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Running
on
Zero
File size: 1,627 Bytes
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import os, torch
# from PIL import Image
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
root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
def infer(prompt):
ckpt_dir = f'{root_dir}/weights/Kolors'
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.enable_model_cpu_offload()
image = pipe(
prompt=prompt,
height=1024,
width=1024,
num_inference_steps=50,
guidance_scale=5.0,
num_images_per_prompt=1,
generator= torch.Generator(pipe.device).manual_seed(66)).images[0]
image.save(f'{root_dir}/scripts/outputs/sample_test.jpg')
if __name__ == '__main__':
import fire
fire.Fire(infer)
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