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e024971dc3 |
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README.md
247
README.md
@@ -10,7 +10,7 @@ The main novelty seems to be an extra layer of indirection with the prior networ
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This model is SOTA for text-to-image for now.
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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>
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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
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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.
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@@ -47,7 +47,7 @@ clip = CLIP(
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use_all_token_embeds = True, # whether to use fine-grained contrastive learning (FILIP)
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decoupled_contrastive_learning = True, # use decoupled contrastive learning (DCL) objective function, removing positive pairs from the denominator of the InfoNCE loss (CLOOB + DCL)
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extra_latent_projection = True, # whether to use separate projections for text-to-image vs image-to-text comparisons (CLOOB)
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use_visual_ssl = True, # whether to do self supervised learning on images
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use_visual_ssl = True, # whether to do self supervised learning on iages
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visual_ssl_type = 'simclr', # can be either 'simclr' or 'simsiam', depending on using DeCLIP or SLIP
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use_mlm = False, # use masked language learning (MLM) on text (DeCLIP)
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text_ssl_loss_weight = 0.05, # weight for text MLM loss
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@@ -110,8 +110,7 @@ decoder = Decoder(
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unet = unet,
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clip = clip,
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timesteps = 100,
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image_cond_drop_prob = 0.1,
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text_cond_drop_prob = 0.5
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cond_drop_prob = 0.2
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).cuda()
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# mock images (get a lot of this)
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@@ -230,8 +229,7 @@ decoder = Decoder(
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unet = (unet1, unet2), # insert both unets in order of low resolution to highest resolution (you can have as many stages as you want here)
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image_sizes = (256, 512), # resolutions, 256 for first unet, 512 for second. these must be unique and in ascending order (matches with the unets passed in)
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timesteps = 1000,
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image_cond_drop_prob = 0.1,
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text_cond_drop_prob = 0.5
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cond_drop_prob = 0.2
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).cuda()
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# mock images (get a lot of this)
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@@ -350,8 +348,7 @@ decoder = Decoder(
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image_sizes = (128, 256),
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clip = clip,
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timesteps = 100,
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image_cond_drop_prob = 0.1,
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text_cond_drop_prob = 0.5,
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cond_drop_prob = 0.2,
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condition_on_text_encodings = False # set this to True if you wish to condition on text during training and sampling
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).cuda()
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@@ -433,8 +430,8 @@ images = torch.randn(4, 3, 256, 256).cuda()
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# precompute the text and image embeddings
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# here using the diffusion prior class, but could be done with CLIP alone
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clip_image_embeds = diffusion_prior.clip.embed_image(images).image_embed
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clip_text_embeds = diffusion_prior.clip.embed_text(text).text_embed
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clip_image_embeds = diffusion_prior.get_image_embed(images)
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clip_text_embeds = diffusion_prior.get_text_cond(text).get('text_embed')
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# feed text and images into diffusion prior network
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@@ -498,105 +495,14 @@ loss.backward()
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# now the diffusion prior can generate image embeddings from the text embeddings
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```
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## OpenAI CLIP
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Although there is the possibility they are using an unreleased, more powerful CLIP, you can use one of the released ones, if you do not wish to train your own CLIP from scratch. This will also allow the community to more quickly validate the conclusions of the paper.
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To use a pretrained OpenAI CLIP, simply import `OpenAIClipAdapter` and pass it into the `DiffusionPrior` or `Decoder` like so
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```python
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import torch
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from dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder, OpenAIClipAdapter
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# openai pretrained clip - defaults to ViT/B-32
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clip = OpenAIClipAdapter()
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# mock data
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text = torch.randint(0, 49408, (4, 256)).cuda()
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images = torch.randn(4, 3, 256, 256).cuda()
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# prior networks (with transformer)
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prior_network = DiffusionPriorNetwork(
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dim = 512,
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depth = 6,
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dim_head = 64,
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heads = 8
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).cuda()
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diffusion_prior = DiffusionPrior(
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net = prior_network,
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clip = clip,
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timesteps = 100,
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cond_drop_prob = 0.2
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).cuda()
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loss = diffusion_prior(text, images)
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loss.backward()
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# do above for many steps ...
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# decoder (with unet)
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unet1 = Unet(
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dim = 128,
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image_embed_dim = 512,
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cond_dim = 128,
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channels = 3,
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dim_mults=(1, 2, 4, 8)
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).cuda()
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unet2 = Unet(
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dim = 16,
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image_embed_dim = 512,
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cond_dim = 128,
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channels = 3,
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dim_mults = (1, 2, 4, 8, 16)
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).cuda()
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decoder = Decoder(
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unet = (unet1, unet2),
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image_sizes = (128, 256),
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clip = clip,
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timesteps = 100,
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image_cond_drop_prob = 0.1,
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text_cond_drop_prob = 0.5,
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condition_on_text_encodings = False # set this to True if you wish to condition on text during training and sampling
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).cuda()
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for unet_number in (1, 2):
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loss = decoder(images, unet_number = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much
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loss.backward()
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# do above for many steps
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dalle2 = DALLE2(
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prior = diffusion_prior,
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decoder = decoder
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)
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images = dalle2(
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['a butterfly trying to escape a tornado'],
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cond_scale = 2. # classifier free guidance strength (> 1 would strengthen the condition)
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)
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# save your image (in this example, of size 256x256)
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```
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Now you'll just have to worry about training the Prior and the Decoder!
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## Experimental
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### DALL-E2 with Latent Diffusion
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This repository decides to take the next step and offer DALL-E v2 combined with <a href="https://huggingface.co/spaces/multimodalart/latentdiffusion">latent diffusion</a>, from Rombach et al.
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This repository decides to take the next step and offer DALL-E2 combined with <a href="https://huggingface.co/spaces/multimodalart/latentdiffusion">latent diffusion</a>, from Rombach et al.
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You can use it as follows. Latent diffusion can be limited to just the first U-Net in the cascade, or to any number you wish.
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The repository also comes equipped with all the necessary settings to recreate `ViT-VQGan` from the <a href="https://arxiv.org/abs/2110.04627">Improved VQGans</a> paper. Furthermore, the <a href="https://github.com/lucidrains/vector-quantize-pytorch">vector quantization</a> library also comes equipped to do <a href="https://arxiv.org/abs/2203.01941">residual or multi-headed quantization</a>, which I believe will give an even further boost in performance to the autoencoder.
