Update modeling_latent_diffusion.py

modeling_latent_diffusion.py

@@ -1,16 +1,12 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-
-import numpy as np
 import tqdm
 import torch
-import torch.nn as nn
 
 from diffusers import DiffusionPipeline
 
-from .modeling_vae import AutoencoderKL
-from .configuration_ldmbert import LDMBertConfig
-from .modeling_ldmbert import LDMBertModel
+# add these relative imports here, so we can load from hub
+from .modeling_vae import AutoencoderKL # NOQA
+from .configuration_ldmbert import LDMBertConfig # NOQA
+from .modeling_ldmbert import LDMBertModel # NOQA
 
 class LatentDiffusion(DiffusionPipeline):
     def __init__(self, vqvae, bert, tokenizer, unet, noise_scheduler):
@@ -95,4 +91,4 @@ class LatentDiffusion(DiffusionPipeline):
         image = self.vqvae.decode(image)
         image = torch.clamp((image+1.0)/2.0, min=0.0, max=1.0)
 
-        return image
+        return image