add hw6 markups

app.py

@@ -8,7 +8,7 @@ import torch
 # import spaces #[uncomment to use ZeroGPU]
 from diffusers import (ControlNetModel, StableDiffusionControlNetPipeline,
                        StableDiffusionPipeline)
-from peft import LoraConfig, PeftModel
+from peft import PeftModel
 from PIL import Image
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -53,40 +53,6 @@ MODEL_NAME = "CompVis/stable-diffusion-v1-4"
 CKPT_DIR = "sd-14-lora-1000"
 
 
-def get_lora_sd_pipeline(
-    ckpt_dir=CKPT_DIR,
-    base_model_name_or_path=None,
-    dtype=torch.float16,
-    device="cuda",
-    adapter_name="default",
-):
-    unet_sub_dir = os.path.join(ckpt_dir, "unet")
-    text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
-    if os.path.exists(text_encoder_sub_dir) and base_model_name_or_path is None:
-        config = LoraConfig.from_pretrained(text_encoder_sub_dir)
-        base_model_name_or_path = config.base_model_name_or_path
-
-    if base_model_name_or_path is None:
-        raise ValueError("Please specify the base model name or path")
-
-    pipe = StableDiffusionPipeline.from_pretrained(
-        base_model_name_or_path, torch_dtype=dtype
-    ).to(device)
-    pipe.unet = PeftModel.from_pretrained(
-        pipe.unet, unet_sub_dir, adapter_name=adapter_name
-    )
-
-    if os.path.exists(text_encoder_sub_dir):
-        pipe.text_encoder = PeftModel.from_pretrained(
-            pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name
-        )
-
-    if dtype in (torch.float16, torch.bfloat16):
-        pipe.unet.half()
-        pipe.text_encoder.half()
-
-    return pipe
-
 
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(