import gradio as gr import numpy as np import random import torch from PIL import Image import os import spaces from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor 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 kolors.models.unet_2d_condition import UNet2DConditionModel from diffusers import AutoencoderKL, EulerDiscreteScheduler from huggingface_hub import snapshot_download device = "cuda" root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) ckpt_dir = f'{root_dir}/weights/Kolors' snapshot_download(repo_id="Kwai-Kolors/Kolors", local_dir=ckpt_dir) snapshot_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-Plus", local_dir=f"{root_dir}/weights/Kolors-IP-Adapter-Plus") # Load models text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dtype=torch.float16).half().to(device) tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder') vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device) scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler") unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device) image_encoder = CLIPVisionModelWithProjection.from_pretrained( f'{root_dir}/weights/Kolors-IP-Adapter-Plus/image_encoder', ignore_mismatched_sizes=True ).to(dtype=torch.float16, device=device) 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 ).to(device) 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"]) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 1024 # ---------------------------------------------- # 수정된 부분: infer 함수 내에서 hidden_prompt를 앞에 추가 # ---------------------------------------------- @spaces.GPU(duration=80) def infer( user_prompt, ip_adapter_image, ip_adapter_scale=0.5, negative_prompt="", seed=100, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=50, progress=gr.Progress(track_tqdm=True) ): # 숨겨진(기본/필수) 프롬프트 hidden_prompt = ( "Studio Ghibli animation style, featuring whimsical characters with expressive eyes " "and fluid movements. Lush, detailed natural environments with ethereal lighting " "and soft color palettes of blues, greens, and warm earth tones." ) # 실제로 파이프라인에 전달할 최종 프롬프트 prompt = f"{hidden_prompt}, {user_prompt}" if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator(device="cuda").manual_seed(seed) pipe.to("cuda") image_encoder.to("cuda") pipe.image_encoder = image_encoder pipe.set_ip_adapter_scale([ip_adapter_scale]) image = pipe( prompt=prompt, ip_adapter_image=[ip_adapter_image], negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, num_images_per_prompt=1, generator=generator, ).images[0] return image, seed examples = [ [ "dancing", "gh1.jpg", 0.5 ], [ "studio ghibli style", "gh2.jpg", 0.5 ], [ "studio ghibli style", "gh3.webp", 0.5 ], [ "studio ghibli style", "gh4.jpg", 0.5 ], ] css = """ #col-container { margin: 0 auto; max-width: 720px; } #result img{ object-position: top; } #result .image-container{ height: 100% } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(f""" # Beyond Ghibli Reimagined """) with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) run_button = gr.Button("Run", scale=0) with gr.Row(): with gr.Column(): ip_adapter_image = gr.Image(label="IP-Adapter Image", type="pil") ip_adapter_scale = gr.Slider( label="Image influence scale", info="Use 1 for creating variations", minimum=0.0, maximum=1.0, step=0.05, value=0.5, ) result = gr.Image(label="Result", elem_id="result") with gr.Accordion("Advanced Settings", open=False): negative_prompt = gr.Text( label="Negative prompt", max_lines=1, placeholder=( "Copy(worst quality, low quality:1.4), bad anatomy, bad hands, text, error, " "missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, " "normal quality, jpeg artifacts, signature, watermark, username, blurry, " "artist name, (deformed iris, deformed pupils:1.2), (semi-realistic, cgi, " "3d, render:1.1), amateur, (poorly drawn hands, poorly drawn face:1.2)" ), ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(): width = gr.Slider( label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=5.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=100, step=1, value=50, ) gr.Examples( examples=examples, fn=infer, inputs=[prompt, ip_adapter_image, ip_adapter_scale], outputs=[result, seed], cache_examples="lazy" ) gr.on( triggers=[run_button.click, prompt.submit], fn=infer, inputs=[ prompt, ip_adapter_image, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps ], outputs=[result, seed] ) demo.queue().launch()