddim returns (x_prev, pred_x0)
-            res = (None, res[0])
-
+    def update_step(self, last_latent):
         if self.mask is not None:
-            self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
+            self.last_latent = self.init_latent * self.mask + self.nmask * last_latent
         else:
-            self.last_latent = res[1]
+            self.last_latent = last_latent
 
         sd_samplers_common.store_latent(self.last_latent)
 
@@ -120,8 +115,15 @@ class VanillaStableDiffusionSampler:
         state.sampling_step = self.step
         shared.total_tqdm.update()
 
+    def after_sample(self, x, ts, cond, uncond, res):
+        if not self.is_unipc:
+            self.update_step(res[1])
+
         return x, ts, cond, uncond, res
 
+    def unipc_after_update(self, x, model_x):
+        self.update_step(x)
+
     def initialize(self, p):
         self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
         if self.eta != 0.0:
@@ -131,7 +133,7 @@ class VanillaStableDiffusionSampler:
             if hasattr(self.sampler, fieldname):
                 setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
         if self.is_unipc:
-            self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r))
+            self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r), lambda x, mx: self.unipc_after_update(x, mx))
 
         self.mask = p.mask if hasattr(p, 'mask') else None
         self.nmask = p.nmask if hasattr(p, 'nmask') else None