From 8a5a585954c852ddba167d9ec1ec34c8cb9ec92c Mon Sep 17 00:00:00 2001
From: Saifeddine ALOUI <aloui.seifeddine@gmail.com>
Date: Fri, 28 Mar 2025 21:44:43 +0100
Subject: [PATCH] removed useless stuff
---
lollms/app.py | 73 +-
lollms/paths.py | 2 +-
lollms/services/tti/comfyui/lollms_comfyui.py | 487 -----
lollms/services/tti/dalle/lollms_dalle.py | 204 --
.../tti/diffusers/lollms_diffusers.py | 324 ----
.../lollms_diffusers_client.py | 238 ---
lollms/services/tti/fooocus/lollms_fooocus.py | 277 ---
.../tti/midjourney/lollms_midjourney.py | 334 ----
lollms/services/tti/sd/lollms_sd.py | 1689 -----------------
.../ttv/cogvideo_x/lollms_cogvideo_x.py | 181 --
.../services/ttv/lumalabs/lollms_lumalabs.py | 163 --
.../ttv/novita_ai/lollms_novita_ai.py | 371 ----
lollms/ttm.py | 179 +-
lollms/utilities.py | 189 ++
14 files changed, 365 insertions(+), 4346 deletions(-)
delete mode 100644 lollms/services/tti/comfyui/lollms_comfyui.py
delete mode 100644 lollms/services/tti/dalle/lollms_dalle.py
delete mode 100644 lollms/services/tti/diffusers/lollms_diffusers.py
delete mode 100644 lollms/services/tti/diffusers_client/lollms_diffusers_client.py
delete mode 100644 lollms/services/tti/fooocus/lollms_fooocus.py
delete mode 100644 lollms/services/tti/midjourney/lollms_midjourney.py
delete mode 100644 lollms/services/tti/sd/lollms_sd.py
delete mode 100644 lollms/services/ttv/cogvideo_x/lollms_cogvideo_x.py
delete mode 100644 lollms/services/ttv/lumalabs/lollms_lumalabs.py
delete mode 100644 lollms/services/ttv/novita_ai/lollms_novita_ai.py
diff --git a/lollms/app.py b/lollms/app.py
index 4fd3ce3..ac4433b 100644
--- a/lollms/app.py
+++ b/lollms/app.py
@@ -618,43 +618,52 @@ class LollmsApplication(LoLLMsCom):
self.warning(f"Couldn't load vllm")
ASCIIColors.execute_with_animation("Loading TTT services", start_ttt,ASCIIColors.color_blue)
- def start_stt(*args, **kwargs):
- self.stt = self.load_service_from_folder(self.lollms_paths.services_zoo_path/"stt", self.config.active_stt_service)
- ASCIIColors.execute_with_animation("Loading loacal STT services", start_stt, ASCIIColors.color_blue)
+ if self.config.active_stt_service:
+ def start_stt(*args, **kwargs):
+ self.stt = self.load_service_from_folder(self.lollms_paths.services_zoo_path/"stt", self.config.active_stt_service)
+ ASCIIColors.execute_with_animation("Loading loacal STT services", start_stt, ASCIIColors.color_blue)
- def start_tts(*args, **kwargs):
- if self.config.active_tts_service == "xtts":
- ASCIIColors.yellow("Loading XTTS")
- try:
- from lollms.services.tts.xtts.lollms_xtts import LollmsXTTS
+ # def start_tts(*args, **kwargs):
+ # if self.config.active_tts_service == "xtts":
+ # ASCIIColors.yellow("Loading XTTS")
+ # try:
+ # from lollms.services.tts.xtts.lollms_xtts import LollmsXTTS
- self.tts = LollmsXTTS(
- self
- )
- except Exception as ex:
- trace_exception(ex)
- self.warning(f"Couldn't load XTTS")
- if self.config.active_tts_service == "eleven_labs_tts":
- from lollms.services.tts.eleven_labs_tts.lollms_eleven_labs_tts import LollmsElevenLabsTTS
- self.tts = LollmsElevenLabsTTS(self)
- elif self.config.active_tts_service == "openai_tts":
- from lollms.services.tts.open_ai_tts.lollms_openai_tts import LollmsOpenAITTS
- self.tts = LollmsOpenAITTS(self)
- elif self.config.active_tts_service == "fish_tts":
- from lollms.services.tts.fish.lollms_fish_tts import LollmsFishAudioTTS
- self.tts = LollmsFishAudioTTS(self)
+ # self.tts = LollmsXTTS(
+ # self
+ # )
+ # except Exception as ex:
+ # trace_exception(ex)
+ # self.warning(f"Couldn't load XTTS")
+ # if self.config.active_tts_service == "eleven_labs_tts":
+ # from lollms.services.tts.eleven_labs_tts.lollms_eleven_labs_tts import LollmsElevenLabsTTS
+ # self.tts = LollmsElevenLabsTTS(self)
+ # elif self.config.active_tts_service == "openai_tts":
+ # from lollms.services.tts.open_ai_tts.lollms_openai_tts import LollmsOpenAITTS
+ # self.tts = LollmsOpenAITTS(self)
+ # elif self.config.active_tts_service == "fish_tts":
+ # from lollms.services.tts.fish.lollms_fish_tts import LollmsFishAudioTTS
+ # self.tts = LollmsFishAudioTTS(self)
+ if self.config.active_tts_service:
+ def start_tts(*args, **kwargs):
+ self.tti = self.load_service_from_folder(self.lollms_paths.services_zoo_path/"tts", self.config.active_tts_service)
+ ASCIIColors.execute_with_animation("Loading TTS services", start_tts, ASCIIColors.color_blue)
- ASCIIColors.execute_with_animation("Loading TTS services", start_tts, ASCIIColors.color_blue)
+ if self.config.active_tti_service:
+ def start_tti(*args, **kwargs):
+ self.tti = self.load_service_from_folder(self.lollms_paths.services_zoo_path/"tti", self.config.active_tti_service)
+ ASCIIColors.execute_with_animation("Loading loacal TTI services", start_tti, ASCIIColors.color_blue)
- def start_tti(*args, **kwargs):
- self.tti = self.load_service_from_folder(self.lollms_paths.services_zoo_path/"tti", self.config.active_tti_service)
- ASCIIColors.execute_with_animation("Loading loacal TTI services", start_tti, ASCIIColors.color_blue)
+ if self.config.active_ttm_service:
+ def start_ttm(*args, **kwargs):
+ self.ttv = self.load_service_from_folder(self.lollms_paths.services_zoo_path/"ttv", self.config.active_ttm_service)
+ ASCIIColors.execute_with_animation("Loading loacal TTM services", start_ttm, ASCIIColors.color_blue)
+ print("OK")
- def start_ttv(*args, **kwargs):
- self.ttv = self.load_service_from_folder(self.lollms_paths.services_zoo_path/"ttv", self.config.active_ttv_service)
-
-
- ASCIIColors.execute_with_animation("Loading loacal TTV services", start_ttv, ASCIIColors.color_blue)
+ if self.config.active_ttv_service:
+ def start_ttv(*args, **kwargs):
+ self.ttv = self.load_service_from_folder(self.lollms_paths.services_zoo_path/"ttv", self.config.active_ttv_service)
+ ASCIIColors.execute_with_animation("Loading loacal TTV services", start_ttv, ASCIIColors.color_blue)
print("OK")
diff --git a/lollms/paths.py b/lollms/paths.py
index 8e43c7e..1939b72 100644
--- a/lollms/paths.py
+++ b/lollms/paths.py
@@ -22,7 +22,7 @@ services_zoo_repo = "https://github.com/ParisNeo/lollms_services_zoo.git"
functions_zoo_repo = "https://github.com/ParisNeo/lollms_functions_zoo.git"
gptqlora_repo = "https://github.com/ParisNeo/gptqlora.git"
-lollms_webui_version = "v19 (codename Omni 🔗)"
+lollms_webui_version = "v19.2242 (codename Twins 🔗)"
# Now we speify the personal folders
class LollmsPaths:
diff --git a/lollms/services/tti/comfyui/lollms_comfyui.py b/lollms/services/tti/comfyui/lollms_comfyui.py
deleted file mode 100644
index 1d4a33e..0000000
--- a/lollms/services/tti/comfyui/lollms_comfyui.py
+++ /dev/null
@@ -1,487 +0,0 @@
-# Title LollmsComfyUI
-# Licence: GPL-3.0
-# Author : Paris Neo
-# Forked from comfyanonymous's Comfyui nodes system
-# check it out : https://github.com/comfyanonymous/ComfyUI
-# Here is a copy of the LICENCE https://github.com/comfyanonymous/ComfyUI/blob/main/LICENSE
-# All rights are reserved
-
-from pathlib import Path
-import sys
-from lollms.app import LollmsApplication
-from lollms.paths import LollmsPaths
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-import time
-import io
-import sys
-import requests
-import os
-import base64
-import subprocess
-import time
-import json
-import platform
-from dataclasses import dataclass
-from PIL import Image, PngImagePlugin
-from enum import Enum
-from typing import List, Dict, Any
-import uuid
-from ascii_colors import ASCIIColors, trace_exception
-from lollms.paths import LollmsPaths
-from lollms.utilities import git_pull, show_yes_no_dialog, PackageManager
-from lollms.tti import LollmsTTI
-import subprocess
-import shutil
-from tqdm import tqdm
-import threading
-import pipmaster as pm
-
-if not pm.is_installed("websocket"):
- pm.install("websocket-client")
-import websocket
-if not pm.is_installed("urllib"):
- pm.install("urllib")
-from urllib import request, parse
-
-
-
-def download_file(url, folder_path, local_filename):
- # Make sure 'folder_path' exists
- folder_path.mkdir(parents=True, exist_ok=True)
-
- with requests.get(url, stream=True) as r:
- r.raise_for_status()
- total_size = int(r.headers.get('content-length', 0))
- progress_bar = tqdm(total=total_size, unit='B', unit_scale=True)
- with open(folder_path / local_filename, 'wb') as f:
- for chunk in r.iter_content(chunk_size=8192):
- f.write(chunk)
- progress_bar.update(len(chunk))
- progress_bar.close()
-
- return local_filename
-
-
-
-
-def get_comfyui(lollms_paths:LollmsPaths):
- root_dir = lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- comfyui_folder = shared_folder / "comfyui"
- comfyui_script_path = comfyui_folder / "main.py"
- git_pull(comfyui_folder)
-
- if comfyui_script_path.exists():
- ASCIIColors.success("comfyui found.")
- ASCIIColors.success("Loading source file...",end="")
- # use importlib to load the module from the file path
- from lollms.services.tti.comfyui.lollms_comfyui import LollmsComfyUI
- ASCIIColors.success("ok")
- return LollmsComfyUI
-
-class LollmsComfyUI(LollmsTTI):
- has_controlnet = False
- def __init__(self, app:LollmsApplication, output_folder:str|Path=None):
- """
- Initializes the LollmsDalle binding.
-
- Args:
- api_key (str): The API key for authentication.
- output_folder (Path|str): The output folder where to put the generated data
- """
- service_config = TypedConfig(
- ConfigTemplate([
- {
- "name": "base_url",
- "type": "str",
- "value": "http://127.0.0.1:8188/",
- "help": "The base URL for the service. This is the address where the service is hosted (e.g., http://127.0.0.1:8188/)."
- },
- {
- "name": "wm",
- "type": "str",
- "value": "lollms",
- "help": "Watermarking text or identifier to be used in the service."
- },
- {
- "name": "max_retries",
- "type": "int",
- "value": 50,
- "help": "The maximum number of retries to attempt before determining that the service is unavailable."
- },
- {
- "name": "local_service",
- "type": "bool",
- "value": False,
- "help": "If set to true, a local instance of the service will be installed and used."
- },
- {
- "name": "start_service_at_startup",
- "type": "bool",
- "value": False,
- "help": "If set to true, the service will automatically start at startup. This also enables the local service option."
- },
- {
- "name": "share",
- "type": "bool",
- "value": False,
- "help": "If set to true, the server will be accessible from outside your local machine (e.g., over the internet)."
- }
- ]),
- BaseConfig(config={
- })
- )
- super().__init__("comfyui",app, service_config)
- # Get the current directory
- lollms_paths = app.lollms_paths
- self.app = app
- root_dir = lollms_paths.personal_path
-
- # If this is requiring a local service then verify if it is on
- if self.service_config.local_service:
- if not self.verify_comfyui():
- self.install()
-
- self.comfyui_url = self.service_config.base_url+"/comfyuiapi/v1"
- shared_folder = root_dir/"shared"
- self.comfyui_folder = shared_folder / "comfyui"
- self.output_dir = root_dir / "outputs/comfyui"
- self.output_dir.mkdir(parents=True, exist_ok=True)
-
- ASCIIColors.red(" _ ____ _ _ __ __ _____ _____ __ _ ")
- ASCIIColors.red("| | / __ \| | | | | \/ |/ ____|/ ____| / _| (_)")
- ASCIIColors.red("| | | | | | | | | | \ / | (___ | | ___ _ __ ___ | |_ _ _ _ _ _ ")
- ASCIIColors.red("| | | | | | | | | | |\/| |\___ \| | / _ \| '_ ` _ \| _| | | | | | | |")
- ASCIIColors.red("| |___| |__| | |____| |____| | | |____) | |___| (_) | | | | | | | | |_| | |_| | |")
- ASCIIColors.red("|______\____/|______|______|_| |_|_____/ \_____\___/|_| |_| |_|_| \__, |\__,_|_|")
- ASCIIColors.red(" ______ __/ | ")
- ASCIIColors.red(" |______| |___/ ")
-
- ASCIIColors.red(" Forked from comfyanonymous's Comfyui nodes system")
- ASCIIColors.red(" Integration in lollms by ParisNeo")
-
- if not self.wait_for_service(1,False) and self.service_config.local_service and self.service_config.start_service_at_startup and self.service_config.base_url is None:
- ASCIIColors.info("Loading lollms_comfyui")
- if platform.system() == "Windows":
- ASCIIColors.info("Running on windows")
- script_path = self.comfyui_folder / "main.py"
-
- if self.service_config.share:
- pass # TODO: implement
- #run_python_script_in_env("comfyui", str(script_path), cwd=self.comfyui_folder, wait=False)
- # subprocess.Popen("conda activate " + str(script_path) +" --share", cwd=self.comfyui_folder)
- else:
- pass # TODO: implement
- # run_python_script_in_env("comfyui", str(script_path), cwd=self.comfyui_folder, wait=False)
- # subprocess.Popen(script_path, cwd=self.comfyui_folder)
- else:
- ASCIIColors.info("Running on linux/MacOs")
- script_path = str(self.comfyui_folder / "lollms_comfyui.sh")
- ASCIIColors.info(f"launcher path: {script_path}")
- ASCIIColors.info(f"comfyui path: {self.comfyui_folder}")
-
- if self.service_config.share:
- pass # TODO: implement
- # run_script_in_env("comfyui","bash " + script_path +" --share", cwd=self.comfyui_folder)
- # subprocess.Popen("conda activate " + str(script_path) +" --share", cwd=self.comfyui_folder)
- else:
- pass # TODO: implement
- # run_script_in_env("comfyui","bash " + script_path, cwd=self.comfyui_folder)
- ASCIIColors.info("Process done")
- ASCIIColors.success("Launching Comfyui succeeded")
-
- # Wait until the service is available at http://127.0.0.1:8188//
-
- def verify_comfyui(self):
- # Clone repository
- root_dir = self.app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- comfyui_folder = shared_folder / "comfyui"
- return comfyui_folder.exists()
-
- def install(self):
- root_dir = self.app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- comfyui_folder = shared_folder / "comfyui"
- if comfyui_folder.exists():
- if show_yes_no_dialog("warning!","I have detected that there is a previous installation of Comfyui.\nShould I remove it and continue installing?"):
- shutil.rmtree(comfyui_folder)
- elif show_yes_no_dialog("warning!","Continue installation?"):
- ASCIIColors.cyan("Installing comfyui conda environment with python 3.10")
- create_conda_env("comfyui","3.10")
- ASCIIColors.cyan("Done")
- return
- else:
- return
-
- subprocess.run(["git", "clone", "https://github.com/ParisNeo/ComfyUI.git", str(comfyui_folder)])
- subprocess.run(["git", "clone", "https://github.com/ParisNeo/ComfyUI-Manager.git", str(comfyui_folder/"custom_nodes/ComfyUI-Manager")])
- subprocess.run(["git", "clone", "https://github.com/AlekPet/ComfyUI_Custom_Nodes_AlekPet.git", str(comfyui_folder/"custom_nodes/ComfyUI_Custom_Nodes_AlekPet")])
- subprocess.run(["git", "clone", "https://github.com/ParisNeo/lollms_nodes_suite.git", str(comfyui_folder/"custom_nodes/lollms_nodes_suite")])
-
-
- subprocess.run(["git", "clone", "https://github.com/jags111/efficiency-nodes-comfyui.git", str(comfyui_folder/"custom_nodes/efficiency-nodes-comfyui")])
- subprocess.run(["git", "clone", "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet.git", str(comfyui_folder/"custom_nodes/ComfyUI-Advanced-ControlNet")])
- subprocess.run(["git", "clone", "https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite.git", str(comfyui_folder/"custom_nodes/ComfyUI-VideoHelperSuite")])
- subprocess.run(["git", "clone", "https://github.com/LykosAI/ComfyUI-Inference-Core-Nodes.git", str(comfyui_folder/"custom_nodes/ComfyUI-Inference-Core-Nodes")])
- subprocess.run(["git", "clone", "https://github.com/Fannovel16/comfyui_controlnet_aux.git", str(comfyui_folder/"custom_nodes/comfyui_controlnet_aux")])
- subprocess.run(["git", "clone", "https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved.git", str(comfyui_folder/"custom_nodes/ComfyUI-AnimateDiff-Evolved")])
-
- if show_yes_no_dialog("warning!","You will need to install an image generation model.\nDo you want to install an image model from civitai?\nI suggest Juggernaut XL.\nIt is a very good model.\nyou can always install more models afterwards in your comfyui folder/models.checkpoints"):
- download_file("https://civitai.com/api/download/models/357609", comfyui_folder/"models/checkpoints","Juggernaut_XL.safetensors")
-
- if show_yes_no_dialog("warning!","Do you want to install a video model from hugging face?\nIsuggest SVD XL."):
- download_file("https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/svd_xt.safetensors", comfyui_folder/"models/checkpoints","svd_xt.safetensors")
-
- if show_yes_no_dialog("warning!","Do you want to install all control net models?"):
- (comfyui_folder/"models/controlnet").mkdir(parents=True, exist_ok=True)
- download_file("https://huggingface.co/thibaud/controlnet-openpose-sdxl-1.0/resolve/main/OpenPoseXL2.safetensors", comfyui_folder/"models/controlnet","OpenPoseXL2.safetensors")
- download_file("https://huggingface.co/diffusers/controlnet-depth-sdxl-1.0/resolve/main/diffusion_pytorch_model.safetensors", comfyui_folder/"models/controlnet","DepthMap_XL.safetensors")
-
-
- if show_yes_no_dialog("warning!","Do you want to install all animation models?"):
- (comfyui_folder/"models/animatediff_models").mkdir(parents=True, exist_ok=True)
- download_file("https://huggingface.co/guoyww/animatediff/resolve/cd71ae134a27ec6008b968d6419952b0c0494cf2/mm_sdxl_v10_beta.ckpt", comfyui_folder/"models/animatediff_models","mm_sdxl_v10_beta.ckpt")
-
- # TODO: fix
- # create_conda_env("comfyui","3.10")
- # if self.app.config.hardware_mode in ["nvidia", "nvidia-tensorcores"]:
- # run_python_script_in_env("comfyui", "-m pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121")
- # if self.app.config.hardware_mode in ["amd", "amd-noavx"]:
- # run_python_script_in_env("comfyui", "-m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7")
- # elif self.app.config.hardware_mode in ["cpu", "cpu-noavx"]:
- # run_python_script_in_env("comfyui", "-m pip install --pre torch torchvision torchaudio")
- # run_python_script_in_env("comfyui", f"-m pip install -r {comfyui_folder}/requirements.txt")
-
- self.app.comfyui = LollmsComfyUI(self.app)
- ASCIIColors.green("Comfyui installed successfully")
- self.app.HideBlockingMessage()
-
-
- def upgrade(self):
- root_dir = self.app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- comfyui_folder = shared_folder / "comfyui"
- if not comfyui_folder.exists():
- self.app.InfoMessage("Comfyui is not installed, install it first")
- return
-
- subprocess.run(["git", "pull", str(comfyui_folder)])
- subprocess.run(["git", "pull", str(comfyui_folder/"custom_nodes/ComfyUI-Manager")])
- subprocess.run(["git", "pull", str(comfyui_folder/"custom_nodes/efficiency-nodes-comfyui")])
- ASCIIColors.success("DONE")
-
-
- def wait_for_service_in_another_thread(self, max_retries=150, show_warning=True):
- thread = threading.Thread(target=self.wait_for_service, args=(max_retries, show_warning))
- thread.start()
- return thread
-
- @staticmethod
- def get_models_list(app):
- return [str(f.name) for f in (app.lollms_paths.personal_path/"shared"/"comfyui"/"models"/"checkpoints").iterdir()]
-
- def wait_for_service(self, max_retries = 50, show_warning=True):
- url = f"{self.comfyui_base_url}"
- # Adjust this value as needed
- retries = 0
-
- while retries < max_retries or max_retries<0:
- try:
- response = requests.get(url)
- if response.status_code == 200:
- print("Service is available.")
