finish pndm

src/diffusers/pipelines/pipeline_pndm.py

@@ -32,9 +32,6 @@ class PNDM(DiffusionPipeline):
         if torch_device is None:
             torch_device = "cuda" if torch.cuda.is_available() else "cpu"
 
-        num_trained_timesteps = self.noise_scheduler.timesteps
-        inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps)
-
         self.unet.to(torch_device)
 
         # Sample gaussian noise to begin loop
@@ -44,55 +41,22 @@ class PNDM(DiffusionPipeline):
         )
         image = image.to(torch_device)
 
-        seq = list(inference_step_times)
-        seq_next = [-1] + list(seq[:-1])
-        model = self.unet
-
-        warmup_time_steps = list(reversed([(t + 5) // 10 * 10 for t in range(seq[-4], seq[-1], 5)]))
+        warmup_time_steps = self.noise_scheduler.get_warmup_time_steps(num_inference_steps)
+        prev_image = image
+        for t in tqdm.tqdm(range(len(warmup_time_steps))):
+            t_orig = warmup_time_steps[t]
+            residual = self.unet(image, t_orig)
 
-        cur_residual = 0
+            if t % 4 == 0:
                 prev_image = image
-        ets = []
-        for i in range(len(warmup_time_steps)):
-            t = warmup_time_steps[i] * torch.ones(image.shape[0])
-            t_next = (warmup_time_steps[i + 1] if i < len(warmup_time_steps) - 1 else warmup_time_steps[-1]) * torch.ones(image.shape[0])
 
-            residual = model(image.to("cuda"), t.to("cuda"))
-            residual = residual.to("cpu")
+            image = self.noise_scheduler.step_warm_up(residual, prev_image, t, num_inference_steps)
 
-            if i % 4 == 0:
-                cur_residual += 1 / 6 * residual
-                ets.append(residual)
-                prev_image = image
-            elif (i - 1) % 4 == 0:
-                cur_residual += 1 / 3 * residual
-            elif (i - 2) % 4 == 0:
-                cur_residual += 1 / 3 * residual
-            elif (i - 3) % 4 == 0:
-                cur_residual += 1 / 6 * residual
-                residual = cur_residual
-                cur_residual = 0
-
-            image = image.to("cpu")
-            t_2 = warmup_time_steps[4 * (i // 4)] * torch.ones(image.shape[0])
-            image = self.noise_scheduler.transfer(prev_image.to("cpu"), t_2, t_next, residual)
-
-        step_idx = len(seq) - 4
-        while step_idx >= 0:
-            i = seq[step_idx]
-            j = seq_next[step_idx]
-
-            t = (torch.ones(image.shape[0]) * i)
-            t_next = (torch.ones(image.shape[0]) * j)
-
-            residual = model(image.to("cuda"), t.to("cuda"))
-            residual = residual.to("cpu")
-            ets.append(residual)
-            residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
-
-            img_next = self.noise_scheduler.transfer(image.to("cpu"), t, t_next, residual)
-            image = img_next
-
-            step_idx = step_idx - 1
+        timesteps = self.noise_scheduler.get_time_steps(num_inference_steps)
+        for t in tqdm.tqdm(range(len(timesteps))):
+            t_orig = timesteps[t]
+            residual = self.unet(image, t_orig)
+
+            image = self.noise_scheduler.step(residual, image, t, num_inference_steps)
 
         return image

src/diffusers/schedulers/scheduling_pndm.py

@@ -55,6 +55,15 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
 
         self.set_format(tensor_format=tensor_format)
 
+        # hardcode for now
+        self.pndm_order = 4
+        self.cur_residual = 0
+
+        # running values
+        self.ets = []
+        self.warmup_time_steps = {}
+        self.time_steps = {}
+
     #        self.register_buffer("betas", betas.to(torch.float32))
     #        self.register_buffer("alphas", alphas.to(torch.float32))
     #        self.register_buffer("alphas_cumprod", alphas_cumprod.to(torch.float32))
@@ -83,51 +92,62 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
             return self.one
         return self.alphas_cumprod[time_step]
 
-    def step(self, img, t_start, t_end, model, ets):
-#        img_next = self.method(img_n, t_start, t_end, model, self.alphas_cump, self.ets)
-#def gen_order_4(img, t, t_next, model, alphas_cump, ets):
-        t_next, t = t_start, t_end
+    def get_warmup_time_steps(self, num_inference_steps):
+        if num_inference_steps in self.warmup_time_steps:
+            return self.warmup_time_steps[num_inference_steps]
 