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```python
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import torch
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from dalle2_pytorch import Unet, Decoder, CLIP, VQGanVAE
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@@ -620,7 +526,7 @@ clip = CLIP(
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# 3 unets for the decoder (a la cascading DDPM)
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# first two unets are doing latent diffusion
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# vqgan-vae must be trained beforehand
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# vqgan-vae must be trained before hand
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vae1 = VQGanVAE(
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dim = 32,
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@@ -673,8 +579,7 @@ decoder = Decoder(
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unet = (unet1, unet2, unet3), # insert unets in order of low resolution to highest resolution (you can have as many stages as you want here)
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image_sizes = (256, 512, 1024), # resolutions, 256 for first unet, 512 for second, 1024 for third
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timesteps = 100,
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image_cond_drop_prob = 0.1,
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text_cond_drop_prob = 0.5
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cond_drop_prob = 0.2
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).cuda()
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# mock images (get a lot of this)
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@@ -708,83 +613,7 @@ images = decoder.sample(mock_image_embed) # (1, 3, 1024, 1024)
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## Training wrapper (wip)
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### Decoder Training
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Training the `Decoder` may be confusing, as one needs to keep track of an optimizer for each of the `Unet`(s) separately. Each `Unet` will also need its own corresponding exponential moving average. The `DecoderTrainer` hopes to make this simple, as shown below
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```python
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import torch
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from dalle2_pytorch import DALLE2, Unet, Decoder, CLIP, DecoderTrainer
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clip = CLIP(
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dim_text = 512,
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dim_image = 512,
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dim_latent = 512,
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num_text_tokens = 49408,
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text_enc_depth = 6,
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text_seq_len = 256,
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text_heads = 8,
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visual_enc_depth = 6,
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visual_image_size = 256,
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visual_patch_size = 32,
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visual_heads = 8
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).cuda()
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# mock data
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text = torch.randint(0, 49408, (4, 256)).cuda()
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images = torch.randn(4, 3, 256, 256).cuda()
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# decoder (with unet)
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unet1 = Unet(
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dim = 128,
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image_embed_dim = 512,
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text_embed_dim = 512,
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cond_dim = 128,
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channels = 3,
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dim_mults=(1, 2, 4, 8)
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).cuda()
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unet2 = Unet(
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dim = 16,
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image_embed_dim = 512,
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text_embed_dim = 512,
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cond_dim = 128,
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channels = 3,
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dim_mults = (1, 2, 4, 8, 16),
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cond_on_text_encodings = True
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).cuda()
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decoder = Decoder(
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unet = (unet1, unet2),
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image_sizes = (128, 256),
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clip = clip,
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timesteps = 1000,
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condition_on_text_encodings = True
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).cuda()
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decoder_trainer = DecoderTrainer(
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decoder,
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lr = 3e-4,
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wd = 1e-2,
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ema_beta = 0.99,
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ema_update_after_step = 1000,
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ema_update_every = 10,
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)
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for unet_number in (1, 2):
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loss = decoder_trainer(images, text = text, unet_number = unet_number) # use the decoder_trainer forward
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loss.backward()
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decoder_trainer.update(unet_number) # update the specific unet as well as its exponential moving average
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# after much training
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# you can sample from the exponentially moving averaged unets as so
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mock_image_embed = torch.randn(4, 512).cuda()
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images = decoder_trainer.sample(mock_image_embed, text = text) # (4, 3, 256, 256)
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```
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Offer training wrappers
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## CLI (wip)
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@@ -816,27 +645,13 @@ Once built, images will be saved to the same directory the command is invoked
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- [x] use attention-based upsampling https://arxiv.org/abs/2112.11435
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- [x] use inheritance just this once for sharing logic between decoder and prior network ddpms
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- [x] bring in vit-vqgan https://arxiv.org/abs/2110.04627 for the latent diffusion
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- [x] abstract interface for CLIP adapter class, so other CLIPs can be brought in
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- [x] take care of mixed precision as well as gradient accumulation within decoder trainer
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- [x] just take care of the training for the decoder in a wrapper class, as each unet in the cascade will need its own optimizer
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- [x] bring in tools to train vqgan-vae
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- [x] add convnext backbone for vqgan-vae (in addition to vit [vit-vqgan] + resnet)
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- [x] make sure DDPMs can be run with traditional resnet blocks (but leave convnext as an option for experimentation)
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- [x] make sure for the latter unets in the cascade, one can train on crops for learning super resolution (constrain the unet to be only convolutions in that case, or allow conv-like attention with rel pos bias)
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- [ ] 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)
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- [ ] abstract interface for CLIP adapter class, so other CLIPs can be brought in
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- [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet
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- [ ] copy the cascading ddpm code to a separate repo (perhaps https://github.com/lucidrains/denoising-diffusion-pytorch) as the main contribution of dalle2 really is just the prior network
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- [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
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- [ ] pull logic for training diffusion prior into a class DiffusionPriorTrainer, for eventual script based + CLI based training
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- [ ] train on a toy task, offer in colab
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- [ ] think about how best to design a declarative training config that handles preencoding for prior and training of multiple networks in decoder
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- [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
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- [ ] bring in cross-scale embedding from iclr paper https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/crossformer.py#L14
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- [ ] figure out if possible to augment with external memory, as described in https://arxiv.org/abs/2204.11824
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- [ ] test out grid attention in cascading ddpm locally, decide whether to keep or remove
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- [ ] use an experimental tracker agnostic setup, as done <a href="https://github.com/lucidrains/tf-bind-transformer#simple-trainer-class-for-fine-tuning">here</a>
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- [ ] interface out the vqgan-vae so a pretrained one can be pulled off the shelf to validate latent diffusion + DALL-E2
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- [ ] make sure FILIP works with DALL-E2 from x-clip https://arxiv.org/abs/2111.07783
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- [ ] make sure resnet | convnext block hyperparameters can be configurable across unet depth (groups and expansion factor)
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- [ ] bring in tools to train vqgan-vae
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## Citations
