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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")
# base = DiffusionPipeline.from_pretrained(base_model,
# torch_dtype=torch.float16, use_safetensors=True
# )
# # 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
return None
# 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() |