Update app.py

app.py

@@ -3,6 +3,7 @@ import gradio as gr
 from diffusers import AnimateDiffSparseControlNetPipeline, AutoencoderKL, MotionAdapter, SparseControlNetModel, AnimateDiffPipeline, EulerAncestralDiscreteScheduler
 from diffusers.schedulers import DPMSolverMultistepScheduler
 from diffusers.utils import export_to_gif, load_image
+from peft import LoraConfig, get_peft_model
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -10,6 +11,7 @@ def generate_video(prompt, negative_prompt, num_inference_steps, conditioning_fr
     motion_adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3", torch_dtype=torch.float16).to(device)
     controlnet = SparseControlNetModel.from_pretrained("guoyww/animatediff-sparsectrl-scribble", torch_dtype=torch.float16).to(device)
     vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16).to(device)
+    
     pipe = AnimateDiffSparseControlNetPipeline.from_pretrained(
         "SG161222/Realistic_Vision_V5.1_noVAE",
         motion_adapter=motion_adapter,
@@ -19,6 +21,10 @@ def generate_video(prompt, negative_prompt, num_inference_steps, conditioning_fr
     ).to(device)
     
     pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, beta_schedule="linear", algorithm_type="dpmsolver++", use_karras_sigmas=True)
+
+    # PEFT 사용하여 Lora 로드
+    lora_config = LoraConfig(r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"])
+    pipe = get_peft_model(pipe, lora_config)
     pipe.load_lora_weights("guoyww/animatediff-motion-lora-v1-5-3", adapter_name="motion_lora")
     pipe.fuse_lora(lora_scale=1.0)