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@@ -868,22 +683,12 @@ Once built, images will be saved to the same directory the command is invoked
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```bibtex
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@inproceedings{Liu2022ACF,
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title = {A ConvNet for the 2020s},
|
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title = {A ConvNet for the 2020https://arxiv.org/abs/2112.11435s},
|
||||
author = {Zhuang Liu and Hanzi Mao and Chaozheng Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
|
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year = {2022}
|
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}
|
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```
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|
||||
```bibtex
|
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@article{shen2019efficient,
|
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author = {Zhuoran Shen and Mingyuan Zhang and Haiyu Zhao and Shuai Yi and Hongsheng Li},
|
||||
title = {Efficient Attention: Attention with Linear Complexities},
|
||||
journal = {CoRR},
|
||||
year = {2018},
|
||||
url = {http://arxiv.org/abs/1812.01243},
|
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}
|
||||
```
|
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|
||||
```bibtex
|
||||
@inproceedings{Tu2022MaxViTMV,
|
||||
title = {MaxViT: Multi-Axis Vision Transformer},
|
||||
@@ -892,6 +697,16 @@ Once built, images will be saved to the same directory the command is invoked
|
||||
}
|
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```
|
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|
||||
```bibtex
|
||||
@article{Arar2021LearnedQF,
|
||||
title = {Learned Queries for Efficient Local Attention},
|
||||
author = {Moab Arar and Ariel Shamir and Amit H. Bermano},
|
||||
journal = {ArXiv},
|
||||
year = {2021},
|
||||
volume = {abs/2112.11435}
|
||||
}
|
||||
```
|
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|
||||
```bibtex
|
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@article{Yu2021VectorquantizedIM,
|
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title = {Vector-quantized Image Modeling with Improved VQGAN},
|
||||
@@ -902,14 +717,4 @@ Once built, images will be saved to the same directory the command is invoked
|
||||
}
|
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```
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|
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```bibtex
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@article{Shleifer2021NormFormerIT,
|
||||
title = {NormFormer: Improved Transformer Pretraining with Extra Normalization},
|
||||
author = {Sam Shleifer and Jason Weston and Myle Ott},
|
||||
journal = {ArXiv},
|
||||
year = {2021},
|
||||
volume = {abs/2110.09456}
|
||||
}
|
||||
```
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||||
*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>
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*Creating noise from data is easy; creating data from noise is generative modeling.* - Yang Song's <a href="https://arxiv.org/abs/2011.13456">paper</a>
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@@ -1,6 +1,4 @@
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from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder
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from dalle2_pytorch.dalle2_pytorch import OpenAIClipAdapter
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from dalle2_pytorch.train import DecoderTrainer
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from dalle2_pytorch.vqgan_vae import VQGanVAE
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from x_clip import CLIP
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|
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130
dalle2_pytorch/attention.py
Normal file
130
dalle2_pytorch/attention.py
Normal file
@@ -0,0 +1,130 @@
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import torch
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from torch import nn, einsum
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import torch.nn.functional as F
|
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|
||||
from einops import rearrange, repeat
|
||||
|
||||
class LayerNormChan(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim,
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||||
eps = 1e-5
|
||||
):
|
||||
super().__init__()
|
||||
self.eps = eps
|
||||
self.gamma = nn.Parameter(torch.ones(1, dim, 1, 1))
|
||||
|
||||
def forward(self, x):
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||||
var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
|
||||
mean = torch.mean(x, dim = 1, keepdim = True)
|
||||
return (x - mean) / (var + self.eps).sqrt() * self.gamma
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||||
|
||||
# attention-based upsampling
|
||||
# from https://arxiv.org/abs/2112.11435
|
||||
|
||||
class QueryAndAttend(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
dim,
|
||||
num_queries = 1,
|
||||
dim_head = 32,
|
||||
heads = 8,
|
||||
window_size = 3
|
||||
):
|
||||
super().__init__()
|
||||
self.scale = dim_head ** -0.5
|
||||
inner_dim = dim_head * heads
|
||||
self.heads = heads
|
||||
self.dim_head = dim_head
|
||||
self.window_size = window_size
|
||||
self.num_queries = num_queries
|
||||
|
||||
self.rel_pos_bias = nn.Parameter(torch.randn(heads, num_queries, window_size * window_size, 1, 1))
|
||||
|
||||
self.queries = nn.Parameter(torch.randn(heads, num_queries, dim_head))
|
||||
self.to_kv = nn.Conv2d(dim, dim_head * 2, 1, bias = False)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
nn.Conv2d(inner_dim, dim * 2, 1, bias = False),
|
||||
nn.Tanh(),
|
||||
nn.Conv2d(dim * 2, dim, 1, bias = False)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
einstein notation
|
||||
b - batch
|
||||
h - heads
|
||||
l - num queries
|
||||
d - head dimension
|
||||
x - height
|
||||
y - width
|
||||
j - source sequence for attending to (kernel size squared in this case)
|
||||
"""
|
||||
|
||||
wsz, heads, dim_head, num_queries = self.window_size, self.heads, self.dim_head, self.num_queries
|
||||
batch, _, height, width = x.shape
|
||||
|
||||
is_one_query = self.num_queries == 1
|
||||
|
||||
# queries, keys, values
|
||||
|
||||
q = self.queries * self.scale
|
||||
k, v = self.to_kv(x).chunk(2, dim = 1)
|
||||
|
||||
# similarities
|
||||
|
||||
sim = einsum('h l d, b d x y -> b h l x y', q, k)
|
||||
sim = rearrange(sim, 'b ... x y -> b (...) x y')
|
||||
|
||||
# unfold the similarity scores, with float(-inf) as padding value
|
||||
|
||||
mask_value = -torch.finfo(sim.dtype).max
|
||||
sim = F.pad(sim, ((wsz // 2,) * 4), value = mask_value)
|
||||
sim = F.unfold(sim, kernel_size = wsz)
|
||||
sim = rearrange(sim, 'b (h l j) (x y) -> b h l j x y', h = heads, l = num_queries, x = height, y = width)
|
||||
|
||||
# rel pos bias
|
||||
|
||||
sim = sim + self.rel_pos_bias
|
||||
|
||||
# numerically stable attention
|
||||
|
||||
sim = sim - sim.amax(dim = -3, keepdim = True).detach()
|
||||
attn = sim.softmax(dim = -3)
|
||||
|
||||
# unfold values
|
||||
|
||||
v = F.pad(v, ((wsz // 2,) * 4), value = 0.)
|
||||
v = F.unfold(v, kernel_size = wsz)
|
||||
v = rearrange(v, 'b (d j) (x y) -> b d j x y', d = dim_head, x = height, y = width)
|
||||
|
||||
# aggregate values
|
||||
|
||||
out = einsum('b h l j x y, b d j x y -> b l h d x y', attn, v)
|
||||
|
||||
# combine heads
|
||||
|
||||
out = rearrange(out, 'b l h d x y -> (b l) (h d) x y')
|
||||
out = self.to_out(out)
|
||||
out = rearrange(out, '(b l) d x y -> b l d x y', b = batch)
|
||||
|
||||
# return original input if one query
|
||||
|
||||
if is_one_query:
|
||||
out = rearrange(out, 'b 1 ... -> b ...')
|
||||
|
||||
return out
|
||||
|
||||
class QueryAttnUpsample(nn.Module):
|
||||
def __init__(self, dim, **kwargs):
|
||||
super().__init__()
|
||||
self.norm = LayerNormChan(dim)
|
||||
self.qna = QueryAndAttend(dim = dim, num_queries = 4, **kwargs)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.norm(x)
|
||||
out = self.qna(x)
|
||||
out = rearrange(out, 'b (w1 w2) c h w -> b c (h w1) (w w2)', w1 = 2, w2 = 2)
|
||||
return out
|
||||
@@ -1,7 +1,6 @@
|
||||
import click
|
||||
import torch
|
||||
import torchvision.transforms as T
|
||||
from functools import reduce
|
||||
from pathlib import Path
|
||||
|
||||
from dalle2_pytorch import DALLE2, Decoder, DiffusionPrior
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,29 +0,0 @@
|
||||
from torch.optim import AdamW, Adam
|
||||
|
||||
def separate_weight_decayable_params(params):
|
||||
no_wd_params = set([param for param in params if param.ndim < 2])
|
||||
wd_params = set(params) - no_wd_params
|
||||
return wd_params, no_wd_params
|
||||
|
||||
def get_optimizer(
|
||||
params,
|
||||
lr = 3e-4,
|
||||
wd = 1e-2,
|
||||
betas = (0.9, 0.999),
|
||||
filter_by_requires_grad = False
|
||||
):
|
||||
if filter_by_requires_grad:
|
||||
params = list(filter(lambda t: t.requires_grad, params))
|
||||
|
||||
if wd == 0:
|
||||
return Adam(params, lr = lr, betas = betas)
|
||||
|
||||
params = set(params)
|
||||
wd_params, no_wd_params = separate_weight_decayable_params(params)
|
||||
|
||||
param_groups = [
|
||||
{'params': list(wd_params)},
|
||||
{'params': list(no_wd_params), 'weight_decay': 0},
|
||||
]
|
||||
|
||||
return AdamW(param_groups, lr = lr, weight_decay = wd, betas = betas)
|
||||
@@ -1,43 +1,6 @@
|
||||
import copy
|
||||
from functools import partial
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.cuda.amp import autocast, GradScaler
|
||||
|
||||
from dalle2_pytorch.dalle2_pytorch import Decoder
|
||||
from dalle2_pytorch.optimizer import get_optimizer
|
||||
|
||||
# helper functions
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
def cast_tuple(val, length = 1):
|
||||
return val if isinstance(val, tuple) else ((val,) * length)
|
||||
|
||||
def pick_and_pop(keys, d):
|
||||
values = list(map(lambda key: d.pop(key), keys))
|
||||
return dict(zip(keys, values))
|
||||
|
||||
def group_dict_by_key(cond, d):
|
||||
return_val = [dict(),dict()]
|
||||
for key in d.keys():
|
||||
match = bool(cond(key))
|
||||
ind = int(not match)
|
||||
return_val[ind][key] = d[key]
|
||||
return (*return_val,)
|
||||
|
||||
def string_begins_with(prefix, str):
|
||||
return str.startswith(prefix)
|
||||
|
||||
def group_by_key_prefix(prefix, d):
|
||||
return group_dict_by_key(partial(string_begins_with, prefix), d)
|
||||
|
||||
def groupby_prefix_and_trim(prefix, d):
|
||||
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
|
||||
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
|
||||
return kwargs_without_prefix, kwargs
|
||||
|
||||
# exponential moving average wrapper
|
||||
|
||||
@@ -46,16 +9,16 @@ class EMA(nn.Module):
|
||||
self,
|
||||
model,
|
||||
beta = 0.99,
|
||||
update_after_step = 1000,
|
||||
update_every = 10,
|
||||
ema_update_after_step = 1000,
|
||||
ema_update_every = 10,
|
||||
):
|
||||
super().__init__()
|
||||
self.beta = beta
|
||||
self.online_model = model
|
||||
self.ema_model = copy.deepcopy(model)
|
||||
|
||||
self.update_after_step = update_after_step # only start EMA after this step number, starting at 0
|
||||
self.update_every = update_every
|
||||
self.ema_update_after_step = ema_update_after_step # only start EMA after this step number, starting at 0
|
||||
self.ema_update_every = ema_update_every
|
||||
|
||||
self.register_buffer('initted', torch.Tensor([False]))
|
||||
self.register_buffer('step', torch.tensor([0.]))
