remove slip

disco.py

@@ -246,6 +246,8 @@ if skip_for_run_all == False:
 
       Remove Super Resolution
 
+      Remove SLIP Models
+
       '''
   )
 
@@ -439,7 +441,6 @@ model_secondary_downloaded = False
 
 if is_colab:
   gitclone("https://github.com/openai/CLIP")
-  #gitclone("https://github.com/facebookresearch/SLIP.git")
   gitclone("https://github.com/crowsonkb/guided-diffusion")
   gitclone("https://github.com/assafshocher/ResizeRight.git")
   gitclone("https://github.com/MSFTserver/pytorch3d-lite.git")
@@ -468,7 +469,6 @@ if not os.path.exists(f'{model_path}/dpt_large-midas-2f21e586.pt'):
 import sys
 import torch
 
-# sys.path.append('./SLIP')
 sys.path.append('./pytorch3d-lite')
 sys.path.append('./ResizeRight')
 sys.path.append('./MiDaS')
@@ -496,7 +496,6 @@ sys.path.append('./CLIP')
 sys.path.append('./guided-diffusion')
 import clip
 from resize_right import resize
-# from models import SLIP_VITB16, SLIP, SLIP_VITL16
 from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
 from datetime import datetime
 import numpy as np
@@ -1636,8 +1635,6 @@ RN50 = True #@param{type:"boolean"}
 RN50x4 = False #@param{type:"boolean"}
 RN50x16 = False #@param{type:"boolean"}
 RN50x64 = False #@param{type:"boolean"}
-SLIPB16 = False #@param{type:"boolean"}
-SLIPL16 = False #@param{type:"boolean"}
 
 #@markdown If you're having issues with model downloads, check this to compare SHA's:
 check_model_SHA = False #@param{type:"boolean"}
@@ -1771,20 +1768,6 @@ if RN50x16 is True: clip_models.append(clip.load('RN50x16', jit=False)[0].eval()
 if RN50x64 is True: clip_models.append(clip.load('RN50x64', jit=False)[0].eval().requires_grad_(False).to(device)) 
 if RN101 is True: clip_models.append(clip.load('RN101', jit=False)[0].eval().requires_grad_(False).to(device)) 
 
-if SLIPB16:
-  SLIPB16model = SLIP_VITB16(ssl_mlp_dim=4096, ssl_emb_dim=256)
-  if not os.path.exists(f'{model_path}/slip_base_100ep.pt'):
-    wget("https://dl.fbaipublicfiles.com/slip/slip_base_100ep.pt", model_path)
-  sd = torch.load(f'{model_path}/slip_base_100ep.pt')
-  real_sd = {}
-  for k, v in sd['state_dict'].items():
-    real_sd['.'.join(k.split('.')[1:])] = v
-  del sd
-  SLIPB16model.load_state_dict(real_sd)
-  SLIPB16model.requires_grad_(False).eval().to(device)
-
-  clip_models.append(SLIPL16model)
-
 normalize = T.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
 lpips_model = lpips.LPIPS(net='vgg').to(device)