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import torch |
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from transformers import CLIPVisionModelWithProjection,CLIPImageProcessor |
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from diffusers.utils import load_image |
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import os,sys |
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from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256_ipadapter import StableDiffusionXLPipeline |
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from kolors.models.modeling_chatglm import ChatGLMModel |
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from kolors.models.tokenization_chatglm import ChatGLMTokenizer |
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from diffusers import AutoencoderKL |
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from kolors.models.unet_2d_condition import UNet2DConditionModel |
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from diffusers import EulerDiscreteScheduler |
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from PIL import Image |
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root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) |
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def infer( ip_img_path, prompt ): |
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ckpt_dir = f'{root_dir}/weights/Kolors' |
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text_encoder = ChatGLMModel.from_pretrained( |
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f'{ckpt_dir}/text_encoder', |
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torch_dtype=torch.float16).half() |
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tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder') |
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vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half() |
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scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler") |
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unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half() |
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image_encoder = CLIPVisionModelWithProjection.from_pretrained( f'{root_dir}/weights/Kolors-IP-Adapter-Plus/image_encoder', ignore_mismatched_sizes=True).to(dtype=torch.float16) |
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ip_img_size = 336 |
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clip_image_processor = CLIPImageProcessor( size=ip_img_size, crop_size=ip_img_size ) |
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pipe = StableDiffusionXLPipeline( |
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vae=vae, |
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text_encoder=text_encoder, |
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tokenizer=tokenizer, |
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unet=unet, |
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scheduler=scheduler, |
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image_encoder=image_encoder, |
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feature_extractor=clip_image_processor, |
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force_zeros_for_empty_prompt=False |
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) |
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pipe = pipe.to("cuda") |
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pipe.enable_model_cpu_offload() |
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if hasattr(pipe.unet, 'encoder_hid_proj'): |
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pipe.unet.text_encoder_hid_proj = pipe.unet.encoder_hid_proj |
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pipe.load_ip_adapter( f'{root_dir}/weights/Kolors-IP-Adapter-Plus' , subfolder="", weight_name=["ip_adapter_plus_general.bin"]) |
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basename = ip_img_path.rsplit('/',1)[-1].rsplit('.',1)[0] |
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ip_adapter_img = Image.open( ip_img_path ) |
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generator = torch.Generator(device="cpu").manual_seed(66) |
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for scale in [0.5]: |
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pipe.set_ip_adapter_scale([ scale ]) |
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image = pipe( |
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prompt= prompt , |
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ip_adapter_image=[ ip_adapter_img ], |
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negative_prompt="", |
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height=1024, |
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width=1024, |
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num_inference_steps= 50, |
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guidance_scale=5.0, |
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num_images_per_prompt=1, |
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generator=generator, |
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).images[0] |
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image.save(f'{root_dir}/scripts/outputs/sample_ip_{basename}.jpg') |
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if __name__ == '__main__': |
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import fire |
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fire.Fire(infer) |
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