update

common.py

@@ -0,0 +1,40 @@
+BACKGROUND_COLOR = "white"
+COLOR = "black"
+
+import streamlit as st
+
+def set_page_container_style(
+    max_width: int = 1100,
+    max_width_100_percent: bool = False,
+    padding_top: int = 1,
+    padding_right: int = 10,
+    padding_left: int = 1,
+    padding_bottom: int = 10,
+    color: str = COLOR,
+    background_color: str = BACKGROUND_COLOR,
+):
+    if max_width_100_percent:
+        max_width_str = f"max-width: 100%;"
+    else:
+        max_width_str = f"max-width: {max_width}px;"
+    st.markdown(
+        f"""
+            <style>
+                .reportview-container .css-1lcbmhc .css-1outpf7 {{
+                    padding-top: 35px;
+                }}
+                .reportview-container .main .block-container {{
+                    {max_width_str}
+                    padding-top: {padding_top}rem;
+                    padding-right: {padding_right}rem;
+                    padding-left: {padding_left}rem;
+                    padding-bottom: {padding_bottom}rem;
+                }}
+                .reportview-container .main {{
+                    color: {color};
+                    background-color: {background_color};
+                }}
+            </style>
+            """,
+        unsafe_allow_html=True,
+    )

core.py

@@ -0,0 +1,278 @@
+import base64
+import io
+import random
+import time
+from typing import List
+from PIL import Image
+import aiohttp
+import asyncio
+import requests
+import streamlit as st
+import requests
+import zipfile
+import io
+import pandas as pd
+from utils import icon
+from streamlit_image_select import image_select
+from PIL import Image
+import random
+import time
+import base64
+from typing import List
+import aiohttp
+import asyncio
+import plotly.express as px
+from common import set_page_container_style
+
+
+def pil_image_to_base64(image: Image.Image) -> str:
+    image_stream = io.BytesIO()
+    image.save(image_stream, format="PNG")
+    base64_image = base64.b64encode(image_stream.getvalue()).decode("utf-8")
+
+    return base64_image
+
+
+def get_or_create_eventloop():
+    try:
+        return asyncio.get_event_loop()
+    except RuntimeError as ex:
+        if "There is no current event loop in thread" in str(ex):
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            return asyncio.get_event_loop()
+
+
+model_config = {
+    "RealisticVision": {
+        "ratio": {
+            "square": (512, 512),
+            "tall": (512, 768),
+            "wide": (768, 512),
+        },
+        "num_inference_steps": 30,
+        "guidance_scale": 7.0,
+        "clip_skip": 2,
+    },
+    "AnimeV3": {
+        "num_inference_steps": 25,
+        "guidance_scale": 7,
+        "clip_skip": 2,
+        "ratio": {
+            "square": (1024, 1024),
+            "tall": (672, 1024),
+            "wide": (1024, 672),
+        },
+    },
+    "DreamShaper": {
+        "num_inference_steps": 35,
+        "guidance_scale": 7,
+        "clip_skip": 2,
+        "ratio": {
+            "square": (512, 512),
+            "tall": (512, 768),
+            "wide": (768, 512),
+        },
+    },
+    "RealitiesEdgeXL": {
+        "num_inference_steps": 7,
+        "guidance_scale": 2.5,
+        "clip_skip": 2,
+        "ratio": {
+            "square": (1024, 1024),
+            "tall": (672, 1024),
+            "wide": (1024, 672),
+        },
+    },
+}
+
+
+def base64_to_image(base64_string):
+    return Image.open(io.BytesIO(base64.b64decode(base64_string)))
+
+
+async def call_niche_api(url, data) -> List[Image.Image]:
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.post(url, json=data) as response:
+                response = await response.json()
+        return base64_to_image(response)
+    except Exception as e:
+        print(e)
+        return None
+
+
+async def get_output(url, datas):
+    tasks = [asyncio.create_task(call_niche_api(url, data)) for data in datas]
+    return await asyncio.gather(*tasks)
+
+
+def main_page(
+    submitted: bool,
+    model_name: str,
+    prompt: str,
+    negative_prompt: str,
+    aspect_ratio: str,
+    num_images: int,
+    uid: str,
+    secret_key: str,
+    seed: str,
+    conditional_image: str,
+    controlnet_conditioning_scale: list,
+    pipeline_type: str,
+    api_token: str,
+    generated_images_placeholder,
+) -> None:
+    """Main page layout and logic for generating images.
+
+    Args:
+        submitted (bool): Flag indicating whether the form has been submitted.
+        width (int): Width of the output image.
+        height (int): Height of the output image.
+        num_inference_steps (int): Number of denoising steps.
+        guidance_scale (float): Scale for classifier-free guidance.