-        noise_ = model(img.to("cuda"), t.to("cuda"))
-        noise_ = noise_.to("cpu")
+        inference_step_times = list(range(0, self.timesteps, self.timesteps // num_inference_steps))
 
-        t_list = [t, (t+t_next)/2, t_next]
-        if len(ets) > 2:
-            ets.append(noise_)
-            noise = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
-        else:
-            noise = self.runge_kutta(img, t_list, model, ets, noise_)
+        warmup_time_steps = np.array(inference_step_times[-self.pndm_order:]).repeat(2) + np.tile(np.array([0, self.timesteps // num_inference_steps // 2]), self.pndm_order)
+        self.warmup_time_steps[num_inference_steps] = list(reversed(warmup_time_steps[:-1].repeat(2)[1:-1]))
+
+        return self.warmup_time_steps[num_inference_steps]
 
-        img_next = self.transfer(img.to("cpu"), t, t_next, noise)
-        return img_next, ets
+    def get_time_steps(self, num_inference_steps):
+        if num_inference_steps in self.time_steps:
+            return self.time_steps[num_inference_steps]
 
-    def runge_kutta(self, x, t_list, model, ets, noise_):
-        model = model.to("cuda")
-        x = x.to("cpu")
+        inference_step_times = list(range(0, self.timesteps, self.timesteps // num_inference_steps))
+        self.time_steps[num_inference_steps] = list(reversed(inference_step_times[:-3]))
 
-        e_1 = noise_
-        ets.append(e_1)
-        x_2 = self.transfer(x, t_list[0], t_list[1], e_1)
+        return self.time_steps[num_inference_steps]
 
-        e_2 = model(x_2.to("cuda"), t_list[1].to("cuda"))
-        e_2 = e_2.to("cpu")
-        x_3 = self.transfer(x, t_list[0], t_list[1], e_2)
+    def step_warm_up(self, residual, image, t, num_inference_steps):
+        warmup_time_steps = self.get_warmup_time_steps(num_inference_steps)
 
-        e_3 = model(x_3.to("cuda"), t_list[1].to("cuda"))
-        e_3 = e_3.to("cpu")
-        x_4 = self.transfer(x, t_list[0], t_list[2], e_3)
+        t_prev = warmup_time_steps[t // 4 * 4]
+        t_next = warmup_time_steps[min(t + 1, len(warmup_time_steps) - 1)]
 
-        e_4 = model(x_4.to("cuda"), t_list[2].to("cuda"))
-        e_4 = e_4.to("cpu")
+        if t % 4 == 0:
+            self.cur_residual += 1 / 6 * residual
+            self.ets.append(residual)
+        elif (t - 1) % 4 == 0:
+            self.cur_residual += 1 / 3 * residual
+        elif (t - 2) % 4 == 0:
+            self.cur_residual += 1 / 3 * residual
+        elif (t - 3) % 4 == 0:
+            residual = self.cur_residual + 1 / 6 * residual
+            self.cur_residual = 0
 
-        et = (1 / 6) * (e_1 + 2 * e_2 + 2 * e_3 + e_4)
+        return self.transfer(image, t_prev, t_next, residual)
 
-        return et
+    def step(self, residual, image, t, num_inference_steps):
+        timesteps = self.get_time_steps(num_inference_steps)
+
+        t_prev = timesteps[t]
+        t_next = timesteps[min(t + 1, len(timesteps) - 1)]
+        self.ets.append(residual)
+
+        residual = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
+
+        return self.transfer(image, t_prev, t_next, residual)
 
     def transfer(self, x, t, t_next, et):
-        alphas_cump = self.alphas_cumprod
-        at = alphas_cump[t.long() + 1].view(-1, 1, 1, 1)
-        at_next = alphas_cump[t_next.long() + 1].view(-1, 1, 1, 1)
+        # TODO(Patrick): clean up to be compatible with numpy and give better names
+
+        alphas_cump = self.alphas_cumprod.to(x.device)
+        at = alphas_cump[t + 1].view(-1, 1, 1, 1)
+        at_next = alphas_cump[t_next + 1].view(-1, 1, 1, 1)
 
         x_delta = (at_next - at) * ((1 / (at.sqrt() * (at.sqrt() + at_next.sqrt()))) * x - 1 / (at.sqrt() * (((1 - at_next) * at).sqrt() + ((1 - at) * at_next).sqrt())) * et)