- if self.app is not None:
- self.app.success("Comfyui Service is now available.")
- return True
- except requests.exceptions.RequestException:
- pass
-
- retries += 1
- time.sleep(1)
- if show_warning:
- print("Service did not become available within the given time.")
- if self.app is not None:
- self.app.error("Comfyui Service did not become available within the given time.")
- return False
-
- def paint(
- self,
- positive_prompt,
- negative_prompt,
- sampler_name="",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None
- ):
- if output_path is None:
- output_path = self.output_dir
- client_id = str(uuid.uuid4())
- url = self.comfyui_base_url[7:-1]
-
- def queue_prompt(prompt):
- p = {"prompt": prompt, "client_id": client_id}
- data = json.dumps(p).encode('utf-8')
- full_url = "http://{}/prompt".format(url)
- req = request.Request(full_url, data=data)
- output = request.urlopen(req).read()
- return json.loads(output)
-
- def get_image(filename, subfolder):
- data = {"filename": filename, "subfolder": subfolder}
- url_values = parse.urlencode(data)
- full_url = "http://{}/view?{}".format(url, url_values)
- with request.urlopen(full_url) as response:
- return response.read()
-
- def get_history(prompt_id):
- url_values = "http://{}/history/{}".format(url, prompt_id)
- with request.urlopen(url_values) as response:
- return json.loads(response.read())
-
- def get_images(ws, prompt):
- prompt_id = queue_prompt(prompt)['prompt_id']
- output_images = {}
- while True:
- out = ws.recv()
- if isinstance(out, str):
- message = json.loads(out)
- if message['type'] == 'executing':
- data = message['data']
- if data['node'] is None and data['prompt_id'] == prompt_id:
- break #Execution is done
- else:
- continue #previews are binary data
-
- history = get_history(prompt_id)[prompt_id]
- for o in history['outputs']:
- for node_id in history['outputs']:
- node_output = history['outputs'][node_id]
- if 'images' in node_output:
- images_output = []
- for image in node_output['images']:
- if image["type"]=="output":
- image_data = get_image(image['filename'], image['subfolder'])
- images_output.append(image_data)
-
- return images_output
-
- def save_images(images:dict, folder_path:str|Path):
- # Create the folder if it doesn't exist
- folder = Path(folder_path)
- folder.mkdir(parents=True, exist_ok=True)
-
- # Save each image to the folder
- for i, img_data in enumerate(images):
- img_path = folder / f'image_{i+1}.png'
- with open(img_path, 'wb') as img_file:
- img_file.write(img_data)
-
- # Return the path to the first image
- return str(folder / 'image_1.png')
- prompt_text = """
- {
- "1": {
- "inputs": {
- "base_ckpt_name": "juggernaut.safetensors",
- "base_clip_skip": -2,
- "refiner_ckpt_name": "None",
- "refiner_clip_skip": -2,
- "positive_ascore": 6,
- "negative_ascore": 2,
- "vae_name": "Baked VAE",
- "positive": "smart robot icon, slick, flat design, high res, W in the center, black background",
- "negative": "ugly, deformed, badly rendered, fuzzy",
- "token_normalization": "none",
- "weight_interpretation": "comfy",
- "empty_latent_width": 1024,
- "empty_latent_height": 1024,
- "batch_size": 1
- },
- "class_type": "Eff. Loader SDXL",
- "_meta": {
- "title": "Eff. Loader SDXL"
- }
- },
- "2": {
- "inputs": {
- "noise_seed": 74738751167752,
- "steps": 20,
- "cfg": 7,
- "sampler_name": "euler",
- "scheduler": "normal",
- "start_at_step": 0,
- "refine_at_step": -1,
- "preview_method": "auto",
- "vae_decode": "true",
- "sdxl_tuple": [
- "1",
- 0
- ],
- "latent_image": [
- "1",
- 1
- ],
- "optional_vae": [
- "1",
- 2
- ]
- },
- "class_type": "KSampler SDXL (Eff.)",
- "_meta": {
- "title": "KSampler SDXL (Eff.)"
- }
- },
- "3": {
- "inputs": {
- "filename_prefix": "ComfyUI",
- "images": [
- "2",
- 3
- ]
- },
- "class_type": "SaveImage",
- "_meta": {
- "title": "Save Image"
- }
- }
- }
- """
-
-
- prompt = json.loads(prompt_text)
- #set the text prompt for our positive CLIPTextEncode
- prompt["1"]["inputs"]["positive"] = prompt_text
- prompt["1"]["inputs"]["negative"] = negative_prompt
- prompt["1"]["inputs"]["empty_latent_width"] = width
- prompt["1"]["inputs"]["empty_latent_height"] = height
-
- prompt["1"]["inputs"]["base_ckpt_name"] = self.app.config.comfyui_model
-
- ws = websocket.WebSocket()
- ws.connect("ws://{}/ws?clientId={}".format(url, client_id))
- images = get_images(ws, prompt)
-
- return save_images(images, output_path), {"prompt":prompt,"negative_prompt":negative_prompt}
-
-
- def paint_from_images(self, positive_prompt: str,
- images: List[str],
- negative_prompt: str = "",
- sampler_name="",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None
- ) -> List[Dict[str, str]]:
- return None
-
diff --git a/lollms/services/tti/dalle/lollms_dalle.py b/lollms/services/tti/dalle/lollms_dalle.py
deleted file mode 100644
index df78637..0000000
--- a/lollms/services/tti/dalle/lollms_dalle.py
+++ /dev/null
@@ -1,204 +0,0 @@
-# Title LollmsDalle
-# Licence: Apache 2.0
-# Author : Paris Neo
-
-
-from pathlib import Path
-import sys
-from lollms.app import LollmsApplication
-from lollms.paths import LollmsPaths
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-import time
-import io
-import sys
-import requests
-import os
-import base64
-import subprocess
-import time
-import json
-import platform
-from dataclasses import dataclass
-from PIL import Image, PngImagePlugin
-from enum import Enum
-from typing import List, Dict, Any
-
-from ascii_colors import ASCIIColors, trace_exception
-from lollms.paths import LollmsPaths
-from lollms.utilities import PackageManager, find_next_available_filename
-from lollms.tti import LollmsTTI
-import subprocess
-import shutil
-from tqdm import tqdm
-import threading
-from io import BytesIO
-import os
-
-
-class LollmsDalle(LollmsTTI):
- def __init__(self, app, output_folder:str|Path=None):
- """
- Initializes the LollmsDalle binding.
-
- Args:
- api_key (str): The API key for authentication.
- output_folder (Path|str): The output folder where to put the generated data
- """
- # Check for the OPENAI_KEY environment variable if no API key is provided
- api_key = os.getenv("OPENAI_KEY","")
- service_config = TypedConfig(
- ConfigTemplate([
- {"name":"api_key", "type":"str", "value":api_key, "help":"A valid Open AI key to generate text using anthropic api"},
- {"name":"generation_engine", "type":"str", "value":"dall-e-3", "options":["dall-e-2","dall-e-3"], "help":"The engine to be used"},
- ]),
- BaseConfig(config={
- "api_key": "", # use avx2
- })
- )
-
- super().__init__("dall-e", app, service_config, output_folder)
-
- def settings_updated(self):
- pass
-
- def paint(
- self,
- positive_prompt,
- negative_prompt,
- sampler_name="Euler",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None,
- generation_engine=None
- ):
- if output_path is None:
- output_path = self.output_path
- if generation_engine is None:
- generation_engine = self.service_config.generation_engine
- if not PackageManager.check_package_installed("openai"):
- PackageManager.install_package("openai")
- import openai
- openai.api_key = self.service_config.api_key
- if generation_engine=="dall-e-2":
- supported_resolutions = [
- [512, 512],
- [1024, 1024],
- ]
- # Find the closest resolution
- closest_resolution = min(supported_resolutions, key=lambda res: abs(res[0] - width) + abs(res[1] - height))
-
- else:
- supported_resolutions = [
- [1024, 1024],
- [1024, 1792],
- [1792, 1024]
- ]
- # Find the closest resolution
- if width>height:
- closest_resolution = [1792, 1024]
- elif width<height:
- closest_resolution = [1024, 1792]
- else:
- closest_resolution = [1024, 1024]
-
-
- # Update the width and height
- width = closest_resolution[0]
- height = closest_resolution[1]
-
-
- response = openai.images.generate(
- model=generation_engine,
- prompt=positive_prompt.strip(),
- quality="standard",
- size=f"{width}x{height}",
- n=1,
-
- )
- # download image to outputs
- output_dir = Path(output_path)
- output_dir.mkdir(parents=True, exist_ok=True)
- image_url = response.data[0].url
-
- # Get the image data from the URL
- response = requests.get(image_url)
-
- if response.status_code == 200:
- # Generate the full path for the image file
- file_name = output_dir/find_next_available_filename(output_dir, "img_dalle_") # You can change the filename if needed
-
- # Save the image to the specified folder
- with open(file_name, "wb") as file:
- file.write(response.content)
- ASCIIColors.yellow(f"Image saved to {file_name}")
- else:
- ASCIIColors.red("Failed to download the image")
-
- return file_name, {"positive_prompt":positive_prompt}
-
- def paint_from_images(self, positive_prompt: str, images: List[str], negative_prompt: str = "") -> List[Dict[str, str]]:
- if output_path is None:
- output_path = self.output_path
- if not PackageManager.check_package_installed("openai"):
- PackageManager.install_package("openai")
- import openai
- openai.api_key = self.service_config.api_key
- generation_engine="dall-e-2"
- supported_resolutions = [
- [512, 512],
- [1024, 1024],
- ]
- # Find the closest resolution
- closest_resolution = min(supported_resolutions, key=lambda res: abs(res[0] - width) + abs(res[1] - height))
-
-
-
- # Update the width and height
- width = closest_resolution[0]
- height = closest_resolution[1]
-
- # Read the image file from disk and resize it
- image = Image.open(images[0])
- width, height = width, height
- image = image.resize((width, height))
-
- # Convert the image to a BytesIO object
- byte_stream = BytesIO()
- image.save(byte_stream, format='PNG')
- byte_array = byte_stream.getvalue()
- response = openai.images.create_variation(
- image=byte_array,
- n=1,
- model=generation_engine, # for now only dalle 2 supports variations
- size=f"{width}x{height}"
- )
- # download image to outputs
- output_dir = Path(output_path)
- output_dir.mkdir(parents=True, exist_ok=True)
- image_url = response.data[0].url
-
- # Get the image data from the URL
- response = requests.get(image_url)
-
- if response.status_code == 200:
- # Generate the full path for the image file
- file_name = output_dir/find_next_available_filename(output_dir, "img_dalle_") # You can change the filename if needed
-
- # Save the image to the specified folder
- with open(file_name, "wb") as file:
- file.write(response.content)
- ASCIIColors.yellow(f"Image saved to {file_name}")
- else:
- ASCIIColors.red("Failed to download the image")
-
- return file_name
-
-
- @staticmethod
- def get(app:LollmsApplication):
- return LollmsDalle
\ No newline at end of file
diff --git a/lollms/services/tti/diffusers/lollms_diffusers.py b/lollms/services/tti/diffusers/lollms_diffusers.py
deleted file mode 100644
index a437591..0000000
--- a/lollms/services/tti/diffusers/lollms_diffusers.py
+++ /dev/null
@@ -1,324 +0,0 @@
-# Title LollmsDiffusers
-# Licence: MIT
-# Author : Paris Neo
- # All rights are reserved
-
-from pathlib import Path
-import sys
-from lollms.app import LollmsApplication
-from lollms.utilities import PackageManager, check_and_install_torch, find_next_available_filename
-
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-import sys
-import requests
-from typing import List, Dict, Any
-
-from ascii_colors import ASCIIColors, trace_exception
-from lollms.paths import LollmsPaths
-from lollms.tti import LollmsTTI
-from lollms.utilities import git_pull
-from tqdm import tqdm
-
-import pipmaster as pm
-if not pm.is_installed("torch"):
- ASCIIColors.yellow("Diffusers: Torch not found. Installing it")
- pm.install_multiple(["torch","torchvision","torchaudio"], "https://download.pytorch.org/whl/cu121", force_reinstall=True)
-
-import torch
-if not torch.cuda.is_available():
- ASCIIColors.yellow("Diffusers: Torch not using cuda. Reinstalling it")
- pm.install_multiple(["torch","torchvision","torchaudio"], "https://download.pytorch.org/whl/cu121", force_reinstall=True)
-if not pm.is_installed("transformers"):
- pm.install("transformers")
-
-if not pm.is_installed("diffusers"):
- pm.install("diffusers")
-
-
-
-def adjust_dimensions(value: int) -> int:
- """Adjusts the given value to be divisible by 8."""
- return (value // 8) * 8
-
-def download_file(url, folder_path, local_filename):
- # Make sure 'folder_path' exists
- folder_path.mkdir(parents=True, exist_ok=True)
-
- with requests.get(url, stream=True) as r:
- r.raise_for_status()
- total_size = int(r.headers.get('content-length', 0))
- progress_bar = tqdm(total=total_size, unit='B', unit_scale=True)
- with open(folder_path / local_filename, 'wb') as f:
- for chunk in r.iter_content(chunk_size=8192):
- f.write(chunk)
- progress_bar.update(len(chunk))
- progress_bar.close()
-
- return local_filename
-
-def install_model(lollms_app:LollmsApplication, model_url):
- root_dir = lollms_app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- diffusers_folder = shared_folder / "diffusers"
-
-
-
- import torch
- from diffusers import PixArtSigmaPipeline
-
- # You can replace the checkpoint id with "PixArt-alpha/PixArt-Sigma-XL-2-512-MS" too.
- pipe = PixArtSigmaPipeline.from_pretrained(
- "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", torch_dtype=torch.float16
- )
-
-
-
-
-
-
-def upgrade_diffusers(lollms_app:LollmsApplication):
- PackageManager.install_or_update("diffusers")
- PackageManager.install_or_update("xformers")
-
-
-class LollmsDiffusers(LollmsTTI):
- has_controlnet = False
- def __init__(self, app, output_folder:str|Path=None):
- """
- Initializes the LollmsDalle binding.
-
- Args:
- api_key (str): The API key for authentication.
- output_folder (Path|str): The output folder where to put the generated data
- """
- service_config = TypedConfig(
- ConfigTemplate([
- {"name":"model", "type":"str", "value":"v2ray/stable-diffusion-3-medium-diffusers", "help":"The model to be used"},
- {"name":"wm", "type":"str", "value":"lollms", "help":"The water marking"},
- ]),
- BaseConfig(config={
- "api_key": "", # use avx2
- })
- )
-
- super().__init__("diffusers", app, service_config)
- if not pm.is_installed("torch"):
- pm.install("torch torchvision torchaudio", "https://download.pytorch.org/whl/cu121")
-
- if not pm.is_installed("transformers"):
- pm.install("transformers")
-
- if not pm.is_installed("diffusers"):
- pm.install("diffusers")
-
- super().__init__("diffusers",app)
- self.ready = False
- # Get the current directory
- lollms_paths = app.lollms_paths
- root_dir = lollms_paths.personal_path
-
- shared_folder = root_dir/"shared"
- self.diffusers_folder = shared_folder / "diffusers"
- self.output_dir = root_dir / "outputs/diffusers"
- self.tti_models_dir = self.diffusers_folder / "models"
- self.output_dir.mkdir(parents=True, exist_ok=True)
- self.tti_models_dir.mkdir(parents=True, exist_ok=True)
-
- ASCIIColors.red("")
- ASCIIColors.red(" _ _ _ _ _ __ __ ")
- ASCIIColors.red(" | | | | | | (_)/ _|/ _| ")
- ASCIIColors.red(" | | ___ | | |_ __ ___ ___ __| |_| |_| |_ _ _ ___ ___ _ __ ___ ")
- ASCIIColors.red(" | | / _ \| | | '_ ` _ \/ __| / _` | | _| _| | | / __|/ _ \ '__/ __| ")
- ASCIIColors.red(" | |___| (_) | | | | | | | \__ \| (_| | | | | | | |_| \__ \ __/ | \__ \ ")
- ASCIIColors.red(" |______\___/|_|_|_| |_| |_|___/ \__,_|_|_| |_| \__,_|___/\___|_| |___/ ")
- ASCIIColors.red(" ______ ")
- ASCIIColors.red(" |______| ")
- ASCIIColors.red("")
- ASCIIColors.yellow(f"Using model: {self.service_config.model}")
- import torch
-
- try:
- if "stable-diffusion-3" in self.service_config.model:
- from diffusers import StableDiffusion3Pipeline # AutoPipelineForImage2Image#PixArtSigmaPipeline
- self.tti_model = StableDiffusion3Pipeline.from_pretrained(
- self.service_config.model, torch_dtype=torch.float16, cache_dir=self.tti_models_dir,
- use_safetensors=True,
- )
- self.iti_model = None
- else:
- from diffusers import AutoPipelineForText2Image # AutoPipelineForImage2Image#PixArtSigmaPipeline
- self.tti_model = AutoPipelineForText2Image.from_pretrained(
- self.service_config.model, torch_dtype=torch.float16, cache_dir=self.tti_models_dir,
- use_safetensors=True,
- )
- self.iti_model = None
-
- # AutoPipelineForText2Image
- # self.tti_model = StableDiffusionPipeline.from_pretrained(
- # "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, cache_dir=self.tti_models_dir,
- # use_safetensors=True,
- # ) # self.service_config.model
- # Enable memory optimizations.
- try:
- if app.config.diffusers_offloading_mode=="sequential_cpu_offload":
- self.tti_model.enable_sequential_cpu_offload()
- elif app.coinfig.diffusers_offloading_mode=="model_cpu_offload":
- self.tti_model.enable_model_cpu_offload()
- except:
- pass
- except Exception as ex:
- self.tti_model= None
- trace_exception(ex)
-
-
- def install_diffusers(self):
- root_dir = self.app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- diffusers_folder = shared_folder / "diffusers"
- diffusers_folder.mkdir(exist_ok=True, parents=True)
- models_dir = diffusers_folder / "models"
- models_dir.mkdir(parents=True, exist_ok=True)
-
- pm.install("diffusers")
- pm.install("xformers")
-
-
- @staticmethod
- def verify(app:LollmsApplication):
- # Clone repository
- root_dir = app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- diffusers_folder = shared_folder / "diffusers"
- return diffusers_folder.exists()
-
- def get(app:LollmsApplication):
- root_dir = app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- diffusers_folder = shared_folder / "diffusers"
- diffusers_script_path = diffusers_folder / "lollms_diffusers.py"
- git_pull(diffusers_folder)
-
- if diffusers_script_path.exists():
- ASCIIColors.success("lollms_diffusers found.")
