fix ddim to use alpha_cumprod

README.md

@@ -1126,7 +1126,6 @@ For detailed information on training the diffusion prior, please refer to the [d
 - [x] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
 - [x] add the final combination of upsample feature maps, used in unet squared, seems to have an effect in local experiments
 - [ ] consider elucidated dalle2 https://arxiv.org/abs/2206.00364
-- [ ] add simple outpainting, text-guided 2x size the image for starters
 - [ ] interface out the vqgan-vae so a pretrained one can be pulled off the shelf to validate latent diffusion + DALL-E2
 
 ## Citations

dalle2_pytorch/dalle2_pytorch.py

@@ -1070,7 +1070,7 @@ class DiffusionPriorNetwork(nn.Module):
 
         null_text_embeds = self.null_text_embeds.to(text_embed.dtype)
 
-        text_embed = torch.where(
+        text_embeds = torch.where(
             text_keep_mask,
             text_embed,
             null_text_embeds
@@ -1259,7 +1259,7 @@ class DiffusionPrior(nn.Module):
     def p_sample_loop_ddim(self, shape, text_cond, *, timesteps, eta = 1., cond_scale = 1.):
         batch, device, alphas, total_timesteps = shape[0], self.device, self.noise_scheduler.alphas_cumprod_prev, self.noise_scheduler.num_timesteps
 
-        times = torch.linspace(-1., total_timesteps, steps = timesteps + 1)[:-1]
+        times = torch.linspace(0., total_timesteps, steps = timesteps + 2)[:-1]
 
         times = list(reversed(times.int().tolist()))
         time_pairs = list(zip(times[:-1], times[1:]))
@@ -1281,14 +1281,12 @@ class DiffusionPrior(nn.Module):
 
             pred = self.net.forward_with_cond_scale(image_embed, time_cond, self_cond = self_cond, cond_scale = cond_scale, **text_cond)
 
-            # derive x0
-
             if self.predict_x_start:
                 x_start = pred
+                pred_noise = self.noise_scheduler.predict_noise_from_start(image_embed, t = time_cond, x0 = pred)
             else:
-                x_start = self.noise_scheduler.predict_start_from_noise(image_embed, t = time_cond, noise = pred_noise)
-
-            # clip x0 before maybe predicting noise
+                x_start = self.noise_scheduler.predict_start_from_noise(image_embed, t = time_cond, noise = pred)
+                pred_noise = pred
 
             if not self.predict_x_start:
                 x_start.clamp_(-1., 1.)
@@ -1296,17 +1294,6 @@ class DiffusionPrior(nn.Module):
             if self.predict_x_start and self.sampling_clamp_l2norm:
                 x_start = self.l2norm_clamp_embed(x_start)
 
-            # predict noise
-
-            if self.predict_x_start:
-                pred_noise = self.noise_scheduler.predict_noise_from_start(image_embed, t = time_cond, x0 = x_start)
-            else:
-                pred_noise = pred
-
-            if time_next < 0:
-                image_embed = x_start
-                continue
-
             c1 = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
             c2 = ((1 - alpha_next) - torch.square(c1)).sqrt()
             noise = torch.randn_like(image_embed) if time_next > 0 else 0.
@@ -2906,25 +2893,16 @@ class Decoder(nn.Module):
 
                 pred, _ = self.parse_unet_output(learned_variance, unet_output)
 
-                # predict x0
-
                 if predict_x_start:
                     x_start = pred
+                    pred_noise = noise_scheduler.predict_noise_from_start(img, t = time_cond, x0 = pred)
                 else:
                     x_start = noise_scheduler.predict_start_from_noise(img, t = time_cond, noise = pred)
-
-                # maybe clip x0
+                    pred_noise = pred
 
                 if clip_denoised:
                     x_start = self.dynamic_threshold(x_start)
 
-                # predict noise
-
-                if predict_x_start:
-                    pred_noise = noise_scheduler.predict_noise_from_start(img, t = time_cond, x0 = pred)
-                else:
-                    pred_noise = pred
-
                 c1 = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
                 c2 = ((1 - alpha_next) - torch.square(c1)).sqrt()
                 noise = torch.randn_like(img) if not is_last_timestep else 0.

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.10.5'
+__version__ = '1.10.2'