+        prompt_strength (float): Prompt strength when using img2img/inpaint.
+        prompt (str): Text prompt for the image generation.
+        negative_prompt (str): Text prompt for elements to avoid in the image.
+    """
+    if submitted:
+        if secret_key != api_token and uid != "-1":
+            st.error("Invalid secret key")
+            return
+        try:
+            uid = int(uid)
+        except ValueError:
+            uid = -1
+        width, height = model_config[model_name]["ratio"][aspect_ratio.lower()]
+        width = int(width)
+        height = int(height)
+        num_inference_steps = model_config[model_name]["num_inference_steps"]
+        guidance_scale = model_config[model_name]["guidance_scale"]
+
+        with st.status(
+            "👩🏾‍🍳 Whipping up your words into art...", expanded=True
+        ) as status:
+            try:
+                # Only call the API if the "Submit" button was pressed
+                if submitted:
+                    start_time = time.time()
+                    # Calling the replicate API to get the image
+                    with generated_images_placeholder.container():
+                        try:
+                            seed = int(seed)
+                        except ValueError:
+                            seed = -1
+                        if seed >= 0:
+                            seeds = [int(seed) + i for i in range(num_images)]
+                        else:
+                            seeds = [random.randint(0, 1e9) for _ in range(num_images)]
+                        all_images = []  # List to store all generated images
+                        data = {
+                            "key": api_token,
+                            "prompt": prompt,  # prompt
+                            "model_name": model_name,  # See avaialble models in https://github.com/NicheTensor/NicheImage/blob/main/configs/model_config.yaml
+                            "seed": seed,  # -1 means random seed
+                            "miner_uid": int(
+                                uid
+                            ),  # specify miner uid, -1 means random miner selected by validator
+                            "pipeline_type": pipeline_type,
+                            "conditional_image": conditional_image,
+                            "pipeline_params": {  # params feed to diffusers pipeline, see all params here https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.__call__
+                                "width": width,
+                                "height": height,
+                                "num_inference_steps": num_inference_steps,
+                                "guidance_scale": guidance_scale,
+                                "negative_prompt": negative_prompt,
+                                "controlnet_conditioning_scale": controlnet_conditioning_scale,
+                                "clip_skip": model_config[model_name]["clip_skip"],
+                            },
+                        }
+                        duplicate_data = [data.copy() for _ in range(num_images)]
+                        for i, d in enumerate(duplicate_data):
+                            d["seed"] = seeds[i]
+                        # Call the NicheImage API
+                        loop = get_or_create_eventloop()
+                        asyncio.set_event_loop(loop)
+                        output = loop.run_until_complete(
+                            get_output(
+                                "http://proxy_client_nicheimage.nichetensor.com:10003/generate",
+                                duplicate_data,
+                            )
+                        )
+                        while len(output) < 4:
+                            output.append(None)
+                        for i, image in enumerate(output):
+                            if not image:
+                                output[i] = Image.new("RGB", (width, height), (0, 0, 0))
+                        print(output)
+                        if output:
+                            st.toast("Your image has been generated!", icon="😍")
+                            end_time = time.time()
+                            status.update(
+                                label=f"✅ Images generated in {round(end_time-start_time, 3)} seconds",
+                                state="complete",
+                                expanded=False,
+                            )
+
+                            # Save generated image to session state
+                            st.session_state.generated_image = output
+                            captions = [f"Image {i+1} 🎈" for i in range(4)]
+                            all_images = []
+                            # Displaying the image
+                            _, main_col, _ = st.columns([0.15, 0.7, 0.15])
+                            with main_col:
+                                cols_1 = st.columns(2)
+                                cols_2 = st.columns(2)
+                                with st.container(border=True):
+                                    for i, image in enumerate(
+                                        st.session_state.generated_image[:2]
+                                    ):
+                                        cols_1[i].image(
+                                            image,
+                                            caption=captions[i],
+                                            use_column_width=True,
+                                            output_format="PNG",
+                                        )
+                                        # Add image to the list
+                                        all_images.append(image)
+                                    for i, image in enumerate(
+                                        st.session_state.generated_image[2:]
+                                    ):
+                                        cols_2[i].image(
