import gradio as gr
import torch
import numpy as np
import diffusers
import os
import random
import spaces
from PIL import Image
hf_token = os.environ.get("HF_TOKEN")
from diffusers import AutoPipelineForText2Image
from diffusers.utils import load_image
device = "cuda" #if torch.cuda.is_available() else "cpu"
pipe = AutoPipelineForText2Image.from_pretrained("briaai/BRIA-2.3", torch_dtype=torch.float16, force_zeros_for_empty_prompt=False).to(device)
pipe.load_ip_adapter("briaai/Image-Prompt", subfolder='models', weight_name="ip_adapter_bria.bin")
pipe.to(device)
# default_negative_prompt= "" #"Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
MAX_SEED = np.iinfo(np.int32).max
@spaces.GPU(enable_queue=True)
def predict(prompt, upload_images, ip_adapter_scale=0.5, negative_prompt="", seed=100, randomize_seed=False, center_crop=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=50, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
ip_adapter_images = []
for img in upload_images:
ip_adapter_images.append(load_image(img))
# ip_adapter_images = [Image.open(image) for image in ip_adapter_images]
# # Optionally resize images if center crop is not selected
# if not center_crop:
# ip_adapter_images = [image.resize((224, 224)) for image in ip_adapter_images]
generator = torch.Generator(device="cuda").manual_seed(seed)
pipe.set_ip_adapter_scale([ip_adapter_scale])
image = pipe(
prompt=prompt,
ip_adapter_image=[ip_adapter_images],
negative_prompt=negative_prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=1,
generator=generator,
).images[0]
return image, seed
def swap_to_gallery(images):
return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)
def remove_back_to_files():
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
examples = [
["high quality", "example1.png", 1.0, "", 1000, False, False, 1152, 896],
["capybara", "example2.png", 0.7, "", 1000, False, False, 1152, 896],
]
css="""
#col-container {
margin: 0 auto;
max-width: 1024px;
}
#result img{
object-position: top;
}
#result .image-container{
height: 100%
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Bria's Image-Prompt-Adapter
""")
with gr.Row():
with gr.Column():
# ip_adapter_images = gr.Gallery(label="Input Images", elem_id="image-gallery").style(grid=[2], preview=True)
# ip_adapter_images = gr.Gallery(label="Input Images", elem_id="image-gallery", show_label=True)#.style(grid=[2])
# ip_adapter_images = gr.Gallery(columns=4, interactive=True, label="Input Images")
files = gr.File(
label="Input Image/s",
file_types=["image"],
file_count="multiple"
)
uploaded_files = gr.Gallery(label="Your images", visible=False, columns=5, rows=1, height=200)
with gr.Column(visible=False) as clear_button:
remove_and_reupload = gr.ClearButton(value="Remove and upload new ones", components=files, size="sm")
ip_adapter_scale = gr.Slider(
label="Image Input Scale",
info="Use 1 for creating image variations",
minimum=0.0,
maximum=1.0,
step=0.05,
value=1.0,
)
with gr.Column():
result = gr.Image(label="Result", elem_id="result", format="png")
prompt = gr.Text(
label="Prompt",
show_label=True,
lines=1,
placeholder="Enter your prompt",
container=True,
info='For image variation, leave empty or try a prompt like: "high quality".'
)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=2048,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=2048,
step=32,
value=1024,
)
run_button = gr.Button("Run", scale=0)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=1000,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
center_crop = gr.Checkbox(label="Center Crop image", value=False, info="If not checked, the IP-Adapter image input would be resized to a square.")
# with gr.Row():
# width = gr.Slider(
# label="Width",
# minimum=256,
# maximum=2048,
# step=32,
# value=1024,
# )
# height = gr.Slider(
# label="Height",
# minimum=256,
# maximum=2048,
# step=32,
# value=1024,
# )
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=7.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=100,
step=1,
value=25,
)
files.upload(fn=swap_to_gallery, inputs=files, outputs=[uploaded_files, clear_button, files])
remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files, clear_button, files])
# gr.Examples(
# examples=examples,
# fn=predict,
# inputs=[prompt, ip_adapter_images, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height],
# outputs=[result, seed],
# cache_examples="lazy"
# )
gr.on(
triggers=[run_button.click, prompt.submit],
fn=predict,
inputs=[prompt, files, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height, guidance_scale, num_inference_steps],
outputs=[result, seed]
)
demo.queue(max_size=25,api_open=False).launch(show_api=False)
# image_blocks.queue(max_size=25,api_open=False).launch(show_api=False)