complete inpainting ability using inpaint_image and inpaint_mask passed into sample function for decoder

README.md

@@ -1049,8 +1049,8 @@ Once built, images will be saved to the same directory the command is invoked
 - [x] test out grid attention in cascading ddpm locally, decide whether to keep or remove https://arxiv.org/abs/2204.01697 (keeping, seems to be fine)
 - [x] allow for unet to be able to condition non-cross attention style as well
 - [x] speed up inference, read up on papers (ddim)
-- [ ] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
+- [x] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
-- [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet (test out unet² in ddpm repo) - consider https://github.com/lucidrains/uformer-pytorch attention-based unet
+- [ ] try out the nested unet from https://arxiv.org/abs/2005.09007 after hearing several positive testimonies from researchers, for segmentation anyhow
 - [ ] 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

@@ -2415,20 +2415,51 @@ class Decoder(nn.Module):
         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
 
     @torch.no_grad()
-    def p_sample_loop_ddpm(self, unet, shape, image_embed, noise_scheduler, predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_cond_img = None, text_encodings = None, cond_scale = 1, is_latent_diffusion = False, lowres_noise_level = None):
+    def p_sample_loop_ddpm(
+        self,
+        unet,
+        shape,
+        image_embed,
+        noise_scheduler,
+        predict_x_start = False,
+        learned_variance = False,
+        clip_denoised = True,
+        lowres_cond_img = None,
+        text_encodings = None,
+        cond_scale = 1,
+        is_latent_diffusion = False,
+        lowres_noise_level = None,
+        inpaint_image = None,
+        inpaint_mask = None
+    ):
         device = self.device
 
         b = shape[0]
         img = torch.randn(shape, device = device)
 
+        if exists(inpaint_image):
+            inpaint_image = self.normalize_img(inpaint_image)
+            inpaint_image = resize_image_to(inpaint_image, shape[-1], nearest = True)
+            inpaint_mask = rearrange(inpaint_mask, 'b h w -> b 1 h w').float()
+            inpaint_mask = resize_image_to(inpaint_mask, shape[-1], nearest = True)
+            inpaint_mask = inpaint_mask.bool()
+
         if not is_latent_diffusion:
             lowres_cond_img = maybe(self.normalize_img)(lowres_cond_img)
 
         for i in tqdm(reversed(range(0, noise_scheduler.num_timesteps)), desc = 'sampling loop time step', total = noise_scheduler.num_timesteps):
+            times = torch.full((b,), i, device = device, dtype = torch.long)
+
+            if exists(inpaint_image):
+                # following the repaint paper
+                # https://arxiv.org/abs/2201.09865
+                noised_inpaint_image = noise_scheduler.q_sample(inpaint_image, t = times)
+                img = (img * ~inpaint_mask) + (noised_inpaint_image * inpaint_mask)
+
             img = self.p_sample(
                 unet,
                 img,
-                torch.full((b,), i, device = device, dtype = torch.long),
+                times,
                 image_embed = image_embed,
                 text_encodings = text_encodings,
                 cond_scale = cond_scale,
@@ -2440,11 +2471,32 @@ class Decoder(nn.Module):
                 clip_denoised = clip_denoised
             )
 
+        if exists(inpaint_image):
+            img = (img * ~inpaint_mask) + (inpaint_image * inpaint_mask)
+
         unnormalize_img = self.unnormalize_img(img)
         return unnormalize_img
 
     @torch.no_grad()
-    def p_sample_loop_ddim(self, unet, shape, image_embed, noise_scheduler, timesteps, eta = 1., predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_cond_img = None, text_encodings = None, cond_scale = 1, is_latent_diffusion = False, lowres_noise_level = None):
+    def p_sample_loop_ddim(
+        self,
+        unet,
+        shape,
+        image_embed,
+        noise_scheduler,
+        timesteps,
+        eta = 1.,
+        predict_x_start = False,
+        learned_variance = False,
+        clip_denoised = True,
+        lowres_cond_img = None,
+        text_encodings = None,
+        cond_scale = 1,
+        is_latent_diffusion = False,
+        lowres_noise_level = None,
+        inpaint_image = None,
+        inpaint_mask = None
+    ):
         batch, device, total_timesteps, alphas, eta = shape[0], self.device, noise_scheduler.num_timesteps, noise_scheduler.alphas_cumprod_prev, self.ddim_sampling_eta
 
         times = torch.linspace(0., total_timesteps, steps = timesteps + 2)[:-1]
@@ -2452,6 +2504,13 @@ class Decoder(nn.Module):
         times = list(reversed(times.int().tolist()))
         time_pairs = list(zip(times[:-1], times[1:]))
 
