aljaz/DALLE2-pytorch
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cleanup

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Phil Wang
2022-06-20 11:14:50 -07:00
parent 893f270012
commit 56883910fb
2 changed files with 6 additions and 77 deletions
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27
README.md
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@@ -1017,33 +1017,6 @@ The most significant parameters for the script are as follows:
- `clip`, default = `None` # Signals the prior to use pre-computed embeddings
#### Loading and Saving the DiffusionPrior model
Two methods are provided, load_diffusion_model and save_diffusion_model, the names being self-explanatory.
```python
from dalle2_pytorch.train import load_diffusion_model, save_diffusion_model
```
##### Loading
load_diffusion_model(dprior_path, device)
dprior_path : path to saved model(.pth)
device : the cuda device you're running on
##### Saving
save_diffusion_model(save_path, model, optimizer, scaler, config, image_embed_dim)
save_path : path to save at
model : object of Diffusion_Prior
optimizer : optimizer object - see train_diffusion_prior.py for how to create one.
e.g: optimizer = get_optimizer(diffusion_prior.net.parameters(), wd=weight_decay, lr=learning_rate)
scaler : a GradScaler object.
e.g: scaler = GradScaler(enabled=amp)
config : config object created in train_diffusion_prior.py - see file for example.
image_embed_dim - the dimension of the image_embedding
e.g: 768
## CLI (wip)
```bash

56
dalle2_pytorch/trainer.py
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@@ -145,44 +145,6 @@ def split_args_and_kwargs(*args, split_size = None, **kwargs):
chunk_size_frac = chunk_size / batch_size
yield chunk_size_frac, (chunked_args, chunked_kwargs)
# saving and loading functions
# for diffusion prior
def load_diffusion_model(dprior_path, device):
dprior_path = Path(dprior_path)
assert dprior_path.exists(), 'Dprior model file does not exist'
loaded_obj = torch.load(str(dprior_path), map_location='cpu')
# Get hyperparameters of loaded model
dpn_config = loaded_obj['hparams']['diffusion_prior_network']
dp_config = loaded_obj['hparams']['diffusion_prior']
image_embed_dim = loaded_obj['image_embed_dim']['image_embed_dim']
# Create DiffusionPriorNetwork and DiffusionPrior with loaded hyperparameters
# DiffusionPriorNetwork
prior_network = DiffusionPriorNetwork( dim = image_embed_dim, **dpn_config).to(device)
# DiffusionPrior with text embeddings and image embeddings pre-computed
diffusion_prior = DiffusionPrior(net = prior_network, **dp_config, image_embed_dim = image_embed_dim).to(device)
# Load state dict from saved model
diffusion_prior.load_state_dict(loaded_obj['model'])
return diffusion_prior, loaded_obj
def save_diffusion_model(save_path, model, optimizer, scaler, config, image_embed_dim):
# Saving State Dict
print_ribbon('Saving checkpoint')
state_dict = dict(model=model.state_dict(),
optimizer=optimizer.state_dict(),
scaler=scaler.state_dict(),
hparams = config,
image_embed_dim = {"image_embed_dim":image_embed_dim})
torch.save(state_dict, save_path+'/'+str(time.time())+'_saved_model.pth')
# exponential moving average wrapper
class EMA(nn.Module):
@@ -505,26 +467,20 @@ class DiffusionPriorTrainer(nn.Module):
@cast_torch_tensor
@prior_sample_in_chunks
def p_sample_loop(self, *args, **kwargs):
if self.use_ema:
return self.ema_diffusion_prior.ema_model.p_sample_loop(*args, **kwargs)
else:
return self.diffusion_prior.p_sample_loop(*args, **kwargs)
model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
return model.p_sample_loop(*args, **kwargs)
@torch.no_grad()
@cast_torch_tensor
@prior_sample_in_chunks
def sample(self, *args, **kwargs):
if self.use_ema:
return self.ema_diffusion_prior.ema_model.sample(*args, **kwargs)
else:
return self.diffusion_prior.sample(*args, **kwargs)
model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
return model.sample(*args, **kwargs)
@torch.no_grad()
def sample_batch_size(self, *args, **kwargs):
if self.use_ema:
return self.ema_diffusion_prior.ema_model.sample_batch_size(*args, **kwargs)
else:
return self.diffusion_prior.sample_batch_size(*args, **kwargs)
model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
return model.sample_batch_size(*args, **kwargs)
@torch.no_grad()
@cast_torch_tensor