Update app.py

app.py

@@ -1,6 +1,8 @@
 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
@@ -14,6 +16,9 @@ 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"
 
@@ -32,8 +37,13 @@ pipe = StableDiffusionPipeline.from_pretrained(
     torch_dtype=torch.float16,
     scheduler=noise_scheduler,
     vae=vae,
+    feature_extractor=safety_feature_extractor,
+    safety_checker=None
 )
 
+#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)
 
@@ -88,8 +98,8 @@ css = '''
 h1{margin-bottom: 0 !important}
 '''
 with gr.Blocks(css=css) as demo:
-    gr.Markdown("# IP-Adapter-FaceID demo")
-    gr.Markdown("Demo for the [h94/IP-Adapter-FaceID model](https://huggingface.co/h94/IP-Adapter-FaceID) - 'preserve face structure' uses the plus v2 model. Non-commercial license")
+    gr.Markdown("# IP-Adapter-FaceID Plus demo")
+    gr.Markdown("Demo for the [h94/IP-Adapter-FaceID model](https://huggingface.co/h94/IP-Adapter-FaceID) - Non-commercial license")
     with gr.Row():
         with gr.Column():
             files = gr.Files(
@@ -106,10 +116,10 @@ with gr.Blocks(css=css) as demo:
             style = gr.Radio(label="Generation type", info="For stylized try prompts like 'a watercolor painting of a woman'", choices=["Photorealistic", "Stylized"], value="Photorealistic")
             submit = gr.Button("Submit")
             with gr.Accordion(open=False, label="Advanced Options"):
-                preserve = gr.Checkbox(label="Preserve Face Structure", info="Higher quality, less versatility (the face structure of your first photo will be preserved)", value=True)
+                preserve = gr.Checkbox(label="Preserve Face Structure", info="Higher quality, less versatility (the face structure of your first photo will be preserved). Unchecking this will use the v1 model.", value=True)
                 face_strength = gr.Slider(label="Face Structure strength", info="Only applied if preserve face structure is checked", value=1.3, step=0.1, minimum=0, maximum=3)
                 likeness_strength = gr.Slider(label="Face Embed strength", value=1.0, step=0.1, minimum=0, maximum=5)
-                nfaa_negative_prompts = gr.Textbox(label="Appended Negative Prompts", info="Negative prompts to steer generations towards safe for all audiences outputs", value="bad quality, bad anatomy")    
+                nfaa_negative_prompts = gr.Textbox(label="Appended Negative Prompts", info="Negative prompts to steer generations towards safe for all audiences outputs", value="naked, bikini, skimpy, scanty, bare skin, lingerie, swimsuit, exposed, see-through")    
         with gr.Column():
             gallery = gr.Gallery(label="Generated Images")
         style.change(fn=change_style,