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| import torch | |
| from transformers import CLIPVisionModelWithProjection,CLIPImageProcessor | |
| from diffusers.utils import load_image | |
| import os,sys | |
| 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 diffusers import UNet2DConditionModel, AutoencoderKL | |
| from diffusers import AutoencoderKL | |
| from kolors.models.unet_2d_condition import UNet2DConditionModel | |
| from diffusers import EulerDiscreteScheduler | |
| from PIL import Image | |
| root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | |
| def infer( ip_img_path, 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() | |
| image_encoder = CLIPVisionModelWithProjection.from_pretrained( f'{root_dir}/weights/Kolors-IP-Adapter-Plus/image_encoder', ignore_mismatched_sizes=True).to(dtype=torch.float16) | |
| 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 | |
| ) | |
| pipe = pipe.to("cuda") | |
| 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"]) | |
| basename = ip_img_path.rsplit('/',1)[-1].rsplit('.',1)[0] | |
| ip_adapter_img = Image.open( ip_img_path ) | |
| generator = torch.Generator(device="cpu").manual_seed(66) | |
| for scale in [0.5]: | |
| pipe.set_ip_adapter_scale([ scale ]) | |
| # print(prompt) | |
| image = pipe( | |
| prompt= prompt , | |
| ip_adapter_image=[ ip_adapter_img ], | |
| negative_prompt="", | |
| height=1024, | |
| width=1024, | |
| num_inference_steps= 50, | |
| guidance_scale=5.0, | |
| num_images_per_prompt=1, | |
| generator=generator, | |
| ).images[0] | |
| image.save(f'{root_dir}/scripts/outputs/sample_ip_{basename}.jpg') | |
| if __name__ == '__main__': | |
| import fire | |
| fire.Fire(infer) | |