- ASCIIColors.success("Loading source file...",end="")
- # use importlib to load the module from the file path
- from lollms.services.tti.diffusers.lollms_diffusers import LollmsDiffusers
- ASCIIColors.success("ok")
- return LollmsDiffusers
-
- def get_scheduler_by_name(self, scheduler_name="LMS"):
- if scheduler_name == "LMS":
- from diffusers import LMSDiscreteScheduler
- return LMSDiscreteScheduler(
- beta_start=0.00085,
- beta_end=0.012,
- beta_schedule="scaled_linear"
- )
- elif scheduler_name == "Euler":
- from diffusers import EulerDiscreteScheduler
- return LMSDiscreteScheduler()
- elif scheduler_name == "DDPMS":
- from diffusers import DDPMScheduler
- return DDPMScheduler()
- elif scheduler_name == "DDIMS":
- from diffusers import DDIMScheduler
- return DDIMScheduler()
-
-
-
- def paint(
- self,
- positive_prompt,
- negative_prompt,
- sampler_name="",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None
- ):
- import torch
- if sampler_name!="":
- sc = self.get_scheduler_by_name(sampler_name)
- if sc:
- self.tti_model.scheduler = sc
- width = adjust_dimensions(int(width))
- height = adjust_dimensions(int(height))
-
-
- def process_output_path(output_path, self_output_dir):
- if output_path is None:
- output_path = Path(self_output_dir)
- fn = find_next_available_filename(output_path, "diff_img_")
- else:
- output_path = Path(output_path)
- if output_path.is_file():
- fn = output_path
- elif output_path.is_dir():
- fn = find_next_available_filename(output_path, "diff_img_")
- else:
- # If the path doesn't exist, assume it's intended to be a file
- fn = output_path
-
- return fn
-
-
- # Usage in the original context
- if output_path is None:
- output_path = self.output_dir
-
- fn = process_output_path(output_path, self.output_dir)
-
- if seed!=-1:
- generator = torch.Generator("cuda").manual_seed(seed)
- image = self.tti_model(positive_prompt, negative_prompt=negative_prompt, height=height, width=width, guidance_scale=scale, num_inference_steps=steps, generator=generator).images[0]
- else:
- image = self.tti_model(positive_prompt, negative_prompt=negative_prompt, height=height, width=width, guidance_scale=scale, num_inference_steps=steps).images[0]
- # Save the image
- image.save(fn)
- return fn, {"prompt":positive_prompt, "negative_prompt":negative_prompt}
-
- def paint_from_images(self, positive_prompt: str,
- image: str,
- negative_prompt: str = "",
- sampler_name="",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None
- ) -> List[Dict[str, str]]:
- import torch
- from diffusers.utils import make_image_grid, load_image
-
- if not self.iti_model:
- from diffusers import AutoPipelineForImage2Image
-
- self.iti_model = AutoPipelineForImage2Image.from_pretrained(
- self.self.service_config.model, torch_dtype=torch.float16, variant="fp16", use_safetensors=True
- )
- if sampler_name!="":
- sc = self.get_scheduler_by_name(sampler_name)
- if sc:
- self.iti_model.scheduler = sc
-
- img = load_image(image)
- if output_path is None:
- output_path = self.output_dir
- if seed!=-1:
- generator = torch.Generator("cuda").manual_seed(seed)
- image = self.titi_model(positive_prompt,image=img, negative_prompt=negative_prompt, height=height, width=width, guidance_scale=scale, num_inference_steps=steps, generator=generator).images[0]
- else:
- image = self.iti_model(positive_prompt,image=img, negative_prompt=negative_prompt, height=height, width=width, guidance_scale=scale, num_inference_steps=steps).images[0]
- output_path = Path(output_path)
- fn = find_next_available_filename(output_path,"diff_img_")
- # Save the image
- image.save(fn)
- return fn, {"prompt":positive_prompt, "negative_prompt":negative_prompt}
-
diff --git a/lollms/services/tti/diffusers_client/lollms_diffusers_client.py b/lollms/services/tti/diffusers_client/lollms_diffusers_client.py
deleted file mode 100644
index 70a9c21..0000000
--- a/lollms/services/tti/diffusers_client/lollms_diffusers_client.py
+++ /dev/null
@@ -1,238 +0,0 @@
-# Title LollmsDiffusers
-# Licence: MIT
-# Author : Paris Neo
- # All rights are reserved
-
-from pathlib import Path
-import sys
-from lollms.app import LollmsApplication
-from lollms.utilities import PackageManager, check_and_install_torch, find_next_available_filename, install_cuda, check_torch_version
-
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-import sys
-import requests
-from typing import List, Dict, Any
-
-from ascii_colors import ASCIIColors, trace_exception
-from lollms.paths import LollmsPaths
-from lollms.tti import LollmsTTI
-from lollms.utilities import git_pull
-from tqdm import tqdm
-from PIL import Image
-import threading
-import base64
-from PIL import Image
-import io
-import pipmaster as pm
-
-
-def adjust_dimensions(value: int) -> int:
- """Adjusts the given value to be divisible by 8."""
- return (value // 8) * 8
-
-def download_file(url, folder_path, local_filename):
- # Make sure 'folder_path' exists
- folder_path.mkdir(parents=True, exist_ok=True)
-
- with requests.get(url, stream=True) as r:
- r.raise_for_status()
- total_size = int(r.headers.get('content-length', 0))
- progress_bar = tqdm(total=total_size, unit='B', unit_scale=True)
- with open(folder_path / local_filename, 'wb') as f:
- for chunk in r.iter_content(chunk_size=8192):
- f.write(chunk)
- progress_bar.update(len(chunk))
- progress_bar.close()
-
- return local_filename
-
-
-def install_diffusers(lollms_app:LollmsApplication):
- root_dir = lollms_app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- diffusers_folder = shared_folder / "diffusers"
- diffusers_folder.mkdir(exist_ok=True, parents=True)
- models_dir = diffusers_folder / "models"
- models_dir.mkdir(parents=True, exist_ok=True)
-
- PackageManager.reinstall("diffusers")
- PackageManager.reinstall("xformers")
-
-
-
-
-def upgrade_diffusers(lollms_app:LollmsApplication):
- PackageManager.install_or_update("diffusers")
- PackageManager.install_or_update("xformers")
-
-
-class LollmsDiffusersClient(LollmsTTI):
- has_controlnet = False
- def __init__(self, app, output_folder:str|Path=None):
- """
- Initializes the LollmsDalle binding.
-
- Args:
- api_key (str): The API key for authentication.
- output_folder (Path|str): The output folder where to put the generated data
- """
- service_config = TypedConfig(
- ConfigTemplate([
- {
- "name": "base_url",
- "type": "str",
- "value": "http://127.0.0.1:8188/",
- "help": "The base URL for the service. This is the address where the service is hosted (e.g., http://127.0.0.1:8188/)."
- },
- {
- "name": "wm",
- "type": "str",
- "value": "lollms",
- "help": "Watermarking text or identifier to be used in the service."
- },
- {"name":"model", "type":"str", "value":"v2ray/stable-diffusion-3-medium-diffusers", "help":"The model to be used"},
- {"name":"wm", "type":"str", "value":"lollms", "help":"The water marking"},
- ]),
- BaseConfig(config={
- "api_key": "", # use avx2
- })
- )
-
- super().__init__("diffusers_client", app, service_config)
- self.ready = False
- # Get the current directory
- lollms_paths = app.lollms_paths
- root_dir = lollms_paths.personal_path
-
-
- shared_folder = root_dir/"shared"
- self.diffusers_folder = shared_folder / "diffusers"
- self.output_dir = root_dir / "outputs/diffusers"
- self.models_dir = self.diffusers_folder / "models"
- self.output_dir.mkdir(parents=True, exist_ok=True)
- self.models_dir.mkdir(parents=True, exist_ok=True)
-
-
- ASCIIColors.green(" _ _ _ _ _ __ __ ")
- ASCIIColors.green(" | | | | | | (_)/ _|/ _| ")
- ASCIIColors.green(" | | ___ | | |_ __ ___ ___ __| |_| |_| |_ _ _ ___ ___ _ __ ___ ")
- ASCIIColors.green(" | | / _ \| | | '_ ` _ \/ __| / _` | | _| _| | | / __|/ _ \ '__/ __| ")
- ASCIIColors.green(" | |___| (_) | | | | | | | \__ \| (_| | | | | | | |_| \__ \ __/ | \__ \ ")
- ASCIIColors.green(" |______\___/|_|_|_| |_| |_|___/ \__,_|_|_| |_| \__,_|___/\___|_| |___/ ")
- ASCIIColors.green(" ______ ")
- ASCIIColors.green(" |______| ")
-
-
- @staticmethod
- def verify(app:LollmsApplication):
- # Clone repository
- root_dir = app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- diffusers_folder = shared_folder / "diffusers"
- return diffusers_folder.exists()
-
- def get(app:LollmsApplication):
- root_dir = app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- diffusers_folder = shared_folder / "diffusers"
- diffusers_script_path = diffusers_folder / "lollms_diffusers.py"
- git_pull(diffusers_folder)
-
- if diffusers_script_path.exists():
- ASCIIColors.success("lollms_diffusers found.")
- ASCIIColors.success("Loading source file...",end="")
- # use importlib to load the module from the file path
- from lollms.services.tti.diffusers.lollms_diffusers import LollmsDiffusers
- ASCIIColors.success("ok")
- return LollmsDiffusers
- def paint(
- self,
- positive_prompt,
- negative_prompt="",
- sampler_name="",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None
- ):
- url = f"{self.service_config.base_url}/generate-image"
-
- payload = {
- "positive_prompt": positive_prompt,
- "negative_prompt": negative_prompt,
- "sampler_name": sampler_name,
- "seed": seed,
- "scale": scale,
- "steps": steps,
- "width": width,
- "height": height,
- "restore_faces": restore_faces
- }
-
- try:
- response = requests.post(url, json=payload)
- response.raise_for_status()
- result = response.json()
-
- # Assuming the server returns the image path
- server_image_path = result['image_path']
-
- # If output_path is not provided, use the server's image path
- if output_path is None:
- output_path = server_image_path
- else:
- # Copy the image from server path to output_path
- # This part needs to be implemented based on how you want to handle file transfer
- pass
-
- return {
- "image_path": output_path,
- "prompt": result['prompt'],
- "negative_prompt": result['negative_prompt']
- }
-
- except requests.exceptions.RequestException as e:
- print(f"An error occurred: {e}")
- return None
-
- def save_image(self, image_data, output_path):
- image = Image.open(io.BytesIO(base64.b64decode(image_data)))
- image.save(output_path)
- print(f"Image saved to {output_path}")
-
- def paint_from_images(self, positive_prompt: str,
- images: List[str],
- negative_prompt: str = "",
- sampler_name="",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None
- ) -> List[Dict[str, str]]:
- import torch
- if sampler_name!="":
- sc = self.get_scheduler_by_name(sampler_name)
- if sc:
- self.model.scheduler = sc
-
- if output_path is None:
- output_path = self.output_dir
- if seed!=-1:
- generator = torch.Generator("cuda").manual_seed(seed)
- image = self.model(positive_prompt, negative_prompt=negative_prompt, height=height, width=width, guidance_scale=scale, num_inference_steps=steps, generator=generator).images[0]
- else:
- image = self.model(positive_prompt, negative_prompt=negative_prompt, height=height, width=width, guidance_scale=scale, num_inference_steps=steps).images[0]
- output_path = Path(output_path)
- fn = find_next_available_filename(output_path,"diff_img_")
- # Save the image
- image.save(fn)
- return fn, {"prompt":positive_prompt, "negative_prompt":negative_prompt}
-
diff --git a/lollms/services/tti/fooocus/lollms_fooocus.py b/lollms/services/tti/fooocus/lollms_fooocus.py
deleted file mode 100644
index f562da9..0000000
--- a/lollms/services/tti/fooocus/lollms_fooocus.py
+++ /dev/null
@@ -1,277 +0,0 @@
-# Title LollmsFooocus
-# Licence: MIT
-# Author : Paris Neo
- # All rights are reserved
-
-from pathlib import Path
-import sys
-from lollms.app import LollmsApplication
-from lollms.paths import LollmsPaths
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-from lollms.utilities import PackageManager, check_and_install_torch, find_next_available_filename
-import time
-import io
-import sys
-import requests
-import os
-import base64
-import subprocess
-import time
-import json
-import platform
-from dataclasses import dataclass
-from PIL import Image, PngImagePlugin
-from enum import Enum
-from typing import List, Dict, Any
-
-from ascii_colors import ASCIIColors, trace_exception
-from lollms.paths import LollmsPaths
-from lollms.tti import LollmsTTI
-from lollms.utilities import git_pull, show_yes_no_dialog, run_script_in_env, create_conda_env
-import subprocess
-import shutil
-from tqdm import tqdm
-import threading
-
-
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-
-def download_file(url, folder_path, local_filename):
- # Make sure 'folder_path' exists
- folder_path.mkdir(parents=True, exist_ok=True)
-
- with requests.get(url, stream=True) as r:
- r.raise_for_status()
- total_size = int(r.headers.get('content-length', 0))
- progress_bar = tqdm(total=total_size, unit='B', unit_scale=True)
- with open(folder_path / local_filename, 'wb') as f:
- for chunk in r.iter_content(chunk_size=8192):
- f.write(chunk)
- progress_bar.update(len(chunk))
- progress_bar.close()
-
- return local_filename
-
-
-def install_fooocus(lollms_app:LollmsApplication):
- root_dir = lollms_app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- fooocus_folder = shared_folder / "fooocus"
- fooocus_folder.mkdir(exist_ok=True, parents=True)
- if not PackageManager.check_package_installed("fooocus"):
- PackageManager.install_or_update("gradio_client")
-
-
-def upgrade_fooocus(lollms_app:LollmsApplication):
- PackageManager.install_or_update("fooocus")
- PackageManager.install_or_update("xformers")
-
-
-class LollmsFooocus(LollmsTTI):
- def __init__(self, app, output_folder:str|Path=None):
- """
- Initializes the LollmsDalle binding.
-
- Args:
- api_key (str): The API key for authentication.
- output_folder (Path|str): The output folder where to put the generated data
- """
- service_config = TypedConfig(
- ConfigTemplate([
- {
- "name": "base_url",
- "type": "str",
- "value": "localhost:1024",
- "help": "The base URL for the service. This is the address where the service is hosted (e.g., http://127.0.0.1:8188/)."
- },
- {
- "name": "wm",
- "type": "str",
- "value": "lollms",
- "help": "Watermarking text or identifier to be used in the service."
- },
- {"name":"model", "type":"str", "value":"v2ray/stable-diffusion-3-medium-diffusers", "help":"The model to be used"},
- {"name":"wm", "type":"str", "value":"lollms", "help":"The water marking"},
- ]),
- BaseConfig(config={
- "api_key": "", # use avx2
- })
- )
-
- super().__init__("fooocus", app, service_config)
- self.ready = False
- # Get the current directory
- lollms_paths = app.lollms_paths
- root_dir = lollms_paths.personal_path
-
- shared_folder = root_dir/"shared"
- self.fooocus_folder = shared_folder / "fooocus"
- self.output_dir = root_dir / "outputs/fooocus"
- self.models_dir = self.fooocus_folder / "models"
- self.output_dir.mkdir(parents=True, exist_ok=True)
- self.models_dir.mkdir(parents=True, exist_ok=True)
-
- ASCIIColors.red(" _ _ _ ______ ")
- ASCIIColors.red("| | | | | | ___| ")
- ASCIIColors.red("| | ___ | | |_ __ ___ ___ | |_ ___ ___ ___ ___ _ _ ___ ")
- ASCIIColors.red("| | / _ \| | | '_ ` _ \/ __| | _/ _ \ / _ \ / _ \ / __| | | / __|")
- ASCIIColors.red("| |___| (_) | | | | | | | \__ \ | || (_) | (_) | (_) | (__| |_| \__ \ ")
- ASCIIColors.red("\_____/\___/|_|_|_| |_| |_|___/ \_| \___/ \___/ \___/ \___|\__,_|___/")
- ASCIIColors.red(" ______ ")
- ASCIIColors.red(" |______| ")
- if not PackageManager.check_package_installed("gradio_client"):
- PackageManager.install_or_update("gradio_client")
- from gradio_client import Client
- self.client = Client(base_url)
-
-
- @staticmethod
- def verify(app:LollmsApplication):
- # Clone repository
- root_dir = app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- fooocus_folder = shared_folder / "fooocus"
- return fooocus_folder.exists()
-
- def get(app:LollmsApplication):
- root_dir = app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- fooocus_folder = shared_folder / "fooocus"
- fooocus_script_path = fooocus_folder / "lollms_fooocus.py"
- git_pull(fooocus_folder)
-
- if fooocus_script_path.exists():
- ASCIIColors.success("lollms_fooocus found.")
