init

app.py

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+import os
+
+import gradio as gr
+import numpy as np
+import torch
+import torchvision.transforms as T
+
+from clip_interrogator import Config, Interrogator
+
+from ditail import DitailDemo, seed_everything
+
+BASE_MODEL = {
+    'sd1.5': 'runwayml/stable-diffusion-v1-5',
+    # 'sd1.5': './ditail/model/stable-diffusion-v1-5'
+    'realistic vision': 'stablediffusionapi/realistic-vision-v51',
+    'pastel mix (anime)': 'stablediffusionapi/pastel-mix-stylized-anime',
+    'chaos (abstract)': 'MAPS-research/Chaos3.0',
+}
+
+# LoRA trigger words
+LORA_TRIGGER_WORD = {
+    'none': [],
+    'film': ['film overlay', 'film grain'],
+    'snow': ['snow'],
+    'flat': ['sdh', 'flat illustration'],
+    'minecraft': ['minecraft square style', 'cg, computer graphics'],
+    'animeoutline': ['lineart', 'monochrome'],
+    # 'caravaggio': ['oil painting', 'in the style of caravaggio'],
+    'impressionism': ['impressionist', 'in the style of Monet'],
+    'pop': ['POP ART'],
+    'shinkai_makoto': ['shinkai makoto', 'kimi no na wa.', 'tenki no ko', 'kotonoha no niwa'],
+}
+
+
+class WebApp():
+    def __init__(self, debug_mode=False):
+        self.args_base = {
+            "seed": 42,
+            "device": "cuda",
+            "output_dir": "output_demo",
+            "caption_model_name": "blip-large",
+            "clip_model_name": "ViT-L-14/openai",
+            "inv_model": "stablediffusionapi/realistic-vision-v51",
+            "spl_model": "runwayml/stable-diffusion-v1-5",
+            "inv_steps": 50,
+            "spl_steps": 50,
+            "img": None,
+            "pos_prompt": '',
+            "neg_prompt": 'worst quality, blurry, NSFW',
+            "alpha": 3.0,
+            "beta": 0.5,
+            "omega": 15,
+            "mask": None,
+            "lora": "none",
+            "lora_dir": "./ditail/lora",
+            "lora_scale": 0.7,
+            "no_injection": False,
+        }
+
+        self.args_input = {} # for gr.components only
+        self.gr_loras = list(LORA_TRIGGER_WORD.keys())
+
+        self.gtag = os.environ.get('GTag')
+
+        self.ga_script = f"""
+            <script async src="https://www.googletagmanager.com/gtag/js?id={self.gtag}"></script>
+            """
+        self.ga_load = f"""
+            function() {{
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){{dataLayer.push(arguments);}}
+                gtag('js', new Date());
+
+                gtag('config', '{self.gtag}');
+            }}
+            """
+
+        self.debug_mode = debug_mode # turn off clip interrogator when debugging for faster building speed
+        if not self.debug_mode:
+            self.init_interrogator()
+
+
+    def init_interrogator(self):
+        # init clip interrogator
+        config = Config()
+        config.clip_model_name = self.args_base['clip_model_name']
+        config.caption_model_name = self.args_base['caption_model_name']
+        self.ci = Interrogator(config)
+        self.ci.config.chunk_size = 2048 if self.ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+        self.ci.config.flavor_intermediate_count = 2048 if self.ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+
+    def title(self):
+        gr.HTML(
+                """
+                <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
+                <div>
+                    <h1 >Diffusion Cocktail 🍸: Fused Generation from Diffusion Models</h1>
+                    <div style="display: flex; justify-content: center; align-items: center; text-align: center; margin: 20px; gap: 10px;>
+                        <a class="flex-item" href="https://arxiv.org/abs/your-arxiv-id" target="_blank">
+                            <img src="https://img.shields.io/badge/arXiv-paper-darkred.svg" alt="arXiv Paper">
+                        </a>
+                        <a class="flex-item" href="https://MAPS-research.github.io/Ditail" target="_blank">
+                            <img src="https://img.shields.io/badge/Project_Page-Diffusion_Cocktail-yellow.svg" alt="Project Page">
+                        </a>
+                        <a class="flex-item" href="https://github.com/MAPS-research/Ditail" target="_blank">
+                            <img src="https://img.shields.io/badge/Github-Code-green.svg" alt="GitHub Code">
