local run

.env_template

@@ -0,0 +1,4 @@
+TOKEN=<telegram-bot-token >
+API_ID=<telegram-id-api-id >
+API_HASH=<telegram-id-api-hash>
+SD_URL=<stable-diffusion-api-url>

.gitignore

@@ -1,3 +1,8 @@
 *.png
 .env
-.session
+*.session
+vscode/
+venv/
+*.session-journal
+logs/stable_diff_telegram_bot.log
+*.session

README.md

@@ -1,16 +1,16 @@
 # AI Powered Art in a Telegram Bot!
 
-this is a txt2img bot to converse with SDweb bot [API](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API) running on tami telegram channel
+this is a txt2img/img2img bot to converse with SDweb bot [API](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API) running on tami telegram channel
 
-# How to
+## How to
-
+### txt2img
-supported invocation:  
 `/draw <text>` - send prompt text to the bot and it will draw an image  
 you can add `negative_prompt` using `ng: <text>`  
 you can add `denoised intermediate steps` using `steps: <text>`  
 
 basicly anything the `/controlnet/txt2img` API payload supports  
 like, 
+
 ```json
 {
   "prompt": "",
@@ -22,6 +22,7 @@ like,
   "cfg_scale": 7
 }
 ```
+
 examples:  
 `/draw a city street`  
 and without people  
@@ -32,26 +33,40 @@ with more steps
 to change the model use:  
 `/getmodels` - to get a list of models and then click to set it. 
 
-
-
 - note1: Anything after ng will be considered as nergative prompt. a.k.a things you do not want to see in your diffusion!  
 - note2: on [negative_prompt](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Negative-prompt) (aka ng):  
 thia is a bit of a black art. i took the recommended defaults for the `Deliberate` model from this fun [alt-model spreadsheet](https://docs.google.com/spreadsheets/d/1Q0bYKRfVOTUHQbUsIISCztpdZXzfo9kOoAy17Qhz3hI/edit#gid=797387129).    
-~~and you (currntly) can only ADD to it, not replace.~~  
 - note3: on `steps` - step of 1 will generate only the first "step" of bot hallucinations. the default is 40. higher will take longer and will give "better" image. range is hardcoded 1-70.  
 see ![video](https://user-images.githubusercontent.com/57876960/212490617-f0444799-50e5-485e-bc5d-9c24a9146d38.mp4)
+
+### img2img
+`/img <prompt> ds:<0.0-1.0>` - reply to an image with a prompt text and it will draw an image
+
+you can add `denoising_strength` using `ds:<float>`  
+Set that low (like 0.2) if you just want to slightly change things. defaults to 0.4
+
+basicly anything the `/controlnet/img2img` API payload supports  
+
 ## Setup
 
-Install requirements
+Install requirements using venv
+
+```bash
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+
+Install requirements using conda
 
 ```bash
 conda create -n sdw python=3.8
 conda activate sdw
 pip install -r requirements.txt
 ```
-(note: conda is not strictly necessary, but it is recommended)
 
-## Original readme
+## Original README
 
 My Bot uses [Automatic1111's WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) as the backend.
 Follow the directions on their repo for setup instructions.
@@ -62,15 +77,23 @@ arguments such as `--xformers` to use xformers memory efficient attention.
 You can use the web ui interface that Automatic1111 provides to select the model and VAE to use.
 Their repo has documentation on how to do so. I also recommend doing a test generation
 
