app

app.py

@@ -1,142 +1,602 @@
+import os
+import random
+
 import gradio as gr
 import numpy as np
-import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import PIL.Image
+from PIL import ImageOps
 import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import BitsAndBytesConfig
+import torchvision.transforms.functional as TF
+from diffusers import (
+    AutoencoderKL,
+    EulerAncestralDiscreteScheduler,
+    StableDiffusionXLAdapterPipeline,
+    T2IAdapter,
+)
+
+import urllib.parse
+import requests
+from io import BytesIO
+import json
+
+from pathlib import Path
+import uuid
+import os, uuid
+from azure.identity import DefaultAzureCredential
+from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient
+
+from datetime import datetime
+
+
+
+class DEFAULTS:
+    NEGATIVE_PROMPT = " extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured"
+    REWRITING_PROMPT = (
+        "Rewrite the image caption by making it shorter (but retain all information about relative position), "
+        "remove information about style of objects or colors of background and foreground, and, most importantly, remove all details "
+        "that suggests it is a sketch. Write it as a Google image search query:"
+    )
+    MOONDREAM_PROMPT = "Describe this image."
+    NUM_STEPS = 25
+    GUIDANCE_SCALE = 5
+    ADAPTER_CONDITIONING_SCALE = 0.8
+    ADAPTER_CONDITIONING_FACTOR = 0.8
+    SEED = 1231245
+    RANDOMIZE_SEED = True
+
+
+
+DESCRIPTION = '''# Sketch to Image/Caption to Bing Search :)
+'''
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+
+style_list = [
+    {
+        "name": "(No style)",
+        "prompt": "{prompt}",
+        "negative_prompt": "",
+    },
+    {
+        "name": "Cinematic",
+        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
+        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
+    },
+    {
+        "name": "3D Model",
+        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
+        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
+    },
+    {
+        "name": "Anime",
+        "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime,  highly detailed",
+        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
+    },
+    {
+        "name": "Digital Art",
+        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
+        "negative_prompt": "photo, photorealistic, realism, ugly",
+    },
+    {
+        "name": "Photographic",
+        "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
+        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
+    },
+    {
+        "name": "Pixel art",
+        "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
+        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
+    },
+    {
+        "name": "Fantasy art",
+        "prompt": "ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
+        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
+    },
+    {
+        "name": "Neonpunk",
+        "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
+        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
+    },
+    {
+        "name": "Manga",
+        "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
+        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
+    },
+]
+
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "Photographic"  # "(No style)"
+
+
+def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    return p.replace("{prompt}", positive), n + negative
+
+
+# with open("azure_connection_string.txt", "r") as f:
+#     CONNECTION_STRING = f.read().strip()
+CONNECTION_STRING = os.getenv("AZURE_CONNECTION_STRING")
+
+
+def upload_pil_image_to_azure(image, connection_string=CONNECTION_STRING):
+    image_name = f"{uuid.uuid4()}.png"
+    image_bytes = BytesIO()
+    image.save(image_bytes, format="PNG")
+    image_bytes.seek(0)
+
+    try:
+        # Create the BlobServiceClient object
+        blob_service_client = BlobServiceClient.from_connection_string(connection_string)
+        # Create a blob client using the local file name as the name for the blob
+        blob_client = blob_service_client.get_blob_client(container="blob-image-hosting", blob=image_name)
+        # Upload the created file and retrieve the URL
+        blob_client.upload_blob(image_bytes)
+        file_url = blob_client.url
+    except Exception as ex:
+        print('Exception:')
+        print(ex)
+        file_url = None
+    # If this function did not fail, upload was successful
+    return file_url
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
 
 if torch.cuda.is_available():
-    torch_dtype = torch.float16
+    if torch.cuda.device_count() > 1:
+        device_0, device_1 = torch.device("cuda:0"), torch.device("cuda:1")
+    else:
+        device_0, device_1 = torch.device("cuda:0"), torch.device("cuda:0")
 else:
-    torch_dtype = torch.float32
+    device_0, device_1 = torch.device("cpu"), torch.device("cpu")
+
+if torch.cuda.is_available():
+    model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+    adapter = T2IAdapter.from_pretrained(
+        "TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
+    )
+    scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+    pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
+        model_id,
+        vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16),
+        adapter=adapter,
+        scheduler=scheduler,
+        torch_dtype=torch.float16,
+        variant="fp16",
+    )
+    pipe.to(device_0)
+else:
+    pipe = None
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
 
