Update app.py

app.py

@@ -5,7 +5,7 @@ import logging
 import torch
 from PIL import Image
 import spaces
-from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForText2Image
 import copy
 import random
 import time
@@ -32,7 +32,7 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 base_model = "John6666/hyper-flux1-dev-fp8-flux"
 taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
 good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
-pipe = StableDiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=good_vae).to(device)
+pipe = AutoPipelineForText2Image.from_pretrained(base_model, torch_dtype=dtype, vae=good_vae).to(device)
 
 model_id = ("zer0int/LongCLIP-GmP-ViT-L-14")
 config = CLIPConfig.from_pretrained(model_id)
@@ -44,9 +44,6 @@ pipe.text_encoder = clip_model.text_model
 pipe.tokenizer_max_length = 248
 pipe.text_encoder.dtype = torch.bfloat16
 
-pipe.transformer.enable_layerwise_casting(
-            storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16
-        )
 MAX_SEED = 2**32-1
 
 class calculateDuration: