just use an assert to make sure clip image channels is never different than the channels of the diffusion prior and decoder, if clip is given

README.md

@@ -12,7 +12,7 @@ This model is SOTA for text-to-image for now.
 
 Please join <a href="https://discord.gg/xBPBXfcFHd"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a> if you are interested in helping out with the replication with the <a href="https://laion.ai/">LAION</a> community | <a href="https://www.youtube.com/watch?v=AIOE1l1W0Tw">Yannic Interview</a>
 
-As of 5/23/22, it is no longer SOTA. SOTA will be <a href="https://github.com/lucidrains/imagen-pytorch">here</a>. Jax versions as well as text-to-video project will be shifted towards the Imagen architecture, as it is way simpler.
+There was enough interest for a <a href="https://github.com/lucidrains/dalle2-jax">Jax version</a>. I will also eventually extend this to <a href="https://github.com/lucidrains/dalle2-video">text to video</a>, once the repository is in a good place.
 
 ## Status
 
@@ -26,7 +26,7 @@ As of 5/23/22, it is no longer SOTA. SOTA will be <a href="https://github.com/lu
 
 ## Pre-Trained Models
 - LAION is training prior models. Checkpoints are available on <a href="https://huggingface.co/zenglishuci/conditioned-prior">🤗huggingface</a> and the training statistics are available on <a href="https://wandb.ai/nousr_laion/conditioned-prior/reports/LAION-DALLE2-PyTorch-Prior--VmlldzoyMDI2OTIx">🐝WANDB</a>.
-- Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/jkrtg0so?workspace=user-veldrovive">In-progress test run</a> 🚧
+- Decoder 🚧
 - DALL-E 2 🚧
 
 ## Install
@@ -1195,12 +1195,4 @@ This library would not have gotten to this working state without the help of
 }
 ```
 
-```bibtex
-@misc{Saharia2022,
-    title   = {Imagen: unprecedented photorealism × deep level of language understanding},
-    author  = {Chitwan Saharia*, William Chan*, Saurabh Saxena†, Lala Li†, Jay Whang†, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho†, David Fleet†, Mohammad Norouzi*},
-    year    = {2022}
-}
-```
-
 *Creating noise from data is easy; creating data from noise is generative modeling.* - <a href="https://arxiv.org/abs/2011.13456">Yang Song's paper</a>

dalle2_pytorch/dalle2_pytorch.py

@@ -890,7 +890,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
         )
 
         if exists(clip):
-            assert image_channels == clip.image_channels, f'channels of image ({image_channels}) should be equal to the channels that CLIP accepts ({clip.image_channels})'
+            assert image_channels == clip.image_channels, f'channels of image ({channels}) should be equal to the channels that CLIP accepts ({clip.image_channels})'
 
             if isinstance(clip, CLIP):
                 clip = XClipAdapter(clip, **clip_adapter_overrides)
@@ -1704,8 +1704,6 @@ class Decoder(BaseGaussianDiffusion):
         vb_loss_weight = 0.001,
         unconditional = False,
         auto_normalize_img = True,                  # whether to take care of normalizing the image from [0, 1] to [-1, 1] and back automatically - you can turn this off if you want to pass in the [-1, 1] ranged image yourself from the dataloader
-        use_dynamic_thres = False,                  # from the Imagen paper
-        dynamic_thres_percentile = 0.9
     ):
         super().__init__(
             beta_schedule = beta_schedule,
@@ -1828,11 +1826,6 @@ class Decoder(BaseGaussianDiffusion):
         self.clip_denoised = clip_denoised
         self.clip_x_start = clip_x_start
 
-        # dynamic thresholding settings, if clipping denoised during sampling
-
-        self.use_dynamic_thres = use_dynamic_thres
-        self.dynamic_thres_percentile = dynamic_thres_percentile
-
         # normalize and unnormalize image functions
 
         self.normalize_img = normalize_neg_one_to_one if auto_normalize_img else identity
@@ -1875,21 +1868,7 @@ class Decoder(BaseGaussianDiffusion):
             x_recon = self.predict_start_from_noise(x, t = t, noise = pred)
 
         if clip_denoised:
-            # s is the threshold amount
+            x_recon.clamp_(-1., 1.)
-            # static thresholding would just be s = 1
-            s = 1.
-            if self.use_dynamic_thres:
-                s = torch.quantile(
-                    rearrange(x_recon, 'b ... -> b (...)').abs(),
-                    self.dynamic_thres_percentile,
-                    dim = -1
-                )
-
-                s.clamp_(min = 1.)
-                s = s.view(-1, *((1,) * (x_recon.ndim - 1)))
-
-            # clip by threshold, depending on whether static or dynamic
-            x_recon = x_recon.clamp(-s, s) / s
 
         model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
 

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.5.0',
+  version = '0.4.12',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',