-#@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+MAX_SEED = np.iinfo(np.int32).max
 
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image, seed
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+    return seed
+
+nf4_config = BitsAndBytesConfig(
+   load_in_4bit=True,
+   bnb_4bit_quant_type="nf4",
+   bnb_4bit_use_double_quant=True,
+   bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+vlmodel_id = "vikhyatk/moondream2"
+vlmodel_revision = "2024-07-23"
+vlmodel = AutoModelForCausalLM.from_pretrained(
+    vlmodel_id, trust_remote_code=True, revision=vlmodel_revision, device_map={"": device_1},
+    torch_dtype=torch.float16, attn_implementation="flash_attention_2")
+vltokenizer = AutoTokenizer.from_pretrained(vlmodel_id, revision=vlmodel_revision)
+
+rewrite_model_name = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+rewrite_model = AutoModelForCausalLM.from_pretrained(
+    rewrite_model_name,
+    device_map={"": device_1},
+    quantization_config=nf4_config,
+    # load_in_8bit=True,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2")
+rewrite_tokenizer = AutoTokenizer.from_pretrained(rewrite_model_name)
+
+
+def caption_image_with_recaption(pil_image, moondream_prompt, rewriting_prompt, user_prompt=""):
+    enc_image = vlmodel.encode_image(pil_image)
+    img_caption = vlmodel.answer_question(enc_image, moondream_prompt, vltokenizer)
+    rewritten_caption = rewrite_prompt(img_caption, rewriting_prompt, user_prompt=user_prompt)
+    rewritten_caption = rewritten_caption.strip('"').replace("\n", " ")
+    return img_caption, rewritten_caption
+
+
+def rewrite_prompt(image_cap: str, guide: str, user_prompt: str = "") -> str:
+    prompt = f"{guide}\n{image_cap}"
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": prompt}
+    ]
+    text = rewrite_tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    model_inputs = rewrite_tokenizer([text], return_tensors="pt").to(device_1)
+    generated_ids = rewrite_model.generate(model_inputs.input_ids, max_new_tokens=128)
+    generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
+    response = rewrite_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return response
+
+
+def run_full(
+    image,
+    user_prompt: str,
+    negative_prompt: str,
+    rewriting_prompt: str,
+    moondream_prompt: str,
+    style_name: str = DEFAULT_STYLE_NAME,
+    num_steps: int = 25,
+    guidance_scale: float = 5,
+    adapter_conditioning_scale: float = 0.8,
+    adapter_conditioning_factor: float = 0.8,
+    seed: int = 0,
+    progress=None,
+) -> PIL.Image.Image:
+    # image is a white background with black sketch
+    image = ImageOps.invert(image)
+    # Threshold the image to get a binary sketch
+    image = TF.to_tensor(image) > 0.5
+    image = TF.to_pil_image(image.to(torch.float32))
+
+    full_log = []
+    if user_prompt == "":
+        pre_caption = True
+        start_time = datetime.now()
+        img_caption, rewritten_caption = caption_image_with_recaption(
+            pil_image=image, rewriting_prompt=rewriting_prompt, moondream_prompt=moondream_prompt)
+        full_log.append(f"Combined captioning time: {datetime.now() - start_time}")
+        full_log.append(f"img_caption (pre): {img_caption}")
+        full_log.append(f"rewritten_caption (pre): {rewritten_caption}")
+        drawing_prompt = rewritten_caption
+    else:
+        pre_caption = False
+        drawing_prompt = user_prompt
+    full_log.append(f"Pre-caption: {pre_caption}")
+
+    # Generate image
+    start_time = datetime.now()
+    drawing_prompt, negative_prompt = apply_style(style_name, drawing_prompt, negative_prompt)
+    generator = torch.Generator(device=device_0).manual_seed(seed)
+    out_img = pipe(
+        prompt=drawing_prompt,
+        negative_prompt=negative_prompt,
+        image=image,
+        num_inference_steps=num_steps,
+        generator=generator,
+        guidance_scale=guidance_scale,
+        adapter_conditioning_scale=adapter_conditioning_scale,
+        adapter_conditioning_factor=adapter_conditioning_factor,
+    ).images[0]
