Update app.py

app.py

@@ -1,2 +1,198 @@
+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
-exec(os.environ.get('APP'))
+
+# 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
+
+@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():
+                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")
+
+    # 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)