|
||||
@@ -63,7 +26,7 @@ class EMA(nn.Module):
|
||||
def update(self):
|
||||
self.step += 1
|
||||
|
||||
if self.step <= self.update_after_step or (self.step % self.update_every) != 0:
|
||||
if self.step <= self.ema_update_after_step or (self.step % self.ema_update_every) != 0:
|
||||
return
|
||||
|
||||
if not self.initted:
|
||||
@@ -72,7 +35,7 @@ class EMA(nn.Module):
|
||||
|
||||
self.update_moving_average(self.ema_model, self.online_model)
|
||||
|
||||
def update_moving_average(self, ma_model, current_model):
|
||||
def update_moving_average(ma_model, current_model):
|
||||
def calculate_ema(beta, old, new):
|
||||
if not exists(old):
|
||||
return new
|
||||
@@ -88,112 +51,3 @@ class EMA(nn.Module):
|
||||
|
||||
def __call__(self, *args, **kwargs):
|
||||
return self.ema_model(*args, **kwargs)
|
||||
|
||||
# trainers
|
||||
|
||||
class DecoderTrainer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
decoder,
|
||||
use_ema = True,
|
||||
lr = 3e-4,
|
||||
wd = 1e-2,
|
||||
max_grad_norm = None,
|
||||
amp = False,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__()
|
||||
assert isinstance(decoder, Decoder)
|
||||
ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
|
||||
|
||||
self.decoder = decoder
|
||||
self.num_unets = len(self.decoder.unets)
|
||||
|
||||
self.use_ema = use_ema
|
||||
|
||||
if use_ema:
|
||||
has_lazy_linear = any([type(module) == nn.LazyLinear for module in decoder.modules()])
|
||||
assert not has_lazy_linear, 'you must set the text_embed_dim on your u-nets if you plan on doing automatic exponential moving average'
|
||||
|
||||
self.ema_unets = nn.ModuleList([])
|
||||
|
||||
self.amp = amp
|
||||
|
||||
# be able to finely customize learning rate, weight decay
|
||||
# per unet
|
||||
|
||||
lr, wd = map(partial(cast_tuple, length = self.num_unets), (lr, wd))
|
||||
|
||||
for ind, (unet, unet_lr, unet_wd) in enumerate(zip(self.decoder.unets, lr, wd)):
|
||||
optimizer = get_optimizer(
|
||||
unet.parameters(),
|
||||
lr = unet_lr,
|
||||
wd = unet_wd,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
setattr(self, f'optim{ind}', optimizer) # cannot use pytorch ModuleList for some reason with optimizers
|
||||
|
||||
if self.use_ema:
|
||||
self.ema_unets.append(EMA(unet, **ema_kwargs))
|
||||
|
||||
scaler = GradScaler(enabled = amp)
|
||||
setattr(self, f'scaler{ind}', scaler)
|
||||
|
||||
# gradient clipping if needed
|
||||
|
||||
self.max_grad_norm = max_grad_norm
|
||||
|
||||
@property
|
||||
def unets(self):
|
||||
return nn.ModuleList([ema.ema_model for ema in self.ema_unets])
|
||||
|
||||
def scale(self, loss, *, unet_number):
|
||||
assert 1 <= unet_number <= self.num_unets
|
||||
index = unet_number - 1
|
||||
scaler = getattr(self, f'scaler{index}')
|
||||
return scaler.scale(loss)
|
||||
|
||||
def update(self, unet_number):
|
||||
assert 1 <= unet_number <= self.num_unets
|
||||
index = unet_number - 1
|
||||
unet = self.decoder.unets[index]
|
||||
|
||||
optimizer = getattr(self, f'optim{index}')
|
||||
scaler = getattr(self, f'scaler{index}')
|
||||
|
||||
if exists(self.max_grad_norm):
|
||||
scaler.unscale_(optimizer)
|
||||
nn.utils.clip_grad_norm_(unet.parameters(), self.max_grad_norm)
|
||||
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
|
||||
if self.use_ema:
|
||||
ema_unet = self.ema_unets[index]
|
||||
ema_unet.update()
|
||||
|
||||
@torch.no_grad()
|
||||
def sample(self, *args, **kwargs):
|
||||
if self.use_ema:
|
||||
trainable_unets = self.decoder.unets
|
||||
self.decoder.unets = self.unets # swap in exponential moving averaged unets for sampling
|
||||
|
||||
output = self.decoder.sample(*args, **kwargs)
|
||||
|
||||
if self.use_ema:
|
||||
self.decoder.unets = trainable_unets # restore original training unets
|
||||
return output
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x,
|
||||
*,
|
||||
unet_number,
|
||||
divisor = 1,
|
||||
**kwargs
|
||||
):
|
||||
with autocast(enabled = self.amp):
|
||||
loss = self.decoder(x, unet_number = unet_number, **kwargs)
|
||||
return self.scale(loss / divisor, unet_number = unet_number)
|
||||
|
||||
@@ -1,266 +0,0 @@
|
||||
from math import sqrt
|
||||
import copy
|
||||
from random import choice
|
||||
from pathlib import Path
|
||||
from shutil import rmtree
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from PIL import Image
|
||||
from torchvision.datasets import ImageFolder
|
||||
import torchvision.transforms as T
|
||||
from torch.utils.data import Dataset, DataLoader, random_split
|
||||
from torchvision.utils import make_grid, save_image
|
||||
|
||||
from einops import rearrange
|
||||
|
||||
from dalle2_pytorch.train import EMA
|
||||
from dalle2_pytorch.vqgan_vae import VQGanVAE
|
||||
from dalle2_pytorch.optimizer import get_optimizer
|
||||
|
||||
# helpers
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
def noop(*args, **kwargs):
|
||||
pass
|
||||
|
||||
def cycle(dl):
|
||||
while True:
|
||||
for data in dl:
|
||||
yield data
|
||||
|
||||
def cast_tuple(t):
|
||||
return t if isinstance(t, (tuple, list)) else (t,)
|
||||
|
||||
def yes_or_no(question):
|
||||
answer = input(f'{question} (y/n) ')
|
||||
return answer.lower() in ('yes', 'y')
|
||||
|
||||
def accum_log(log, new_logs):
|
||||
for key, new_value in new_logs.items():
|
||||
old_value = log.get(key, 0.)