- ASCIIColors.success("Loading source file...",end="")
- # use importlib to load the module from the file path
- from lollms.services.fooocus.lollms_fooocus import LollmsFooocus
- ASCIIColors.success("ok")
- return LollmsFooocus
-
-
- def paint(
- self,
- positive_prompt,
- negative_prompt,
- sampler_name="Euler",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None
- ):
- if output_path is None:
- output_path = self.output_dir
-
- self.client.predict()
- image = self.model(positive_prompt, negative_prompt=negative_prompt, guidance_scale=scale, num_inference_steps=steps,).images[0]
- output_path = Path(output_path)
- fn = find_next_available_filename(output_path,"diff_img_")
- # Save the image
- image.save(fn)
- return fn, {"prompt":positive_prompt, "negative_prompt":negative_prompt}
-
-
-
-# from gradio_client import Client
-
-# client = Client("https://fooocus.mydomain.fr/",verify_ssl=False)
-# result = client.predict(
-# True, # bool in 'Generate Image Grid for Each Batch' Checkbox component
-# "Howdy!", # str in 'parameter_11' Textbox component
-# "Howdy!", # str in 'Negative Prompt' Textbox component
-# ["Fooocus V2"], # List[str] in 'Selected Styles' Checkboxgroup component
-# "Quality", # str in 'Performance' Radio component
-# '704×1408 <span style="color: grey;"> ∣ 1:2</span>', # str in 'Aspect Ratios' Radio component
-# 1, # int | float (numeric value between 1 and 32) in 'Image Number' Slider component
-# "png", # str in 'Output Format' Radio component
-# "Howdy!", # str in 'Seed' Textbox component
-# True, # bool in 'Read wildcards in order' Checkbox component
-# 0, # int | float (numeric value between 0.0 and 30.0) in 'Image Sharpness' Slider component
-# 1, # int | float (numeric value between 1.0 and 30.0) in 'Guidance Scale' Slider component
-# "animaPencilXL_v100.safetensors", # str (Option from: ['animaPencilXL_v100.safetensors', 'juggernautXL_v8Rundiffusion.safetensors', 'realisticStockPhoto_v20.safetensors', 'sd_xl_base_1.0_0.9vae.safetensors', 'sd_xl_refiner_1.0_0.9vae.safetensors']) in 'Base Model (SDXL only)' Dropdown component
-# "None", # str (Option from: ['None', 'animaPencilXL_v100.safetensors', 'juggernautXL_v8Rundiffusion.safetensors', 'realisticStockPhoto_v20.safetensors', 'sd_xl_base_1.0_0.9vae.safetensors', 'sd_xl_refiner_1.0_0.9vae.safetensors']) in 'Refiner (SDXL or SD 1.5)' Dropdown component
-# 0.1, # int | float (numeric value between 0.1 and 1.0) in 'Refiner Switch At' Slider component
-# True, # bool in 'Enable' Checkbox component
-# "None", # str (Option from: ['None', 'sd_xl_offset_example-lora_1.0.safetensors', 'SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors', 'sdxl_lcm_lora.safetensors', 'sdxl_lightning_4step_lora.safetensors']) in 'LoRA 1' Dropdown component
-# -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component
-# True, # bool in 'Enable' Checkbox component
-# "None", # str (Option from: ['None', 'sd_xl_offset_example-lora_1.0.safetensors', 'SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors', 'sdxl_lcm_lora.safetensors', 'sdxl_lightning_4step_lora.safetensors']) in 'LoRA 2' Dropdown component
-# -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component
-# True, # bool in 'Enable' Checkbox component
-# "None", # str (Option from: ['None', 'sd_xl_offset_example-lora_1.0.safetensors', 'SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors', 'sdxl_lcm_lora.safetensors', 'sdxl_lightning_4step_lora.safetensors']) in 'LoRA 3' Dropdown component
-# -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component
-# True, # bool in 'Enable' Checkbox component
-# "None", # str (Option from: ['None', 'sd_xl_offset_example-lora_1.0.safetensors', 'SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors', 'sdxl_lcm_lora.safetensors', 'sdxl_lightning_4step_lora.safetensors']) in 'LoRA 4' Dropdown component
-# -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component
-# True, # bool in 'Enable' Checkbox component
-# "None", # str (Option from: ['None', 'sd_xl_offset_example-lora_1.0.safetensors', 'SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors', 'sdxl_lcm_lora.safetensors', 'sdxl_lightning_4step_lora.safetensors']) in 'LoRA 5' Dropdown component
-# -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component
-# True, # bool in 'Input Image' Checkbox component
-# "Howdy!", # str in 'parameter_91' Textbox component
-# "Disabled", # str in 'Upscale or Variation:' Radio component
-# "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image) in 'Drag above image to here' Image component
-# ["Left"], # List[str] in 'Outpaint Direction' Checkboxgroup component
-# "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image) in 'Drag inpaint or outpaint image to here' Image component
-# "Howdy!", # str in 'Inpaint Additional Prompt' Textbox component
-# "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image) in 'Mask Upload' Image component
-# True, # bool in 'Disable Preview' Checkbox component
-# True, # bool in 'Disable Intermediate Results' Checkbox component
-# True, # bool in 'Disable seed increment' Checkbox component
-# 0.1, # int | float (numeric value between 0.1 and 3.0) in 'Positive ADM Guidance Scaler' Slider component
-# 0.1, # int | float (numeric value between 0.1 and 3.0) in 'Negative ADM Guidance Scaler' Slider component
-# 0, # int | float (numeric value between 0.0 and 1.0) in 'ADM Guidance End At Step' Slider component
-# 1, # int | float (numeric value between 1.0 and 30.0) in 'CFG Mimicking from TSNR' Slider component
-# "euler", # str (Option from: ['euler', 'euler_ancestral', 'heun', 'heunpp2', 'dpm_2', 'dpm_2_ancestral', 'lms', 'dpm_fast', 'dpm_adaptive', 'dpmpp_2s_ancestral', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu', 'ddpm', 'lcm', 'ddim', 'uni_pc', 'uni_pc_bh2']) in 'Sampler' Dropdown component
-# "normal", # str (Option from: ['normal', 'karras', 'exponential', 'sgm_uniform', 'simple', 'ddim_uniform', 'lcm', 'turbo']) in 'Scheduler' Dropdown component
-# -1, # int | float (numeric value between -1 and 200) in 'Forced Overwrite of Sampling Step' Slider component
-# -1, # int | float (numeric value between -1 and 200) in 'Forced Overwrite of Refiner Switch Step' Slider component
-# -1, # int | float (numeric value between -1 and 2048) in 'Forced Overwrite of Generating Width' Slider component
-# -1, # int | float (numeric value between -1 and 2048) in 'Forced Overwrite of Generating Height' Slider component
-# -1, # int | float (numeric value between -1 and 1.0) in 'Forced Overwrite of Denoising Strength of "Vary"' Slider component
-# -1, # int | float (numeric value between -1 and 1.0) in 'Forced Overwrite of Denoising Strength of "Upscale"' Slider component
-# True, # bool in 'Mixing Image Prompt and Vary/Upscale' Checkbox component
-# True, # bool in 'Mixing Image Prompt and Inpaint' Checkbox component
-# True, # bool in 'Debug Preprocessors' Checkbox component
-# True, # bool in 'Skip Preprocessors' Checkbox component
-# 1, # int | float (numeric value between 1 and 255) in 'Canny Low Threshold' Slider component
-# 1, # int | float (numeric value between 1 and 255) in 'Canny High Threshold' Slider component
-# "joint", # str (Option from: ['joint', 'separate', 'vae']) in 'Refiner swap method' Dropdown component
-# 0, # int | float (numeric value between 0.0 and 1.0) in 'Softness of ControlNet' Slider component
-# True, # bool in 'Enabled' Checkbox component
-# 0, # int | float (numeric value between 0 and 2) in 'B1' Slider component
-# 0, # int | float (numeric value between 0 and 2) in 'B2' Slider component
-# 0, # int | float (numeric value between 0 and 4) in 'S1' Slider component
-# 0, # int | float (numeric value between 0 and 4) in 'S2' Slider component
-# True, # bool in 'Debug Inpaint Preprocessing' Checkbox component
-# True, # bool in 'Disable initial latent in inpaint' Checkbox component
-# "None", # str (Option from: ['None', 'v1', 'v2.5', 'v2.6']) in 'Inpaint Engine' Dropdown component
-# 0, # int | float (numeric value between 0.0 and 1.0) in 'Inpaint Denoising Strength' Slider component
-# 0, # int | float (numeric value between 0.0 and 1.0) in 'Inpaint Respective Field' Slider component
-# True, # bool in 'Enable Mask Upload' Checkbox component
-# True, # bool in 'Invert Mask' Checkbox component
-# -64, # int | float (numeric value between -64 and 64) in 'Mask Erode or Dilate' Slider component
-# True, # bool in 'Save Metadata to Images' Checkbox component
-# "fooocus", # str in 'Metadata Scheme' Radio component
-# "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image) in 'Image' Image component
-# 0, # int | float (numeric value between 0.0 and 1.0) in 'Stop At' Slider component
-# 0, # int | float (numeric value between 0.0 and 2.0) in 'Weight' Slider component
-# "ImagePrompt", # str in 'Type' Radio component
-# "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image) in 'Image' Image component
-# 0, # int | float (numeric value between 0.0 and 1.0) in 'Stop At' Slider component
-# 0, # int | float (numeric value between 0.0 and 2.0) in 'Weight' Slider component
-# "ImagePrompt", # str in 'Type' Radio component
-# "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image)in 'Image' Image component
-# 0, # int | float (numeric value between 0.0 and 1.0) in 'Stop At' Slider component
-# 0, # int | float (numeric value between 0.0 and 2.0) in 'Weight' Slider component
-# "ImagePrompt", # str in 'Type' Radio component
-# "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image) in 'Image' Image component
-# 0, # int | float (numeric value between 0.0 and 1.0) in 'Stop At' Slider component
-# 0, # int | float (numeric value between 0.0 and 2.0) in 'Weight' Slider component
-# "ImagePrompt", # str in 'Type' Radio component
-# fn_index=40
-# )
-# print(result)
diff --git a/lollms/services/tti/midjourney/lollms_midjourney.py b/lollms/services/tti/midjourney/lollms_midjourney.py
deleted file mode 100644
index 7964c69..0000000
--- a/lollms/services/tti/midjourney/lollms_midjourney.py
+++ /dev/null
@@ -1,334 +0,0 @@
-# Title LollmsMidjourney
-# Licence: Apache 2.0
-# Author : Paris Neo
-
-
-from pathlib import Path
-import sys
-from lollms.app import LollmsApplication
-from lollms.paths import LollmsPaths
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-import time
-import io
-import sys
-import requests
-import os
-import base64
-import subprocess
-import time
-import json
-import platform
-from dataclasses import dataclass
-from PIL import Image, PngImagePlugin
-from enum import Enum
-from typing import List, Dict, Any
-
-from ascii_colors import ASCIIColors, trace_exception
-from lollms.paths import LollmsPaths
-from lollms.utilities import PackageManager, find_next_available_filename
-from lollms.tti import LollmsTTI
-import subprocess
-import shutil
-from tqdm import tqdm
-
-import os
-import requests
-from PIL import Image
-
-
-MIDJOURNEY_API_URL = "https://api.imaginepro.ai/api/v1/nova"
-
-
-
-def split_image(file_path, folder_path, i):
- with Image.open(file_path) as img:
- width, height = img.size
-
- # Calculate the size of each quadrant
- quad_width = width // 2
- quad_height = height // 2
-
- quadrants = [
- (0, 0, quad_width, quad_height),
- (quad_width, 0, width, quad_height),
- (0, quad_height, quad_width, height),
- (quad_width, quad_height, width, height)
- ]
-
- split_paths = []
- for index, box in enumerate(quadrants):
- quadrant = img.crop(box)
- split_path = os.path.join(folder_path, f"midjourney_{i}_{index+1}.png")
- quadrant.save(split_path)
- split_paths.append(split_path)
-
- return split_paths
-
-class LollmsMidjourney(LollmsTTI):
- def __init__(self, app, output_folder:str|Path=None):
- """
- Initializes the LollmsDalle binding.
-
- Args:
- api_key (str): The API key for authentication.
- output_folder (Path|str): The output folder where to put the generated data
- """
- # Check for the MIDJOURNEY_KEY environment variable if no API key is provided
- api_key = os.getenv("MIDJOURNEY_KEY","")
- service_config = TypedConfig(
- ConfigTemplate([
- {
- "name": "api_key",
- "type": "str",
- "value": api_key,
- "help": "A valid API key for Midjourney, used to access the text generation service via the Anthropic API."
- },
- {
- "name": "timeout",
- "type": "int",
- "value": 300,
- "help": "The maximum time (in seconds) to wait for a response from the API before timing out."
- },
- {
- "name": "retries",
- "type": "int",
- "value": 2,
- "help": "The number of times to retry the request if it fails or times out."
- },
- {
- "name": "interval",
- "type": "int",
- "value": 1,
- "help": "The time interval (in seconds) between retry attempts."
- }
- ]),
- BaseConfig(config={
- "api_key": "", # use avx2
- })
- )
-
- super().__init__("midjourney", app, service_config)
- self.output_folder = output_folder
-
- self.session = requests.Session()
- self.headers = {
- "Authorization": f"Bearer {self.service_config.api_key}",
- "Content-Type": "application/json"
- }
- def settings_updated(self):
- self.session = requests.Session()
- self.headers = {
- "Authorization": f"Bearer {self.service_config.api_key}",
- "Content-Type": "application/json"
- }
-
- def send_prompt(self, prompt: str) -> Dict[str, Any]:
- """
- Send a prompt to the MidJourney API to generate an image.
-
- Args:
- prompt (str): The prompt for image generation.
-
- Returns:
- Dict[str, Any]: The response from the API.
- """
- url = f"{MIDJOURNEY_API_URL}/imagine"
- payload = {"prompt": prompt}
- response = self.session.post(url, json=payload, headers=self.headers)
- response.raise_for_status()
- return response.json()
-
- def check_progress(self, message_id: str) -> Dict[str, Any]:
- """
- Check the progress of the image generation.
-
- Args:
- message_id (str): The message ID from the initial request.
-
- Returns:
- Dict[str, Any]: The response from the API.
- """
- url = f"{MIDJOURNEY_API_URL}/message/{message_id}"
- response = self.session.get(url, headers=self.headers)
- response.raise_for_status()
- return response.json()
-
- def upscale_image(self, message_id: str, button: str) -> Dict[str, Any]:
- """
- Upscale the generated image.
-
- Args:
- message_id (str): The message ID from the initial request.
- button (str): The button action for upscaling.
-
- Returns:
- Dict[str, Any]: The response from the API.
- """
- url = f"{MIDJOURNEY_API_URL}/button"
- payload = {"messageId": message_id, "button": button}
- response = self.session.post(url, json=payload, headers=self.headers)
- response.raise_for_status()
- return response.json()
-
- def send_prompt_with_retry(self, prompt: str, retries: int = 3) -> Dict[str, Any]:
- """
- Send a prompt to the MidJourney API with retry mechanism.
-
- Args:
- prompt (str): The prompt for image generation.
- retries (int): Number of retry attempts.
-
- Returns:
- Dict[str, Any]: The response from the API.
- """
- for attempt in range(retries):
- try:
- return self.send_prompt(prompt)
- except requests.exceptions.RequestException as e:
- if attempt < retries - 1:
- ASCIIColors.warning(f"Attempt {attempt + 1} failed: {e}. Retrying...")
- time.sleep(2 ** attempt)
- else:
- ASCIIColors.error(f"All {retries} attempts failed.")
- raise e
-
- def poll_progress(self, message_id: str, timeout: int = 300, interval: int = 5) -> Dict[str, Any]:
- """
- Poll the progress of the image generation until it's done or timeout.
-
- Args:
- message_id (str): The message ID from the initial request.
- timeout (int): The maximum time to wait for the image generation.
- interval (int): The interval between polling attempts.
-
- Returns:
- Dict[str, Any]: The response from the API.
- """
- start_time = time.time()
- with tqdm(total=100, desc="Image Generation Progress", unit="%") as pbar:
- while time.time() - start_time < timeout:
- progress_response = self.check_progress(message_id)
- if progress_response.get("status") == "DONE":
- pbar.update(100 - pbar.n) # Ensure the progress bar is complete
- print(progress_response)
- return progress_response
- elif progress_response.get("status") == "FAIL":
- ASCIIColors.error("Image generation failed.")
- return {"error": "Image generation failed"}
-
- progress = progress_response.get("progress", 0)
- pbar.update(progress - pbar.n) # Update the progress bar
- time.sleep(interval)
-
- ASCIIColors.error("Timeout while waiting for image generation.")
- return {"error": "Timeout while waiting for image generation"}
-
-
-
- def download_image(self, uri, folder_path, split=False):
- if not os.path.exists(folder_path):
- os.makedirs(folder_path)
-
- i = 1
- while True:
- file_path = os.path.join(folder_path, f"midjourney_{i}.png")
- if not os.path.exists(file_path):
- break
- i += 1
-
- response = requests.get(uri)
- if response.status_code == 200:
- with open(file_path, 'wb') as file:
- file.write(response.content)
- print(f"Image downloaded and saved as {file_path}")
-
- if split:
- return split_image(file_path, folder_path, i)
- else:
- return [file_path]
- else:
- print(f"Failed to download image. Status code: {response.status_code}")
- return None
-
- def get_nearest_aspect_ratio(self, width: int, height: int) -> str:
- # Define the available aspect ratios
- aspect_ratios = {
- "1:2": 0.5,
- "2:3": 0.6667,
- "3:4": 0.75,
- "4:5": 0.8,
- "1:1": 1,
- "5:4": 1.25,
- "4:3": 1.3333,
- "3:2": 1.5,
- "16:9": 1.7778,
- "7:4": 1.75,
- "2:1": 2
- }
-
- # Calculate the input aspect ratio
- input_ratio = width / height
-
- # Find the nearest aspect ratio
- nearest_ratio = min(aspect_ratios.items(), key=lambda x: abs(x[1] - input_ratio))
-
- # Return the formatted string
- return f"--ar {nearest_ratio[0]}"
-
- def paint(
- self,
- positive_prompt,
- negative_prompt,
- sampler_name="Euler",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None
- ):
- if output_path is None:
- output_path = self.output_path
-
- try:
- # Send prompt and get initial response
- positive_prompt += self.get_nearest_aspect_ratio(width, height)
- initial_response = self.send_prompt_with_retry(positive_prompt, self.service_config.retries)
- message_id = initial_response.get("messageId")
- if not message_id:
- raise ValueError("No messageId returned from initial prompt")
-
- # Poll progress until image generation is done
- progress_response = self.poll_progress(message_id, self.service_config.timeout, self.service_config.interval)
- if "error" in progress_response:
- raise ValueError(progress_response["error"])
-
- if width<=1024:
- file_names = self.download_image(progress_response["uri"], output_path, True)
-
- return file_names[0], {"prompt":positive_prompt, "negative_prompt":negative_prompt}
-
- # Upscale the generated image
- upscale_response = self.upscale_image(message_id, "U1")
- message_id = upscale_response.get("messageId")
- if not message_id:
- raise ValueError("No messageId returned from initial prompt")
-
- # Poll progress until image generation is done
- progress_response = self.poll_progress(message_id, self.service_config.timeout, self.service_config.interval)
- if "error" in progress_response:
- raise ValueError(progress_response["error"])
-
- file_name = self.download_image(progress_response["uri"], output_path)
- return file_name, {"prompt":positive_prompt, "negative_prompt":negative_prompt}
-
- except Exception as e:
- trace_exception(e)
- ASCIIColors.error(f"An error occurred: {e}")
- return "", {"prompt":positive_prompt, "negative_prompt":negative_prompt}
-
- @staticmethod
- def get(app:LollmsApplication):
- return LollmsMidjourney
\ No newline at end of file
diff --git a/lollms/services/tti/sd/lollms_sd.py b/lollms/services/tti/sd/lollms_sd.py
deleted file mode 100644
index 66b905e..0000000
--- a/lollms/services/tti/sd/lollms_sd.py
+++ /dev/null
@@ -1,1689 +0,0 @@
-# Title LollmsSD
-# Licence: MIT
-# Author : Paris Neo
-# Adapted from the work of mix1009's sdwebuiapi
-# check it out : https://github.com/mix1009/sdwebuiapi/tree/main
-# Here is a copy of the LICENCE https://github.com/mix1009/sdwebuiapi/blob/main/LICENSE
-# All rights are reserved
-
-from pathlib import Path
-import sys
-from lollms.app import LollmsApplication
-from lollms.paths import LollmsPaths
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-import time
-import io
-import sys
-import requests
-import os
-import base64
-import subprocess
-import time
-import json
-import platform
-from dataclasses import dataclass
-from PIL import Image, PngImagePlugin
-from enum import Enum
-from typing import List, Dict, Any
-
-from ascii_colors import ASCIIColors, trace_exception
-from lollms.paths import LollmsPaths
-from lollms.tti import LollmsTTI
-from lollms.utilities import git_pull, show_yes_no_dialog, EnvironmentManager
-import subprocess
-import shutil
-from tqdm import tqdm
-import threading
-
-em = EnvironmentManager()
-
-
-def download_file(url, folder_path, local_filename):
- # Make sure 'folder_path' exists
- folder_path.mkdir(parents=True, exist_ok=True)
-
- with requests.get(url, stream=True) as r:
- r.raise_for_status()
- total_size = int(r.headers.get('content-length', 0))
- progress_bar = tqdm(total=total_size, unit='B', unit_scale=True)
- with open(folder_path / local_filename, 'wb') as f:
- for chunk in r.iter_content(chunk_size=8192):
- f.write(chunk)
- progress_bar.update(len(chunk))
- progress_bar.close()
-
- return local_filename
-
-def install_model(lollms_app:LollmsApplication, model_url):
- root_dir = lollms_app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- sd_folder = shared_folder / "auto_sd"
- download_file(model_url, sd_folder/"models/Stable-diffusion","Juggernaut-XL_v9_RunDiffusionPhoto_v2.safetensors")
-
-def install_sd(lollms_app:LollmsApplication):
- root_dir = lollms_app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- sd_folder = shared_folder / "auto_sd"
- ASCIIColors.cyan("Installing autosd conda environment with python 3.10")
- em.create_env("autosd","3.10")
- ASCIIColors.cyan("Done")
- if os.path.exists(str(sd_folder)):
- print("Repository already exists. Pulling latest changes...")
- try:
- subprocess.run(["git", "-C", str(sd_folder), "pull"], check=True)
- subprocess.run(["git", "-C", str(sd_folder/"extensions/SD-CN-Animation"), "pull"])
- except:
- subprocess.run(["git", "clone", "https://github.com/ParisNeo/stable-diffusion-webui.git", str(sd_folder)])
- else:
- print("Cloning repository...")
