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from PIL import Image
import gradio as gr
from diffusers import DiffusionPipeline
import torch
import numpy as np
import roop.globals
from roop.core import (
start,
decode_execution_providers,
suggest_max_memory,
suggest_execution_threads,
)
from roop.processors.frame.core import get_frame_processors_modules
from roop.utilities import normalize_output_path
import os
from PIL import Image
base_model="stabilityai/stable-diffusion-xl-base-1.0"
base = DiffusionPipeline.from_pretrained(base_model,
torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
lora_model_path = "./pytorch_weights.safetensors"
base.load_lora_weights(lora_model_path)
def swap_face(source_file, target_file,doFaceEnhancer):
source_path = "input.jpg"
target_path = "target.jpg"
# source_image = Image.fromarray(source_file)
# source_image.save(source_path)
# target_image = Image.fromarray(target_file)
# target_image.save(target_path)
# Open and save the source image
source_image = Image.open(source_file)
source_image.save(source_path)
# Open and save the target image
target_image = Image.open(target_file)
target_image.save(target_path)
print("source_path: ", source_path)
print("target_path: ", target_path)
roop.globals.source_path = source_path
roop.globals.target_path = target_path
output_path = "output.jpg"
roop.globals.output_path = normalize_output_path(
roop.globals.source_path, roop.globals.target_path, output_path
)
if doFaceEnhancer == True:
roop.globals.frame_processors = ["face_swapper","face_enhancer"]
else:
roop.globals.frame_processors = ["face_swapper"]
roop.globals.headless = True
roop.globals.keep_fps = True
roop.globals.keep_audio = True
roop.globals.keep_frames = False
roop.globals.many_faces = False
roop.globals.video_encoder = "libx264"
roop.globals.video_quality = 18
roop.globals.max_memory = suggest_max_memory()
roop.globals.execution_providers = decode_execution_providers(["cpu"])
roop.globals.execution_threads = suggest_execution_threads()
print(
"start process",
roop.globals.source_path,
roop.globals.target_path,
roop.globals.output_path,
)
for frame_processor in get_frame_processors_modules(
roop.globals.frame_processors
):
if not frame_processor.pre_check():
return
start()
return output_path
def generate_image(prompt, height=1024, width=1024):
image = base(
prompt=prompt,
negative_prompt="(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck",
num_inference_steps=40,
guidance_scale=3,
height=int(height),
width=int(width),
num_images_per_prompt=1,
)
for i in range(len(image.images)):
# Save the generated images
original_image_path = f"./output{i}.png"
image.images[i].save(original_image_path)
# The source image for face swapping
source_image_path = "./roop/IMG_0991.png"
# Run the face swap and optionally face enhancement
swapped_image_path = swap_face(source_image_path, original_image_path, False)
# Load the swapped image and add to the output list
swapped_image = Image.open(swapped_image_path)
return swapped_image
examples = [
["xkx man as superman. Uhd, 8k", 1024, 1024],
["photo of xkx man on a beach. Amazing scenery, waves, sun. Uhd, 8k", 1024, 1536],
]
# Set up the Gradio interface
interface = gr.Interface(
fn=generate_image,
inputs=[
gr.Text(label="Prompt"),
gr.Number(label="Height"),
gr.Number(label="Width")
],
outputs=gr.Image(label="Image"),
examples=examples
)
interface.launch() |