Update app.py

app.py

@@ -2,8 +2,6 @@ import torch
 import spaces
 from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
 from transformers import AutoFeatureExtractor
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from ip_adapter.ip_adapter_faceid import IPAdapterFaceID, IPAdapterFaceIDPlus
 from huggingface_hub import hf_hub_download
 from insightface.app import FaceAnalysis
 from insightface.utils import face_align
@@ -16,10 +14,6 @@ image_encoder_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
 ip_ckpt = hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid_sd15.bin", repo_type="model")
 ip_plus_ckpt = hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid-plusv2_sd15.bin", repo_type="model")
 
-safety_model_id = "CompVis/stable-diffusion-safety-checker"
-safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
-safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
-
 device = "cuda"
 
 noise_scheduler = DDIMScheduler(
@@ -37,13 +31,9 @@ pipe = StableDiffusionPipeline.from_pretrained(
     torch_dtype=torch.float16,
     scheduler=noise_scheduler,
     vae=vae,
-    feature_extractor=safety_feature_extractor,
-    safety_checker=safety_checker
+    safety_checker=None  # Disconnected the safety checker
 ).to(device)
 
-#pipe.load_lora_weights("h94/IP-Adapter-FaceID", weight_name="ip-adapter-faceid-plusv2_sd15_lora.safetensors")
-#pipe.fuse_lora()
-
 ip_model = IPAdapterFaceID(pipe, ip_ckpt, device)
 ip_model_plus = IPAdapterFaceIDPlus(pipe, image_encoder_path, ip_plus_ckpt, device)
 
@@ -62,7 +52,7 @@ def generate_image(images, prompt, negative_prompt, preserve_face_structure, fac
         faceid_embed = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
         faceid_all_embeds.append(faceid_embed)
         if(first_iteration and preserve_face_structure):
-            face_image = face_align.norm_crop(face, landmark=faces[0].kps, image_size=224) # you can also segment the face
+            face_image = face_align.norm_crop(face, landmark=faces[0].kps, image_size=224)
             first_iteration = False
             
     average_embedding = torch.mean(torch.stack(faceid_all_embeds, dim=0), dim=0)