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3 Commits

Author SHA1 Message Date
Phil Wang
72bf159331 update 2022-05-24 08:25:40 -07:00
Phil Wang
e5e47cfecb link to aidan's test run 2022-05-23 12:41:46 -07:00
Phil Wang
fa533962bd 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 2022-05-22 22:43:14 -07:00
3 changed files with 6 additions and 3 deletions

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@@ -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>
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.
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.
## Status
@@ -26,7 +26,7 @@ There was enough interest for a <a href="https://github.com/lucidrains/dalle2-ja
## 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 🚧
- Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/jkrtg0so?workspace=user-veldrovive">In-progress test run</a> 🚧
- DALL-E 2 🚧
## Install

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@@ -890,6 +890,8 @@ 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})'
if isinstance(clip, CLIP):
clip = XClipAdapter(clip, **clip_adapter_overrides)
elif isinstance(clip, CoCa):
@@ -1721,6 +1723,7 @@ class Decoder(BaseGaussianDiffusion):
self.clip = None
if exists(clip):
assert not unconditional, 'clip must not be given if doing unconditional image training'
assert 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)

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@@ -10,7 +10,7 @@ setup(
'dream = dalle2_pytorch.cli:dream'
],
},
version = '0.4.11',
version = '0.4.14',
license='MIT',
description = 'DALL-E 2',
author = 'Phil Wang',