-Create a file called `.env` in the same folder as `main.py`. Inside the `.env` file,
+Copy a file called `.env_template` into `.env_template` in the same folder as `main.py`.
-create a line `TOKEN = xxxx`, where xxxx is your telegram bot token.
+In the `.env` file fill out the following environment variables:
-create a line `API_ID = xxxx`, where xxxx is your telegram id api id.
+`TOKEN = xxxx`, where xxxx is your telegram bot token.
-create a line `API_HASH = xxxx`, where xxxx is your telegram id api hash.
+`API_ID = xxxx`, where xxxx is your telegram id api id.
-create a line `SD_URL = xxxx`, where xxxx is your sd api url.
+`API_HASH = xxxx`, where xxxx is your telegram id api hash.
+`SD_URL = xxxx`, where xxxx is your sd api url.
+
+
+To get the API_ID and API_HASH, you need to create a new application on the Telegram's developer website. Here are the steps:
+
+1. Open browser and visit https://my.telegram.org and log in with your Telegram account.
+2. Click on "API development tools".
+3. Fill out the form to create a new application. You can enter any valid details you want.
+4. After you've created the application, you'll be given the API_ID and API_HASH.
+5. Once you have these, you can add them to your .env file:
 
 Now, you can run the bot
 
 `python main.py`
 
-
-

dpic.oy.py

@@ -0,0 +1,163 @@
+from datetime import datetime
+import urllib.request
+import base64
+import json
+import time
+import os
+url="pop-os.local"
+webui_server_url = f'http://{url}:7860'
+
+out_dir = 'api_out'
+out_dir_t2i = os.path.join(out_dir, 'txt2img')
+out_dir_i2i = os.path.join(out_dir, 'img2img')
+os.makedirs(out_dir_t2i, exist_ok=True)
+os.makedirs(out_dir_i2i, exist_ok=True)
+
+
+def timestamp():
+    return datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S")
+
+
+def encode_file_to_base64(path):
+    with open(path, 'rb') as file:
+        return base64.b64encode(file.read()).decode('utf-8')
+
+
+def decode_and_save_base64(base64_str, save_path):
+    with open(save_path, "wb") as file:
+        file.write(base64.b64decode(base64_str))
+
+
+def call_api(api_endpoint, **payload):
+    data = json.dumps(payload).encode('utf-8')
+    request = urllib.request.Request(
+        f'{webui_server_url}/{api_endpoint}',
+        headers={'Content-Type': 'application/json'},
+        data=data,
+    )
+    response = urllib.request.urlopen(request)
+    return json.loads(response.read().decode('utf-8'))
+
+
+def call_txt2img_api(**payload):
+    response = call_api('sdapi/v1/txt2img', **payload)
+    for index, image in enumerate(response.get('images')):
+        save_path = os.path.join(out_dir_t2i, f'txt2img-{timestamp()}-{index}.png')
+        decode_and_save_base64(image, save_path)
+
+
+def call_img2img_api(**payload):
+    response = call_api('sdapi/v1/img2img', **payload)
+    for index, image in enumerate(response.get('images')):
+        save_path = os.path.join(out_dir_i2i, f'img2img-{timestamp()}-{index}.png')
+        decode_and_save_base64(image, save_path)
+
+
+if __name__ == '__main__':
+    payload = {
+        "prompt": "masterpiece, (best quality:1.1), 1girl <lora:lora_model:1>",  # extra networks also in prompts
+        "negative_prompt": "",
+        "seed": 1,
+        "steps": 20,
+        "width": 512,
+        "height": 512,
+        "cfg_scale": 7,
+        "sampler_name": "DPM++ 2M",
+        "n_iter": 1,
+        "batch_size": 1,
+
+        # example args for x/y/z plot
