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import gradio as gr
import numpy as np
import random
import torch
from PIL import Image
import os
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256_ipadapter import StableDiffusionXLPipeline
from kolors.models.modeling_chatglm import ChatGLMModel
from kolors.models.tokenization_chatglm import ChatGLMTokenizer
from kolors.models.unet_2d_condition import UNet2DConditionModel
from diffusers import AutoencoderKL, EulerDiscreteScheduler
from huggingface_hub import snapshot_download
import spaces
device = "cuda"
root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
ckpt_dir = f'{root_dir}/weights/Kolors'
snapshot_download(repo_id="Kwai-Kolors/Kolors", local_dir=ckpt_dir)
snapshot_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-Plus", local_dir=f"{root_dir}/weights/Kolors-IP-Adapter-Plus")
# Load models
text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dtype=torch.float16).half().to(device)
tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device)
scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
f'{root_dir}/weights/Kolors-IP-Adapter-Plus/image_encoder',
ignore_mismatched_sizes=True
).to(dtype=torch.float16, device=device)
ip_img_size = 336
clip_image_processor = CLIPImageProcessor(size=ip_img_size, crop_size=ip_img_size)
pipe = StableDiffusionXLPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
image_encoder=image_encoder,
feature_extractor=clip_image_processor,
force_zeros_for_empty_prompt=False
).to(device)
#pipe = pipe.to(device)
#pipe.enable_model_cpu_offload()
if hasattr(pipe.unet, 'encoder_hid_proj'):
pipe.unet.text_encoder_hid_proj = pipe.unet.encoder_hid_proj
pipe.load_ip_adapter(f'{root_dir}/weights/Kolors-IP-Adapter-Plus', subfolder="", weight_name=["ip_adapter_plus_general.bin"])
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU
def infer(prompt, ip_adapter_image, ip_adapter_scale=0.5, negative_prompt="", seed=100, randomize_seed=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)
generator = torch.Generator(device="cuda").manual_seed(seed)
pipe.to("cuda")
image_encoder.to("cuda")
pipe.image_encoder = image_encoder
pipe.set_ip_adapter_scale([ip_adapter_scale])
image = pipe(
prompt=prompt,
ip_adapter_image=[ip_adapter_image],
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
examples = [
["A dog", "minta.jpeg", 0.4],
["A capybara", "king-min.png", 0.5],
["A cat", "blue_hair.png", 0.5],
["", "meow.jpeg", 1.0],
]
css="""
#col-container {
margin: 0 auto;
max-width: 720px;
}
#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"""
# Kolors IP-Adapter - image reference and variations
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
with gr.Row():
with gr.Column():
ip_adapter_image = gr.Image(label="IP-Adapter Image", type="pil")
ip_adapter_scale = gr.Slider(
label="Image influence scale",
info="Use 1 for creating variations",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.5,
)
result = gr.Image(label="Result", elem_id="result")
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=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=5.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=100,
step=1,
value=100,
)
gr.Examples(
examples=examples,
fn=infer,
inputs=[prompt, ip_adapter_image, ip_adapter_scale],
outputs=[result, seed],
cache_examples="lazy"
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[prompt, ip_adapter_image, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result, seed]
)
# 포트 7890 설정, 대기열 활성화, API 활성화
demo.launch()
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