added app and requirements

app.py

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+import torch
+from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
+from PIL import Image
+from ip_adapter.ip_adapter_faceid import IPAdapterFaceIDPlus
+import cv2
+from insightface.app import FaceAnalysis
+from insightface.utils import face_align
+
+
+base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE"
+vae_model_path = "stabilityai/sd-vae-ft-mse"
+#image_encoder_path = "h94/IP-Adapter/models/image_encoder"
+image_encoder_path = "image_encoder"
+ip_ckpt = "IP-Adapter-FaceID/ip-adapter-faceid-plus_sd15.bin"
+
+
+if torch.cuda.is_available():
+    device = 'cuda'
+    torch_dtype = torch.float16
+else:
+    device = 'cpu'
+    torch_dtype = torch.float32
+print(f'Using device: {device}')
+
+noise_scheduler = DDIMScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    clip_sample=False,
+    set_alpha_to_one=False,
+    steps_offset=1,
+)
+vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch_dtype)
+pipe = StableDiffusionPipeline.from_pretrained(
+    base_model_path,
+    torch_dtype=torch_dtype,
+    scheduler=noise_scheduler,
+    vae=vae,
+    feature_extractor=None,
+    safety_checker=None
+)
+
+
+ip_model = IPAdapterFaceIDPlus(pipe, image_encoder_path, ip_ckpt, device, num_tokens=4, torch_dtype=torch_dtype)
+
+
+
+app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+app.prepare(ctx_id=0, det_size=(640, 640), det_thresh=0.2)
+
+def generate_images(img_filepath, prompt, n_images=3,
+                    negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality, blurry",
+                    img_prompt_scale=0.5,
+                    num_inference_steps=30,
+                    seed=None):
+    print(prompt)
+    image = cv2.imread(img_filepath)
+    faces = app.get(image)
+
+    faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
+    face_image = face_align.norm_crop(image, landmark=faces[0].kps, image_size=224) # you can also segment the face
+    images = ip_model.generate(
+        prompt=prompt, negative_prompt=negative_prompt, face_image=face_image, faceid_embeds=faceid_embeds,
+        num_samples=n_images, width=512, height=512, num_inference_steps=num_inference_steps, seed=seed,
+        scale=img_prompt_scale, # with scale=1 I get weird images
+    )
+    return [images, Image.fromarray(face_image[..., [2, 1, 0]])]
+
+import gradio as gr
+with gr.Blocks() as demo:
+    gr.Markdown(
+    """
+    # IP-Adapter-FaceID-plus
+    Generate images conditioned on a image prompt and a text prompt. Learn more here: https://huggingface.co/h94/IP-Adapter-FaceID
+    """)
+    with gr.Row():
+        with gr.Column():
+            demo_inputs = []
+            demo_inputs.append(gr.Image(type='filepath', label='image prompt'))
+            demo_inputs.append(gr.Textbox(label='text prompt', value='headshot of a man, green moss wall in the background'))
+            demo_inputs.append(gr.Slider(maximum=3, minimum=1, value=3, step=1, label='number of images'))
+            with gr.Accordion(label='Advanced options', open=False):
+                demo_inputs.append(gr.Textbox(label='negative text prompt', value="monochrome, lowres, bad anatomy, worst quality, low quality, blurry"))
+                demo_inputs.append(gr.Slider(maximum=1, minimum=0, value=0.5, step=0.05, label='image prompt scale'))
+            btn = gr.Button("Generate")
+
+        with gr.Column():
+            demo_outputs = []
+            demo_outputs.append(gr.Gallery(label='generated images'))
+            demo_outputs.append(gr.Image(label='detected face', height=224, width=224))
+    btn.click(generate_images, inputs=demo_inputs, outputs=demo_outputs)
+    sample_prompts = [
+        'headshot of a man, green moss wall in the background',
+        'linkedin profile picture of a macdonalds worker',
+        'LinkedIn profile picture of a beautiful man dressed in a suit, huge explosion in the background',
+        ]
+    gr.Examples(sample_prompts, inputs=demo_inputs[1], label='Sample prompts')
+
+demo.launch(share=True, debug=True)

requirements.txt

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+insightface
+onnxruntime
+diffusers==0.22.1
+einops
+accelerate
+git+https://github.com/ironbar/IP-Adapter.git@add-cpu-support