- subprocess.run(["git", "clone", "https://github.com/ParisNeo/stable-diffusion-webui.git", str(sd_folder)])
- subprocess.run(["git", "clone", "https://github.com/Mikubill/sd-webui-controlnet", str(sd_folder/"extensions/sd-webui-controlnet")])
- subprocess.run(["git", "clone", "https://github.com/deforum-art/sd-webui-deforum.git", str(sd_folder/"extensions/sd-webui-deforum")])
- subprocess.run(["git", "clone", "https://github.com/ParisNeo/SD-CN-Animation.git", str(sd_folder/"extensions/SD-CN-Animation")])
-
- if show_yes_no_dialog("warning!","Do you want to install a model?\nI suggest Juggernaut-XL_v9.\nYou can install models manually by putting them inside your persona folder/auto_sd/models/stable_diffusion"):
- download_file("https://huggingface.co/RunDiffusion/Juggernaut-XL-v9/resolve/main/Juggernaut-XL_v9_RunDiffusionPhoto_v2.safetensors", sd_folder/"models/Stable-diffusion","Juggernaut-XL_v9_RunDiffusionPhoto_v2.safetensors")
-
- lollms_app.sd = LollmsSD(lollms_app)
- ASCIIColors.green("Stable diffusion installed successfully")
-
-
-def upgrade_sd(lollms_app:LollmsApplication):
- root_dir = lollms_app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- sd_folder = shared_folder / "auto_sd"
- if os.path.exists(str(sd_folder)):
- print("Repository already exists. Pulling latest changes...")
- try:
- subprocess.run(["git", "-C", str(sd_folder), "pull"], check=True)
- except:
- subprocess.run(["git", "clone", "https://github.com/ParisNeo/stable-diffusion-webui.git", str(sd_folder)])
-
- try:
- subprocess.run(["git", "-C", str(sd_folder/"extensions/sd-webui-deforum"), "pull"])
- except:
- subprocess.run(["git", "clone", "https://github.com/deforum-art/sd-webui-deforum.git", str(sd_folder/"extensions/sd-webui-deforum")])
-
- try:
- subprocess.run(["git", "-C", str(sd_folder/"extensions/sd-webui-controlnet"), "pull"])
- except:
- subprocess.run(["git", "clone", "https://github.com/Mikubill/sd-webui-controlnet", str(sd_folder/"extensions/sd-webui-controlnet")])
-
- try:
- subprocess.run(["git", "-C", str(sd_folder/"extensions/SD-CN-Animation"), "pull"])
- except:
- subprocess.run(["git", "clone", "https://github.com/ParisNeo/SD-CN-Animation.git", str(sd_folder/"extensions/SD-CN-Animation")])
- else:
- print("Cloning repository...")
- subprocess.run(["git", "clone", "https://github.com/ParisNeo/stable-diffusion-webui.git", str(sd_folder)])
- subprocess.run(["git", "clone", "https://github.com/deforum-art/sd-webui-deforum.git", str(sd_folder/"extensions/sd-webui-deforum")])
- subprocess.run(["git", "clone", "https://github.com/ParisNeo/SD-CN-Animation.git", str(sd_folder/"extensions/SD-CN-Animation")])
-
-
-def raw_b64_img(image: Image) -> str:
- # XXX controlnet only accepts RAW base64 without headers
- with io.BytesIO() as output_bytes:
- metadata = None
- for key, value in image.info.items():
- if isinstance(key, str) and isinstance(value, str):
- if metadata is None:
- metadata = PngImagePlugin.PngInfo()
- metadata.add_text(key, value)
- image.save(output_bytes, format="PNG", pnginfo=metadata)
-
- bytes_data = output_bytes.getvalue()
-
- return str(base64.b64encode(bytes_data), "utf-8")
-
-
-def b64_img(image: Image) -> str:
- return "data:image/png;base64," + raw_b64_img(image)
-
-
-class Upscaler(str, Enum):
- none = "None"
- Lanczos = "Lanczos"
- Nearest = "Nearest"
- LDSR = "LDSR"
- BSRGAN = "BSRGAN"
- ESRGAN_4x = "ESRGAN_4x"
- R_ESRGAN_General_4xV3 = "R-ESRGAN General 4xV3"
- ScuNET_GAN = "ScuNET GAN"
- ScuNET_PSNR = "ScuNET PSNR"
- SwinIR_4x = "SwinIR 4x"
-
-
-class HiResUpscaler(str, Enum):
- none = "None"
- Latent = "Latent"
- LatentAntialiased = "Latent (antialiased)"
- LatentBicubic = "Latent (bicubic)"
- LatentBicubicAntialiased = "Latent (bicubic antialiased)"
- LatentNearest = "Latent (nearist)"
- LatentNearestExact = "Latent (nearist-exact)"
- Lanczos = "Lanczos"
- Nearest = "Nearest"
- ESRGAN_4x = "ESRGAN_4x"
- LDSR = "LDSR"
- ScuNET_GAN = "ScuNET GAN"
- ScuNET_PSNR = "ScuNET PSNR"
- SwinIR_4x = "SwinIR 4x"
-
-@dataclass
-class WebUIApiResult:
- images: list
- parameters: dict
- info: dict
-
- @property
- def image(self):
- return self.images[0]
-
-
-
-class ControlNetUnit:
- def __init__(
- self,
- input_image: Image = None,
- mask: Image = None,
- module: str = "none",
- model: str = "None",
- weight: float = 1.0,
- resize_mode: str = "Resize and Fill",
- lowvram: bool = False,
- processor_res: int = 512,
- threshold_a: float = 64,
- threshold_b: float = 64,
- guidance: float = 1.0,
- guidance_start: float = 0.0,
- guidance_end: float = 1.0,
- control_mode: int = 0,
- pixel_perfect: bool = False,
- guessmode: int = None, # deprecated: use control_mode
- ):
- self.input_image = input_image
- self.mask = mask
- self.module = module
- self.model = model
- self.weight = weight
- self.resize_mode = resize_mode
- self.lowvram = lowvram
- self.processor_res = processor_res
- self.threshold_a = threshold_a
- self.threshold_b = threshold_b
- self.guidance = guidance
- self.guidance_start = guidance_start
- self.guidance_end = guidance_end
- if guessmode:
- print(
- "ControlNetUnit guessmode is deprecated. Please use control_mode instead."
- )
- control_mode = guessmode
- self.control_mode = control_mode
- self.pixel_perfect = pixel_perfect
-
- def to_dict(self):
- return {
- "input_image": raw_b64_img(self.input_image) if self.input_image else "",
- "mask": raw_b64_img(self.mask) if self.mask is not None else None,
- "module": self.module,
- "model": self.model,
- "weight": self.weight,
- "resize_mode": self.resize_mode,
- "lowvram": self.lowvram,
- "processor_res": self.processor_res,
- "threshold_a": self.threshold_a,
- "threshold_b": self.threshold_b,
- "guidance": self.guidance,
- "guidance_start": self.guidance_start,
- "guidance_end": self.guidance_end,
- "control_mode": self.control_mode,
- "pixel_perfect": self.pixel_perfect,
- }
-
-class LollmsSD(LollmsTTI):
- has_controlnet = False
- def __init__(self, app:LollmsApplication, output_folder:str|Path=None):
- """
- Initializes the LollmsDalle binding.
-
- Args:
- api_key (str): The API key for authentication.
- output_folder (Path|str): The output folder where to put the generated data
- """
- service_config = TypedConfig(
- ConfigTemplate([
- {
- "name": "base_url",
- "type": "str",
- "value": "http://127.0.0.1:7860",
- "help": "Watermarking text or identifier to be used in the generated content."
- },
- {
- "name": "wm",
- "type": "str",
- "value": "Artbot",
- "help": "Watermarking text or identifier to be used in the generated content."
- },
- {
- "name": "max_retries",
- "type": "int",
- "value": 50,
- "help": "The maximum number of retries to attempt before determining that the service is unavailable."
- },
- {
- "name": "sampler",
- "type": "str",
- "value": "Euler a",
- "help": "The sampler algorithm to use for image generation. Example: 'Euler a'."
- },
- {
- "name": "steps",
- "type": "int",
- "value": 20,
- "help": "The number of steps to use in the generation process. Higher values may improve quality but increase processing time."
- },
- {
- "name": "use_https",
- "type": "bool",
- "value": False,
- "help": "If set to true, the service will use HTTPS for secure communication."
- },
- {
- "name": "username",
- "type": "str",
- "value": None,
- "help": "The username for authentication, if required by the service."
- },
- {
- "name": "password",
- "type": "str",
- "value": None,
- "help": "The password for authentication, if required by the service."
- },
- {
- "name": "local_service",
- "type": "bool",
- "value": False,
- "help": "If set to true, a local instance of the service will be installed and used."
- },
- {
- "name": "start_service_at_startup",
- "type": "bool",
- "value": False,
- "help": "If set to true, the service will automatically start at startup. This also enables the local service option."
- },
- {
- "name": "share",
- "type": "bool",
- "value": False,
- "help": "If set to true, the server will be accessible from outside your local machine (e.g., over the internet)."
- }
- ]),
- BaseConfig(config={
- "api_key": "", # use avx2
- })
- )
-
- super().__init__("stable_diffusion", app, service_config, output_folder)
-
- self.ready = False
- # Get the current directory
- lollms_paths = app.lollms_paths
- root_dir = lollms_paths.personal_path
-
- # Store the path to the script
- if service_config.base_url is None:
- self.service_config.base_url = "http://127.0.0.1:7860"
- if service_config.local_service and service_config.start_service_at_startup:
- if not LollmsSD.verify(app):
- install_sd(app)
-
-
- self.sd_base_url = self.service_config.base_url+"/sdapi/v1"
- shared_folder = root_dir/"shared"
- self.sd_folder = shared_folder / "auto_sd"
- self.output_dir = root_dir / "outputs/sd"
- self.output_dir.mkdir(parents=True, exist_ok=True)
-
-
- ASCIIColors.red(" ")
- ASCIIColors.red(" __ _____ __ __ _____ _____ _____ ____ ")
- ASCIIColors.red("| | | | | | | | | __| | __| \ ")
- ASCIIColors.red("| |__| | | |__| |__| | | |__ | |__ | | |")
- ASCIIColors.red("|_____|_____|_____|_____|_|_|_|_____|_____|_____|____/ ")
- ASCIIColors.red(" |_____| ")
-
- ASCIIColors.red(" Forked from Auto1111's Stable diffusion api")
- ASCIIColors.red(" Integration in lollms by ParisNeo using mix1009's sdwebuiapi ")
-
- if not self.wait_for_service(1,False) and service_config.local_service and service_config.start_service_at_startup:
- ASCIIColors.info("Loading lollms_sd")
- os.environ['SD_WEBUI_RESTARTING'] = '1' # To forbid sd webui from showing on the browser automatically
- # Launch the Flask service using the appropriate script for the platform
- if platform.system() == "Windows":
- ASCIIColors.info("Running on windows")
- script_path = self.sd_folder / "lollms_sd.bat"
- if self.service_config.share:
- pass # TODO : implement
- # run_script_in_env("autosd", str(script_path) +" --share", cwd=self.sd_folder)
- else:
- pass # TODO : implement
- # run_script_in_env("autosd", str(script_path), cwd=self.sd_folder)
- else:
- ASCIIColors.info("Running on linux/MacOs")
- script_path = str(self.sd_folder / "lollms_sd.sh")
- ASCIIColors.info(f"launcher path: {script_path}")
- ASCIIColors.info(f"sd path: {self.sd_folder}")
-
- if self.service_config.share:
- pass # TODO: implement
- # run_script_in_env("autosd","bash " + script_path +" --share", cwd=self.sd_folder)
- else:
- pass # TODO: implement
- # run_script_in_env("autosd","bash " + script_path, cwd=self.sd_folder)
- ASCIIColors.info("Process done")
- ASCIIColors.success("Launching Auto1111's SD succeeded")
-
- # Wait until the service is available at http://127.0.0.1:7860/
-
- self.default_sampler = self.service_config.sampler
- self.default_steps = self.service_config.steps
-
- self.session = requests.Session()
-
- if self.service_config.username and self.service_config.password:
- self.set_auth(self.service_config.username, self.service_config.password)
- else:
- self.check_controlnet()
- def settings_updated(self):
- pass # TODO: implement
- @staticmethod
- def verify(app:LollmsApplication):
- # Clone repository
- root_dir = app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- sd_folder = shared_folder / "auto_sd"
- return sd_folder.exists()
-
- def get(app:LollmsApplication):
- root_dir = app.lollms_paths.personal_path
- shared_folder = root_dir/"shared"
- sd_folder = shared_folder / "auto_sd"
- sd_script_path = sd_folder / "lollms_sd.py"
- git_pull(sd_folder)
-
- if sd_script_path.exists():
- ASCIIColors.success("lollms_sd found.")
- ASCIIColors.success("Loading source file...",end="")
- # use importlib to load the module from the file path
- from lollms.services.tti.sd.lollms_sd import LollmsSD
- ASCIIColors.success("ok")
- return LollmsSD
-
-
- def paint(
- self,
- positive_prompt,
- negative_prompt,
- sampler_name="Euler",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None
- ):
- if output_path is None:
- output_path = self.output_dir
- infos = {}
- img_paths = []
-
- try:
- generated = self.txt2img(
- positive_prompt,
- negative_prompt=negative_prompt,
- sampler_name=sampler_name,
- seed=seed,
- cfg_scale=scale,
- steps=steps,
- width=width,
- height=height,
- tiling=False,
- restore_faces=restore_faces,
- styles=None,
- script_name="",
- )
- """
- images: list
- parameters: dict
- info: dict
- """
- for img in generated.images:
- img_paths.append(self.saveImage(img, output_path))
- infos = generated.info
- except Exception as ex:
- ASCIIColors.error("Couldn't generate the image")
- trace_exception(ex)
-
-
- return img_paths[0] if len(img_paths)>0 else None, infos
-
- def paint_from_images(self, positive_prompt: str,
- images: List[str],
- negative_prompt: str = "",
- sampler_name="",
- seed=-1,
- scale=7.5,
- steps=20,
- img2img_denoising_strength=0.9,
- width=512,
- height=512,
- restore_faces=True,
- output_path=None
- ) -> List[Dict[str, str]]:
- if output_path is None:
- output_path = self.output_dir
- infos = {}
- img_paths = []
- try:
- generated = self.img2img(
- positive_prompt,
- negative_prompt,
- [self.loadImage(images[-1])],
- sampler_name=sampler_name,
- seed=seed,
- cfg_scale=scale,
- steps=steps,
- width=int(width),
- height=int(height),
- denoising_strength=img2img_denoising_strength,
- tiling=False,
- restore_faces=restore_faces,
- styles=None,
- script_name="",
- )
- """
- images: list
- parameters: dict
- info: dict
- """
- for img in generated.images:
- img_paths.append(self.saveImage(img, output_path))
- infos = generated.info
- except Exception as ex:
- ASCIIColors.error("Couldn't generate the image")
- trace_exception(ex)
-
- return img_paths[0] if len(img_paths)>0 else None, infos
-
- def check_controlnet(self):
- try:
- scripts = self.get_scripts()
- self.has_controlnet = "controlnet m2m" in scripts["txt2img"]
- except:
- pass
-
- def set_auth(self, username, password):
- self.session.auth = (username, password)
- self.check_controlnet()
-
- def _to_api_result(self, response):
- if response.status_code != 200:
- raise RuntimeError(response.status_code, response.text)
-
- r = response.json()
- images = []
- if "images" in r.keys():
- images = [Image.open(io.BytesIO(base64.b64decode(i))) for i in r["images"]]
- elif "image" in r.keys():
- images = [Image.open(io.BytesIO(base64.b64decode(r["image"])))]
-
- info = ""
- if "info" in r.keys():
- try:
- info = json.loads(r["info"])
- except:
- info = r["info"]
- elif "html_info" in r.keys():
- info = r["html_info"]
- elif "caption" in r.keys():
- info = r["caption"]
-
- parameters = ""
- if "parameters" in r.keys():
- parameters = r["parameters"]
-
- return WebUIApiResult(images, parameters, info)
-
- async def _to_api_result_async(self, response):
- if response.status != 200:
- raise RuntimeError(response.status, await response.text())
-
- r = await response.json()
- images = []
- if "images" in r.keys():
- images = [Image.open(io.BytesIO(base64.b64decode(i))) for i in r["images"]]
- elif "image" in r.keys():
- images = [Image.open(io.BytesIO(base64.b64decode(r["image"])))]
-
- info = ""
- if "info" in r.keys():
- try:
- info = json.loads(r["info"])
- except:
- info = r["info"]
- elif "html_info" in r.keys():
- info = r["html_info"]
- elif "caption" in r.keys():
- info = r["caption"]
-
- parameters = ""
- if "parameters" in r.keys():
- parameters = r["parameters"]
-
- return WebUIApiResult(images, parameters, info)
-
- def loadImage(self, file_path:str)->Image:
- return Image.open(file_path)
-
- def saveImage(self, image:Image, save_folder=None):
- if save_folder is None:
- save_folder = self.output_dir
- save_folder = Path(save_folder)
- if save_folder.is_dir():
- image_name = self.get_available_image_name(save_folder, self.service_config.wm)
- image_path = os.path.join(save_folder, image_name)
- else:
- image_path = save_folder
- image.save(image_path)
- return image_path
-
-
- def txt2img(
- self,
- prompt="",
- negative_prompt="",
- enable_hr=False,
- denoising_strength=0.7,
- firstphase_width=0,
- firstphase_height=0,
- hr_scale=2,
- hr_upscaler=HiResUpscaler.Latent,
- hr_second_pass_steps=0,
- hr_resize_x=0,
- hr_resize_y=0,
- styles=[],
- seed=-1,
- subseed=-1,
- subseed_strength=0.0,
- seed_resize_from_h=0,
- seed_resize_from_w=0,
- sampler_name=None, # use this instead of sampler_index
- batch_size=1,
- n_iter=1,
- steps=None,
- cfg_scale=7.0,
- width=512,
- height=512,
- restore_faces=False,
- tiling=False,
- do_not_save_samples=False,
- do_not_save_grid=False,
- eta=1.0,
- s_churn=0,
- s_tmax=0,
- s_tmin=0,
- s_noise=1,
- override_settings={},
- override_settings_restore_afterwards=True,
- script_args=None, # List of arguments for the script "script_name"
- script_name=None,
- send_images=True,
- save_images=False,
- alwayson_scripts={},
- controlnet_units: List[ControlNetUnit] = [],
- sampler_index=None, # deprecated: use sampler_name
- use_deprecated_controlnet=False,
- use_async=False,
- ):
- if sampler_index is None:
- sampler_index = self.default_sampler
- if sampler_name is None:
- sampler_name = self.default_sampler
- if steps is None:
- steps = self.default_steps
- if script_args is None:
- script_args = []
- payload = {
- "enable_hr": enable_hr,
- "hr_scale": hr_scale,
- "hr_upscaler": hr_upscaler,
- "hr_second_pass_steps": hr_second_pass_steps,
- "hr_resize_x": hr_resize_x,
- "hr_resize_y": hr_resize_y,
- "denoising_strength": denoising_strength,
- "firstphase_width": firstphase_width,
- "firstphase_height": firstphase_height,
- "prompt": prompt,
- "styles": styles,
- "seed": seed,
- "subseed": subseed,
- "subseed_strength": subseed_strength,
- "seed_resize_from_h": seed_resize_from_h,
- "seed_resize_from_w": seed_resize_from_w,
- "batch_size": batch_size,
- "n_iter": n_iter,
- "steps": steps,
- "cfg_scale": cfg_scale,
- "width": width,
- "height": height,
- "restore_faces": restore_faces,
- "tiling": tiling,
- "do_not_save_samples": do_not_save_samples,
- "do_not_save_grid": do_not_save_grid,
- "negative_prompt": negative_prompt,
- "eta": eta,
- "s_churn": s_churn,
- "s_tmax": s_tmax,
- "s_tmin": s_tmin,
- "s_noise": s_noise,
- "override_settings": override_settings,
- "override_settings_restore_afterwards": override_settings_restore_afterwards,
- "sampler_name": sampler_name,
- "sampler_index": sampler_index,
- "script_name": script_name,
- "script_args": script_args,
- "send_images": send_images,
- "save_images": save_images,
- "alwayson_scripts": alwayson_scripts,
- }
-
- if use_deprecated_controlnet and controlnet_units and len(controlnet_units) > 0:
- payload["controlnet_units"] = [x.to_dict() for x in controlnet_units]
- return self.custom_post(
- "controlnet/txt2img", payload=payload, use_async=use_async
- )
-
- if controlnet_units and len(controlnet_units) > 0:
- payload["alwayson_scripts"]["ControlNet"] = {
- "args": [x.to_dict() for x in controlnet_units]
- }
- #elif self.has_controlnet:
- # workaround : if not passed, webui will use previous args!
