Spaces-explorers

from controlnet_aux import AnylineDetector

anyline = AnylineDetector.from_pretrained(
    "TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline"
).to("cuda")

source = Image.open("source.png")
result = anyline(source, detect_resolution=1280)

# pip install gradio_rerun gradio
import gradio as gr
from gradio_rerun import Rerun

gr.Interface(
    inputs=gr.File(file_count="multiple", type="filepath"),
    outputs=Rerun(height=900),
    fn=lambda file_path: file_path,
).launch()

#Prepare a list t of num_of_results values between 0 and 1
t_space = torch.linspace(0, 1, num_of_results)
for t in tqdm(t_space):
    mix_factor = t.item()
    # interpolate between the two face images 
    image = (image1 * (1 - mix_factor) + image2 * mix_factor).astype(np.uint8)
    # interpolate between the two face embedding 
    faceid_embeds = torch.lerp(faceid_embeds1, faceid_embeds2, t)
   #generate interpolated result
    images = ip_model.generate(prompt=prompt, negative_prompt=negative_prompt, face_image=image, faceid_embeds=faceid_embeds, shortcut=v2, num_samples=2, scale=scale, s_scale=s_scale, guidance_scale=guidance_scale, width=width, height=height, num_inference_steps=steps, seed=seed)