Upload app (3).py

app (3).py

@@ -0,0 +1,198 @@
+import gradio as gr
+import numpy as np
+import random
+import torch
+from PIL import Image
+import os
+
+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
+import spaces 
+
+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)
+
+#pipe = pipe.to(device)
+#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"])
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+@spaces.GPU
+def infer(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)):
+    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 = [
+    ["A dog", "minta.jpeg", 0.4],
+    ["A capybara", "king-min.png", 0.5],
+    ["A cat", "blue_hair.png", 0.5],
+    ["", "meow.jpeg", 1.0],
+]
+
+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"""
+        # Kolors IP-Adapter - image reference and variations
+        """)
+        
+        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="Enter a negative prompt",
+            )
+            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=100,
+                )
+            
+        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]
+    )
+
+# 포트 7890 설정, 대기열 활성화, API 활성화
+demo.launch()
+