+        # "script_name": "x/y/z plot",
+        # "script_args": [
+        #     1,
+        #     "10,20",
+        #     [],
+        #     0,
+        #     "",
+        #     [],
+        #     0,
+        #     "",
+        #     [],
+        #     True,
+        #     True,
+        #     False,
+        #     False,
+        #     0,
+        #     False
+        # ],
+
+        # example args for Refiner and ControlNet
+        # "alwayson_scripts": {
+        #     "ControlNet": {
+        #         "args": [
+        #             {
+        #                 "batch_images": "",
+        #                 "control_mode": "Balanced",
+        #                 "enabled": True,
+        #                 "guidance_end": 1,
+        #                 "guidance_start": 0,
+        #                 "image": {
+        #                     "image": encode_file_to_base64(r"B:\path\to\control\img.png"),
+        #                     "mask": None  # base64, None when not need
+        #                 },
+        #                 "input_mode": "simple",
+        #                 "is_ui": True,
+        #                 "loopback": False,
+        #                 "low_vram": False,
+        #                 "model": "control_v11p_sd15_canny [d14c016b]",
+        #                 "module": "canny",
+        #                 "output_dir": "",
+        #                 "pixel_perfect": False,
+        #                 "processor_res": 512,
+        #                 "resize_mode": "Crop and Resize",
+        #                 "threshold_a": 100,
+        #                 "threshold_b": 200,
+        #                 "weight": 1
+        #             }
+        #         ]
+        #     },
+        #     "Refiner": {
+        #         "args": [
+        #             True,
+        #             "sd_xl_refiner_1.0",
+        #             0.5
+        #         ]
+        #     }
+        # },
+        # "enable_hr": True,
+        # "hr_upscaler": "R-ESRGAN 4x+ Anime6B",
+        # "hr_scale": 2,
+        # "denoising_strength": 0.5,
+        # "styles": ['style 1', 'style 2'],
+        # "override_settings": {
+        #     'sd_model_checkpoint': "sd_xl_base_1.0",  # this can use to switch sd model
+        # },
+    }
+    call_txt2img_api(**payload)
+
+    init_images = [
+        encode_file_to_base64(r"../stable-diffusion-webui/output/img2img-images/2024-05-15/00012-357584826.png"),
+        # encode_file_to_base64(r"B:\path\to\img_2.png"),
+        # "https://image.can/also/be/a/http/url.png",
+    ]
+
+    batch_size = 2
+    payload = {
+        "prompt": "1girl, blue hair",
+        "seed": 1,
+        "steps": 20,
+        "width": 512,
+        "height": 512,
+        "denoising_strength": 0.5,
+        "n_iter": 1,
+        "init_images": init_images,
+        "batch_size": batch_size if len(init_images) == 1 else len(init_images),
+        # "mask": encode_file_to_base64(r"B:\path\to\mask.png")
+    }
+    # if len(init_images) > 1 then batch_size should be == len(init_images)
+    # else if len(init_images) == 1 then batch_size can be any value int >= 1
+    call_img2img_api(**payload)
+
+    # there exist a useful extension that allows converting of webui calls to api payload
+    # particularly useful when you wish setup arguments of extensions and scripts
+    # https://github.com/huchenlei/sd-webui-api-payload-display

localAPIRun.py

@@ -0,0 +1,163 @@
+from datetime import datetime
+import urllib.request
+import base64
+import json
+import time
+import os
+url="pop-os.local"
+webui_server_url = f'http://{url}:7860'
+
+out_dir = 'api_out'
+out_dir_t2i = os.path.join(out_dir, 'txt2img')
+out_dir_i2i = os.path.join(out_dir, 'img2img')
+os.makedirs(out_dir_t2i, exist_ok=True)
+os.makedirs(out_dir_i2i, exist_ok=True)
+
+
+def timestamp():
+    return datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S")
+
+
+def encode_file_to_base64(path):
+    with open(path, 'rb') as file:
+        return base64.b64encode(file.read()).decode('utf-8')
+
+
+def decode_and_save_base64(base64_str, save_path):
+    with open(save_path, "wb") as file:
+        file.write(base64.b64decode(base64_str))
+
+
+def call_api(api_endpoint, **payload):
+    data = json.dumps(payload).encode('utf-8')
+    request = urllib.request.Request(
+        f'{webui_server_url}/{api_endpoint}',
+        headers={'Content-Type': 'application/json'},
+        data=data,
+    )
+    response = urllib.request.urlopen(request)
+    return json.loads(response.read().decode('utf-8'))
+
+
+def call_txt2img_api(**payload):
+    response = call_api('sdapi/v1/txt2img', **payload)
+    for index, image in enumerate(response.get('images')):
+        save_path = os.path.join(out_dir_t2i, f'txt2img-{timestamp()}-{index}.png')
+        decode_and_save_base64(image, save_path)
+
+
+def call_img2img_api(**payload):
+    response = call_api('sdapi/v1/img2img', **payload)
+    for index, image in enumerate(response.get('images')):
+        save_path = os.path.join(out_dir_i2i, f'img2img-{timestamp()}-{index}.png')
+        decode_and_save_base64(image, save_path)
+
+
+if __name__ == '__main__':
+    payload = {
+        "prompt": "masterpiece, (best quality:1.1), 1girl <lora:lora_model:1>",  # extra networks also in prompts
+        "negative_prompt": "",
+        "seed": 1,
+        "steps": 20,
+        "width": 512,
+        "height": 512,
+        "cfg_scale": 7,
+        "sampler_name": "DPM++ 2M",
+        "n_iter": 1,
+        "batch_size": 1,
+
+        # example args for x/y/z plot
+        # "script_name": "x/y/z plot",
+        # "script_args": [
+        #     1,
+        #     "10,20",
+        #     [],
+        #     0,
+        #     "",
+        #     [],
+        #     0,
+        #     "",
+        #     [],
+        #     True,
+        #     True,
+        #     False,
+        #     False,
+        #     0,
+        #     False
+        # ],
+
+        # example args for Refiner and ControlNet
+        # "alwayson_scripts": {
+        #     "ControlNet": {
+        #         "args": [
+        #             {
+        #                 "batch_images": "",
+        #                 "control_mode": "Balanced",
+        #                 "enabled": True,
+        #                 "guidance_end": 1,
+        #                 "guidance_start": 0,
+        #                 "image": {
+        #                     "image": encode_file_to_base64(r"B:\path\to\control\img.png"),
+        #                     "mask": None  # base64, None when not need
+        #                 },
+        #                 "input_mode": "simple",
+        #                 "is_ui": True,
+        #                 "loopback": False,
+        #                 "low_vram": False,
+        #                 "model": "control_v11p_sd15_canny [d14c016b]",
+        #                 "module": "canny",
+        #                 "output_dir": "",
+        #                 "pixel_perfect": False,
+        #                 "processor_res": 512,
+        #                 "resize_mode": "Crop and Resize",
+        #                 "threshold_a": 100,
+        #                 "threshold_b": 200,
+        #                 "weight": 1
+        #             }
+        #         ]
+        #     },
+        #     "Refiner": {
+        #         "args": [
+        #             True,
+        #             "sd_xl_refiner_1.0",
+        #             0.5
+        #         ]
+        #     }
+        # },
+        # "enable_hr": True,
+        # "hr_upscaler": "R-ESRGAN 4x+ Anime6B",
+        # "hr_scale": 2,
+        # "denoising_strength": 0.5,
+        # "styles": ['style 1', 'style 2'],
+        # "override_settings": {
+        #     'sd_model_checkpoint': "sd_xl_base_1.0",  # this can use to switch sd model
+        # },
+    }
+    call_txt2img_api(**payload)
+
+    init_images = [
+        encode_file_to_base64(r"../stable-diffusion-webui/output/img2img-images/2024-05-15/00012-357584826.png"),
+        # encode_file_to_base64(r"B:\path\to\img_2.png"),
+        # "https://image.can/also/be/a/http/url.png",
+    ]
+
+    batch_size = 2
+    payload = {
+        "prompt": "1girl, blue hair",
+        "seed": 1,
+        "steps": 20,
+        "width": 512,
+        "height": 512,
+        "denoising_strength": 0.5,
+        "n_iter": 1,
+        "init_images": init_images,
+        "batch_size": batch_size if len(init_images) == 1 else len(init_images),
+        # "mask": encode_file_to_base64(r"B:\path\to\mask.png")
+    }
+    # if len(init_images) > 1 then batch_size should be == len(init_images)
+    # else if len(init_images) == 1 then batch_size can be any value int >= 1
+    call_img2img_api(**payload)
+
+    # there exist a useful extension that allows converting of webui calls to api payload
+    # particularly useful when you wish setup arguments of extensions and scripts
+    # https://github.com/huchenlei/sd-webui-api-payload-display