+                        </a>
+                    </div>
+                </div>
+                </div>
+                """
+                )
+                
+
+    def get_image(self):
+        self.args_input['img'] = gr.Image(label='content image', type='pil', show_share_button=False, elem_classes="input_image")
+    
+    def get_prompts(self):
+        # with gr.Row():
+        generate_prompt = gr.Checkbox(label='generate prompt with clip', value=True)
+        self.args_input['pos_prompt'] = gr.Textbox(label='prompt')
+            
+            
+        # event listeners
+        self.args_input['img'].upload(self._interrogate_image, inputs=[self.args_input['img'], generate_prompt], outputs=[self.args_input['pos_prompt']])
+        generate_prompt.change(self._interrogate_image, inputs=[self.args_input['img'], generate_prompt], outputs=[self.args_input['pos_prompt']])
+
+
+    def _interrogate_image(self, image, generate_prompt):
+        # self.init_interrogator()
+        if hasattr(self, 'ci') and generate_prompt:
+            return self.ci.interrogate_fast(image).split(',')[0].replace('arafed', '')
+        else:
+            return ''
+        
+
+    def get_base_model(self):
+        self.args_input['spl_model'] = gr.Radio(choices=list(BASE_MODEL.keys()), value=list(BASE_MODEL.keys())[0], label='target base model')
+
+    def get_lora(self, num_cols=3):
+        self.args_input['lora'] = gr.State('none')
+        lora_gallery = gr.Gallery(label='target LoRA (optional)', columns=num_cols, value=[(os.path.join(self.args_base['lora_dir'], f"{lora}.jpeg"), lora) for lora in self.gr_loras], allow_preview=False, show_share_button=False, selected_index=0)
+        lora_gallery.select(self._update_lora_selection, inputs=[], outputs=[self.args_input['lora']])
+    
+    def _update_lora_selection(self, selected_state: gr.SelectData):
+        return self.gr_loras[selected_state.index]
+
+    def get_params(self):
+        with gr.Row():
+            with gr.Column():
+                self.args_input['inv_model'] = gr.Radio(choices=list(BASE_MODEL.keys()), value=list(BASE_MODEL.keys())[1], label='inversion base model')
+                self.args_input['neg_prompt'] = gr.Textbox(label='negative prompt', value=self.args_base['neg_prompt'])
+            # with gr.Row():
+                self.args_input['alpha'] = gr.Number(label='positive prompt scaling weight (alpha)', value=self.args_base['alpha'], interactive=True)
+                self.args_input['beta'] = gr.Number(label='negative prompt scaling weight (beta)', value=self.args_base['beta'], interactive=True)
+
+            with gr.Column():
+                self.args_input['omega'] = gr.Slider(label='cfg', value=self.args_base['omega'], maximum=25, interactive=True)
+                
+                self.args_input['inv_steps'] = gr.Slider(minimum=1, maximum=100, label='edit steps', interactive=True, value=self.args_base['inv_steps'], step=1)
+                self.args_input['spl_steps'] = gr.Slider(minimum=1, maximum=100, label='sample steps', interactive=False, value=self.args_base['spl_steps'], step=1, visible=False)
+                # sync inv_steps with spl_steps
+                self.args_input['inv_steps'].change(lambda x: x, inputs=self.args_input['inv_steps'], outputs=self.args_input['spl_steps'])
+
+                self.args_input['lora_scale'] = gr.Slider(minimum=0, maximum=1, label='LoRA scale', value=0.7)
+                self.args_input['seed'] = gr.Number(label='seed', value=self.args_base['seed'], interactive=True, precision=0, step=1)
+
+    def run_ditail(self, *values):
+        self.args = self.args_base.copy()
+        print(self.args_input.keys())
+        for k, v in zip(list(self.args_input.keys()), values):
+            self.args[k] = v
+        # quick fix for example
+        self.args['lora'] = 'none' if not isinstance(self.args['lora'], str) else self.args['lora']
+        print('selected lora: ', self.args['lora'])
+        # map inversion model to url
+        self.args['pos_prompt'] = ', '.join(LORA_TRIGGER_WORD.get(self.args['lora'], [])+[self.args['pos_prompt']])
+        self.args['inv_model'] = BASE_MODEL[self.args['inv_model']]
+        self.args['spl_model'] = BASE_MODEL[self.args['spl_model']]
+        print('selected model: ', self.args['inv_model'], self.args['spl_model'])
+