|
||||
log[key] = old_value + new_value
|
||||
return log
|
||||
|
||||
# classes
|
||||
|
||||
class ImageDataset(Dataset):
|
||||
def __init__(
|
||||
self,
|
||||
folder,
|
||||
image_size,
|
||||
exts = ['jpg', 'jpeg', 'png']
|
||||
):
|
||||
super().__init__()
|
||||
self.folder = folder
|
||||
self.image_size = image_size
|
||||
self.paths = [p for ext in exts for p in Path(f'{folder}').glob(f'**/*.{ext}')]
|
||||
|
||||
print(f'{len(self.paths)} training samples found at {folder}')
|
||||
|
||||
self.transform = T.Compose([
|
||||
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
|
||||
T.Resize(image_size),
|
||||
T.RandomHorizontalFlip(),
|
||||
T.CenterCrop(image_size),
|
||||
T.ToTensor()
|
||||
])
|
||||
|
||||
def __len__(self):
|
||||
return len(self.paths)
|
||||
|
||||
def __getitem__(self, index):
|
||||
path = self.paths[index]
|
||||
img = Image.open(path)
|
||||
return self.transform(img)
|
||||
|
||||
# main trainer class
|
||||
|
||||
class VQGanVAETrainer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
vae,
|
||||
*,
|
||||
num_train_steps,
|
||||
lr,
|
||||
batch_size,
|
||||
folder,
|
||||
grad_accum_every,
|
||||
wd = 0.,
|
||||
save_results_every = 100,
|
||||
save_model_every = 1000,
|
||||
results_folder = './results',
|
||||
valid_frac = 0.05,
|
||||
random_split_seed = 42,
|
||||
ema_beta = 0.995,
|
||||
ema_update_after_step = 2000,
|
||||
ema_update_every = 10,
|
||||
apply_grad_penalty_every = 4,
|
||||
):
|
||||
super().__init__()
|
||||
assert isinstance(vae, VQGanVAE), 'vae must be instance of VQGanVAE'
|
||||
image_size = vae.image_size
|
||||
|
||||
self.vae = vae
|
||||
self.ema_vae = EMA(vae, update_after_step = ema_update_after_step, update_every = ema_update_every)
|
||||
|
||||
self.register_buffer('steps', torch.Tensor([0]))
|
||||
|
||||
self.num_train_steps = num_train_steps
|
||||
self.batch_size = batch_size
|
||||
self.grad_accum_every = grad_accum_every
|
||||
|
||||
all_parameters = set(vae.parameters())
|
||||
discr_parameters = set(vae.discr.parameters())
|
||||
vae_parameters = all_parameters - discr_parameters
|
||||
|
||||
self.optim = get_optimizer(vae_parameters, lr = lr, wd = wd)
|
||||
self.discr_optim = get_optimizer(discr_parameters, lr = lr, wd = wd)
|
||||
|
||||
# create dataset
|
||||
|
||||
self.ds = ImageDataset(folder, image_size = image_size)
|
||||
|
||||
# split for validation
|
||||
|
||||
if valid_frac > 0:
|
||||
train_size = int((1 - valid_frac) * len(self.ds))
|
||||
valid_size = len(self.ds) - train_size
|
||||
self.ds, self.valid_ds = random_split(self.ds, [train_size, valid_size], generator = torch.Generator().manual_seed(random_split_seed))
|
||||
print(f'training with dataset of {len(self.ds)} samples and validating with randomly splitted {len(self.valid_ds)} samples')
|
||||
else:
|
||||
self.valid_ds = self.ds
|
||||
print(f'training with shared training and valid dataset of {len(self.ds)} samples')
|
||||
|
||||
# dataloader
|
||||
|
||||
self.dl = cycle(DataLoader(
|
||||
self.ds,
|
||||
batch_size = batch_size,
|
||||
shuffle = True
|
||||
))
|
||||
|
||||
self.valid_dl = cycle(DataLoader(
|
||||
self.valid_ds,
|
||||
batch_size = batch_size,
|
||||
shuffle = True
|
||||
))
|
||||
|
||||
self.save_model_every = save_model_every
|
||||
self.save_results_every = save_results_every
|
||||
|
||||
self.apply_grad_penalty_every = apply_grad_penalty_every
|
||||
|
||||
self.results_folder = Path(results_folder)
|
||||
|
||||
if len([*self.results_folder.glob('**/*')]) > 0 and yes_or_no('do you want to clear previous experiment checkpoints and results?'):
|
||||
rmtree(str(self.results_folder))
|
||||
|
||||
self.results_folder.mkdir(parents = True, exist_ok = True)
|
||||
|
||||
def train_step(self):
|
||||
device = next(self.vae.parameters()).device
|
||||
steps = int(self.steps.item())
|
||||
apply_grad_penalty = not (steps % self.apply_grad_penalty_every)
|
||||
|
||||
self.vae.train()
|
||||
|
||||
# logs
|
||||
|
||||
logs = {}
|
||||
|
||||
# update vae (generator)
|
||||
|
||||
for _ in range(self.grad_accum_every):
|
||||
img = next(self.dl)
|
||||
img = img.to(device)
|
||||
|
||||
loss = self.vae(
|
||||
img,
|
||||
return_loss = True,
|
||||
apply_grad_penalty = apply_grad_penalty
|
||||
)
|
||||
|
||||
accum_log(logs, {'loss': loss.item() / self.grad_accum_every})
|
||||
|
||||
(loss / self.grad_accum_every).backward()
|
||||
|
||||
self.optim.step()
|
||||
self.optim.zero_grad()
|
||||
|
||||
|
||||
# update discriminator
|
||||
|
||||
if exists(self.vae.discr):
|
||||
discr_loss = 0
|
||||
for _ in range(self.grad_accum_every):
|
||||
img = next(self.dl)
|
||||
img = img.to(device)
|
||||
|
||||
loss = self.vae(img, return_discr_loss = True)
|
||||
accum_log(logs, {'discr_loss': loss.item() / self.grad_accum_every})
|
||||
|
||||
(loss / self.grad_accum_every).backward()
|
||||
|
||||
self.discr_optim.step()
|
||||
self.discr_optim.zero_grad()
|
||||
|
||||
# log
|
||||
|
||||
print(f"{steps}: vae loss: {logs['loss']} - discr loss: {logs['discr_loss']}")
|
||||
|
||||
# update exponential moving averaged generator
|
||||
|
||||
self.ema_vae.update()
|
||||
|
||||
# sample results every so often
|
||||
|
||||
if not (steps % self.save_results_every):
|
||||
for model, filename in ((self.ema_vae.ema_model, f'{steps}.ema'), (self.vae, str(steps))):
|
||||
model.eval()
|
||||
|
||||
imgs = next(self.dl)
|
||||
imgs = imgs.to(device)
|
||||
|
||||
recons = model(imgs)
|
||||
nrows = int(sqrt(self.batch_size))
|
||||
|
||||
imgs_and_recons = torch.stack((imgs, recons), dim = 0)
|
||||
imgs_and_recons = rearrange(imgs_and_recons, 'r b ... -> (b r) ...')
|
||||
|
||||
imgs_and_recons = imgs_and_recons.detach().cpu().float().clamp(0., 1.)