+                                            image,
+                                            caption=captions[i + 2],
+                                            use_column_width=True,
+                                            output_format="PNG",
+                                        )
+
+                        # Save all generated images to session state
+                        st.session_state.all_images = all_images
+                        zip_io = io.BytesIO()
+                        # Download option for each image
+                        with zipfile.ZipFile(zip_io, "w") as zipf:
+                            for i, image in enumerate(st.session_state.all_images):
+                                image_data = io.BytesIO()
+                                image.save(image_data, format="PNG")
+                                image_data.seek(0)
+                                # Write each image to the zip file with a name
+                                zipf.writestr(
+                                    f"output_file_{i+1}.png", image_data.read()
+                                )
+                        # Create a download button for the zip file
+                        st.download_button(
+                            ":red[**Download All Images**]",
+                            data=zip_io.getvalue(),
+                            file_name="output_files.zip",
+                            mime="application/zip",
+                            use_container_width=True,
+                        )
+                status.update(
+                    label="✅ Images generated!", state="complete", expanded=False
+                )
+            except Exception as e:
+                print(e)
+                st.error(f"Encountered an error: {e}", icon="🚨")
+
+    # If not submitted, chill here 🍹
+    else:
+        pass

pages/1_🎨_Text_To_Image.py

@@ -0,0 +1,246 @@
+import streamlit as st
+import base64
+import io
+import random
+import time
+from typing import List
+from PIL import Image
+import aiohttp
+import asyncio
+from streamlit_image_select import image_select
+import requests
+import streamlit as st
+import requests
+import zipfile
+import io
+import pandas as pd
+from core import *
+from utils import icon
+from streamlit_image_select import image_select
+from PIL import Image
+import random
+import time
+import base64
+from typing import List
+import aiohttp
+import asyncio
+import plotly.express as px
+from common import set_page_container_style
+
+replicate_text = "NicheImage - Subnet 23 - Bittensor"
+replicate_logo = "assets/NicheTensorTransparent.png"
+replicate_link = "https://github.com/NicheTensor/NicheImage"
+
+st.set_page_config(
+    page_title="NicheImage Generator", page_icon=replicate_logo, layout="wide"
+)
+set_page_container_style(
+    max_width=1100,
+    max_width_100_percent=True,
+    padding_top=0,
+    padding_right=10,
+    padding_left=5,
+    padding_bottom=10,
+)
+
+
+def get_or_create_eventloop():
+    try:
+        return asyncio.get_event_loop()
+    except RuntimeError as ex:
+        if "There is no current event loop in thread" in str(ex):
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            return asyncio.get_event_loop()
+
+
+# UI configurations
+st.markdown(
+    """<style>
+    #root > div:nth-child(1) > div > div > div > div > section > div {padding-top: 2rem;}
+</style>
+
+""",
+    unsafe_allow_html=True,
+)
+css = """
+<style>
+section.main > div:has(~ footer ) {
+    padding-bottom: 5px;
+}
+</style>
+"""
+st.markdown(css, unsafe_allow_html=True)
+
+# API Tokens and endpoints from `.streamlit/secrets.toml` file
+API_TOKEN = st.secrets["API_TOKEN"]
+# Placeholders for images and gallery
+generated_images_placeholder = st.empty()
+gallery_placeholder = st.empty()
+
+
+def configure_sidebar() -> None:
+    """
+    Setup and display the sidebar elements.
+
+    This function configures the sidebar of the Streamlit application,
+    including the form for user inputs and the resources section.
+    """
+    with st.sidebar:
+        st.image(replicate_logo, use_column_width=True)
+        with st.form("my_form"):
+            model_name = st.selectbox(
+                ":blue[**Select Model**]",
+                options=[
+                    "RealisticVision",
+                    "AnimeV3",
+                    "DreamShaper",
+                    "RealitiesEdgeXL",
+                ],
+            )
+            prompt = st.text_area(
+                ":blue[**Enter prompt ✍🏾**]",
+                value="cinematic still of a shiba inu, fluffy neck, wearing a suit of ornate metal armor",
+            )
+            aspect_ratio = st.selectbox(
+                ":blue[**Aspect Ratio**]", options=["Tall", "Wide", "Square"]
+            )
+            num_images = 4
+            negative_prompt = st.text_area(
+                ":blue[**Negative Prompt 🙅🏽‍♂️**]",
+                value="low quality, blurry, pixelated, noisy, low resolution, defocused, out of focus, overexposed, bad image, nsfw",
+                help="This is a negative prompt, basically type what you don't want to see in the generated image",
+            )
+            with st.expander(
+                "📚 Advanced",
+                expanded=False,
+            ):
+                uid = st.text_input("Specify an UID", value="-1")
+                secret_key = st.text_input("Enter secret key", value="")
+                seed = st.text_input("Seed", value="-1")
+            # The Big Red "Submit" Button!
+            submitted = st.form_submit_button(
+                "Submit", type="primary", use_container_width=True
+            )
+
+        return (
+            submitted,
+            model_name,
+            prompt,
+            negative_prompt,
+            aspect_ratio,
+            num_images,
+            uid,
+            secret_key,
+            seed,
+            "",
+        )
+
+
+def main():
+    """
+    Main function to run the Streamlit application.
