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import spaces
import random
import torch
from huggingface_hub import snapshot_download
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from kolors.pipelines import pipeline_stable_diffusion_xl_chatglm_256_ipadapter, pipeline_stable_diffusion_xl_chatglm_256
from kolors.models.modeling_chatglm import ChatGLMModel
from kolors.models.tokenization_chatglm import ChatGLMTokenizer
from kolors.models import unet_2d_condition
from diffusers import AutoencoderKL, EulerDiscreteScheduler, UNet2DConditionModel
import gradio as gr
import numpy as np
device = "cuda"
ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors")
ckpt_IPA_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-Plus")
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_t2i = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
unet_i2i = unet_2d_condition.UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(f'{ckpt_IPA_dir}/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_t2i = pipeline_stable_diffusion_xl_chatglm_256.StableDiffusionXLPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet_t2i,
scheduler=scheduler,
force_zeros_for_empty_prompt=False
).to(device)
pipe_i2i = pipeline_stable_diffusion_xl_chatglm_256_ipadapter.StableDiffusionXLPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet_i2i,
scheduler=scheduler,
image_encoder=image_encoder,
feature_extractor=clip_image_processor,
force_zeros_for_empty_prompt=False
).to(device)
if hasattr(pipe_i2i.unet, 'encoder_hid_proj'):
pipe_i2i.unet.text_encoder_hid_proj = pipe_i2i.unet.encoder_hid_proj
pipe_i2i.load_ip_adapter(f'{ckpt_IPA_dir}' , 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 = None,
ip_adapter_scale = 0.5,
negative_prompt = "",
seed = 0,
randomize_seed = False,
width = 1024,
height = 1024,
guidance_scale = 5.0,
num_inference_steps = 25
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
if ip_adapter_image is None:
pipe_t2i.to(device)
image = pipe_t2i(
prompt = prompt,
negative_prompt = negative_prompt,
guidance_scale = guidance_scale,
num_inference_steps = num_inference_steps,
width = width,
height = height,
generator = generator
).images[0]
return image
else:
pipe_i2i.to(device)
image_encoder.to(device)
pipe_i2i.image_encoder = image_encoder
pipe_i2i.set_ip_adapter_scale([ip_adapter_scale])
image = pipe_i2i(
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
examples = [
]
css="""
#col-left {
margin: 0 auto;
max-width: 600px;
}
#col-right {
margin: 0 auto;
max-width: 750px;
}
"""
def load_description(fp):
with open(fp, 'r', encoding='utf-8') as f:
content = f.read()
return content
with gr.Blocks(css=css) as Kolors:
gr.HTML(load_description("assets/title.md"))
with gr.Row():
with gr.Column(elem_id="col-left"):
with gr.Row():
prompt = gr.Textbox(
label="Prompt",
placeholder="Enter your prompt",
lines=2
)
with gr.Row():
ip_adapter_image = gr.Image(label="Image Prompt (optional)", type="pil")
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Textbox(
label="Negative prompt",
placeholder="Enter a negative prompt",
visible=True,
)
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=10,
maximum=50,
step=1,
value=25,
)
with gr.Row():
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,
)
with gr.Row():
run_button = gr.Button("Run")
with gr.Column(elem_id="col-right"):
result = gr.Image(label="Result", show_label=False)
with gr.Row():
gr.Examples(
fn = infer,
examples = examples,
inputs = [prompt, ip_adapter_image, ip_adapter_scale],
outputs = [result]
)
run_button.click(
fn = infer,
inputs = [prompt, ip_adapter_image, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs = [result]
)
Kolors.queue().launch(debug=True)
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