update

app.py

@@ -65,7 +65,7 @@ m1_model = AutoModelForCausalLM.from_pretrained(
 #### import diffusion models
 text_encoder = CLIPTextModel.from_pretrained(
     'JingyeChen22/textdiffuser2-full-ft', subfolder="text_encoder", ignore_mismatched_sizes=True
-).half().cuda()
+).cuda()
 tokenizer = CLIPTokenizer.from_pretrained(
     'runwayml/stable-diffusion-v1-5', subfolder="tokenizer"
 )
@@ -83,10 +83,10 @@ for c in alphabet:
 print(len(tokenizer))
 print('***************')
 
-vae = AutoencoderKL.from_pretrained('runwayml/stable-diffusion-v1-5', subfolder="vae").half().cuda()
+vae = AutoencoderKL.from_pretrained('runwayml/stable-diffusion-v1-5', subfolder="vae").cuda()
 unet = UNet2DConditionModel.from_pretrained(
     'JingyeChen22/textdiffuser2-full-ft', subfolder="unet"
-).half().cuda()
+).cuda()
 text_encoder.resize_token_embeddings(len(tokenizer))
 
 
@@ -340,10 +340,10 @@ def text_to_image(prompt,keywords,radio,slider_step,slider_guidance,slider_batch
             scheduler = DDPMScheduler.from_pretrained('runwayml/stable-diffusion-v1-5', subfolder="scheduler") 
             scheduler.set_timesteps(slider_step) 
             noise = torch.randn((slider_batch, 4, 64, 64)).to("cuda") 
-            input = noise.half()
+            input = noise
 
-            encoder_hidden_states_cond = text_encoder(prompts_cond)[0].half()
-            encoder_hidden_states_nocond = text_encoder(prompts_nocond)[0].half()
+            encoder_hidden_states_cond = text_encoder(prompts_cond)[0]
+            encoder_hidden_states_nocond = text_encoder(prompts_nocond)[0]
 
 
             for t in tqdm(scheduler.timesteps):