main.py

@@ -5,235 +5,231 @@ import re
 import os
 import uuid
 import base64
+from datetime import datetime
 from PIL import Image, PngImagePlugin
 from pyrogram import Client, filters
-from pyrogram.types import *
+from pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup
 from dotenv import load_dotenv
 
-# Done! Congratulations on your new bot. You will find it at
+# Load environment variables
-# t.me/gootmornbot
-# You can now add a description, about section and profile picture for your bot, see /help for a list of commands. By the way, when you've finished creating your cool bot, ping our Bot Support if you want a better username for it. Just make sure the bot is fully operational before you do this.
-
-# Use this token to access the HTTP API:
-# Keep your token secure and store it safely, it can be used by anyone to control your bot.
-
-# For a description of the Bot API, see this page: https://core.telegram.org/bots/api
-
 load_dotenv()
 API_ID = os.environ.get("API_ID", None)
 API_HASH = os.environ.get("API_HASH", None)
-TOKEN = os.environ.get("TOKEN", None)
+TOKEN = os.environ.get("TOKEN_givemtxt2img", None)
 SD_URL = os.environ.get("SD_URL", None)
-print(SD_URL)
+
 app = Client("stable", api_id=API_ID, api_hash=API_HASH, bot_token=TOKEN)
+IMAGE_PATH = 'images'  # Do not leave a trailing /
 
-# default params
+# Ensure IMAGE_PATH directory exists
-steps_value_default = 40
+os.makedirs(IMAGE_PATH, exist_ok=True)
 
+def timestamp():
+    return datetime.now().strftime("%Y%m%d-%H%M%S")
+
+def encode_file_to_base64(path):
+    with open(path, 'rb') as file:
+        return base64.b64encode(file.read()).decode('utf-8')
+
+def decode_and_save_base64(base64_str, save_path):
+    with open(save_path, "wb") as file:
+        file.write(base64.b64decode(base64_str))
 
 def parse_input(input_string):
     default_payload = {
         "prompt": "",
-        "negative_prompt": "",
+        "negative_prompt": "ugly, bad face, distorted",
-        "controlnet_input_image": [],
-        "controlnet_mask": [],
-        "controlnet_module": "",
-        "controlnet_model": "",
-        "controlnet_weight": 1,
-        "controlnet_resize_mode": "Scale to Fit (Inner Fit)",
-        "controlnet_lowvram": False,
-        "controlnet_processor_res": 64,
-        "controlnet_threshold_a": 64,
-        "controlnet_threshold_b": 64,
-        "controlnet_guidance": 1,
-        "controlnet_guessmode": True,
         "enable_hr": False,
-        "denoising_strength": 0.5,
+        "Sampler": "DPM++ SDE Karras",
-        "hr_scale": 1.5,
+        "denoising_strength": 0.35,
-        "hr_upscale": "Latent",
-        "seed": -1,
-        "subseed": -1,
-        "subseed_strength": -1,
-        "sampler_index": "",
         "batch_size": 1,
         "n_iter": 1,
-        "steps": 20,
+        "steps": 35,
         "cfg_scale": 7,
         "width": 512,
         "height": 512,
-        "restore_faces": True,
+        "restore_faces": False,
         "override_settings": {},
         "override_settings_restore_afterwards": True,
     }
-    # Initialize an empty payload with the 'prompt' key
+    payload = default_payload.copy()
-    payload = {"prompt": ""}
-
     prompt = []
 
