| 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 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 ]) |
| |
| 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') |
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|
|
| if __name__ == '__main__': |
| import fire |
| fire.Fire(infer) |
| |
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