Update README.md

README.md

@@ -14,30 +14,10 @@ library_name: diffusers
 ---
 
 ```py
+from diffusers import AutoPipelineForText2Image
 import torch
-from diffusers import FluxPipeline
 
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16) # can replace schnell with dev
-pipe.load_lora_weights(janannfndnd/SAB/lora.safetensors)
-pipe.fuse_lora(lora_scale=1.5)
-pipe.load_lora_weights(VideoAditor/Flux-Lora-Realism/flux_realism_lora.safetensors)
-pipe.fuse_lora(lora_scale=1)
-
-# to run on low vram GPUs (i.e. between 4 and 32 GB VRAM)
-pipe.enable_sequential_cpu_offload()
-pipe.vae.enable_slicing()
-pipe.vae.enable_tiling()
-
-pipe.to(torch.float16) # casting here instead of in the pipeline constructor because doing so in the constructor loads all models into CPU memory at once
-
-prompt = ""
-out = pipe(
-    prompt=prompt,
-    guidance_scale=4.5,
-    height=512,
-    width=512,
-    num_inference_steps=10,
-    max_sequence_length=256,
-).images[0]
-out.save("image.png")
+pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
+pipeline.load_lora_weights('janannfndnd/SABA', weight_name='lora.safetensors')
+image = pipeline("your prompt").images[
 ```