Update app.py

app.py

@@ -58,23 +58,19 @@ pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype).to("cuda
 #pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.float16).to("cuda")
 torch.cuda.empty_cache()
 
-clipmodel = 'norm'
-if clipmodel == "long":
-    model_id = "zer0int/LongCLIP-GmP-ViT-L-14"
-    config = CLIPConfig.from_pretrained(model_id)
-    maxtokens = 77
-if clipmodel == "norm":
-    model_id = "zer0int/CLIP-GmP-ViT-L-14"
-    config = CLIPConfig.from_pretrained(model_id)
-    maxtokens = 77
-clip_model = CLIPModel.from_pretrained(model_id, torch_dtype=torch.bfloat16, config=config, ignore_mismatched_sizes=True).to("cuda")
-clip_processor = CLIPProcessor.from_pretrained(model_id, padding="max_length", max_length=maxtokens, ignore_mismatched_sizes=True, return_tensors="pt", truncation=True)
-
+model_id = ("zer0int/LongCLIP-GmP-ViT-L-14")
+config = CLIPConfig.from_pretrained(model_id)
+config.text_config.max_position_embeddings = 248
+clip_model = CLIPModel.from_pretrained(model_id, torch_dtype=torch.bfloat16, config=config, ignore_mismatched_sizes=True)
+clip_processor = CLIPProcessor.from_pretrained(model_id, padding="max_length", max_length=248)
 pipe.tokenizer = clip_processor.tokenizer
 pipe.text_encoder = clip_model.text_model
-pipe.tokenizer_max_length = maxtokens
+pipe.tokenizer_max_length = 248
 pipe.text_encoder.dtype = torch.bfloat16
-pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to("cuda")
+#pipe.text_encoder_2 = t5.text_model
+
+pipe.vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
+
 
 # Load LoRAs from JSON file
 with open('loras.json', 'r') as f: