stylestudio

.gitattributes

@@ -32,5 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-app_exp.py
+*tfevents* filter=lfs diff=lfs merge=lfs -text

app.py

@@ -100,6 +100,7 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
         seed = random.randint(0, MAX_SEED)
     return seed
 
+@spaces.GPU
 def create_image(style_image_pil,
                  prompt,
                  neg_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",

app_exp.py

@@ -1,244 +0,0 @@
-import sys
-sys.path.append("./")
-import gradio as gr
-import spaces
-import torch
-from ip_adapter.utils import BLOCKS as BLOCKS
-import numpy as np
-import random
-from diffusers import (
-    AutoencoderKL,
-    StableDiffusionXLPipeline,
-)
-from ip_adapter import StyleStudio_Adapter
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32
-base_model_path =  "/mnt/agilab/models/sdxl"
-image_encoder_path = "/mnt/agilab/models/ipadapter_sdxl/image_encoder"
-csgo_ckpt = "/mnt/agilab/models/CSGO/csgo_4_32.bin"
-pretrained_vae_name_or_path = '/mnt/agilab/models/madebyollin_sdxl-vae-fp16-fix'
-weight_dtype = torch.float16
-
-vae = AutoencoderKL.from_pretrained(pretrained_vae_name_or_path,torch_dtype=torch.float16)
-pipe = StableDiffusionXLPipeline.from_pretrained(
-    base_model_path,
-    torch_dtype=torch.float16,
-    add_watermarker=False,
-    vae=vae
-)
-pipe.enable_vae_tiling()
-
-target_style_blocks = BLOCKS['style']
-
-csgo = StyleStudio_Adapter(
-        pipe, image_encoder_path, csgo_ckpt, device, num_style_tokens=32,
-        target_style_blocks=target_style_blocks,
-        controlnet_adapter=False,
-        style_model_resampler=True,
-
-        fuSAttn=True,
-        end_fusion=20,
-        adainIP=True,
-        )
-
-MAX_SEED = np.iinfo(np.int32).max
-
-
-def get_example():
-    case = [
-        [
-            './assets/style1.jpg',
-            "A red apple",
-            7.0,
-            42,
-            10,
-         ],
-        [
-            './assets/style2.jpg',
-            "A black car",
-            7.0,
-            42,
-            10,
-         ],
-        [
-            './assets/style3.jpg',
-            "A orange bus",
-            7.0,
-            42,
-            10,
-         ],
-    ]
-    return case
-
-def run_for_examples(style_image_pil, prompt, guidance_scale, seed, end_fusion):
-    
-    return create_image(
-        style_image_pil=style_image_pil,
-        prompt=prompt,
-        neg_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
-        guidance_scale=guidance_scale,
-        num_inference_steps=50,
-        seed=seed,
-        end_fusion=end_fusion,
-        use_SAttn=True,
-        crossModalAdaIN=True,
-    )
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-def create_image(style_image_pil,
-                 prompt,
-                 neg_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
-                 guidance_scale=7,
-                 num_inference_steps=50,
-                 end_fusion=20,
-                 crossModalAdaIN=True,
-                 use_SAttn=True,
-                 seed=42,
-):
-
-    style_image = style_image_pil
-
-    generator = torch.Generator(device).manual_seed(seed)
-    init_latents = torch.randn((1, 4, 128, 128), generator=generator, device="cuda", dtype=torch.float16)
-    num_sample=1
-    if use_SAttn:
-        num_sample=2
-        init_latents = init_latents.repeat(num_sample, 1, 1, 1)
-    with torch.no_grad():
-        images = csgo.generate(pil_style_image=style_image,
-                                prompt=prompt,
-                                negative_prompt=neg_prompt,
-                                height=1024,
-                                width=1024,
-                                guidance_scale=guidance_scale,
-                                num_images_per_prompt=1,
-                                num_samples=num_sample,
-                                num_inference_steps=num_inference_steps,
-                                end_fusion=end_fusion,
-                                cross_modal_adain=crossModalAdaIN,
-                                use_SAttn=use_SAttn,
-                                
-                                generator=generator,
-                                latents=init_latents,
-                                )
-
-    if use_SAttn:
-        return [images[1]]
-    else:
-        return [images[0]]
-
-# Description
-title = r"""
-<h1 align="center">StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements</h1>
-"""
-
-description = r"""
-<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/Westlake-AGI-Lab/StyleStudio' target='_blank'><b>StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements</b></a>.<br>
-How to use:<br>
-1. Upload a style image.
-2. <b>Enter your desired prompt</b>.
-3. Click the <b>Submit</b> button to begin customization.
-4. Share your stylized photo with your friends and enjoy! 😊
-
-Advanced usage:<br>
-1. Click advanced options.
-2. Choose different guidance and steps.
-3. Set the timing for the Teacher Model's participation.
-4. 
-"""
-
-article = r"""
----
-📝 **Tips**
-<br>
-1. As the value of end_fusion <b>increases</b>, the style gradually diminishes. 
-Therefore, it is suggested to set end_fusion to be between 1/5 and 1/3 of the number of inference steps (num inference steps).
-2. If you want to experience style-based CFG, see the details on the <a href="https://github.com/Westlake-AGI-Lab/StyleStudio">GitHub repo</a>.
-
----
-📝 **Citation**
-<br>
-If our work is helpful for your research or applications, please cite us via:
-```bibtex
-
-```
-📧 **Contact**
-<br>
-If you have any questions, please feel free to open an issue or directly reach us out at <b>leimingkun@westlake.edu.cn</b>.
-"""
-
-block = gr.Blocks(css="footer {visibility: hidden}").queue(max_size=10, api_open=False)
-with block:
-    gr.Markdown(title)
-    gr.Markdown(description)
-
-    with gr.Tabs():
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    with gr.Column():
-                        style_image_pil = gr.Image(label="Style Image", type='pil')
-
-                prompt = gr.Textbox(label="Prompt",
-                                    value="A red apple")
-                
-                neg_prompt = gr.Textbox(label="Negative Prompt",
-                                    value="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry")
-
-                with gr.Accordion(open=True, label="Advanced Options"):
-
-                    guidance_scale = gr.Slider(minimum=1, maximum=15.0, step=0.01, value=7.0, label="guidance scale")
-                    
-                    num_inference_steps = gr.Slider(minimum=5, maximum=200.0, step=1.0, value=50,
-                                                    label="num inference steps")
-                    
-                    end_fusion = gr.Slider(minimum=0, maximum=200, step=1.0, value=20.0, label="end fusion")
-                    
-                    seed = gr.Slider(minimum=-1000000, maximum=1000000, value=42, step=1, label="Seed Value")
-                    
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
-                    
-                    crossModalAdaIN = gr.Checkbox(label="Cross Modal AdaIN", value=True)
-                    use_SAttn = gr.Checkbox(label="Teacher Model", value=True)
-
-                generate_button = gr.Button("Generate Image")
-
-            with gr.Column():
-                generated_image = gr.Gallery(label="Generated Image")
-
-        generate_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-            api_name=False,
-        ).then(
-            fn=create_image,
-            inputs=[
-                    style_image_pil,
-                    prompt,
-                    neg_prompt,
-                    guidance_scale,
-                    num_inference_steps,
-                    end_fusion,
-                    crossModalAdaIN,
-                    use_SAttn,
-                    seed,],
-            outputs=[generated_image])
-
-    gr.Examples(
-        examples=get_example(),
-        inputs=[style_image_pil, prompt, guidance_scale, seed, end_fusion],
-        fn=run_for_examples,
-        outputs=[generated_image],
-        cache_examples=False,
-    )
-
-    gr.Markdown(article)
-
-block.launch(server_name="0.0.0.0", server_port=1234)