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import os
import gradio as gr
import requests
from PIL import Image
from io import BytesIO
from base64 import b64encode
SEGMIND_MODEL_URL = "https://api.segmind.com/v1/inpaint-auto"
def urlToB64(imgUrl):
return str(b64encode(requests.get(imgUrl).content))[2:-1]
def imageToB64(img):
buffered = BytesIO()
img.save(buffered, format="JPEG")
return str(b64encode(buffered.getvalue()))[2:-1]
def generate_image(
upload_method,
img_url,
uploaded_img,
prompt,
negative_prompt,
cn_model,
cn_processor,
base_model
):
if upload_method == "URL":
if not img_url:
raise ValueError("Image URL is required.")
img_b64 = urlToB64(img_url)
else:
if not uploaded_img:
raise ValueError("Image upload is required.")
img_b64 = imageToB64(uploaded_img)
data = {
"image": img_b64,
"prompt": prompt,
"negative_prompt": negative_prompt,
"samples": 1,
"base_model": base_model,
"cn_model": cn_model,
"cn_processor": cn_processor,
"scheduler": "DPM++ 2M SDE Karras",
"num_inference_steps": 25,
"guidance_scale": 7.5,
"seed": -1,
"strength": 0.9,
"base64": False,
}
response = requests.post(
SEGMIND_MODEL_URL,
json=data,
headers={"x-api-key": os.environ['SEGMIND_API_KEY']}
)
output_img = Image.open(BytesIO(response.content))
return output_img
def invertBox(upload_method):
# Return gr.update objects with visibility settings
if upload_method == "URL":
return gr.update(visible=True), gr.update(visible=False)
else:
return gr.update(visible=False), gr.update(visible=True)
with gr.Blocks() as demo:
gr.Markdown("### Photo Background Changer")
gr.Markdown(
"Change the bavkground of the image in one click to anything that you can imagine"
)
with gr.Row():
upload_method = gr.Radio(
choices=["URL", "Upload"], label="Choose Image Upload Method", value="URL"
)
img_url = gr.Textbox(label="Image URL")
uploaded_img = gr.Image(type="pil", label="Upload Image", visible=False)
upload_method.change(
invertBox, inputs=upload_method, outputs=[img_url, uploaded_img]
)
with gr.Row():
prompt = gr.Textbox(label="Prompt")
negative_prompt = gr.Textbox(
label="Negative Prompt",
value="disfigured, deformed, ugly, floating in air, blur, haze, uneven edges, improper blending, animated, cartoon",
)
with gr.Row():
cn_model = gr.Dropdown(
label="Select Controlnet Model",
choices=["Canny", "Depth", "SoftEdge", "OpenPose"],
value="Depth",
)
cn_processor = gr.Dropdown(
label="Select Controlnet Processor",
choices=[
"canny",
"depth",
"depth_leres",
"depth_leres++",
"hed",
"hed_safe",
"mediapipe_face",
"mlsd",
"normal_map",
"openpose",
"openpose_hand",
"openpose_face",
"openpose_faceonly",
"openpose_full",
"dw_openpose_full",
"animal_openpose",
"clip_vision",
"revision_clipvision",
"revision_ignore_prompt",
"ip-adapter_clip_sd15",
"ip-adapter_clip_sdxl_plus_vith",
"ip-adapter_clip_sdxl",
"color",
"pidinet",
"pidinet_safe",
"pidinet_sketch",
"pidinet_scribble",
"scribble_xdog",
"scribble_hed",
"segmentation",
"threshold",
"depth_zoe",
"normal_bae",
"oneformer_coco",
"oneformer_ade20k",
"lineart",
"lineart_coarse",
"lineart_anime",
"lineart_standard",
"shuffle",
"tile_resample",
"invert",
"lineart_anime_denoise",
"reference_only",
"reference_adain",
"reference_adain+attn",
"inpaint",
"inpaint_only",
"inpaint_only+lama",
"tile_colorfix",
"tile_colorfix+sharp",
"recolor_luminance",
"recolor_intensity",
"blur_gaussian",
"anime_face_segment",
],
value="canny",
)
with gr.Row():
base_model = gr.Dropdown(
label="Select Base SD Model to use",
choices=["Real Vision XL", "SDXL", "Juggernaut XL", "DreamShaper XL"],
value="Juggernaut XL",
)
with gr.Row():
generate_btn = gr.Button("Generate Image")
output_image = gr.Image(type="pil")
generate_btn.click(
fn=generate_image,
inputs=[
upload_method,
img_url,
uploaded_img,
prompt,
negative_prompt,
cn_model,
cn_processor,
base_model
],
outputs=[output_image],
)
demo.launch(debug=True) |