+
+    This function initializes the sidebar configuration and the main page layout.
+    It retrieves the user inputs from the sidebar, and passes them to the main page function.
+    The main page function then generates images based on these inputs.
+    """
+    (
+        submitted,
+        model_name,
+        prompt,
+        negative_prompt,
+        aspect_ratio,
+        num_images,
+        uid,
+        secret_key,
+        seed,
+        conditional_image,
+    ) = configure_sidebar()
+    main_page(
+        submitted,
+        model_name,
+        prompt,
+        negative_prompt,
+        aspect_ratio,
+        num_images,
+        uid,
+        secret_key,
+        seed,
+        conditional_image,
+        [],
+        "txt2img",
+        API_TOKEN,
+        generated_images_placeholder,
+    )
+    if not submitted:
+        with gallery_placeholder.container():
+            with st.container():
+                st.info(
+                    "👩🏾‍🍳 :blue[**Realistic Vision Style - [CivitAI](https://civitai.com/models/4201/realistic-vision-v60-b1)**]"
+                )
+                st.balloons()
+            with st.container(border=True):
+                _ = image_select(
+                    label="",
+                    images=[
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/401d2674-e1f8-4976-a615-36110d0b76b3/original=true/ref-res-1.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d2050c06-e916-4091-857a-66bdafcaf6d9/original=true/00029-913302605.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/0012d0e9-6d77-4981-b16e-13ac9f3eeb53/original=true/00011-836818560.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/088542b8-00dc-4135-8115-f2086ebb4ffb/original=true/06772-2405195618-HDR,UHD,8K,Highly%20detailed,best%20quality,masterpiece,_lora_catman_0.8_,maomi,blurry%20background,blurry,sunglasses,hat,hands%20in%20poc.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/3b23b565-9b7b-4e5b-9a40-ff9b5e80565d/original=true/00076-329187343.jpeg",
+                    ],
+                    use_container_width=False,
+                )
+            with st.container():
+                st.info(
+                    "👩🏾‍🍳 :blue[**DreamShaper Style - [CivitAI](https://civitai.com/models/4384/dreamshaper)**]"
+                )
+            with st.container(border=True):
+                _ = image_select(
+                    label="",
+                    images=[
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/af837eea-2ccc-4801-b916-75ecb270c382/original=true/71584-4007479174-masterpiece,%20a%20dinosaur%20(in%20the%20museum_1.2),%20background%20is%20museum%20exhibition,%20(art%20by%20YRAX_1.1),%20saturated%20colors,%20concept%20art,.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/77940ee3-062a-4d2b-95c4-52c69b79fffd/original=true/00524-2430470379.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1649bcd9-4417-4c83-97f8-674373b67d61/original=true/42445-1548916933-wabstyle,%20monochrome,%20_lora_wabstyle_1_%20a%20dog.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/09264ecb-38c6-42ed-a2f7-a7ab5ab662fb/original=true/00262-5775713%20-%20Kopie.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a25faadc-7370-426d-9e95-1e102a10e508/original=true/00613-787076454-(a%20Glass%20ring_1.3),%20masterpiece,%20best%20quality,%20_lora_RingArt_Sora_0.8_,%20(style%20of%20Ellen%20Gallagher_1.3)__(masterpiece,%20best%20quali%20(1).jpeg",
+                    ],
+                    use_container_width=False,
+                )
+            with st.container():
+                st.info(
+                    "👩🏾‍🍳 :blue[**AnimeV3 Style - [CivitAI](https://civitai.com/models/146113/newdream-sdxl)**]"
+                )
+            with st.container(border=True):
+                _ = image_select(
+                    label="",
+                    images=[
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/c788e346-7af1-4135-928a-c944634a4a51/original=true/Soldier.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/dc86e78e-7587-45b8-b709-b6f611af68fa/original=true/pirate%20(3).jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/cb6aa6c4-6cd6-4efd-84a8-4b3917ca4d24/original=true/00000-1761366536.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/61cad059-f9d8-446f-b33d-b22e078ac5fb/original=true/Scream.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/58d55973-12c9-4854-afbc-4fbd8f5434ef/original=true/CarSakura.jpeg",
+                    ],
+                    use_container_width=False,
+                )
+            with st.container():
+                st.info(
+                    "👩🏾‍🍳 :blue[**RealitiesEdgeXL Style - [CivitAI](https://civitai.com/models/129666?modelVersionId=356472)**]"
+                )
+            with st.container(border=True):
+                _ = image_select(
+                    label="",
+                    images=[