-    # Find all occurrences of keys (words ending with a colon)
     matches = re.finditer(r"(\w+):", input_string)
     last_index = 0
 
-    # Iterate over the found keys
     for match in matches:
-        key = match.group(1).lower()  # Convert the key to lowercase
+        key = match.group(1).lower()
         value_start_index = match.end()
 
-        # If there's text between the last key and the current key, add it to the prompt
         if last_index != match.start():
             prompt.append(input_string[last_index: match.start()].strip())
         last_index = value_start_index
+        if key == "ds":
+            key = "denoising_strength"
+        if key == "ng":
+            key = "negative_prompt"
             
-        # Check if the key is in the default payload
         if key in default_payload:
-            # Extract the value for the current key
+            value_end_index = re.search(r"(?=\s+\w+:|$)", input_string[value_start_index:]).start()
-            value_end_index = re.search(
+            value = input_string[value_start_index: value_start_index + value_end_index].strip()
-                r"(?=\s+\w+:|$)", input_string[value_start_index:]
-            ).start()
-            value = input_string[
-                value_start_index : value_start_index + value_end_index
-            ].strip()
-
-            # Check if the default value for the key is an integer
-            if isinstance(default_payload[key], int):
-                # If the value is a valid integer, store it as an integer in the payload
-                if value.isdigit():
-                    payload[key] = int(value)
-            else:
-                # If the default value for the key is not an integer, store the value as is in the payload
             payload[key] = value
-
             last_index += value_end_index
         else:
-            # If the key is not in the default payload, add it to the prompt
             prompt.append(f"{key}:")
 
-    # Join the prompt words and store it in the payload
     payload["prompt"] = " ".join(prompt)
-
-    # If the prompt is empty, set the input string as the prompt
     if not payload["prompt"]:
         payload["prompt"] = input_string.strip()
 
-    # Return the final payload
     return payload
 
 
+def call_api(api_endpoint, payload):
+    try:
+        response = requests.post(f'{SD_URL}/{api_endpoint}', json=payload)
+        response.raise_for_status()
+        return response.json()
+    except requests.RequestException as e:
+        print(f"API call failed: {e}")
+        return None
+
+def process_images(images, user_id, user_name):
+    def generate_unique_name():
+        unique_id = str(uuid.uuid4())[:7]
+        return f"{user_name}-{unique_id}"
+
+    word = generate_unique_name()
+
+    for i in images:
+        image = Image.open(io.BytesIO(base64.b64decode(i.split(",", 1)[0])))
+
+        png_payload = {"image": "data:image/png;base64," + i}
+        response2 = requests.post(f"{SD_URL}/sdapi/v1/png-info", json=png_payload)
+        response2.raise_for_status()
+
+        pnginfo = PngImagePlugin.PngInfo()
+        pnginfo.add_text("parameters", response2.json().get("info"))
+        image.save(f"{IMAGE_PATH}/{word}.png", pnginfo=pnginfo)
+
+        return word, response2.json().get("info")
+
 @app.on_message(filters.command(["draw"]))
 def draw(client, message):
     msgs = message.text.split(" ", 1)
     if len(msgs) == 1:
-        message.reply_text(
+        message.reply_text("Format :\n/draw < text to image >\nng: < negative (optional) >\nsteps: < steps value (1-70, optional) >")
-            "Format :\n/draw < text to image >\nng: < negative (optional) >\nsteps: < steps value (1-70, optional) >"
-        )
         return
 
     payload = parse_input(msgs[1])
     print(payload)
 