- # payload["alwayson_scripts"]["ControlNet"] = {"args": []}
- print(self.sd_base_url)
- return self.post_and_get_api_result(
- f"{self.sd_base_url}/txt2img", payload, use_async
- )
-
- def post_and_get_api_result(self, url, json, use_async):
- if use_async:
- import asyncio
-
- return asyncio.ensure_future(self.async_post(url=url, json=json))
- else:
- response = self.session.post(url=url, json=json)
- return self._to_api_result(response)
-
- async def async_post(self, url, json):
- import aiohttp
-
- async with aiohttp.ClientSession() as session:
- auth = aiohttp.BasicAuth(self.session.auth[0], self.session.auth[1]) if self.session.auth else None
- async with session.post(url, json=json, auth=auth) as response:
- return await self._to_api_result_async(response)
-
- def img2img(
- self,
- prompt="",
- negative_prompt="",
- images=[], # list of PIL Image
- resize_mode=0,
- denoising_strength=0.75,
- image_cfg_scale=1.5,
- mask_image=None, # PIL Image mask
- mask_blur=4,
- inpainting_fill=0,
- inpaint_full_res=True,
- inpaint_full_res_padding=0,
- inpainting_mask_invert=0,
- initial_noise_multiplier=1,
- styles=[],
- seed=-1,
- subseed=-1,
- subseed_strength=0,
- seed_resize_from_h=0,
- seed_resize_from_w=0,
- sampler_name=None, # use this instead of sampler_index
- batch_size=1,
- n_iter=1,
- steps=None,
- cfg_scale=7.0,
- width=512,
- height=512,
- restore_faces=False,
- tiling=False,
- do_not_save_samples=False,
- do_not_save_grid=False,
- eta=1.0,
- s_churn=0,
- s_tmax=0,
- s_tmin=0,
- s_noise=1,
- override_settings={},
- override_settings_restore_afterwards=True,
- script_args=None, # List of arguments for the script "script_name"
- sampler_index=None, # deprecated: use sampler_name
- include_init_images=False,
- script_name=None,
- send_images=True,
- save_images=False,
- alwayson_scripts={},
- controlnet_units: List[ControlNetUnit] = [],
- use_deprecated_controlnet=False,
- use_async=False,
- ):
- if sampler_name is None:
- sampler_name = self.default_sampler
- if sampler_index is None:
- sampler_index = self.default_sampler
- if steps is None:
- steps = self.default_steps
- if script_args is None:
- script_args = []
-
- payload = {
- "init_images": [b64_img(x) for x in images],
- "resize_mode": resize_mode,
- "denoising_strength": denoising_strength,
- "mask_blur": mask_blur,
- "inpainting_fill": inpainting_fill,
- "inpaint_full_res": inpaint_full_res,
- "inpaint_full_res_padding": inpaint_full_res_padding,
- "inpainting_mask_invert": inpainting_mask_invert,
- "initial_noise_multiplier": initial_noise_multiplier,
- "prompt": prompt,
- "styles": styles,
- "seed": seed,
- "subseed": subseed,
- "subseed_strength": subseed_strength,
- "seed_resize_from_h": seed_resize_from_h,
- "seed_resize_from_w": seed_resize_from_w,
- "batch_size": batch_size,
- "n_iter": n_iter,
- "steps": steps,
- "cfg_scale": cfg_scale,
- "image_cfg_scale": image_cfg_scale,
- "width": width,
- "height": height,
- "restore_faces": restore_faces,
- "tiling": tiling,
- "do_not_save_samples": do_not_save_samples,
- "do_not_save_grid": do_not_save_grid,
- "negative_prompt": negative_prompt,
- "eta": eta,
- "s_churn": s_churn,
- "s_tmax": s_tmax,
- "s_tmin": s_tmin,
- "s_noise": s_noise,
- "override_settings": override_settings,
- "override_settings_restore_afterwards": override_settings_restore_afterwards,
- "sampler_name": sampler_name,
- "sampler_index": sampler_index,
- "include_init_images": include_init_images,
- "script_name": script_name,
- "script_args": script_args,
- "send_images": send_images,
- "save_images": save_images,
- "alwayson_scripts": alwayson_scripts,
- }
- if mask_image is not None:
- payload["mask"] = b64_img(mask_image)
-
- if use_deprecated_controlnet and controlnet_units and len(controlnet_units) > 0:
- payload["controlnet_units"] = [x.to_dict() for x in controlnet_units]
- return self.custom_post(
- "controlnet/img2img", payload=payload, use_async=use_async
- )
-
- if controlnet_units and len(controlnet_units) > 0:
- payload["alwayson_scripts"]["ControlNet"] = {
- "args": [x.to_dict() for x in controlnet_units]
- }
- #elif self.has_controlnet:
- # payload["alwayson_scripts"]["ControlNet"] = {"args": []}
-
- return self.post_and_get_api_result(
- f"{self.sd_base_url}/img2img", payload, use_async
- )
-
- def extra_single_image(
- self,
- image, # PIL Image
- resize_mode=0,
- show_extras_results=True,
- gfpgan_visibility=0,
- codeformer_visibility=0,
- codeformer_weight=0,
- upscaling_resize=2,
- upscaling_resize_w=512,
- upscaling_resize_h=512,
- upscaling_crop=True,
- upscaler_1="None",
- upscaler_2="None",
- extras_upscaler_2_visibility=0,
- upscale_first=False,
- use_async=False,
- ):
- payload = {
- "resize_mode": resize_mode,
- "show_extras_results": show_extras_results,
- "gfpgan_visibility": gfpgan_visibility,
- "codeformer_visibility": codeformer_visibility,
- "codeformer_weight": codeformer_weight,
- "upscaling_resize": upscaling_resize,
- "upscaling_resize_w": upscaling_resize_w,
- "upscaling_resize_h": upscaling_resize_h,
- "upscaling_crop": upscaling_crop,
- "upscaler_1": upscaler_1,
- "upscaler_2": upscaler_2,
- "extras_upscaler_2_visibility": extras_upscaler_2_visibility,
- "upscale_first": upscale_first,
- "image": b64_img(image),
- }
-
- return self.post_and_get_api_result(
- f"{self.sd_base_url}/extra-single-image", payload, use_async
- )
-
- def extra_batch_images(
- self,
- images, # list of PIL images
- name_list=None, # list of image names
- resize_mode=0,
- show_extras_results=True,
- gfpgan_visibility=0,
- codeformer_visibility=0,
- codeformer_weight=0,
- upscaling_resize=2,
- upscaling_resize_w=512,
- upscaling_resize_h=512,
- upscaling_crop=True,
- upscaler_1="None",
- upscaler_2="None",
- extras_upscaler_2_visibility=0,
- upscale_first=False,
- use_async=False,
- ):
- if name_list is not None:
- if len(name_list) != len(images):
- raise RuntimeError("len(images) != len(name_list)")
- else:
- name_list = [f"image{i + 1:05}" for i in range(len(images))]
- images = [b64_img(x) for x in images]
-
- image_list = []
- for name, image in zip(name_list, images):
- image_list.append({"data": image, "name": name})
-
- payload = {
- "resize_mode": resize_mode,
- "show_extras_results": show_extras_results,
- "gfpgan_visibility": gfpgan_visibility,
- "codeformer_visibility": codeformer_visibility,
- "codeformer_weight": codeformer_weight,
- "upscaling_resize": upscaling_resize,
- "upscaling_resize_w": upscaling_resize_w,
- "upscaling_resize_h": upscaling_resize_h,
- "upscaling_crop": upscaling_crop,
- "upscaler_1": upscaler_1,
- "upscaler_2": upscaler_2,
- "extras_upscaler_2_visibility": extras_upscaler_2_visibility,
- "upscale_first": upscale_first,
- "imageList": image_list,
- }
-
- return self.post_and_get_api_result(
- f"{self.sd_base_url}/extra-batch-images", payload, use_async
- )
-
- # XXX 500 error (2022/12/26)
- def png_info(self, image):
- payload = {
- "image": b64_img(image),
- }
-
- response = self.session.post(url=f"{self.sd_base_url}/png-info", json=payload)
- return self._to_api_result(response)
-
- """
- :param image pass base64 encoded image or PIL Image
- :param model "clip" or "deepdanbooru"
- """
- def interrogate(self, image, model="clip"):
- payload = {
- "image": b64_img(image) if isinstance(image, Image.Image) else b64_img(Image.open(image)),
- "model": model,
- }
-
- response = self.session.post(url=f"{self.sd_base_url}/interrogate", json=payload)
- return self._to_api_result(response)
-
- def interrupt(self):
- response = self.session.post(url=f"{self.sd_base_url}/interrupt")
- return response.json()
-
- def skip(self):
- response = self.session.post(url=f"{self.sd_base_url}/skip")
- return response.json()
-
- def get_options(self):
- response = self.session.get(url=f"{self.sd_base_url}/options")
- return response.json()
-
- def set_options(self, options):
- response = self.session.post(url=f"{self.sd_base_url}/options", json=options)
- return response.json()
-
- def get_cmd_flags(self):
- response = self.session.get(url=f"{self.sd_base_url}/cmd-flags")
- return response.json()
-
- def get_progress(self):
- response = self.session.get(url=f"{self.sd_base_url}/progress")
- return response.json()
-
- def get_cmd_flags(self):
- response = self.session.get(url=f"{self.sd_base_url}/cmd-flags")
- return response.json()
-
- def get_samplers(self):
- response = self.session.get(url=f"{self.sd_base_url}/samplers")
- return response.json()
-
- def get_sd_vae(self):
- response = self.session.get(url=f"{self.sd_base_url}/sd-vae")
- return response.json()
-
- def get_upscalers(self):
- response = self.session.get(url=f"{self.sd_base_url}/upscalers")
- return response.json()
-
- def get_latent_upscale_modes(self):
- response = self.session.get(url=f"{self.sd_base_url}/latent-upscale-modes")
- return response.json()
-
- def get_loras(self):
- response = self.session.get(url=f"{self.sd_base_url}/loras")
- return response.json()
-
- def get_sd_models(self):
- response = self.session.get(url=f"{self.sd_base_url}/sd-models")
- return response.json()
-
- def get_hypernetworks(self):
- response = self.session.get(url=f"{self.sd_base_url}/hypernetworks")
- return response.json()
-
- def get_face_restorers(self):
- response = self.session.get(url=f"{self.sd_base_url}/face-restorers")
- return response.json()
-
- def get_realesrgan_models(self):
- response = self.session.get(url=f"{self.sd_base_url}/realesrgan-models")
- return response.json()
-
- def get_prompt_styles(self):
- response = self.session.get(url=f"{self.sd_base_url}/prompt-styles")
- return response.json()
-
- def get_artist_categories(self): # deprecated ?
- response = self.session.get(url=f"{self.sd_base_url}/artist-categories")
- return response.json()
-
- def get_artists(self): # deprecated ?
- response = self.session.get(url=f"{self.sd_base_url}/artists")
- return response.json()
-
- def refresh_checkpoints(self):
- response = self.session.post(url=f"{self.sd_base_url}/refresh-checkpoints")
- return response.json()
-
- def get_scripts(self):
- response = self.session.get(url=f"{self.sd_base_url}/scripts")
- return response.json()
-
- def get_embeddings(self):
- response = self.session.get(url=f"{self.sd_base_url}/embeddings")
- return response.json()
-
- def get_memory(self):
- response = self.session.get(url=f"{self.sd_base_url}/memory")
- return response.json()
-
- def get_endpoint(self, endpoint, baseurl):
- if baseurl:
- return f"{self.sd_base_url}/{endpoint}"
- else:
- from urllib.parse import urlparse, urlunparse
-
- parsed_url = urlparse(self.service_config.base_url)
- basehost = parsed_url.netloc
- parsed_url2 = (parsed_url[0], basehost, endpoint, "", "", "")
- return urlunparse(parsed_url2)
-
- def custom_get(self, endpoint, baseurl=False):
- url = self.get_endpoint(endpoint, baseurl)
- response = self.session.get(url=url)
- return response.json()
-
- def custom_post(self, endpoint, payload={}, baseurl=False, use_async=False):
- url = self.get_endpoint(endpoint, baseurl)
- if use_async:
- import asyncio
-
- return asyncio.ensure_future(self.async_post(url=url, json=payload))
- else:
- response = self.session.post(url=url, json=payload)
- return self._to_api_result(response)
-
- def controlnet_version(self):
- r = self.custom_get("controlnet/version")
- return r["version"]
-
- def controlnet_model_list(self):
- r = self.custom_get("controlnet/model_list")
- return r["model_list"]
-
- def controlnet_module_list(self):
- r = self.custom_get("controlnet/module_list")
- return r["module_list"]
-
- def controlnet_detect(
- self, images, module="none", processor_res=512, threshold_a=64, threshold_b=64
- ):
- input_images = [b64_img(x) for x in images]
- payload = {
- "controlnet_module": module,
- "controlnet_input_images": input_images,
- "controlnet_processor_res": processor_res,
- "controlnet_threshold_a": threshold_a,
- "controlnet_threshold_b": threshold_b,
- }
- r = self.custom_post("controlnet/detect", payload=payload)
- return r
-
- def util_get_model_names(self):
- return sorted([x["title"] for x in self.get_sd_models()])
-
- def util_set_model(self, name, find_closest=True):
- if find_closest:
- name = name.lower()
- models = self.util_get_model_names()
- found_model = None
- if name in models:
- found_model = name
- elif find_closest:
- import difflib
-
- def str_simularity(a, b):
- return difflib.SequenceMatcher(None, a, b).ratio()
-
- max_sim = 0.0
- max_model = models[0]
- for model in models:
- sim = str_simularity(name, model)
- if sim >= max_sim:
- max_sim = sim
- max_model = model
- found_model = max_model
- if found_model:
- print(f"loading {found_model}")
- options = {}
- options["sd_model_checkpoint"] = found_model
- self.set_options(options)
- print(f"model changed to {found_model}")
- else:
- print("model not found")
-
- def util_get_current_model(self):
- return self.get_options()["sd_model_checkpoint"]
-
- def util_wait_for_ready(self, check_interval=5.0):
- import time
-
- while True:
- result = self.get_progress()
- progress = result["progress"]
- job_count = result["state"]["job_count"]
- if progress == 0.0 and job_count == 0:
- break
- else:
- print(f"[WAIT]: progress = {progress:.4f}, job_count = {job_count}")
- time.sleep(check_interval)
-
-
- def wait_for_service_in_another_thread(self, max_retries=150, show_warning=True):
- thread = threading.Thread(target=self.wait_for_service, args=(max_retries, show_warning))
- thread.start()
- return thread
-
- def wait_for_service(self, max_retries = 50, show_warning=True):
- url = f"{self.sd_base_url}/internal/ping"
- # Adjust this value as needed
- retries = 0
-
- while retries < max_retries or max_retries<0:
- try:
- response = requests.get(url)
- if response.status_code == 200:
- print("Service is available.")
- if self.app is not None:
- self.app.success("SD Service is now available.")
- self.ready = True
- return True
- except requests.exceptions.RequestException:
- pass
-
- retries += 1
- time.sleep(1)
- if show_warning:
- print("Service did not become available within the given time.")
- if self.app is not None:
- self.app.error("SD Service did not become available within the given time.")
- return False
-
- def get_available_image_name(self, save_folder, base_name):
- index = 0
- while True:
- image_name = f"{base_name}_{index}.png"
- image_path = os.path.join(save_folder, image_name)
- if not os.path.exists(image_path):
- return image_name
- index += 1
-
-
-
-## Interface for extensions
-
-
-# https://github.com/mix1009/model-keyword
-@dataclass
-class ModelKeywordResult:
- keywords: list
- model: str
- oldhash: str
- match_source: str
-
-
-class ModelKeywordInterface:
- def __init__(self, webuiapi):
- self.api = webuiapi
-
- def get_keywords(self):
- result = self.api.custom_get("model_keyword/get_keywords")
- keywords = result["keywords"]
- model = result["model"]
- oldhash = result["hash"]
- match_source = result["match_source"]
- return ModelKeywordResult(keywords, model, oldhash, match_source)
-
-
-
-
-
-# https://github.com/Klace/stable-diffusion-webui-instruct-pix2pix
-class InstructPix2PixInterface:
- def __init__(self, webuiapi):
- self.api = webuiapi
-
- def img2img(
- self,
- images=[],
- prompt: str = "",
- negative_prompt: str = "",
- output_batches: int = 1,
- sampler: str = "Euler a",
- steps: int = 20,
- seed: int = 0,
- randomize_seed: bool = True,
- text_cfg: float = 7.5,
- image_cfg: float = 1.5,
- randomize_cfg: bool = False,
- output_image_width: int = 512,
- ):
- init_images = [b64_img(x) for x in images]
- payload = {
- "init_images": init_images,
- "prompt": prompt,
- "negative_prompt": negative_prompt,
- "output_batches": output_batches,
- "sampler": sampler,
- "steps": steps,
- "seed": seed,
- "randomize_seed": randomize_seed,
- "text_cfg": text_cfg,
- "image_cfg": image_cfg,
- "randomize_cfg": randomize_cfg,
- "output_image_width": output_image_width,
- }
- return self.api.custom_post("instruct-pix2pix/img2img", payload=payload)
-
-
-#https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg
-class RemBGInterface:
- def __init__(self, webuiapi):
- self.api = webuiapi
-
- def rembg(
- self,
- input_image: str = "", #image string (?)