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7ae8de93-3634-4fb2-acb6-7ba66a630670/original=true/00044-3526680654.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d7a3e977-5433-46d0-82ba-4946386bd28e/original=true/00135-1518921975.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/c3852385-4c73-4821-ac6f-b9f0323c7d6f/original=true/08323--3358745561.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7f6be0cf-4b58-4c16-aa8f-4c396fb135b2/original=true/ComfyUI-1-_01583_.jpeg",
+                        "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e9ecf8ad-8f3b-4fc3-9eca-c59389e84a3f/original=true/08314--1054985950.jpeg",
+                    ],
+                    use_container_width=False,
+                )
+
+
+if __name__ == "__main__":
+    main()

pages/2_🎨_Image_To_Image.py

@@ -0,0 +1,190 @@
+import streamlit as st
+import base64
+import io
+import random
+import time
+from typing import List
+from PIL import Image
+import aiohttp
+import asyncio
+import requests
+import streamlit as st
+import requests
+import zipfile
+import io
+import pandas as pd
+from core import *
+from utils import icon
+from streamlit_image_select import image_select
+from PIL import Image
+import random
+import time
+import base64
+from typing import List
+import aiohttp
+import asyncio
+import plotly.express as px
+from common import set_page_container_style
+
+replicate_text = "NicheImage - Subnet 23 - Bittensor"
+replicate_logo = "assets/NicheTensorTransparent.png"
+replicate_link = "https://github.com/NicheTensor/NicheImage"
+
+st.set_page_config(
+    page_title="NicheImage Generator", page_icon=replicate_logo, layout="wide"
+)
+set_page_container_style(
+    max_width=1100,
+    max_width_100_percent=True,
+    padding_top=0,
+    padding_right=10,
+    padding_left=5,
+    padding_bottom=10,
+)
+
+
+def get_or_create_eventloop():
+    try:
+        return asyncio.get_event_loop()
+    except RuntimeError as ex:
+        if "There is no current event loop in thread" in str(ex):
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            return asyncio.get_event_loop()
+
+
+# UI configurations
+st.markdown(
+    """<style>
+    #root > div:nth-child(1) > div > div > div > div > section > div {padding-top: 2rem;}
+</style>
+
+""",
+    unsafe_allow_html=True,
+)
+css = """
+<style>
+section.main > div:has(~ footer ) {
+    padding-bottom: 5px;
+}
+</style>
+"""
+st.markdown(css, unsafe_allow_html=True)
+
+# API Tokens and endpoints from `.streamlit/secrets.toml` file
+API_TOKEN = st.secrets["API_TOKEN"]
+# Placeholders for images and gallery
+generated_images_placeholder = st.empty()
+gallery_placeholder = st.empty()
+
+
+def configure_sidebar() -> None:
+    """
+    Setup and display the sidebar elements.
+
+    This function configures the sidebar of the Streamlit application,
+    including the form for user inputs and the resources section.
+    """
+    with st.sidebar:
+        st.image(replicate_logo, use_column_width=True)
+        with st.form("my_form"):
+            model_name = st.selectbox(
+                ":blue[**Select Model**]",
+                options=["DreamShaper"],
+            )
+            prompt = st.text_area(
+                ":blue[**Enter prompt ✍🏾**]",
+                value="cinematic still of a shiba inu, fluffy neck, wearing a suit of ornate metal armor",
+            )
+            # aspect_ratio = st.selectbox(
+            #     ":blue[**Aspect Ratio**]", options=["Tall", "Wide", "Square"]
+            # )
+            aspect_ratio = "Square"
+            conditional_image = st.file_uploader(
+                ":blue[**Upload your image**]",
+                type=["png", "jpg", "jpeg"],
+                help="Upload an image to condition the generation",
+            )
+            if conditional_image:
+                st.image(conditional_image)
+            # num_images = st.slider(":blue[**Number of images to generate**]", 1, 4, 1)
+            num_images = 4
+            negative_prompt = st.text_area(
+                ":blue[**Negative Prompt 🙅🏽‍♂️**]",
+                value="low quality, blurry, pixelated, noisy, low resolution, defocused, out of focus, overexposed, bad image, nsfw",
+                help="This is a negative prompt, basically type what you don't want to see in the generated image",
+            )
+            with st.expander(
+                "📚 Advanced",
+                expanded=False,
+            ):
+                uid = st.text_input("Specify an UID", value="-1")
+                secret_key = st.text_input("Enter secret key", value="")
+                seed = st.text_input("Seed", value="-1")
+            # The Big Red "Submit" Button!
+            submitted = st.form_submit_button(
+                "Submit", type="primary", use_container_width=True
+            )
+
+        return (
+            submitted,
+            model_name,
+            prompt,
+            negative_prompt,
+            aspect_ratio,
+            num_images,
+            uid,
+            secret_key,
+            seed,
+            conditional_image,
+        )
+
+
+def main():
+    """
+    Main function to run the Streamlit application.