|
||||
grid = make_grid(imgs_and_recons, nrow = 2, normalize = True, value_range = (0, 1))
|
||||
|
||||
logs['reconstructions'] = grid
|
||||
|
||||
save_image(grid, str(self.results_folder / f'{filename}.png'))
|
||||
|
||||
print(f'{steps}: saving to {str(self.results_folder)}')
|
||||
|
||||
# save model every so often
|
||||
|
||||
if not (steps % self.save_model_every):
|
||||
state_dict = self.vae.state_dict()
|
||||
model_path = str(self.results_folder / f'vae.{steps}.pt')
|
||||
torch.save(state_dict, model_path)
|
||||
|
||||
ema_state_dict = self.ema_vae.state_dict()
|
||||
model_path = str(self.results_folder / f'vae.{steps}.ema.pt')
|
||||
torch.save(ema_state_dict, model_path)
|
||||
|
||||
print(f'{steps}: saving model to {str(self.results_folder)}')
|
||||
|
||||
self.steps += 1
|
||||
return logs
|
||||
|
||||
def train(self, log_fn = noop):
|
||||
device = next(self.vae.parameters()).device
|
||||
|
||||
while self.steps < self.num_train_steps:
|
||||
logs = self.train_step()
|
||||
log_fn(logs)
|
||||
|
||||
print('training complete')
|
||||
@@ -15,6 +15,8 @@ from einops import rearrange, reduce, repeat
|
||||
from einops_exts import rearrange_many
|
||||
from einops.layers.torch import Rearrange
|
||||
|
||||
from dalle2_pytorch.attention import QueryAttnUpsample
|
||||
|
||||
# constants
|
||||
|
||||
MList = nn.ModuleList
|
||||
@@ -285,10 +287,6 @@ class ResnetEncDec(nn.Module):
|
||||
def get_encoded_fmap_size(self, image_size):
|
||||
return image_size // (2 ** self.layers)
|
||||
|
||||
@property
|
||||
def last_dec_layer(self):
|
||||
return self.decoders[-1].weight
|
||||
|
||||
def encode(self, x):
|
||||
for enc in self.encoders:
|
||||
x = enc(x)
|
||||
@@ -331,112 +329,6 @@ class ResBlock(nn.Module):
|
||||
def forward(self, x):
|
||||
return self.net(x) + x
|
||||
|
||||
# convnext enc dec
|
||||
|
||||
class ChanLayerNorm(nn.Module):
|
||||
def __init__(self, dim, eps = 1e-5):
|
||||
super().__init__()
|
||||
self.eps = eps
|
||||
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
|
||||
|
||||
def forward(self, x):
|
||||
var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
|
||||
mean = torch.mean(x, dim = 1, keepdim = True)
|
||||
return (x - mean) / (var + self.eps).sqrt() * self.g
|
||||
|
||||
class ConvNext(nn.Module):
|
||||
def __init__(self, dim, mult = 4, kernel_size = 3, ds_kernel_size = 7):
|
||||
super().__init__()
|
||||
inner_dim = int(dim * mult)
|
||||
self.net = nn.Sequential(
|
||||
nn.Conv2d(dim, dim, ds_kernel_size, padding = ds_kernel_size // 2, groups = dim),
|
||||
ChanLayerNorm(dim),
|
||||
nn.Conv2d(dim, inner_dim, kernel_size, padding = kernel_size // 2),
|
||||
nn.GELU(),
|
||||
nn.Conv2d(inner_dim, dim, kernel_size, padding = kernel_size // 2)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.net(x) + x
|
||||
|
||||
class ConvNextEncDec(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
*,
|
||||
channels = 3,
|
||||
layers = 4,
|
||||
layer_mults = None,
|
||||
num_blocks = 1,
|
||||
first_conv_kernel_size = 5,
|
||||
use_attn = True,
|
||||
attn_dim_head = 64,
|
||||
attn_heads = 8,
|
||||
attn_dropout = 0.,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.layers = layers
|
||||
|
||||
self.encoders = MList([])
|
||||
self.decoders = MList([])
|
||||
|
||||
layer_mults = default(layer_mults, list(map(lambda t: 2 ** t, range(layers))))
|
||||
assert len(layer_mults) == layers, 'layer multipliers must be equal to designated number of layers'
|
||||
|
||||
layer_dims = [dim * mult for mult in layer_mults]
|
||||
dims = (dim, *layer_dims)
|
||||
|
||||
self.encoded_dim = dims[-1]
|
||||
|
||||
dim_pairs = zip(dims[:-1], dims[1:])
|
||||
|
||||
append = lambda arr, t: arr.append(t)
|
||||
prepend = lambda arr, t: arr.insert(0, t)
|
||||
|
||||
if not isinstance(num_blocks, tuple):
|
||||
num_blocks = (*((0,) * (layers - 1)), num_blocks)
|
||||
|
||||
if not isinstance(use_attn, tuple):
|
||||
use_attn = (*((False,) * (layers - 1)), use_attn)
|
||||
|
||||
assert len(num_blocks) == layers, 'number of blocks config must be equal to number of layers'
|
||||
assert len(use_attn) == layers
|
||||
|
||||
for layer_index, (dim_in, dim_out), layer_num_blocks, layer_use_attn in zip(range(layers), dim_pairs, num_blocks, use_attn):
|
||||
append(self.encoders, nn.Sequential(nn.Conv2d(dim_in, dim_out, 4, stride = 2, padding = 1), leaky_relu()))
|
||||
prepend(self.decoders, nn.Sequential(nn.ConvTranspose2d(dim_out, dim_in, 4, 2, 1), leaky_relu()))
|
||||
|
||||
if layer_use_attn:
|
||||
prepend(self.decoders, VQGanAttention(dim = dim_out, heads = attn_heads, dim_head = attn_dim_head, dropout = attn_dropout))
|
||||
|
||||
for _ in range(layer_num_blocks):
|
||||
append(self.encoders, ConvNext(dim_out))
|
||||
prepend(self.decoders, ConvNext(dim_out))
|
||||
|
||||
if layer_use_attn:
|
||||
append(self.encoders, VQGanAttention(dim = dim_out, heads = attn_heads, dim_head = attn_dim_head, dropout = attn_dropout))
|
||||
|
||||
prepend(self.encoders, nn.Conv2d(channels, dim, first_conv_kernel_size, padding = first_conv_kernel_size // 2))
|
||||
append(self.decoders, nn.Conv2d(dim, channels, 1))
|
||||
|
||||
def get_encoded_fmap_size(self, image_size):
|
||||
return image_size // (2 ** self.layers)
|
||||
|
||||
@property
|
||||
def last_dec_layer(self):
|
||||
return self.decoders[-1].weight
|
||||
|
||||
def encode(self, x):
|
||||
for enc in self.encoders:
|
||||
x = enc(x)
|
||||
return x
|
||||
|
||||
def decode(self, x):
|
||||
for dec in self.decoders:
|
||||
x = dec(x)
|
||||
return x
|
||||
|
||||
# vqgan attention layer
|
||||
|
||||
class VQGanAttention(nn.Module):
|
||||
@@ -603,10 +495,12 @@ class ViTEncDec(nn.Module):
|
||||
layers = layers
|
||||
),
|
||||
nn.Sequential(
|
||||
nn.Linear(dim, dim * 4, bias = False),
|
||||
nn.Linear(dim, dim * 2, bias = False),
|
||||
nn.Tanh(),
|
||||
nn.Linear(dim * 4, input_dim, bias = False),
|
||||
nn.Linear(dim * 2, dim, bias = False),
|
||||
),
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, input_dim),
|
||||
RearrangeImage(),
|
||||
Rearrange('b h w (p1 p2 c) -> b c (h p1) (w p2)', p1 = patch_size, p2 = patch_size)
|
||||
)
|
||||
@@ -614,10 +508,6 @@ class ViTEncDec(nn.Module):
|
||||
def get_encoded_fmap_size(self, image_size):
|
||||
return image_size // self.patch_size
|
||||
|
||||
@property
|
||||
def last_dec_layer(self):
|
||||
return self.decoder[-3][-1].weight
|
||||
|
||||
def encode(self, x):
|
||||
return self.encoder(x)
|
||||
|
||||
@@ -659,7 +549,6 @@ class VQGanVAE(nn.Module):
|
||||
l2_recon_loss = False,
|
||||
use_hinge_loss = True,
|
||||
vgg = None,
|
||||
vq_codebook_dim = 256,
|
||||
vq_codebook_size = 512,
|
||||
vq_decay = 0.8,
|
||||