+    full_log.append(f"Image generation time: {datetime.now() - start_time}")
+
+    if not pre_caption:
+        start_time = datetime.now()
+        img_caption, rewritten_caption = caption_image_with_recaption(
+            pil_image=out_img,
+            rewriting_prompt=rewriting_prompt,
+            moondream_prompt=moondream_prompt,
+            user_prompt=user_prompt)
+        full_log.append(f"Combined captioning time: {datetime.now() - start_time}")
+        full_log.append(f"img_caption (post): {img_caption}")
+        full_log.append(f"rewritten_caption (post): {rewritten_caption}")
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
-        
-        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)
-        
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            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, #Replace with defaults that work for your model
+    # SERP query
+    bing_serp_query = f"https://www.bing.com/images/search?q={urllib.parse.quote(rewritten_caption)}"
+    md_text = f"### Bing search query\n[{bing_serp_query}]({bing_serp_query})\n"
+
+    # Visual Search query
+    out_img_imgur_url = upload_pil_image_to_azure(out_img)
+    if out_img_imgur_url is None:
+        md_text += "### Bing Visual Search\n**Error:** Failed to upload image to Azure Blob Storage\n"
+        bing_image_search_url = "https://www.bing.com/images"
+    else:
+        imgur_url_quote = urllib.parse.quote(out_img_imgur_url)
+        bing_image_search_url = f"https://www.bing.com/images/search?view=detailv2&iss=SBI&form=SBIIRP&q=imgurl:{imgur_url_quote}"
+        md_text += f"### Bing Visual Search\n[{bing_image_search_url}]({bing_image_search_url})\n"
+
+    # Debug info
+    md_text += f"### Debug: sketch caption\n{img_caption}\n\n### Debug: rewritten caption\n{rewritten_caption}\n"
+
+    # Full log dump
+    md_text += f"### Debug: full log\n{'<br>'.join(full_log)}"
+
+    # return dict
+    return {
+        "image": out_img,
+        "text_search_url": bing_serp_query,
+        "visual_search_url": bing_image_search_url,
+        "logs": md_text,
+    }
+
+
+def run_full_gradio(
+    image,
+    user_prompt: str,
+    negative_prompt: str,
+    rewriting_prompt: str,
+    moondream_prompt: str,
+    style_name: str = DEFAULT_STYLE_NAME,
+    num_steps: int = 25,
+    guidance_scale: float = 5,
+    adapter_conditioning_scale: float = 0.8,
+    adapter_conditioning_factor: float = 0.8,
+    seed: int = 0,
+    progress=gr.Progress(track_tqdm=True),
+) -> PIL.Image.Image:
+    image = image['composite']
+    background = PIL.Image.new('RGBA', image.size, (255, 255, 255))
+    alpha_composite = PIL.Image.alpha_composite(background, image)
+    image = alpha_composite.convert("RGB")
+
+    results = run_full(
+        image=image,
+        user_prompt=user_prompt,
+        negative_prompt=negative_prompt,
+        rewriting_prompt=rewriting_prompt,
+        moondream_prompt=moondream_prompt,
+        style_name=style_name,
+        num_steps=num_steps,
+        guidance_scale=guidance_scale,
+        adapter_conditioning_scale=adapter_conditioning_scale,
+        adapter_conditioning_factor=adapter_conditioning_factor,
+        seed=seed,
+        progress=progress,
+    )
+
+    # construct markdown output
+    return results["image"], results["logs"]
+
+
+def run_full_api(
+    image_url: str,
+    user_prompt: str,
+    progress=gr.Progress(track_tqdm=True),
+) -> str:
+    seed = randomize_seed_fn(0, True)
+    image = PIL.Image.open(BytesIO(requests.get(image_url).content))
+    results = run_full(
+        image=image, user_prompt=user_prompt,
+        negative_prompt=DEFAULTS.NEGATIVE_PROMPT,
+        rewriting_prompt=DEFAULTS.REWRITING_PROMPT,
+        moondream_prompt=DEFAULTS.MOONDREAM_PROMPT,
+        style_name=DEFAULT_STYLE_NAME,
+        num_steps=DEFAULTS.NUM_STEPS,
+        guidance_scale=DEFAULTS.GUIDANCE_SCALE,
+        adapter_conditioning_scale=DEFAULTS.ADAPTER_CONDITIONING_SCALE,
+        adapter_conditioning_factor=DEFAULTS.ADAPTER_CONDITIONING_FACTOR,