-    # The rest of the draw function remains unchanged
+    K = message.reply_text("Please Wait 10-15 Seconds")
-
+    r = call_api('sdapi/v1/txt2img', payload)
-    K = message.reply_text("Please Wait 10-15 Second")
-    r = requests.post(url=f"{SD_URL}/sdapi/v1/txt2img", json=payload).json()
-
-    def genr():
-        unique_id = str(uuid.uuid4())[:7]
-        return f"{message.from_user.first_name}-{unique_id}"
-
-    word = genr()
 
+    if r:
         for i in r["images"]:
-        image = Image.open(io.BytesIO(base64.b64decode(i.split(",", 1)[0])))
+            word, info = process_images([i], message.from_user.id, message.from_user.first_name)
 
-        png_payload = {"image": "data:image/png;base64," + i}
+            seed_value = info.split(", Seed: ")[1].split(",")[0]
-        response2 = requests.post(url=f"{SD_URL}/sdapi/v1/png-info", json=png_payload)
+            caption = f"**[{message.from_user.first_name}](tg://user?id={message.from_user.id})**\n\n"
-
+            # for key, value in payload.items():
-        pnginfo = PngImagePlugin.PngInfo()
+            #     caption += f"{key.capitalize()} - **{value}**\n"
-        pnginfo.add_text("parameters", response2.json().get("info"))
+            prompt = payload["prompt"]
-        image.save(f"{word}.png", pnginfo=pnginfo)
+            caption += f"**{prompt}**\n"
-
-        # Add a flag to check if the user provided a seed value
-        user_provided_seed = "seed" in payload
-
-        info_dict = response2.json()
-        seed_value = info_dict['info'].split(", Seed: ")[1].split(",")[0]
-        # print(seed_value)
-
-        caption = f"**[{message.from_user.first_name}-Kun](tg://user?id={message.from_user.id})**\n\n"
-        for key, value in payload.items():
-            caption += f"{key.capitalize()} - **{value}**\n"
             caption += f"Seed - **{seed_value}**\n"
 
-        message.reply_photo(
+            # Ensure caption is within the allowed length
-            photo=f"{word}.png",
+            if len(caption) > 1024:
-            caption=caption,
+                caption = caption[:1021] + "..."
-        )
 
-
+            message.reply_photo(photo=f"{IMAGE_PATH}/{word}.png", caption=caption)
-        # os.remove(f"{word}.png")
+            K.delete()
+    else:
+        message.reply_text("Failed to generate image. Please try again later.")
         K.delete()
 
+@app.on_message(filters.command(["img"]))
+def img2img(client, message):
+    if not message.reply_to_message or not message.reply_to_message.photo:
+        message.reply_text("reply to an image with \n`/img < prompt > ds:0-1.0`\n\nds stand for `Denoising_strength` parameter. Set that low (like 0.2) if you just want to slightly change things. defaults to 0.4")
+        return
+
+    msgs = message.text.split(" ", 1)
+    print(msgs)
+
+    if len(msgs) == 1:
+        message.reply_text("""Format :\n/img < prompt >\nforce: < 0.1-1.0, default 0.3 >
+                           """)
+        return
+
+    payload = parse_input(" ".join(msgs[1:]))
+    print(payload)
+    photo = message.reply_to_message.photo
+    photo_file = app.download_media(photo)
+    init_image = encode_file_to_base64(photo_file)
+    os.remove(photo_file)  # Clean up downloaded image file
+
+    payload["init_images"] = [init_image]
+
+    K = message.reply_text("Please Wait 10-15 Seconds")
+    r = call_api('sdapi/v1/img2img', payload)
+
+    if r:
+        for i in r["images"]:
+            word, info = process_images([i], message.from_user.id, message.from_user.first_name)
+
+            caption = f"**[{message.from_user.first_name}](tg://user?id={message.from_user.id})**\n\n"
+            prompt = payload["prompt"]
+            caption += f"**{prompt}**\n"
+
+            message.reply_photo(photo=f"{IMAGE_PATH}/{word}.png", caption=caption)
+            K.delete()
+    else:
+        message.reply_text("Failed to process image. Please try again later.")
+        K.delete()
 