+        seed_everything(self.args['seed'])
+        ditail = DitailDemo(self.args)
+        
+        metadata_to_show = ['inv_model', 'spl_model', 'lora', 'lora_scale', 'inv_steps', 'spl_steps', 'pos_prompt', 'alpha', 'neg_prompt', 'beta', 'omega']
+        self.args_to_show = {}
+        for key in metadata_to_show:
+            self.args_to_show[key] = self.args[key ]
+
+        return ditail.run_ditail(), self.args_to_show
+        # return self.args['img'], self.args
+
+    def run_example(self, img, prompt, inv_model, spl_model, lora):
+        return self.run_ditail(img, prompt, spl_model, gr.State(lora), inv_model)
+
+    def show_credits(self):
+        # gr.Markdown(
+        #     """
+        #     ### About Diffusion Cocktail (Ditail)
+        #     * This is a research project by [MAPS Lab](https://whongyi.github.io/MAPS-research), [NYU Shanghai](https://shanghai.nyu.edu)
+        #     * Authors: Haoming Liu ([email protected]), Yuanhe Guo ([email protected]), Hongyi Wen ([email protected])
+        #     """
+        # )
+        gr.Markdown(
+            """
+            ### Model Credits
+            * Diffusion Models are downloaded from [huggingface](https://huggingface.co) and [civitai](https://civitai.com): [stable diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5), [realistic vision](https://huggingface.co/stablediffusionapi/realistic-vision-v51), [pastel mix](https://huggingface.co/stablediffusionapi/pastel-mix-stylized-anime), [chaos3.0](https://civitai.com/models/91534/chaos30)
+            * LoRA Models are downloaded from [civitai](https://civitai.com) and [liblib](https://www.liblib.art): [film](https://civitai.com/models/90393/japan-vibes-film-color), [snow](https://www.liblib.art/modelinfo/f732b23b02f041bdb7f8f3f8a256ca8b), [flat](https://www.liblib.art/modelinfo/76dcb8b59d814960b0244849f2747a15), [minecraft](https://civitai.com/models/113741/minecraft-square-style), [animeoutline](https://civitai.com/models/16014/anime-lineart-manga-like-style), [impressionism](https://civitai.com/models/113383/y5-impressionism-style), [pop](https://civitai.com/models/161450?modelVersionId=188417), [shinkai_makoto](https://civitai.com/models/10626?modelVersionId=12610) 
+            """
+        )
+
+
+    def ui(self):
+        with gr.Blocks(css='.input_image img {object-fit: contain;}', head=self.ga_script) as demo:
+            self.title()
+            with gr.Row():
+                # with gr.Column():
+                self.get_image()
+
+                with gr.Column():
+                    self.get_prompts()
+                    self.get_base_model()
+                    self.get_lora(num_cols=3)
+                    submit_btn = gr.Button("Generate", variant='primary')
+
+            with gr.Accordion("advanced options", open=False):
+                self.get_params()   
+            
+            with gr.Row():
+                output_image = gr.Image(label="output image")
+                # expected_output_image = gr.Image(label="expected output image", visible=False)
+                metadata = gr.JSON(label='metadata')
+
+                submit_btn.click(self.run_ditail,
+                                inputs=list(self.args_input.values()),
+                                outputs=[output_image, metadata],
+                                scroll_to_output=True,
+                                )
+
+            with gr.Row():
+                cache_examples = not self.debug_mode
+                gr.Examples(
+                    examples=[[os.path.join(os.path.dirname(__file__), "example", "Lenna.png"), 'a woman called Lenna wearing a feathered hat', list(BASE_MODEL.keys())[1], list(BASE_MODEL.keys())[2], 'none']],
+                    inputs=[self.args_input['img'], self.args_input['pos_prompt'], self.args_input['inv_model'], self.args_input['spl_model'], gr.Textbox(label='LoRA', visible=False), ],
+                    fn = self.run_example,
+                    outputs=[output_image, metadata],
+                    run_on_click=True,
+                    cache_examples=cache_examples,
+                )
+
+            self.show_credits()
+        
+            demo.load(None, js=self.ga_load)
+        return demo
+
+
+app = WebApp(debug_mode=False)
+demo = app.ui()
+
+
+if __name__ == "__main__":
+    demo.launch(share=True)
+    # demo.launch()
+    
+