vq_commitment_weight = 1.,
|
||||
@@ -682,8 +571,6 @@ class VQGanVAE(nn.Module):
|
||||
enc_dec_klass = ResnetEncDec
|
||||
elif vae_type == 'vit':
|
||||
enc_dec_klass = ViTEncDec
|
||||
elif vae_type == 'convnext':
|
||||
enc_dec_klass = ConvNextEncDec
|
||||
else:
|
||||
raise ValueError(f'{vae_type} not valid')
|
||||
|
||||
@@ -696,7 +583,6 @@ class VQGanVAE(nn.Module):
|
||||
|
||||
self.vq = VQ(
|
||||
dim = self.enc_dec.encoded_dim,
|
||||
codebook_dim = vq_codebook_dim,
|
||||
codebook_size = vq_codebook_size,
|
||||
decay = vq_decay,
|
||||
commitment_weight = vq_commitment_weight,
|
||||
@@ -855,7 +741,7 @@ class VQGanVAE(nn.Module):
|
||||
|
||||
# calculate adaptive weight
|
||||
|
||||
last_dec_layer = self.enc_dec.last_dec_layer
|
||||
last_dec_layer = self.decoders[-1].weight
|
||||
|
||||
norm_grad_wrt_gen_loss = grad_layer_wrt_loss(gen_loss, last_dec_layer).norm(p = 2)
|
||||
norm_grad_wrt_perceptual_loss = grad_layer_wrt_loss(perceptual_loss, last_dec_layer).norm(p = 2)
|
||||
|
||||
8
setup.py
8
setup.py
@@ -10,7 +10,7 @@ setup(
|
||||
'dream = dalle2_pytorch.cli:dream'
|
||||
],
|
||||
},
|
||||
version = '0.0.100',
|
||||
version = '0.0.53',
|
||||
license='MIT',
|
||||
description = 'DALL-E 2',
|
||||
author = 'Phil Wang',
|
||||
@@ -23,19 +23,15 @@ setup(
|
||||
],
|
||||
install_requires=[
|
||||
'click',
|
||||
'clip-anytorch',
|
||||
'einops>=0.4',
|
||||
'einops-exts>=0.0.3',
|
||||
'embedding-reader',
|
||||
'kornia>=0.5.4',
|
||||
'pillow',
|
||||
'resize-right>=0.0.2',
|
||||
'torch>=1.10',
|
||||
'torchvision',
|
||||
'tqdm',
|
||||
'vector-quantize-pytorch',
|
||||
'webdataset',
|
||||
'x-clip>=0.5.1',
|
||||
'x-clip>=0.4.4',
|
||||
'youtokentome'
|
||||
],
|
||||
classifiers=[
|
||||
|
||||
@@ -1,291 +0,0 @@
|
||||
import os
|
||||
import math
|
||||
import argparse
|
||||
import numpy as np
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from embedding_reader import EmbeddingReader
|
||||
from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork
|
||||
from dalle2_pytorch.optimizer import get_optimizer
|
||||
from torch.cuda.amp import autocast,GradScaler
|
||||
|
||||
import time
|
||||
from tqdm import tqdm
|
||||
|
||||
import wandb
|
||||
os.environ["WANDB_SILENT"] = "true"
|
||||
NUM_TEST_EMBEDDINGS = 100 # for cosine similarity reporting during training
|
||||
REPORT_METRICS_EVERY = 100 # for cosine similarity and other metric reporting during training
|
||||
|
||||
|
||||
def eval_model(model,device,image_reader,text_reader,start,end,batch_size,loss_type,phase="Validation"):
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
total_loss = 0.
|
||||
total_samples = 0.
|
||||
|
||||
for emb_images, emb_text in zip(image_reader(batch_size=batch_size, start=start, end=end),
|
||||
text_reader(batch_size=batch_size, start=start, end=end)):
|
||||
|
||||
emb_images_tensor = torch.tensor(emb_images[0]).to(device)
|
||||
emb_text_tensor = torch.tensor(emb_text[0]).to(device)
|
||||
|
||||
batches = emb_images_tensor.shape[0]
|
||||
|
||||
loss = model(text_embed = emb_text_tensor, image_embed = emb_images_tensor)
|
||||
|
||||
total_loss += loss.item() * batches
|
||||
total_samples += batches
|
||||
|
||||
avg_loss = (total_loss / total_samples)
|
||||
wandb.log({f'{phase} {loss_type}': avg_loss})
|
||||
|
||||
def save_model(save_path, state_dict):
|
||||
# Saving State Dict
|
||||
print("====================================== Saving checkpoint ======================================")
|
||||
torch.save(state_dict, save_path+'/'+str(time.time())+'_saved_model.pth')
|
||||
|
||||
def report_cosine_sims(diffusion_prior,image_reader,text_reader,train_set_size,val_set_size,NUM_TEST_EMBEDDINGS,device):
|
||||
cos = nn.CosineSimilarity(dim=1, eps=1e-6)
|
||||
|
||||
tstart = train_set_size+val_set_size
|
||||
tend = train_set_size+val_set_size+NUM_TEST_EMBEDDINGS
|
||||
|
||||
for embt, embi in zip(text_reader(batch_size = NUM_TEST_EMBEDDINGS, start=tstart, end = tend),image_reader(batch_size = NUM_TEST_EMBEDDINGS, start=tstart, end = tend)):
|
||||
text_embed = torch.tensor(embt[0]).to(device)
|
||||
text_embed = text_embed / text_embed.norm(dim=1, keepdim=True)
|
||||
test_text_cond = dict(text_embed = text_embed)
|
||||
|
||||
test_image_embeddings = torch.tensor(embi[0]).to(device)
|
||||
test_image_embeddings = test_image_embeddings / test_image_embeddings.norm(dim=1, keepdim=True)
|
||||
|
||||
predicted_image_embeddings = diffusion_prior.p_sample_loop((NUM_TEST_EMBEDDINGS, 768), text_cond = test_text_cond)
|
||||
predicted_image_embeddings = predicted_image_embeddings / predicted_image_embeddings.norm(dim=1, keepdim=True)
|
||||
|
||||
original_similarity = cos(text_embed,test_image_embeddings).cpu().numpy()
|
||||
predicted_similarity = cos(text_embed,predicted_image_embeddings).cpu().numpy()
|
||||
|
||||
wandb.log({"CosineSimilarity(text_embed,image_embed)": np.mean(original_similarity)})
|
||||
wandb.log({"CosineSimilarity(text_embed,predicted_image_embed)":np.mean(predicted_similarity)})
|
||||
|
||||
return np.mean(predicted_similarity - original_similarity)
|
||||
|
||||
|
||||
|
||||
def train(image_embed_dim,
|
||||
image_embed_url,
|
||||
text_embed_url,
|
||||
batch_size,
|
||||
train_percent,
|
||||
val_percent,
|
||||
test_percent,
|
||||
num_epochs,
|
||||
dp_loss_type,
|
||||
clip,
|
||||
dp_condition_on_text_encodings,
|
||||
dp_timesteps,
|
||||
dp_l2norm_output,
|
||||
dp_normformer,
|
||||
dp_cond_drop_prob,
|
||||
dpn_depth,
|
||||
dpn_dim_head,
|
||||
dpn_heads,
|
||||
save_interval,
|
||||
save_path,
|
||||
device,
|
||||
learning_rate=0.001,
|
||||
max_grad_norm=0.5,
|
||||
weight_decay=0.01,
|
||||
amp=False):
|
||||
|
||||
# DiffusionPriorNetwork
|
||||
prior_network = DiffusionPriorNetwork(
|
||||
dim = image_embed_dim,
|
||||
depth = dpn_depth,
|
||||
dim_head = dpn_dim_head,
|
||||
heads = dpn_heads,
|
||||
normformer = dp_normformer,
|
||||
l2norm_output = dp_l2norm_output).to(device)
|
||||
|
||||
# DiffusionPrior with text embeddings and image embeddings pre-computed
|
||||
diffusion_prior = DiffusionPrior(
|
||||
net = prior_network,
|
||||
clip = clip,
|
||||
image_embed_dim = image_embed_dim,
|
||||
timesteps = dp_timesteps,
|
||||
cond_drop_prob = dp_cond_drop_prob,
|
||||
loss_type = dp_loss_type,
|
||||
condition_on_text_encodings = dp_condition_on_text_encodings).to(device)
|
||||
|
||||
# Get image and text embeddings from the servers
|
||||
print("==============Downloading embeddings - image and text====================")
|
||||
image_reader = EmbeddingReader(embeddings_folder=image_embed_url, file_format="npy")
|
||||
text_reader = EmbeddingReader(embeddings_folder=text_embed_url, file_format="npy")
|
||||
