does not seem right to clip for the prior diffusion part

README.md

@@ -536,6 +536,7 @@ Once built, images will be saved to the same directory the command is invoked
 - [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
 - [ ] bring in tools to train vqgan-vae
 - [ ] for decoder, allow ability to customize objective (predict epsilon vs x0), in case latent diffusion does better with prediction of x0
+- [ ] bring in vit-vqgan https://arxiv.org/abs/2110.04627 for the latent diffusion
 
 ## Citations
 
@@ -573,17 +574,6 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
-```bibtex
-@misc{zhang2019root,
-    title   = {Root Mean Square Layer Normalization},
-    author  = {Biao Zhang and Rico Sennrich},
-    year    = {2019},
-    eprint  = {1910.07467},
-    archivePrefix = {arXiv},
-    primaryClass = {cs.LG}
-}
-```
-
 ```bibtex
 @inproceedings{Tu2022MaxViTMV,
     title   = {MaxViT: Multi-Axis Vision Transformer},

dalle2_pytorch/cli.py

@@ -1,9 +1,51 @@
 import click
+import torch
+import torchvision.transforms as T
+from pathlib import Path
+
+from dalle2_pytorch import DALLE2, Decoder, DiffusionPrior
+
+def safeget(dictionary, keys, default = None):
+    return reduce(lambda d, key: d.get(key, default) if isinstance(d, dict) else default, keys.split('.'), dictionary)
+
+def simple_slugify(text, max_length = 255):
+    return text.replace("-", "_").replace(",", "").replace(" ", "_").replace("|", "--").strip('-_')[:max_length]
+
+def get_pkg_version():
+    from pkg_resources import get_distribution
+    return get_distribution('dalle2_pytorch').version
 
 def main():
     pass
 
 @click.command()
+@click.option('--model', default = './dalle2.pt', help = 'path to trained DALL-E2 model')
+@click.option('--cond_scale', default = 2, help = 'conditioning scale (classifier free guidance) in decoder')
 @click.argument('text')
-def dream(text):
+def dream(
-    return 'not ready yet'
+    model,
+    cond_scale,
+    text
+):
+    model_path = Path(model)
+    full_model_path = str(model_path.resolve())
+    assert model_path.exists(), f'model not found at {full_model_path}'
+    loaded = torch.load(str(model_path))
+
+    version = safeget(loaded, 'version')
+    print(f'loading DALL-E2 from {full_model_path}, saved at version {version} - current package version is {get_pkg_version()}')
+
+    prior_init_params = safeget(loaded, 'init_params.prior')
+    decoder_init_params = safeget(loaded, 'init_params.decoder')
+    model_params = safeget(loaded, 'model_params')
+
+    prior = DiffusionPrior(**prior_init_params)
+    decoder = Decoder(**decoder_init_params)
+
+    dalle2 = DALLE2(prior, decoder)
+    dalle2.load_state_dict(model_params)
+
+    image = dalle2(text, cond_scale = cond_scale)
+
+    pil_image = T.ToPILImage()(image)
+    return pil_image.save(f'./{simple_slugify(text)}.png')

dalle2_pytorch/dalle2_pytorch.py

@@ -591,7 +591,7 @@ class DiffusionPrior(nn.Module):
         else:
             x_recon = self.predict_start_from_noise(x, t = t, noise = self.net(x, t, **text_cond))
 
-        if clip_denoised:
+        if clip_denoised and not self.predict_x0:
             x_recon.clamp_(-1., 1.)
 
         model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
@@ -1451,6 +1451,7 @@ class DALLE2(nn.Module):
         cond_scale = 1.
     ):
         device = next(self.parameters()).device
+        one_text = isinstance(text, str) or (not is_list_str(text) and text.shape[0] == 1)
 
         if isinstance(text, str) or is_list_str(text):
             text = [text] if not isinstance(text, (list, tuple)) else text
@@ -1458,4 +1459,8 @@ class DALLE2(nn.Module):
 
         image_embed = self.prior.sample(text, num_samples_per_batch = self.prior_num_samples)
         images = self.decoder.sample(image_embed, cond_scale = cond_scale)
+
+        if one_text:
+            return images[0]
+
         return images

dalle2_pytorch/vqgan_vae.py

@@ -477,7 +477,8 @@ class VQGanVAE(nn.Module):
         img,
         return_loss = False,
         return_discr_loss = False,
-        return_recons = False
+        return_recons = False,
+        add_gradient_penalty = True
     ):
         batch, channels, height, width, device = *img.shape, img.device
         assert height == self.image_size and width == self.image_size, 'height and width of input image must be equal to {self.image_size}'
@@ -502,11 +503,11 @@ class VQGanVAE(nn.Module):
 
             fmap_discr_logits, img_discr_logits = map(self.discr, (fmap, img))
 
-            gp = gradient_penalty(img, img_discr_logits)
-
             discr_loss = self.discr_loss(fmap_discr_logits, img_discr_logits)
 
-            loss = discr_loss + gp
+            if add_gradient_penalty:
+                gp = gradient_penalty(img, img_discr_logits)
+                loss = discr_loss + gp
 
             if return_recons:
                 return loss, fmap

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.38',
+  version = '0.0.40',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',