+        seed=seed)
+    return results["text_search_url"], results["visual_search_url"], results["logs"]
+
+
+def run_caponly(
+    image,
+    rewriting_prompt: str,
+    moondream_prompt: str,
+    seed: int = 0,
+    progress=None,
+) -> PIL.Image.Image:
+    # image is a white background with black sketch
+    image = ImageOps.invert(image)
+    # Threshold the image to get a binary sketch
+    image = TF.to_tensor(image) > 0.5
+    image = TF.to_pil_image(image.to(torch.float32))
+
+    full_log = []
+    start_time = datetime.now()
+    img_caption, rewritten_caption = caption_image_with_recaption(
+        pil_image=image, rewriting_prompt=rewriting_prompt, moondream_prompt=moondream_prompt)
+    full_log.append(f"Combined captioning time: {datetime.now() - start_time}")
+    full_log.append(f"img_caption (pre): {img_caption}")
+    full_log.append(f"rewritten_caption (pre): {rewritten_caption}")
+    final_prompt = rewritten_caption
+
+    # SERP query
+    bing_serp_query = f"https://www.bing.com/images/search?q={urllib.parse.quote(rewritten_caption)}"
+    md_text = f"### Bing search query\n[{bing_serp_query}]({bing_serp_query})\n"
+
+    # Debug info
+    md_text += f"### Debug: sketch caption\n{img_caption}\n\n### Debug: rewritten caption\n{rewritten_caption}\n"
+
+    # Full log dump
+    md_text += f"### Debug: full log\n{'<br>'.join(full_log)}"
+
+    # return dict
+    return {
+        "text_search_url": bing_serp_query,
+        "logs": md_text,
+    }
+
+
+def run_caponly_api(
+    image_url: str,
+    progress=gr.Progress(track_tqdm=True),
+) -> str:
+    seed = randomize_seed_fn(0, True)
+    image = PIL.Image.open(BytesIO(requests.get(image_url).content))
+    results = run_caponly(
+        image=image,
+        rewriting_prompt=DEFAULTS.REWRITING_PROMPT,
+        moondream_prompt=DEFAULTS.MOONDREAM_PROMPT,
+        seed=seed)
+    return results["text_search_url"], results["logs"]
+
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION, elem_id="description")
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+    )
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Group():
+                image = gr.Sketchpad(
+                    # sources=["canvas"],
+                    # tool="sketch",
+                    type="pil",
+                    image_mode="RGBA",
+                    # invert_colors=True,
+                    layers=False,
+                    canvas_size=(1024, 1024),
+                    brush=gr.Brush(
+                        default_color="black",
+                        colors=None,
+                        default_size=4,
+                        color_mode="fixed",
+                        ),
+                    eraser=gr.Eraser(),
+                    height=440,
+                )
+                prompt = gr.Textbox(label="Prompt")
+                style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
+                run_button = gr.Button("Run")
+            with gr.Accordion("Advanced options", open=False):
+                negative_prompt = gr.Textbox(
+                    label="Negative prompt",
+                    value=DEFAULTS.NEGATIVE_PROMPT,
                 )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
+                rewriting_prompt = gr.Textbox(
+                    label="Rewriting prompt",
+                    value=DEFAULTS.REWRITING_PROMPT,
+                )
+                moondream_prompt = gr.Textbox(
+                    label="Moondream prompt",
+                    value=DEFAULTS.MOONDREAM_PROMPT,
+                )
+                num_steps = gr.Slider(
+                    label="Number of steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=DEFAULTS.NUM_STEPS,
                 )
-            
-            with gr.Row():
-                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
-                    minimum=0.0,
+                    minimum=0.1,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
+                    value=DEFAULTS.GUIDANCE_SCALE,
                 )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