num_data_points = text_reader.count
|
||||
|
||||
# Create save_path if it doesn't exist
|
||||
if not os.path.exists(save_path):
|
||||
os.makedirs(save_path)
|
||||
|
||||
### Training code ###
|
||||
scaler = GradScaler(enabled=amp)
|
||||
optimizer = get_optimizer(diffusion_prior.net.parameters(), wd=weight_decay, lr=learning_rate)
|
||||
epochs = num_epochs
|
||||
|
||||
step = 0
|
||||
t = time.time()
|
||||
|
||||
train_set_size = int(train_percent*num_data_points)
|
||||
val_set_size = int(val_percent*num_data_points)
|
||||
|
||||
for _ in range(epochs):
|
||||
diffusion_prior.train()
|
||||
|
||||
for emb_images,emb_text in zip(image_reader(batch_size=batch_size, start=0, end=train_set_size),
|
||||
text_reader(batch_size=batch_size, start=0, end=train_set_size)):
|
||||
emb_images_tensor = torch.tensor(emb_images[0]).to(device)
|
||||
emb_text_tensor = torch.tensor(emb_text[0]).to(device)
|
||||
|
||||
with autocast(enabled=amp):
|
||||
loss = diffusion_prior(text_embed = emb_text_tensor,image_embed = emb_images_tensor)
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Samples per second
|
||||
step+=1
|
||||
samples_per_sec = batch_size*step/(time.time()-t)
|
||||
# Save checkpoint every save_interval minutes
|
||||
if(int(time.time()-t) >= 60*save_interval):
|
||||
t = time.time()
|
||||
|
||||
save_model(
|
||||
save_path,
|
||||
dict(model=diffusion_prior.state_dict(), optimizer=optimizer.state_dict(), scaler=scaler.state_dict()))
|
||||
|
||||
# Log to wandb
|
||||
wandb.log({"Training loss": loss.item(),
|
||||
"Steps": step,
|
||||
"Samples per second": samples_per_sec})
|
||||
# Log cosineSim(text_embed,predicted_image_embed) - cosineSim(text_embed,image_embed)
|
||||
# Use NUM_TEST_EMBEDDINGS samples from the test set each time
|
||||
# Get embeddings from the most recently saved model
|
||||
if(step % REPORT_METRICS_EVERY) == 0:
|
||||
diff_cosine_sim = report_cosine_sims(diffusion_prior,
|
||||
image_reader,
|
||||
text_reader,
|
||||
train_set_size,
|
||||
val_set_size,
|
||||
NUM_TEST_EMBEDDINGS,
|
||||
device)
|
||||
wandb.log({"Cosine similarity difference": diff_cosine_sim})
|
||||
|
||||
scaler.unscale_(optimizer)
|
||||
nn.utils.clip_grad_norm_(diffusion_prior.parameters(), max_grad_norm)
|
||||
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
|
||||
### Evaluate model(validation run) ###
|
||||
start = train_set_size
|
||||
end=start+val_set_size
|
||||
eval_model(diffusion_prior,device,image_reader,text_reader,start,end,batch_size,dp_loss_type,phase="Validation")
|
||||
|
||||
### Test run ###
|
||||
test_set_size = int(test_percent*train_set_size)
|
||||
start=train_set_size+val_set_size
|
||||
end=num_data_points
|
||||
eval_model(diffusion_prior,device,image_reader,text_reader,start,end,batch_size,dp_loss_type,phase="Test")
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
# Logging
|
||||
parser.add_argument("--wandb-entity", type=str, default="laion")
|
||||
parser.add_argument("--wandb-project", type=str, default="diffusion-prior")
|
||||
parser.add_argument("--wandb-name", type=str, default="laion-dprior")
|
||||
parser.add_argument("--wandb-dataset", type=str, default="LAION-5B")
|
||||
parser.add_argument("--wandb-arch", type=str, default="DiffusionPrior")
|
||||
# URLs for embeddings
|
||||
parser.add_argument("--image-embed-url", type=str, default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/")
|
||||
parser.add_argument("--text-embed-url", type=str, default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/")
|
||||
# Hyperparameters
|
||||
parser.add_argument("--learning-rate", type=float, default=1.1e-4)
|
||||
parser.add_argument("--weight-decay", type=float, default=6.02e-2)
|
||||
parser.add_argument("--max-grad-norm", type=float, default=0.5)
|
||||
parser.add_argument("--batch-size", type=int, default=10**4)
|
||||
parser.add_argument("--num-epochs", type=int, default=5)
|
||||
# Image embed dimension
|
||||
parser.add_argument("--image-embed-dim", type=int, default=768)
|
||||
# Train-test split
|
||||
parser.add_argument("--train-percent", type=float, default=0.7)
|
||||
parser.add_argument("--val-percent", type=float, default=0.2)
|
||||
parser.add_argument("--test-percent", type=float, default=0.1)
|
||||
# LAION training(pre-computed embeddings)
|
||||
# DiffusionPriorNetwork(dpn) parameters
|
||||
parser.add_argument("--dpn-depth", type=int, default=6)
|
||||
parser.add_argument("--dpn-dim-head", type=int, default=64)
|
||||
parser.add_argument("--dpn-heads", type=int, default=8)
|
||||
# DiffusionPrior(dp) parameters
|
||||
parser.add_argument("--dp-condition-on-text-encodings", type=bool, default=False)
|
||||
parser.add_argument("--dp-timesteps", type=int, default=100)
|
||||
parser.add_argument("--dp-l2norm-output", type=bool, default=False)
|
||||
parser.add_argument("--dp-normformer", type=bool, default=False)
|
||||
parser.add_argument("--dp-cond-drop-prob", type=float, default=0.1)
|
||||
parser.add_argument("--dp-loss-type", type=str, default="l2")
|
||||
parser.add_argument("--clip", type=str, default=None)
|
||||
parser.add_argument("--amp", type=bool, default=False)
|
||||
# Model checkpointing interval(minutes)
|
||||
parser.add_argument("--save-interval", type=int, default=30)
|
||||
parser.add_argument("--save-path", type=str, default="./diffusion_prior_checkpoints")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
print("Setting up wandb logging... Please wait...")
|
||||
|
||||
wandb.init(
|
||||
entity=args.wandb_entity,
|
||||
project=args.wandb_project,
|
||||
config={
|
||||
"learning_rate": args.learning_rate,
|
||||
"architecture": args.wandb_arch,
|
||||
"dataset": args.wandb_dataset,
|
||||
"epochs": args.num_epochs,
|
||||
})
|
||||
|
||||
print("wandb logging setup done!")
|
||||
# Obtain the utilized device.
|
||||
|
||||
has_cuda = torch.cuda.is_available()
|
||||
if has_cuda:
|
||||
device = torch.device("cuda:0")
|
||||
torch.cuda.set_device(device)
|
||||
|
||||
# Training loop
|
||||
train(args.image_embed_dim,
|
||||
args.image_embed_url,
|
||||
args.text_embed_url,
|
||||
args.batch_size,
|
||||
args.train_percent,
|
||||
args.val_percent,
|
||||
args.test_percent,
|
||||
args.num_epochs,
|
||||
args.dp_loss_type,
|
||||
args.clip,
|
||||
args.dp_condition_on_text_encodings,
|
||||
args.dp_timesteps,
|
||||
args.dp_l2norm_output,
|
||||
args.dp_normformer,
|
||||
args.dp_cond_drop_prob,
|
||||
args.dpn_depth,
|
||||
args.dpn_dim_head,
|
||||
args.dpn_heads,
|
||||
args.save_interval,
|
||||
args.save_path,
|
||||
device,
|
||||
args.learning_rate,
|
||||
args.max_grad_norm,
|
||||
args.weight_decay,
|
||||
args.amp)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user