add fp32

app.py

@@ -106,13 +106,14 @@ def inference(text, init_image, skip_timesteps, clip_guidance_scale, tv_scale, r
     print('Using device:', device)
     model, diffusion = create_model_and_diffusion(**model_config)
     model.load_state_dict(torch.load('256x256_openai_comics_faces_by_alex_spirin_084000.pt', map_location='cpu'))
-    model.requires_grad_(False).eval().to(device)
+    model.requires_grad_(False).eval().to(device).float()
     for name, param in model.named_parameters():
         if 'qkv' in name or 'norm' in name or 'proj' in name:
             param.requires_grad_()
     if model_config['use_fp16']:
         model.convert_to_fp16()
-    clip_model = clip.load('ViT-B/16', jit=False)[0].eval().requires_grad_(False).to(device)
+    else: model.convert_to_fp32()
+    clip_model = clip.load('ViT-B/16', jit=False)[0].eval().requires_grad_(False).to(device).float()
     clip_size = clip_model.visual.input_resolution
     normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
                                      std=[0.26862954, 0.26130258, 0.27577711])