+                adapter_conditioning_scale = gr.Slider(
+                    label="Adapter conditioning scale",
+                    minimum=0.5,
+                    maximum=1,
+                    step=0.1,
+                    value=DEFAULTS.ADAPTER_CONDITIONING_SCALE,
+                )
+                adapter_conditioning_factor = gr.Slider(
+                    label="Adapter conditioning factor",
+                    info="Fraction of timesteps for which adapter should be applied",
+                    minimum=0.5,
+                    maximum=1,
+                    step=0.1,
+                    value=DEFAULTS.ADAPTER_CONDITIONING_FACTOR,
+                )
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
                     step=1,
-                    value=2, #Replace with defaults that work for your model
+                    value=0,
                 )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Column():
+            result_img = gr.Image(label="Result", height=400, interactive=False)
+            result_caption = gr.Markdown(label="Image caption")
+            result = [result_img, result_caption]
+
+    with gr.Row():
+        gr.Markdown("# API endpoints", elem_id="description")
+    with gr.Row():
+        with gr.Column():
+            with gr.Accordion("Full Experience API", open=False):
+                api_fullexp_image_url = gr.Textbox(label="Image URL")
+                api_fullexp_user_prompt = gr.Textbox(label="User prompt")
+                api_fullexp_run_button = gr.Button("Run API")
+                api_fullexp_text_search_url = gr.Textbox(label="Text search URL")
+                api_fullexp_visual_search_url = gr.Textbox(label="Visual search URL")
+                api_fullexp_logs = gr.Markdown(label="Logs")
+        with gr.Column():
+            with gr.Accordion("Caption Only API", open=False):
+                api_caponly_image_url = gr.Textbox(label="Image URL")
+                api_caponly_run_button = gr.Button("Run API")
+                api_caponly_text_search_url = gr.Textbox(label="Text search URL")
+                api_caponly_logs = gr.Markdown(label="Logs")
+
+    # Gradio components interconnections
+    inputs = [
+        image,
+        prompt,
+        negative_prompt,
+        rewriting_prompt,
+        moondream_prompt,
+        style,
+        num_steps,
+        guidance_scale,
+        adapter_conditioning_scale,
+        adapter_conditioning_factor,
+        seed,
+    ]
+    prompt.submit(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=run_full_gradio,
+        inputs=inputs,
+        outputs=result,
+        api_name=False,
+    )
+    negative_prompt.submit(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=run_full_gradio,
+        inputs=inputs,
+        outputs=result,
+        api_name=False,
+    )
+    run_button.click(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=run_full_gradio,
+        inputs=inputs,
+        outputs=result,
+        api_name=False,
+    )
+
+    # API interconnections
+    api_fullexp_run_button.click(
+        fn=run_full_api,
+        inputs=[api_fullexp_image_url, api_fullexp_user_prompt],
+        outputs=[api_fullexp_text_search_url, api_fullexp_visual_search_url, api_fullexp_logs],
+        api_name="full_experience",
+    )
+    api_caponly_run_button.click(
+        fn=run_caponly_api,
+        inputs=[api_caponly_image_url],
+        outputs=[api_caponly_text_search_url, api_caponly_logs],
+        api_name="caption_only",
     )
 
-demo.queue().launch()
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()

requirements.txt

@@ -3,4 +3,10 @@ diffusers
 invisible_watermark
 torch
 transformers
-xformers
+xformers
+flash-attn
+bitsandbytes
+azure-core
+azure-storage-blob
+azure-identity
+einops

style.css

@@ -0,0 +1,16 @@
+#component-0{
+    max-width: 900px;
+    margin: 0 auto;
+  }
+  
+  #description, h1 {
+    text-align: center;
+  }
+  
+  #duplicate-button {
+    margin: auto;
+    color: #fff;
+    background: #1565c0;
+    border-radius: 100vh;
+  }
+