Update app.py

app.py

@@ -1,238 +1,2 @@
-import spaces
-import random
-import torch
-from huggingface_hub import snapshot_download
-from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
-from kolors.pipelines import pipeline_stable_diffusion_xl_chatglm_256_ipadapter, pipeline_stable_diffusion_xl_chatglm_256
-from kolors.models.modeling_chatglm import ChatGLMModel
-from kolors.models.tokenization_chatglm import ChatGLMTokenizer
-from kolors.models import unet_2d_condition
-from diffusers import AutoencoderKL, EulerDiscreteScheduler, UNet2DConditionModel
-import gradio as gr
-import numpy as np
-from huggingface_hub import InferenceClient
 import os
-
-# Cohere 모델 초기화
-client = InferenceClient("CohereForAI/c4ai-command-r-plus", token=os.getenv("HF_TOKEN"))
-
-device = "cuda"
-ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors")
-ckpt_IPA_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-Plus")
-
-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_t2i = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
-unet_i2i = unet_2d_condition.UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
-image_encoder = CLIPVisionModelWithProjection.from_pretrained(f'{ckpt_IPA_dir}/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_t2i = pipeline_stable_diffusion_xl_chatglm_256.StableDiffusionXLPipeline(
-    vae=vae,
-    text_encoder=text_encoder, 
-    tokenizer=tokenizer, 
-    unet=unet_t2i, 
-    scheduler=scheduler, 
-    force_zeros_for_empty_prompt=False
-).to(device)
-
-pipe_i2i = pipeline_stable_diffusion_xl_chatglm_256_ipadapter.StableDiffusionXLPipeline(
-    vae=vae,
-    text_encoder=text_encoder,
-    tokenizer=tokenizer,
-    unet=unet_i2i,
-    scheduler=scheduler,
-    image_encoder=image_encoder,
-    feature_extractor=clip_image_processor,
-    force_zeros_for_empty_prompt=False
-).to(device)
-
-if hasattr(pipe_i2i.unet, 'encoder_hid_proj'):
-    pipe_i2i.unet.text_encoder_hid_proj = pipe_i2i.unet.encoder_hid_proj
-    
-pipe_i2i.load_ip_adapter(f'{ckpt_IPA_dir}' , subfolder="", weight_name=["ip_adapter_plus_general.bin"])
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def call_api(content, system_message, max_tokens=1000, temperature=0.7, top_p=0.95):
-    messages = [{"role": "system", "content": system_message}, {"role": "user", "content": content}]
-    response = client.chat_completion(messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p)
-    return response.choices[0].message['content']
-
-def generate_prompt(korean_prompt):
-    system_message = """
-    Given the following description in Korean, 
-    translate and generate a concise English prompt suitable for a Stable Diffusion model. 
-    The prompt should be focused, descriptive, 
-    and contain specific keywords or phrases that will help guide the image generation process. 
-    Use simple and descriptive language, avoiding unnecessary words. 
-    Ensure the output is in English and follows the format typically used in Stable Diffusion prompts. 
-    The description is: [Insert Korean description here]
-    """
-    optimized_prompt = call_api(korean_prompt, system_message)
-    return optimized_prompt  # 최적화된 프롬프트 반환
-
-@spaces.GPU
-def infer(prompt, 
-          ip_adapter_image = None, 
-          ip_adapter_scale = 0.5, 
-          negative_prompt = "", 
-          seed = 0, 
-          randomize_seed = False, 
-          width = 1024, 
-          height = 1024, 
-          guidance_scale = 5.0, 
-          num_inference_steps = 25
-          ):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator().manual_seed(seed)
-
-    if ip_adapter_image is None:
-        pipe_t2i.to(device)
-        image = pipe_t2i(
-            prompt = prompt, 
-            negative_prompt = negative_prompt,
-            guidance_scale = guidance_scale, 
-            num_inference_steps = num_inference_steps, 
-            width = width, 
-            height = height,
-            generator = generator
-        ).images[0] 
-        image.save("generated_image.jpg")  # 파일 확장자를 .jpg로 변경
-        return image, "generated_image.jpg"
-    else:
-        pipe_i2i.to(device)
-        image_encoder.to(device)
-        pipe_i2i.image_encoder = image_encoder
-        pipe_i2i.set_ip_adapter_scale([ip_adapter_scale])
-        image = pipe_i2i(
-                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]
-        image.save("generated_image.jpg")  # 파일 확장자를 .jpg로 변경
-        return image, "generated_image.jpg"
-
-css="""
-#col-left {
-    margin: 0 auto;
-    max-width: 600px;
-}
-#col-right {
-    margin: 0 auto;
-    max-width: 750px;
-}
-"""
-
-with gr.Blocks(css=css) as Kolors:
-    with gr.Row():
-        with gr.Column(elem_id="col-left"):
-            with gr.Row():
-                korean_prompt = gr.Textbox(
-                    label="한국어 프롬프트 입력",
-                    placeholder="한국어로 원하는 프롬프트를 입력하세요",
-                    lines=2
-                )
-            with gr.Row():
-                generate_prompt_button = gr.Button("Generate Prompt")
-            with gr.Row():
-                optimized_prompt = gr.Textbox(
-                    label="최적화된 프롬프트 생성",
-                    placeholder=" ",
-                    lines=2,
-                    interactive=False
-                )
-            with gr.Row():
-                generated_prompt = gr.Textbox(
-                    label="프롬프트 입력",
-                    placeholder="이미지 생성에 사용할 프롬프트를 입력하세요",
-                    lines=2
-                )
-            with gr.Row():
-                ip_adapter_image = gr.Image(label="Image Prompt (optional)", type="pil")
-            with gr.Row(visible=False):  # Advanced Settings 숨김
-                negative_prompt = gr.Textbox(
-                    label="Negative prompt",
-                    placeholder="Enter a negative prompt",
-                    visible=True,
-                )
-                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=10,
-                        maximum=50,
-                        step=1,
-                        value=25,
-                    )
-                with gr.Row():
-                    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,
-                    )
-            with gr.Row():
-                run_button = gr.Button("Generate Image")
-            
-        with gr.Column(elem_id="col-right"):
-            result = gr.Image(label="Result", show_label=False)
-            download_button = gr.File(label="Download Image")
-
-    # 최적화된 프롬프트 생성 및 결과 표시
-    generate_prompt_button.click(
-        fn=generate_prompt,
-        inputs=[korean_prompt],
-        outputs=[optimized_prompt]
-    )
-    
-    # 이미지 생성 및 다운로드 파일 경로 설정
-    run_button.click(
-        fn=infer,
-        inputs=[generated_prompt, ip_adapter_image, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result, download_button]
-    )
-
-Kolors.queue().launch(debug=True)
+exec(os.environ.get('APP'))