+
+    This function initializes the sidebar configuration and the main page layout.
+    It retrieves the user inputs from the sidebar, and passes them to the main page function.
+    The main page function then generates images based on these inputs.
+    """
+    (
+        submitted,
+        model_name,
+        prompt,
+        negative_prompt,
+        aspect_ratio,
+        num_images,
+        uid,
+        secret_key,
+        seed,
+        conditional_image,
+    ) = configure_sidebar()
+    if conditional_image:
+        conditional_image = Image.open(conditional_image)
+        conditional_image = pil_image_to_base64(conditional_image)
+    main_page(
+        submitted,
+        model_name,
+        prompt,
+        negative_prompt,
+        aspect_ratio,
+        num_images,
+        uid,
+        secret_key,
+        seed,
+        conditional_image,
+        [],
+        "img2img",
+        API_TOKEN,
+        generated_images_placeholder,
+    )
+    if not submitted:
+        st.info("🎨 Upload your image and imagine the possibilities!")
+        st.image(
+            "https://preview.redd.it/using-crude-drawings-for-composition-img2img-v0-v7adchf52oha1.jpg?auto=webp&s=1251ed1c567a04ec0cf17fe257fbac01b2d903bd"
+        )
+
+
+if __name__ == "__main__":
+    main()

pages/3_🎨_Control_To_Image.py

@@ -0,0 +1,254 @@
+import streamlit as st
+import base64
+import io
+import random
+import time
+from typing import List
+from PIL import Image
+import aiohttp
+import asyncio
+import requests
+import streamlit as st
+import requests
+import zipfile
+import io
+import pandas as pd
+from core import *
+from utils import icon
+from streamlit_image_select import image_select
+from PIL import Image
+import random
+import time
+import base64
+from typing import List
+import aiohttp
+import asyncio
+import plotly.express as px
+from common import set_page_container_style
+
+replicate_text = "NicheImage - Subnet 23 - Bittensor"
+replicate_logo = "assets/NicheTensorTransparent.png"
+replicate_link = "https://github.com/NicheTensor/NicheImage"
+
+st.set_page_config(
+    page_title="NicheImage Generator", page_icon=replicate_logo, layout="wide"
+)
+set_page_container_style(
+    max_width=1100,
+    max_width_100_percent=True,
+    padding_top=0,
+    padding_right=10,
+    padding_left=5,
+    padding_bottom=10,
+)
+
+
+def get_or_create_eventloop():
+    try:
+        return asyncio.get_event_loop()
+    except RuntimeError as ex:
+        if "There is no current event loop in thread" in str(ex):
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            return asyncio.get_event_loop()
+
+
+# UI configurations
+st.markdown(
+    """<style>
+    #root > div:nth-child(1) > div > div > div > div > section > div {padding-top: 2rem;}
+</style>
+
+""",
+    unsafe_allow_html=True,
+)
+css = """
+<style>
+section.main > div:has(~ footer ) {
+    padding-bottom: 5px;
+}
+</style>
+"""
+st.markdown(css, unsafe_allow_html=True)
+
+# API Tokens and endpoints from `.streamlit/secrets.toml` file
+API_TOKEN = st.secrets["API_TOKEN"]
+# Placeholders for images and gallery
+generated_images_placeholder = st.empty()
+gallery_placeholder = st.empty()
+
+
+def configure_sidebar() -> None:
+    """
+    Setup and display the sidebar elements.
+
+    This function configures the sidebar of the Streamlit application,
+    including the form for user inputs and the resources section.
