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import torch
import spaces
from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
from ip_adapter.ip_adapter_faceid import IPAdapterFaceID
from huggingface_hub import hf_hub_download
from insightface.app import FaceAnalysis
import gradio as gr
base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE"
vae_model_path = "stabilityai/sd-vae-ft-mse"
ip_ckpt = hf_hub_download(repo_id='h94/IP-Adapter-FaceID', filename="ip-adapter-faceid_sd15.bin", repo_type="model")
device = "cuda"
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.float16)
pipe = StableDiffusionPipeline.from_pretrained(
base_model_path,
torch_dtype=torch.float16,
scheduler=noise_scheduler,
vae=vae,
#feature_extractor=None,
#safety_checker=None
)
ip_model = IPAdapterFaceID(pipe, ip_ckpt, device)
@spaces.GPU
def generate_image(image, prompt, negative_prompt):
pipe.to(device)
app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))
faces = app.get(image)
faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
image = ip_model.generate(
prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, width=512, height=512, num_inference_steps=30
).images[0]
return image
demo = gr.Interface(fn=generate_image, inputs=[gr.Image(label="Your face"), gr.Textbox(label="Prompt"), gr.Textbox(label="Negative Prompt")], outputs=[gr.Image(label="Generated Image")])
demo.launch() |