- model: str = 'u2net', #[None, 'u2net', 'u2netp', 'u2net_human_seg', 'u2net_cloth_seg','silueta','isnet-general-use','isnet-anime']
- return_mask: bool = False,
- alpha_matting: bool = False,
- alpha_matting_foreground_threshold: int = 240,
- alpha_matting_background_threshold: int = 10,
- alpha_matting_erode_size: int = 10
- ):
-
- payload = {
- "input_image": b64_img(input_image),
- "model": model,
- "return_mask": return_mask,
- "alpha_matting": alpha_matting,
- "alpha_matting_foreground_threshold": alpha_matting_foreground_threshold,
- "alpha_matting_background_threshold": alpha_matting_background_threshold,
- "alpha_matting_erode_size": alpha_matting_erode_size
- }
- return self.api.custom_post("rembg", payload=payload)
-
-
-# https://github.com/Mikubill/sd-webui-controlnet
-class ControlNetInterface:
- def __init__(self, webuiapi, show_deprecation_warning=True):
- self.api = webuiapi
- self.show_deprecation_warning = show_deprecation_warning
-
- def print_deprecation_warning(self):
- print(
- "ControlNetInterface txt2img/img2img is deprecated. Please use normal txt2img/img2img with controlnet_units param"
- )
-
- def txt2img(
- self,
- prompt: str = "",
- negative_prompt: str = "",
- controlnet_input_image: [] = [],
- controlnet_mask: [] = [],
- controlnet_module: str = "",
- controlnet_model: str = "",
- controlnet_weight: float = 0.5,
- controlnet_resize_mode: str = "Scale to Fit (Inner Fit)",
- controlnet_lowvram: bool = False,
- controlnet_processor_res: int = 512,
- controlnet_threshold_a: int = 64,
- controlnet_threshold_b: int = 64,
- controlnet_guidance: float = 1.0,
- enable_hr: bool = False, # hiresfix
- denoising_strength: float = 0.5,
- hr_scale: float = 1.5,
- hr_upscale: str = "Latent",
- guess_mode: bool = True,
- seed: int = -1,
- subseed: int = -1,
- subseed_strength: int = -1,
- sampler_index: str = "Euler a",
- batch_size: int = 1,
- n_iter: int = 1, # Iteration
- steps: int = 20,
- cfg_scale: float = 7,
- width: int = 512,
- height: int = 512,
- restore_faces: bool = False,
- override_settings: Dict[str, Any] = None,
- override_settings_restore_afterwards: bool = True,
- ):
- if self.show_deprecation_warning:
- self.print_deprecation_warning()
-
- controlnet_input_image_b64 = [raw_b64_img(x) for x in controlnet_input_image]
- controlnet_mask_b64 = [raw_b64_img(x) for x in controlnet_mask]
-
- payload = {
- "prompt": prompt,
- "negative_prompt": negative_prompt,
- "controlnet_input_image": controlnet_input_image_b64,
- "controlnet_mask": controlnet_mask_b64,
- "controlnet_module": controlnet_module,
- "controlnet_model": controlnet_model,
- "controlnet_weight": controlnet_weight,
- "controlnet_resize_mode": controlnet_resize_mode,
- "controlnet_lowvram": controlnet_lowvram,
- "controlnet_processor_res": controlnet_processor_res,
- "controlnet_threshold_a": controlnet_threshold_a,
- "controlnet_threshold_b": controlnet_threshold_b,
- "controlnet_guidance": controlnet_guidance,
- "enable_hr": enable_hr,
- "denoising_strength": denoising_strength,
- "hr_scale": hr_scale,
- "hr_upscale": hr_upscale,
- "guess_mode": guess_mode,
- "seed": seed,
- "subseed": subseed,
- "subseed_strength": subseed_strength,
- "sampler_index": sampler_index,
- "batch_size": batch_size,
- "n_iter": n_iter,
- "steps": steps,
- "cfg_scale": cfg_scale,
- "width": width,
- "height": height,
- "restore_faces": restore_faces,
- "override_settings": override_settings,
- "override_settings_restore_afterwards": override_settings_restore_afterwards,
- }
- return self.api.custom_post("controlnet/txt2img", payload=payload)
-
- def img2img(
- self,
- init_images: [] = [],
- mask: str = None,
- mask_blur: int = 30,
- inpainting_fill: int = 0,
- inpaint_full_res: bool = True,
- inpaint_full_res_padding: int = 1,
- inpainting_mask_invert: int = 1,
- resize_mode: int = 0,
- denoising_strength: float = 0.7,
- prompt: str = "",
- negative_prompt: str = "",
- controlnet_input_image: [] = [],
- controlnet_mask: [] = [],
- controlnet_module: str = "",
- controlnet_model: str = "",
- controlnet_weight: float = 1.0,
- controlnet_resize_mode: str = "Scale to Fit (Inner Fit)",
- controlnet_lowvram: bool = False,
- controlnet_processor_res: int = 512,
- controlnet_threshold_a: int = 64,
- controlnet_threshold_b: int = 64,
- controlnet_guidance: float = 1.0,
- guess_mode: bool = True,
- seed: int = -1,
- subseed: int = -1,
- subseed_strength: int = -1,
- sampler_index: str = "",
- batch_size: int = 1,
- n_iter: int = 1, # Iteration
- steps: int = 20,
- cfg_scale: float = 7,
- width: int = 512,
- height: int = 512,
- restore_faces: bool = False,
- include_init_images: bool = True,
- override_settings: Dict[str, Any] = None,
- override_settings_restore_afterwards: bool = True,
- ):
- if self.show_deprecation_warning:
- self.print_deprecation_warning()
-
- init_images_b64 = [raw_b64_img(x) for x in init_images]
- controlnet_input_image_b64 = [raw_b64_img(x) for x in controlnet_input_image]
- controlnet_mask_b64 = [raw_b64_img(x) for x in controlnet_mask]
-
- payload = {
- "init_images": init_images_b64,
- "mask": raw_b64_img(mask) if mask else None,
- "mask_blur": mask_blur,
- "inpainting_fill": inpainting_fill,
- "inpaint_full_res": inpaint_full_res,
- "inpaint_full_res_padding": inpaint_full_res_padding,
- "inpainting_mask_invert": inpainting_mask_invert,
- "resize_mode": resize_mode,
- "denoising_strength": denoising_strength,
- "prompt": prompt,
- "negative_prompt": negative_prompt,
- "controlnet_input_image": controlnet_input_image_b64,
- "controlnet_mask": controlnet_mask_b64,
- "controlnet_module": controlnet_module,
- "controlnet_model": controlnet_model,
- "controlnet_weight": controlnet_weight,
- "controlnet_resize_mode": controlnet_resize_mode,
- "controlnet_lowvram": controlnet_lowvram,
- "controlnet_processor_res": controlnet_processor_res,
- "controlnet_threshold_a": controlnet_threshold_a,
- "controlnet_threshold_b": controlnet_threshold_b,
- "controlnet_guidance": controlnet_guidance,
- "guess_mode": guess_mode,
- "seed": seed,
- "subseed": subseed,
- "subseed_strength": subseed_strength,
- "sampler_index": sampler_index,
- "batch_size": batch_size,
- "n_iter": n_iter,
- "steps": steps,
- "cfg_scale": cfg_scale,
- "width": width,
- "height": height,
- "restore_faces": restore_faces,
- "include_init_images": include_init_images,
- "override_settings": override_settings,
- "override_settings_restore_afterwards": override_settings_restore_afterwards,
- }
- return self.api.custom_post("controlnet/img2img", payload=payload)
-
- def model_list(self):
- r = self.api.custom_get("controlnet/model_list")
- return r["model_list"]
-
-
- """
- def txt_to_img(
- self,
- positive_prompt,
- negative_prompt="",
- denoising_strength=0,
- seed=-1,
- cfg_scale=7,
- steps=50,
- width=512,
- height=512,
- tiling=False,
- sampler_name="Euler",
- upscaler_name="",
- styles=None,
- restore_faces=False,
- save_folder=None,
- script_name=""
- ):
- url = f"{self.sd_base_url}/sdapi/v1/txt2img"
-
- if styles is None:
- styles = []
-
- if save_folder is None:
- save_folder = self.output_dir
-
- data = {
- "enable_hr": False,
- "denoising_strength": denoising_strength,
- "firstphase_width": 0,
- "firstphase_height": 0,
- "hr_scale": 2,
- "hr_upscaler": upscaler_name,
- "hr_second_pass_steps": 0,
- "hr_resize_x": 0,
- "hr_resize_y": 0,
- "hr_sampler_name": sampler_name,
- "hr_prompt": "",
- "hr_negative_prompt": "",
- "prompt": positive_prompt,
- "styles": styles,
- "seed": seed,
- "subseed": -1,
- "subseed_strength": 0,
- "seed_resize_from_h": -1,
- "seed_resize_from_w": -1,
- "sampler_name": sampler_name,
- "batch_size": 1,
- "n_iter": 1,
- "steps": steps,
- "cfg_scale": cfg_scale,
- "width": width,
- "height": height,
- "restore_faces": restore_faces,
- "tiling": tiling,
- "do_not_save_samples": False,
- "do_not_save_grid": True,
- "negative_prompt": negative_prompt,
- "eta": 0,
- "s_min_uncond": 0,
- "s_churn": 0,
- "s_tmax": 0,
- "s_tmin": 0,
- "s_noise": 1,
- "override_settings": {},
- "override_settings_restore_afterwards": True,
- "script_args": [],
- "sampler_index": sampler_name,
- "script_name": script_name,
- "send_images": True,
- "save_images": False,
- "alwayson_scripts": {}
- }
-
- headers = {
- "Content-Type": "application/json"
- }
-
- try:
- response = requests.post(url, json=data, headers=headers)
-
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- response_data = response.json()
- if save_folder:
- image_paths = []
- for i, image_base64 in enumerate(response_data['images']):
- image_name = self.get_available_image_name(save_folder, self.service_config.wm)
- image_path = os.path.join(save_folder, image_name)
- image_data = base64.b64decode(image_base64)
- with open(image_path, 'wb') as f:
- f.write(image_data)
- image_paths.append(image_path)
- response_data['image_paths'] = image_paths
- return response_data
- else:
- # If the request was not successful, print the status code and response text
- print(f"Error: {response.status_code} - {response.text}")
- return None
- except requests.exceptions.RequestException as e:
- print(f"Error: {e}")
- return None
-
- def img_to_img(self,
- init_images,
- prompt="",
- negative_prompt="",
- sampler_name="Euler",
- seed=-1,
- cfg_scale=7,
- steps=50,
- width=512,
- height=512,
- denoising_strength=0.75,
- tiling=False,
- restore_faces=False,
- styles=None,
- save_folder=None,
- script_name=""
- ):
- url = f"{self.sd_base_url}/sdapi/v1/img2img"
-
- if styles is None:
- styles = []
-
- data = {
- "init_images": init_images,
- "resize_mode": 0,
- "denoising_strength": denoising_strength,
- "image_cfg_scale": 0,
- "mask": "string",
- "mask_blur": 0,
- "mask_blur_x": 4,
- "mask_blur_y": 4,
- "inpainting_fill": 0,
- "inpaint_full_res": True,
- "inpaint_full_res_padding": 0,
- "inpainting_mask_invert": 0,
- "initial_noise_multiplier": 0,
- "prompt": prompt,
- "styles": styles,
- "seed": seed,
- "subseed": -1,
- "subseed_strength": 0,
- "seed_resize_from_h": -1,
- "seed_resize_from_w": -1,
- "sampler_name": sampler_name,
- "batch_size": 1,
- "n_iter": 1,
- "steps": steps,
- "cfg_scale": cfg_scale,
- "width": width,
- "height": height,
- "restore_faces": restore_faces,
- "tiling": tiling,
- "do_not_save_samples": False,
- "do_not_save_grid": True,
- "negative_prompt": negative_prompt,
- "eta": 0,
- "s_min_uncond": 0,
- "s_churn": 0,
- "s_tmax": 0,
- "s_tmin": 0,
- "s_noise": 1,
- "override_settings": {},
- "override_settings_restore_afterwards": True,
- "script_args": [],
- "sampler_index": "Euler",
- "include_init_images": False,
- "script_name": script_name,
- "send_images": True,
- "save_images": False,
- "alwayson_scripts": {}
- }
-
- headers = {
- "Content-Type": "application/json"
- }
-
- try:
- response = requests.post(url, json=data, headers=headers)
-
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- response_data = response.json()
- if save_folder:
- image_paths = []
- for i, image_base64 in enumerate(response_data['images']):
- image_name = self.get_available_image_name(save_folder, self.service_config.wm)
- image_path = os.path.join(save_folder, image_name)
- image_data = base64.b64decode(image_base64)
- with open(image_path, 'wb') as f:
- f.write(image_data)
- image_paths.append(image_path)
- response_data['image_paths'] = image_paths
- return response_data
- else:
- # If the request was not successful, print the status code and response text
- print(f"Error: {response.status_code} - {response.text}")
- return None
- except requests.exceptions.RequestException as e:
- print(f"Error: {e}")
- return None
-
-
- """
diff --git a/lollms/services/ttv/cogvideo_x/lollms_cogvideo_x.py b/lollms/services/ttv/cogvideo_x/lollms_cogvideo_x.py
deleted file mode 100644
index ac0ef9f..0000000
--- a/lollms/services/ttv/cogvideo_x/lollms_cogvideo_x.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import os
-import time
-from pathlib import Path
-from typing import List, Optional
-import pipmaster as pm
-
-# Install required libraries if not already present
-if not pm.is_installed("torch"):
- pm.install_multiple(["torch","torchvision"," torchaudio"], "https://download.pytorch.org/whl/cu118") # Adjust CUDA version as needed
-if not pm.is_installed("diffusers"):
- pm.install("diffusers")
-if not pm.is_installed("transformers"):
- pm.install("transformers")
-if not pm.is_installed("accelerate"):
- pm.install("accelerate")
-if not pm.is_installed("imageio-ffmpeg"):
- pm.install("imageio-ffmpeg")
-
-import torch
-from diffusers import CogVideoXPipeline
-from diffusers.utils import export_to_video
-from lollms.app import LollmsApplication
-from lollms.main_config import LOLLMSConfig
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-from lollms.utilities import find_next_available_filename
-from lollms.ttv import LollmsTTV
-from ascii_colors import ASCIIColors
-
-class LollmsCogVideoX(LollmsTTV):
- """
- LollmsCogVideoX is an implementation of LollmsTTV using CogVideoX for text-to-video generation.
- """
-
- def __init__(
- self,
- app: LollmsApplication,
- output_folder: str | Path = None
- ):
- # Define service configuration
- service_config = TypedConfig(
- ConfigTemplate([
- {"name": "model_name", "type": "str", "value": "THUDM/CogVideoX-2b", "options": ["THUDM/CogVideoX-2b", "THUDM/CogVideoX-5b"], "help": "CogVideoX model to use"},
- {"name": "use_gpu", "type": "bool", "value": True, "help": "Use GPU if available"},
- {"name": "dtype", "type": "str", "value": "float16", "options": ["float16", "bfloat16"], "help": "Data type for model precision"},
- ]),
- BaseConfig(config={
- "model_name": "THUDM/CogVideoX-2b", # Default to 2B model (less VRAM-intensive)
- "use_gpu": True,
- "dtype": "float16",
- })
- )
- super().__init__("cogvideox", app, service_config, output_folder)
-
- # Initialize CogVideoX pipeline
- self.pipeline = None
- self.load_pipeline()
-
- def load_pipeline(self):
- """Loads or reloads the CogVideoX pipeline based on config."""
- try:
- dtype = torch.float16 if self.service_config.dtype == "float16" else torch.bfloat16
- self.pipeline = CogVideoXPipeline.from_pretrained(
- self.service_config.model_name,
- torch_dtype=dtype
- )
- if self.service_config.use_gpu and torch.cuda.is_available():
- self.pipeline.to("cuda")
- self.pipeline.enable_model_cpu_offload() # Optimize VRAM usage
- else:
- ASCIIColors.warning("GPU not available or disabled. Running on CPU (slower).")
- ASCIIColors.success(f"Loaded CogVideoX model: {self.service_config.model_name}")
- except Exception as e:
- raise RuntimeError(f"Failed to load CogVideoX pipeline: {str(e)}")
-
- def settings_updated(self):
- """Reloads the pipeline if settings change."""
- self.load_pipeline()
-
- def generate_video(
- self,
- prompt: str,
- negative_prompt: Optional[str] = None,
- model_name: str = "",
- height: int = 480,
- width: int = 720,
- steps: int = 50,
- seed: int = -1,
- nb_frames: int = 49,
- output_dir: str | Path = None,
- ) -> str:
- """
- Generates a video from a text prompt using CogVideoX.
-
- Args:
- prompt (str): The text prompt describing the video content.
- negative_prompt (Optional[str]): Ignored (CogVideoX doesn't support it natively).
- model_name (str): Overrides config model if provided (optional).
- height (int): Desired height of the video (default 480).
- width (int): Desired width of the video (default 720).
- steps (int): Number of inference steps (default 50).
- seed (int): Random seed (default -1 for random).
- nb_frames (int): Number of frames (default 49, ~6 seconds at 8 fps).
- output_dir (str | Path): Optional custom output directory.
-
- Returns:
- str: Path to the generated video file.
- """
- output_path = Path(output_dir) if output_dir else self.output_folder
- output_path.mkdir(exist_ok=True, parents=True)
-
- # Handle unsupported parameters
- if negative_prompt:
- ASCIIColors.warning("Warning: CogVideoX does not support negative prompts. Ignoring negative_prompt.")
- if model_name and model_name != self.service_config.model_name:
- ASCIIColors.warning(f"Overriding config model {self.service_config.model_name} with {model_name}")
- self.service_config.model_name = model_name
- self.load_pipeline()
-
- # Generation parameters
- gen_params = {
- "prompt": prompt,
- "num_frames": nb_frames,
- "num_inference_steps": steps,
- "guidance_scale": 6.0, # Default value from CogVideoX docs
- "height": height,
- "width": width,
- }
- if seed != -1:
- gen_params["generator"] = torch.Generator(device="cuda" if self.service_config.use_gpu else "cpu").manual_seed(seed)
-
- # Generate video
- try:
- ASCIIColors.info("Generating video with CogVideoX...")
- start_time = time.time()
- video_frames = self.pipeline(**gen_params).frames[0] # CogVideoX returns a list of frame batches
- output_filename = find_next_available_filename(output_path, "cogvideox_output.mp4")
- export_to_video(video_frames, output_filename, fps=8)
- elapsed_time = time.time() - start_time
- ASCIIColors.success(f"Video generated and saved to {output_filename} in {elapsed_time:.2f} seconds")
- except Exception as e:
- raise RuntimeError(f"Failed to generate video: {str(e)}")
-
- return str(output_filename)
-
- def generate_video_by_frames(self, prompts: List[str], frames: List[int], negative_prompt: str, fps: int = 8,
- num_inference_steps: int = 50, guidance_scale: float = 6.0,
- seed: Optional[int] = None) -> str:
- """
- Generates a video from a list of prompts. Since CogVideoX doesn't natively support multi-prompt videos,
- this concatenates prompts into a single description.
-
- Args:
- prompts (List[str]): List of prompts for each segment.
- frames (List[int]): Number of frames per segment (summed to total frames).
- negative_prompt (str): Ignored.
- fps (int): Frames per second (default 8).
- num_inference_steps (int): Inference steps (default 50).
- guidance_scale (float): Guidance scale (default 6.0).
- seed (Optional[int]): Random seed.
-
- Returns:
- str: Path to the generated video file.
- """
- if not prompts or not frames:
- raise ValueError("Prompts and frames lists cannot be empty.")
-
- # Combine prompts into a single narrative
- combined_prompt = " ".join(prompts)
- total_frames = sum(frames)
-
- return self.generate_video(
- prompt=combined_prompt,
- negative_prompt=negative_prompt,
- steps=num_inference_steps,
- seed=seed if seed is not None else -1,
- nb_frames=total_frames
- )
-
- def getModels(self):
- """Returns available CogVideoX models."""
- return ["THUDM/CogVideoX-2b", "THUDM/CogVideoX-5b"]
\ No newline at end of file
diff --git a/lollms/services/ttv/lumalabs/lollms_lumalabs.py b/lollms/services/ttv/lumalabs/lollms_lumalabs.py
deleted file mode 100644
index 6a77fcd..0000000
--- a/lollms/services/ttv/lumalabs/lollms_lumalabs.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import os
-import time
-import requests
-from pathlib import Path
-from typing import List, Optional
-import pipmaster as pm
-if not pm.is_installed("lumaai"):
- pm.install("lumaai")
-from lumaai import LumaAI
-from lollms.app import LollmsApplication
-from lollms.main_config import LOLLMSConfig
-from lollms.config import TypedConfig
-from lollms.utilities import find_next_available_filename
-from lollms.service import LollmsSERVICE
-from lollms.ttv import LollmsTTV
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-from ascii_colors import ASCIIColors
-class LollmsLumaLabs(LollmsTTV):
- """
- LollmsLumaLabs is an implementation of LollmsTTV using LumaAI for text-to-image generation.
- Note: LumaAI currently supports image generation, so video output will be limited to single-frame representations.