+    """
+    with st.sidebar:
+        st.image(replicate_logo, use_column_width=True)
+        with st.form("my_form"):
+            model_name = st.selectbox(
+                ":blue[**Select Model**]",
+                options=["DreamShaper"],
+            )
+            prompt = st.text_area(
+                ":blue[**Enter prompt ✍🏾**]",
+                value="cinematic still of a shiba inu, fluffy neck, wearing a suit of ornate metal armor",
+            )
+            aspect_ratio = "Square"
+            conditional_image = st.file_uploader(
+                ":blue[**Upload your image**]",
+                type=["png", "jpg", "jpeg"],
+                help="Upload an image to condition the generation",
+            )
+            if conditional_image:
+                st.image(conditional_image)
+            with st.container(border=True):
+                canny_strength = st.slider(
+                    ":blue[**Canny Strength**]",
+                    min_value=0.0,
+                    max_value=1.0,
+                    value=0.5,
+                    step=0.01,
+                    help="Strength of the Canny ControlNet",
+                )
+                depth_strength = st.slider(
+                    ":blue[**Depth Strength**]",
+                    min_value=0.0,
+                    max_value=1.0,
+                    value=0.5,
+                    step=0.01,
+                    help="Strength of the Depth ControlNet",
+                )
+                mlsd_strength = st.slider(
+                    ":blue[**MLSD Strength**]",
+                    min_value=0.0,
+                    max_value=1.0,
+                    value=0.5,
+                    step=0.01,
+                    help="Strength of the MLSD ControlNet",
+                )
+                controlnet_conditioning_scale = [
+                    canny_strength,
+                    depth_strength,
+                    mlsd_strength,
+                ]
+
+            num_images = 4
+            negative_prompt = st.text_area(
+                ":blue[**Negative Prompt 🙅🏽‍♂️**]",
+                value="low quality, blurry, pixelated, noisy, low resolution, defocused, out of focus, overexposed, bad image, nsfw",
+                help="This is a negative prompt, basically type what you don't want to see in the generated image",
+            )
+            with st.expander(
+                "📚 Advanced",
+                expanded=False,
+            ):
+                uid = st.text_input("Specify an UID", value="-1")
+                secret_key = st.text_input("Enter secret key", value="")
+                seed = st.text_input("Seed", value="-1")
+            # The Big Red "Submit" Button!
+            submitted = st.form_submit_button(
+                "Submit", type="primary", use_container_width=True
+            )
+
+        return (
+            submitted,
+            model_name,
+            prompt,
+            negative_prompt,
+            aspect_ratio,
+            num_images,
+            uid,
+            secret_key,
+            seed,
+            conditional_image,
+            controlnet_conditioning_scale,
+        )
+
+
+def main():
+    """
+    Main function to run the Streamlit application.
+
+    This function initializes the sidebar configuration and the main page layout.
+    It retrieves the user inputs from the sidebar, and passes them to the main page function.
+    The main page function then generates images based on these inputs.
+    """
+    (
+        submitted,
+        model_name,
+        prompt,
+        negative_prompt,
+        aspect_ratio,
+        num_images,
+        uid,
+        secret_key,
+        seed,
+        conditional_image,
+        controlnet_conditioning_scale,
+    ) = configure_sidebar()
+    if conditional_image:
+        conditional_image = Image.open(conditional_image)
+        conditional_image = pil_image_to_base64(conditional_image)
+    main_page(
+        submitted,
+        model_name,
+        prompt,
+        negative_prompt,
+        aspect_ratio,
+        num_images,
+        uid,
+        secret_key,
+        seed,
+        conditional_image,
+        controlnet_conditioning_scale,
+        "controlnet_txt2img",
+        API_TOKEN,
+        generated_images_placeholder,
+    )
+    if not submitted:
+        with generated_images_placeholder.container():
+            st.info("🎨 :blue[**Canny Edge**]")
+            cols = st.columns(2)
+            with cols[0]:
+                st.image(
