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import os, torch
from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256 import StableDiffusionXLPipeline
from kolors.models.modeling_chatglm import ChatGLMModel
from kolors.models.tokenization_chatglm import ChatGLMTokenizer
from diffusers import UNet2DConditionModel, AutoencoderKL
from diffusers import EulerDiscreteScheduler
from peft import (
LoraConfig,
PeftModel,
)
def infer(prompt):
ckpt_dir = "/path/base_model_path"
lora_ckpt = 'trained_models/ktxl_dog_text/checkpoint-1000/'
load_text_encoder = True
text_encoder = ChatGLMModel.from_pretrained(
f'{ckpt_dir}/text_encoder',
torch_dtype=torch.float16).half()
tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half()
scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half()
pipe = StableDiffusionXLPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
force_zeros_for_empty_prompt=False)
pipe = pipe.to("cuda")
pipe.load_lora_weights(lora_ckpt, adapter_name="ktxl-lora")
pipe.set_adapters(["ktxl-lora"], [0.8])
if load_text_encoder:
pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, lora_ckpt)
random_seed = 0
generator = torch.Generator(pipe.device).manual_seed(random_seed)
neg_p = ''
out = pipe(prompt, generator=generator, negative_prompt=neg_p, num_inference_steps=25, width=1024, height=1024, num_images_per_prompt=1, guidance_scale=5).images[0]
out.save("ktxl_test_image.png")
if __name__ == '__main__':
import fire
fire.Fire(infer) |