 @app.on_message(filters.command(["getmodels"]))
 async def get_models(client, message):
-    response = requests.get(url=f"{SD_URL}/sdapi/v1/sd-models")
+    try:
-    if response.status_code == 200:
+        response = requests.get(f"{SD_URL}/sdapi/v1/sd-models")
+        response.raise_for_status()
         models_json = response.json()
-        # create buttons for each model name
-        buttons = []
-        for model in models_json:
-            buttons.append(
-                [
-                    InlineKeyboardButton(
-                        model["title"], callback_data=model["model_name"]
-                    )
-                ]
-            )
-        # send the message
-        await message.reply_text(
-            text="Select a model [checkpoint] to use",
-            reply_markup=InlineKeyboardMarkup(buttons),
-        )
 
+        buttons = [
+            [InlineKeyboardButton(model["title"], callback_data=model["model_name"])]
+            for model in models_json
+        ]
+        await message.reply_text("Select a model [checkpoint] to use", reply_markup=InlineKeyboardMarkup(buttons))
+    except requests.RequestException as e:
+        await message.reply_text(f"Failed to get models: {e}")
 
 @app.on_callback_query()
 async def process_callback(client, callback_query):
-    # if a model button is clicked, set sd_model_checkpoint to the selected model's title
     sd_model_checkpoint = callback_query.data
-
-    # The sd_model_checkpoint needs to be set to the title from /sdapi/v1/sd-models
-    # post using /sdapi/v1/options
-
     options = {"sd_model_checkpoint": sd_model_checkpoint}
 
-    # post the options
+    try:
-    response = requests.post(url=f"{SD_URL}/sdapi/v1/options", json=options)
+        response = requests.post(f"{SD_URL}/sdapi/v1/options", json=options)
-    if response.status_code == 200:
+        response.raise_for_status()
-        # if the post was successful, send a message
+        await callback_query.message.reply_text(f"Checkpoint set to {sd_model_checkpoint}")
-        await callback_query.message.reply_text(
+    except requests.RequestException as e:
-            "checpoint set to " + sd_model_checkpoint
+        await callback_query.message.reply_text(f"Failed to set checkpoint: {e}")
-        )
-    else:
-        # if the post was unsuccessful, send an error message
-        await callback_query.message.reply_text("Error setting options")
 
+# @app.on_message(filters.command(["start"], prefixes=["/", "!"]))
+# async def start(client, message):
+#     buttons = [[InlineKeyboardButton("Add to your group", url="https://t.me/gootmornbot?startgroup=true")]]
+#     await message.reply_text("Hello!\nAsk me to imagine anything\n\n/draw text to image", reply_markup=InlineKeyboardMarkup(buttons))
 
-@app.on_message(filters.command(["start"], prefixes=["/", "!"]))
+user_interactions = {}
-async def start(client, message):
-    # Photo = "https://i.imgur.com/79hHVX6.png"
 
-    buttons = [
+@app.on_message(filters.command(["user_stats"]))
-        [
+def user_stats(client, message):
-            InlineKeyboardButton(
+    stats = "User Interactions:\n\n"
-                "Add to your group", url="https://t.me/gootmornbot?startgroup=true"
+    for user_id, info in user_interactions.items():
-            )
+        stats += f"User: {info['username']} (ID: {user_id})\n"
-        ]
+        stats += f"Commands: {', '.join(info['commands'])}\n\n"
-    ]
-    await message.reply_text(
-        # photo=Photo,
-        text=f"Hello!\nask me to imagine anything\n\n/draw text to image",
-        reply_markup=InlineKeyboardMarkup(buttons),
-    )
     
+    message.reply_text(stats)
 
 app.run()

requirements.txt

@@ -1,4 +1,4 @@
-pyrogram==1.4.16
+pyrogram
 requests
 tgcrypto==1.2.2
 Pillow