+                    "https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_canny.png",
+                    use_column_width=True,
+                )
+            with cols[1]:
+                st.image(
+                    "https://cdn-lfs.huggingface.co/repos/f4/f7/f4f7a7f70b5d098c5f2b46c19c2063eea26d6eba1488e9141804f6402b509cbb/3907d804ef04bcc1dd998ddb8dbd01ad6ffd071d6f13bd2e8a03936f7fe35f13?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27output_bird_canny_1.png%3B+filename%3D%22output_bird_canny_1.png%22%3B&response-content-type=image%2Fpng&Expires=1709913845&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwOTkxMzg0NX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy9mNC9mNy9mNGY3YTdmNzBiNWQwOThjNWYyYjQ2YzE5YzIwNjNlZWEyNmQ2ZWJhMTQ4OGU5MTQxODA0ZjY0MDJiNTA5Y2JiLzM5MDdkODA0ZWYwNGJjYzFkZDk5OGRkYjhkYmQwMWFkNmZmZDA3MWQ2ZjEzYmQyZThhMDM5MzZmN2ZlMzVmMTM%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=JplN20XzO7Y34tz-FkA3-cv4VRfIkzz9Tf8-Re7EdW-7%7EaBEItVnl5SDX44oSk-WeNFPfhcHg-b7RK1zlD6H1n3Zo7Y8JtMR1Z07%7ElZ6S6s80j1vpOrMmnkyB-7DcM9Mvi2ITDPXhOdYGnAK1GER1Zdg-ctVEkf7frNEj%7E5zcqJ1kFyRCJcjPHtgAtIAySOANk6BIZEWLEbZGM3b4swTMnNPUZZw5CYRp2dgGgVEo8i6xalR1l8rqvAxQ3yB4C5nfJURcqQLY1lkk9B58dhR6Afy5ZG-krqilv07R7HqbsS7GWq6w8BCEXA0E9cjQTqzqlpCZG0wH78vNE8GDdF6kQ__&Key-Pair-Id=KVTP0A1DKRTAX",
+                    use_column_width=True,
+                )
+            st.info("🎨 :blue[**Depth Map**]")
+            cols2 = st.columns(2)
+            with st.container():
+                with cols2[0]:
+                    st.image(
+                        "https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_depth.png",
+                        use_column_width=True,
+                    )
+                with cols2[1]:
+                    st.image(
+                        "https://cdn-lfs.huggingface.co/repos/f4/f7/f4f7a7f70b5d098c5f2b46c19c2063eea26d6eba1488e9141804f6402b509cbb/25c89f31165aa3c435b28deeb1e542c68b0dab496727403c1f3c7ded3b78fdd1?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27output_vermeer_depth_2.png%3B+filename%3D%22output_vermeer_depth_2.png%22%3B&response-content-type=image%2Fpng&Expires=1709913950&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwOTkxMzk1MH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy9mNC9mNy9mNGY3YTdmNzBiNWQwOThjNWYyYjQ2YzE5YzIwNjNlZWEyNmQ2ZWJhMTQ4OGU5MTQxODA0ZjY0MDJiNTA5Y2JiLzI1Yzg5ZjMxMTY1YWEzYzQzNWIyOGRlZWIxZTU0MmM2OGIwZGFiNDk2NzI3NDAzYzFmM2M3ZGVkM2I3OGZkZDE%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=b%7EhyTb-HRh2shXtGNEfhNrXU%7EsuQdyE0iTXVKpR76CDxlS147%7EYxiU%7E9JJAL1asz-vfUS5u%7ErtqIRfGkKAcU1DP8lqtGhIfDBi6iPNEyWoAWAGj7mF6y50qj7xeq2Iz4yWeHoiZpzotiWbMqCE6V6QKrHsaU2btHlHjlLsw4owh3ELXjHB6%7ENCKwH0uKLfABOBZzrOJOCilJU8VwiCRsk3QryLD3uywZMGDxyzd1vxnaBR4PTIrZ46FEatcXCpGdZ1ZJngtdiepdLRX4tI41gVk0fWYrQ5wOnMSudCQuCfVXvHRnoPwKUVcmF5mCa7smZykizITEmwg3ctW4W80pBA__&Key-Pair-Id=KVTP0A1DKRTAX",
+                        use_column_width=True,
+                    )
+            st.info("🎨 :blue[**MLSD**]")
+            cols3 = st.columns(2)
+            with st.container():
+                with cols3[0]:
+                    st.image(
+                        "https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_mlsd.png",
+                        use_column_width=True,
+                    )
+                with cols3[1]:
+                    st.image(
+                        "https://cdn-lfs.huggingface.co/repos/f4/f7/f4f7a7f70b5d098c5f2b46c19c2063eea26d6eba1488e9141804f6402b509cbb/92c95cf788f547ef7ea2da0fb45a0232864899ddd93c8f04d88b04f681216ad3?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27output_room_mlsd_0.png%3B+filename%3D%22output_room_mlsd_0.png%22%3B&response-content-type=image%2Fpng&Expires=1709912870&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwOTkxMjg3MH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy9mNC9mNy9mNGY3YTdmNzBiNWQwOThjNWYyYjQ2YzE5YzIwNjNlZWEyNmQ2ZWJhMTQ4OGU5MTQxODA0ZjY0MDJiNTA5Y2JiLzkyYzk1Y2Y3ODhmNTQ3ZWY3ZWEyZGEwZmI0NWEwMjMyODY0ODk5ZGRkOTNjOGYwNGQ4OGIwNGY2ODEyMTZhZDM%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=A9jKF1jw8YO8OFuB7CzbaeQrEdT8wuPZJnT%7E1gZTxBAot9-HBQypW4IMsP7XPCeWnfdJBH4EKcsfzP2RU2-y2%7ENHrJl6wd9n-Wyi5traDPUgoHlSGIe6BvEKJlkzUI7gf87ThVWNc6wI3kHZM0l3dESE0ID2e2aCKGNxUCfaV%7EBdyt-Myr0HbVQtfYR8SjW7Rdj5WCJy9vOZKS3y2nzfPsUZRNtup4Xw3EW%7Ei0BGomdrB%7EfoOj4A0CvuL-La8SxPz1a3AcakD0ti8P0TPIJ9FrjmWLr49t9gJvn2XMYD3mH8VVsSBsGDn6pjgTLS4FK0KvCu-lIqt14v1bF10wCzBA__&Key-Pair-Id=KVTP0A1DKRTAX",
+                        use_column_width=True,
+                    )
+
+
+if __name__ == "__main__":
+    main()