+        if exists(inpaint_image):
+            inpaint_image = self.normalize_img(inpaint_image)
+            inpaint_image = resize_image_to(inpaint_image, shape[-1], nearest = True)
+            inpaint_mask = rearrange(inpaint_mask, 'b h w -> b 1 h w').float()
+            inpaint_mask = resize_image_to(inpaint_mask, shape[-1], nearest = True)
+            inpaint_mask = inpaint_mask.bool()
+
         img = torch.randn(shape, device = device)
 
         if not is_latent_diffusion:
@@ -2463,6 +2522,12 @@ class Decoder(nn.Module):
 
             time_cond = torch.full((batch,), time, device = device, dtype = torch.long)
 
+            if exists(inpaint_image):
+                # following the repaint paper
+                # https://arxiv.org/abs/2201.09865
+                noised_inpaint_image = noise_scheduler.q_sample(inpaint_image, t = time_cond)
+                img = (img * ~inpaint_mask) + (noised_inpaint_image * inpaint_mask)
+
             pred = unet.forward_with_cond_scale(img, time_cond, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, lowres_noise_level = lowres_noise_level)
 
             if learned_variance:
@@ -2486,6 +2551,9 @@ class Decoder(nn.Module):
                   c1 * noise + \
                   c2 * pred_noise
 
+        if exists(inpaint_image):
+            img = (img * ~inpaint_mask) + (inpaint_image * inpaint_mask)
+
         img = self.unnormalize_img(img)
         return img
 
@@ -2585,6 +2653,8 @@ class Decoder(nn.Module):
         cond_scale = 1.,
         stop_at_unet_number = None,
         distributed = False,
+        inpaint_image = None,
+        inpaint_mask = None
     ):
         assert self.unconditional or exists(image_embed), 'image embed must be present on sampling from decoder unless if trained unconditionally'
 
@@ -2598,6 +2668,8 @@ class Decoder(nn.Module):
         assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
         assert not (not self.condition_on_text_encodings and exists(text_encodings)), 'decoder specified not to be conditioned on text, yet it is presented'
 
+        assert not (exists(inpaint_image) ^ exists(inpaint_mask)), 'inpaint_image and inpaint_mask (boolean mask of [batch, height, width]) must be both given for inpainting'
+
         img = None
         is_cuda = next(self.parameters()).is_cuda
 
@@ -2609,6 +2681,8 @@ class Decoder(nn.Module):
             context = self.one_unet_in_gpu(unet = unet) if is_cuda and not distributed else null_context()
 
             with context:
+                # prepare low resolution conditioning for upsamplers
+
                 lowres_cond_img = lowres_noise_level = None
                 shape = (batch_size, channel, image_size, image_size)
 
@@ -2619,12 +2693,16 @@ class Decoder(nn.Module):
                         lowres_noise_level = torch.full((batch_size,), int(self.lowres_noise_sample_level * 1000), dtype = torch.long, device = self.device)
                         lowres_cond_img, _ = lowres_cond.noise_image(lowres_cond_img, lowres_noise_level)
 
+                # latent diffusion
+
                 is_latent_diffusion = isinstance(vae, VQGanVAE)
                 image_size = vae.get_encoded_fmap_size(image_size)
                 shape = (batch_size, vae.encoded_dim, image_size, image_size)
 
                 lowres_cond_img = maybe(vae.encode)(lowres_cond_img)
 
+                # denoising loop for image
+
                 img = self.p_sample_loop(
                     unet,
                     shape,
@@ -2638,7 +2716,9 @@ class Decoder(nn.Module):
                     lowres_noise_level = lowres_noise_level,
                     is_latent_diffusion = is_latent_diffusion,
                     noise_scheduler = noise_scheduler,
-                    timesteps = sample_timesteps
+                    timesteps = sample_timesteps,
+                    inpaint_image = inpaint_image,
+                    inpaint_mask = inpaint_mask
                 )
 
                 img = vae.decode(img)

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.25.2'
+__version__ = '0.26.0'