- """
-
- def __init__(
- self,
- app: LollmsApplication,
- output_folder: str | Path = None
- ):
-
- # Initialize LumaAI client
- api_key = os.environ.get("LUMAAI_API_KEY")
- service_config = TypedConfig(
- ConfigTemplate([
- {"name":"api_key", "type":"str", "value":api_key, "help":"A valid Novita AI key to generate text using anthropic api"},
- ]),
- BaseConfig(config={
- "api_key": "", # use avx2
- })
- )
- super().__init__("lumalabs", app, service_config, output_folder)
- try:
- self.client = LumaAI(auth_token=self.service_config.api_key)
- except:
- ASCIIColors.error("Couldn't create a client")
- self.client = None
-
- def settings_updated(self):
- try:
- self.client = LumaAI(auth_token=self.service_config.api_key)
- except:
- ASCIIColors.error("Couldn't create a client")
- self.client = None
-
-
- def generate_video(
- self,
- prompt: str,
- negative_prompt: Optional[str] = None,
- model_name: str = "",
- height: int = 512,
- width: int = 512,
- steps: int = 20,
- seed: int = -1,
- nb_frames: int = None,
- output_dir: str | Path = None,
- ) -> str:
- """
- Generates a 'video' from a text prompt using LumaAI. Currently limited to a single image due to API constraints.
-
- Args:
- prompt (str): The text prompt describing the content.
- negative_prompt (Optional[str]): Text describing elements to avoid (not supported by LumaAI, ignored).
- model_name (str): Model name (not supported by LumaAI, ignored).
- height (int): Desired height of the output image (default 512, LumaAI may override).
- width (int): Desired width of the output image (default 512, LumaAI may override).
- steps (int): Number of inference steps (default 20, ignored by LumaAI).
- seed (int): Random seed for reproducibility (default -1, ignored by LumaAI).
- nb_frames (int): Number of frames (default None, limited to 1 due to LumaAI image-only support).
- output_dir (str | Path): Optional custom output directory.
-
- Returns:
- str: Path to the generated image file (single-frame 'video').
- """
- output_path = Path(output_dir) if output_dir else self.output_folder
- output_path.mkdir(exist_ok=True, parents=True)
-
- # Warn about unsupported features
- if negative_prompt:
- ASCIIColors.warning("Warning: LumaAI does not support negative prompts. Ignoring negative_prompt.")
- if model_name:
- ASCIIColors.warning("Warning: LumaAI does not support model selection in this implementation. Ignoring model_name.")
- if steps != 20:
- ASCIIColors.warning("Warning: LumaAI controls inference steps internally. Ignoring steps parameter.")
- if seed != -1:
- ASCIIColors.warning("Warning: LumaAI does not support seed specification. Ignoring seed.")
- if nb_frames and nb_frames > 1:
- ASCIIColors.warning("Warning: LumaAI only supports single-image generation. Generating 1 frame instead of requested nb_frames.")
-
- # Note: LumaAI's API (as shown) doesn't support width/height directly in the provided example,
- # but we'll include them in case the API supports it in a newer version
- generation_params = {
- "prompt": prompt,
- # Uncomment and use these if LumaAI supports them in the future:
- # "height": height,
- # "width": width,
- }
-
- # Create generation request
- try:
- generation = self.client.generations.image.create(**generation_params)
- except Exception as e:
- raise RuntimeError(f"Failed to initiate generation: {str(e)}")
-
- # Poll for completion
- completed = False
- while not completed:
- try:
- generation = self.client.generations.get(id=generation.id)
- if generation.state == "completed":
- completed = True
- elif generation.state == "failed":
- raise RuntimeError(f"Generation failed: {generation.failure_reason}")
- print("Dreaming...")
- time.sleep(2)
- except Exception as e:
- raise RuntimeError(f"Error polling generation status: {str(e)}")
-
- # Download the image
- image_url = generation.assets.image
- output_filename = find_next_available_filename(output_path, f"{generation.id}.jpg")
-
- try:
- response = requests.get(image_url, stream=True)
- response.raise_for_status()
- with open(output_filename, 'wb') as file:
- file.write(response.content)
- print(f"File downloaded as {output_filename}")
- except Exception as e:
- raise RuntimeError(f"Failed to download image: {str(e)}")
-
- return str(output_filename)
-
- def generate_video_by_frames(self, prompts: List[str], frames: List[int], negative_prompt: str, fps: int = 8,
- num_inference_steps: int = 50, guidance_scale: float = 6.0,
- seed: Optional[int] = None) -> str:
- """
- Generates a 'video' from a list of prompts. Since LumaAI only supports single images,
- this will generate the first prompt's image and return it as a static representation.
- """
- if not prompts:
- raise ValueError("Prompts list cannot be empty.")
-
- return self.generate_video(
- prompt=prompts[0],
- negative_prompt=negative_prompt,
- seed=seed if seed is not None else -1
- )
-
- def getModels(self):
- """
- Gets the list of models. LumaAI doesn't expose model selection, so returns a placeholder.
- """
- return ["LumaAI_Default"]
\ No newline at end of file
diff --git a/lollms/services/ttv/novita_ai/lollms_novita_ai.py b/lollms/services/ttv/novita_ai/lollms_novita_ai.py
deleted file mode 100644
index 00cfb7b..0000000
--- a/lollms/services/ttv/novita_ai/lollms_novita_ai.py
+++ /dev/null
@@ -1,371 +0,0 @@
-from pathlib import Path
-from typing import List, Optional, Dict, Any
-from lollms.ttv import LollmsTTV
-from lollms.app import LollmsApplication
-from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
-from lollms.utilities import find_next_available_filename
-import requests
-import json
-import os
-import time
-
-class LollmsNovitaAITextToVideo(LollmsTTV):
- """
- A binding for the Novita.ai Text-to-Video API.
- This class allows generating videos from text prompts using the Novita.ai service.
- """
- def __init__(
- self,
- app:LollmsApplication,
- output_folder:str|Path=None
- ):
- """
- Initializes the NovitaAITextToVideo binding.
-
- Args:
- api_key (str): The API key for authentication.
- base_url (str): The base URL for the Novita.ai API. Defaults to "https://api.novita.ai/v3/async".
- """
- # Check for the NOVITA_AI_KEY environment variable if no API key is provided
- api_key = os.getenv("NOVITA_AI_KEY","")
- service_config = TypedConfig(
- ConfigTemplate([
- {"name":"api_key", "type":"str", "value":api_key, "help":"A valid Novita AI key to generate text using anthropic api"},
- {"name":"generation_engine","type":"str","value":"stable_diffusion", "options": ["stable_diffusion", "hunyuan-video-fast", "wan-t2v"], "help":"The engine name"},
- {"name":"sd_model_name","type":"str","value":"darkSushiMixMix_225D_64380.safetensors", "options": ["darkSushiMixMix_225D_64380.safetensors"], "help":"The model name"},
- {"name":"n_frames","type":"int","value":129, "help":"The number of frames in the video"},
- {"name":"guidance_scale", "type":"float", "value":7.5, "help":"The guidance scale for the generation"},
- {"name":"loras", "type":"str", "value":None, "help":"List of LoRA configurations"},
- {"name":"embeddings", "type":"str", "value":None, "help":"List of embedding configurations"},
- {"name":"closed_loop", "type":"bool", "value":False, "help":"Whether to use closed loop generation"},
- {"name":"clip_skip", "type":"int", "value":0, "help":"Number of layers to skip in CLIP"}
- ]),
- BaseConfig(config={
- "api_key": "", # use avx2
- })
- )
-
- super().__init__("novita_ai", app, service_config,output_folder)
- self.sd_model_name = self.service_config.sd_model_name
- self.base_url = "https://api.novita.ai/v3/async"
-
- models = self.getModels()
- service_config.config_template["sd_model_name"]["options"] = [model["model_name"] for model in models]
-
- def settings_updated(self):
- models = self.getModels()
- self.service_config.config_template["sd_model_name"]["options"] = models
-
- def getModels(self):
- """
- Gets the list of models
- """
- url = "https://api.novita.ai/v3/model"
- headers = {
- "Content-Type": "<content-type>",
- "Authorization": f"Bearer {self.service_config.api_key}"
- }
-
- response = requests.request("GET", url, headers=headers)
- js = response.json()
- return js["models"]
-
- def generate_video(
- self,
- prompt: str,
- negative_prompt: Optional[str] = None,
- model_name: str = "",
- height: int = 512,
- width: int = 512,
- steps: int = 20,
- seed: int = -1,
- nb_frames: int = None,
- output_dir:str | Path =None,
- ) -> str:
- """
- Generates a video from text prompts using the Novita.ai API.
-
- Args:
- model_name (str): Name of the model checkpoint.
- height (int): Height of the video, range [256, 1024].
- width (int): Width of the video, range [256, 1024].
- steps (int): Number of denoising steps, range [1, 50].
- prompts (List[Dict[str, Any]]): List of prompts with frames and text descriptions.
- negative_prompt (Optional[str]): Text input to avoid in the video. Defaults to None.
- seed (int): Random seed for reproducibility. Defaults to -1.
- guidance_scale (Optional[float]): Controls adherence to the prompt. Defaults to None.
- loras (Optional[List[Dict[str, Any]]]): List of LoRA parameters. Defaults to None.
- embeddings (Optional[List[Dict[str, Any]]]): List of embeddings. Defaults to None.
- closed_loop (Optional[bool]): Controls animation loop behavior. Defaults to None.
- clip_skip (Optional[int]): Number of layers to skip during optimization. Defaults to None.
-
- Returns:
- str: The task_id for retrieving the generated video.
- """
- if output_dir is None:
- output_dir = self.output_folder
-
- if nb_frames is None:
- nb_frames =self.service_config.n_frames
-
- if self.service_config.generation_engine=="hunyuan-video-fast":
- width, height, nb_frames, steps = self.pin_dimensions_frames_steps(width, height, nb_frames, steps)
- url = "https://api.novita.ai/v3/async/hunyuan-video-fast"
-
- payload = {
- "model_name": "hunyuan-video-fast",
- "width": width,
- "height": height,
- "seed": seed,
- "steps": steps,
- "prompt": prompt,
- "frames": nb_frames
- }
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {self.service_config.api_key}"
- }
-
- response = requests.request("POST", url, json=payload, headers=headers)
- elif self.service_config.generation_engine=="wan-t2v":
- width, height, nb_frames, steps = self.pin_dimensions_frames_steps(width, height, nb_frames, steps)
- url = "https://api.novita.ai/v3/async/wan-t2v"
-
- payload = {
- "model_name": "wan-t2v",
- "width": width,
- "height": height,
- "seed": seed,
- "steps": steps,
- "prompt": prompt,
- "frames": nb_frames
- }
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {self.service_config.api_key}"
- }
-
- response = requests.request("POST", url, json=payload, headers=headers)
-
- elif self.service_config.generation_engine=="stable_diffusion":
- print(response.text)
- if model_name=="":
- model_name = self.sd_model_name
-
-
- url = f"{self.base_url}/txt2video"
- headers = {
- "Authorization": f"Bearer {self.service_config.api_key}",
- "Content-Type": "application/json",
- }
- payload = {
- "extra": {
- "response_video_type": "mp4", # gif
- "enterprise_plan": {"enabled": False}
- },
- "sd_model_name": model_name,
- "height": height,
- "width": width,
- "steps": steps,
- "prompts": [
- {
- "frames": nb_frames,
- "prompt": prompt
- }
- ],
- "negative_prompt": negative_prompt,
- "guidance_scale": self.service_config.guidance_scale,
- "seed": seed,
- "loras": self.service_config.loras,
- "embeddings": self.service_config.embeddings,
- "closed_loop": self.service_config.closed_loop,
- "clip_skip": self.service_config.clip_skip
- }
- # Remove None values from the payload to avoid sending null fields
- payload = {k: v for k, v in payload.items() if v is not None}
-
- response = requests.post(url, headers=headers, data=json.dumps(payload))
- else:
- return "Unsupported engine name"
-
- response.raise_for_status() # Raise an exception for HTTP errors
- task_id = response.json().get("task_id")
-
-
- url = f"https://api.novita.ai/v3/async/task-result?task_id={task_id}"
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {self.service_config.api_key}",
- }
- done = False
- while not done:
- response = requests.request("GET", url, headers=headers)
- infos = response.json()
- if infos["task"]["status"]=="TASK_STATUS_SUCCEED" or infos["task"]["status"]=="TASK_STATUS_FAILED":
- done = True
- time.sleep(1)
- if infos["task"]["status"]=="TASK_STATUS_SUCCEED":
- if output_dir:
- output_dir = Path(output_dir)
- file_name = output_dir/find_next_available_filename(output_dir, "vid_novita_","mp4") # You can change the filename if needed
- self.download_video(infos["videos"][0]["video_url"], file_name )
- return file_name
- return None
-
- def pin_dimensions_frames_steps(self, width, height, nframes, steps):
- # Supported widths
- standard_widths = [480, 640, 720, 864, 1280]
-
- # Width-to-height mapping
- width_height_map = {
- 480: [640, 864], # 480 width supports 640 or 864 height
- 640: [480], # 640 width supports 480 height
- 720: [1280], # 720 width supports 1280 height
- 864: [480], # 864 width supports 480 height
- 1280: [720] # 1280 width supports 720 height
- }
-
- # Supported nframes
- standard_nframes = [85, 129]
-
- # Supported steps range
- min_steps, max_steps = 2, 30
-
- # Pin the width to the nearest standard width
- pinned_width = min(standard_widths, key=lambda x: abs(x - width))
-
- # Pin the height to the nearest supported height for the pinned width
- supported_heights = width_height_map[pinned_width]
- pinned_height = min(supported_heights, key=lambda x: abs(x - height))
-
- # Pin the nframes to the nearest standard nframes
- pinned_nframes = min(standard_nframes, key=lambda x: abs(x - nframes))
-
- # Pin the steps to the valid range (2 to 30)
- pinned_steps = max(min_steps, min(max_steps, steps))
-
- return pinned_width, pinned_height, pinned_nframes, pinned_steps
- def generate_video_by_frames(self, prompts: List[str], frames: List[int], negative_prompt: str, fps: int = 8,
- sd_model_name: str = "",
- height: int = 512,
- width: int = 512,
- steps: int = 20,
- seed: int = -1,
- output_dir:str | Path =None,
- ) -> str:
- """
- Generates a video from a list of prompts and corresponding frames.
-
- Args:
- prompts (List[str]): List of text prompts for each frame.
- frames (List[int]): List of frame indices corresponding to each prompt.
- negative_prompt (str): Text describing elements to avoid in the video.
- fps (int): Frames per second. Default is 8.
- num_inference_steps (int): Number of steps for the model to infer. Default is 50.
- guidance_scale (float): Controls how closely the model adheres to the prompt. Default is 6.0.
- seed (Optional[int]): Random seed for reproducibility. Default is None.
-
- Returns:
- str: The path to the generated video.
- """
- if sd_model_name=="":
- sd_model_name = self.sd_model_name
- if output_dir is None:
- output_dir = self.output_folder
-
-
- url = f"{self.base_url}/txt2video"
- headers = {
- "Authorization": f"Bearer {self.service_config.api_key}",
- "Content-Type": "application/json",
- }
- payload = {
- "extra": {
- "response_video_type": "mp4", # gif
- "enterprise_plan": {"enabled": False}
- },
- "sd_model_name": sd_model_name,
- "height": height,
- "width": width,
- "steps": steps,
- "prompts": [
- {
- "frames": nb_frames,
- "prompt": prompt,
-
- } for nb_frames, prompt in zip(prompts, frames)
- ],
- "negative_prompt": negative_prompt,
- "guidance_scale": guidance_scale,
- "seed": seed,
- "loras": [],
- "embeddings": [],
- "closed_loop": closed_loop,
- "clip_skip": clip_skip
- }
- # Remove None values from the payload to avoid sending null fields
- payload = {k: v for k, v in payload.items() if v is not None}
-
- response = requests.post(url, headers=headers, data=json.dumps(payload))
- response.raise_for_status() # Raise an exception for HTTP errors
- task_id = response.json().get("task_id")
-
-
- url = f"https://api.novita.ai/v3/async/task-result?task_id={task_id}"
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {self.service_config.api_key}",
- }
- done = False
- while not done:
- response = requests.request("GET", url, headers=headers)
- infos = response.json()
- if infos["task"]["status"]=="TASK_STATUS_SUCCEED" or infos["task"]["status"]=="TASK_STATUS_FAILED":
- done = True
- time.sleep(1)
- if infos["task"]["status"]=="TASK_STATUS_SUCCEED":
- if output_dir:
- output_dir = Path(output_dir)
- file_name = output_dir/find_next_available_filename(output_dir, "vid_novita_","mp4") # You can change the filename if needed
- self.download_video(infos["videos"][0]["video_url"], file_name )
- return file_name
- return None
-
- def get_task_result(self, task_id: str) -> Dict[str, Any]:
- """
- Retrieves the result of a video generation task using the task_id.
-
- Args:
- task_id (str): The task_id returned by the generate_video method.
-
- Returns:
- Dict[str, Any]: The task result containing the video URL and other details.
- """
- url = f"{self.base_url}/task-result"
- headers = {
- "Authorization": f"Bearer {self.service_config.api_key}",
- }
- params = {
- "task_id": task_id,
- }
-
- response = requests.get(url, headers=headers, params=params)
- response.raise_for_status() # Raise an exception for HTTP errors
-
- return response.json()
-
- def download_video(self, video_url: str, save_path: Path) -> None:
- """
- Downloads the generated video from the provided URL and saves it to the specified path.
-
- Args:
- video_url (str): The URL of the video to download.
- save_path (Path): The path where the video will be saved.
- """
- response = requests.get(video_url)
- response.raise_for_status() # Raise an exception for HTTP errors
-
- with open(save_path, "wb") as file:
- file.write(response.content)
diff --git a/lollms/ttm.py b/lollms/ttm.py
index 1ace807..bb9d8f5 100644
--- a/lollms/ttm.py
+++ b/lollms/ttm.py
@@ -2,14 +2,15 @@
Lollms TTM Module
=================
-This module is part of the Lollms library, designed to provide Text-to-Music (TTM) functionalities within the LollmsApplication framework. The base class `LollmsTTM` is intended to be inherited and implemented by other classes that provide specific TTM functionalities.
+This module is part of the Lollms library, designed to provide Text-to-Music (TTM) functionalities within the LollmsApplication framework. The base class `LollmsTTM` is intended to be inherited and implemented by other classes that provide specific TTM functionalities using various models or APIs.
Author: ParisNeo, a computer geek passionate about AI
+Inspired by the LollmsTTI structure.
"""
from lollms.app import LollmsApplication
from pathlib import Path
-from typing import List, Dict
+from typing import List, Dict, Optional
from lollms.main_config import LOLLMSConfig
from lollms.config import TypedConfig
from lollms.service import LollmsSERVICE
@@ -17,100 +18,188 @@ from lollms.service import LollmsSERVICE
class LollmsTTM(LollmsSERVICE):
"""
LollmsTTM is a base class for implementing Text-to-Music (TTM) functionalities within the LollmsApplication.
+ Subclasses should implement the actual music generation logic by overriding the `generate` method.
"""
-
+
def __init__(
self,
- name:str,
+ name: str,
app: LollmsApplication,
service_config: TypedConfig,
- output_folder: str|Path=None
+ output_folder: Optional[str | Path] = None
):
"""
- Initializes the LollmsTTI class with the given parameters.
+ Initializes the LollmsTTM class with the given parameters.
Args:
+ name (str): The unique name of the TTM service.
app (LollmsApplication): The instance of the main Lollms application.
- model (str, optional): The TTI model to be used for image generation. Defaults to an empty string.
- api_key (str, optional): API key for accessing external TTI services. Defaults to an empty string.
- output_path (Path or str, optional): Path where the output image files will be saved. Defaults to None.
+ service_config (TypedConfig): Configuration specific to this TTM service.
+ output_folder (str | Path, optional): Path where the output audio files will be saved.
+ If None, defaults to a subfolder named `name` within
+ `app.lollms_paths.personal_outputs_path`.
"""
super().__init__(name, app, service_config)
if output_folder is not None:
self.output_folder = Path(output_folder)
else:
- self.output_folder = app.lollms_paths.personal_outputs_path/name
- self.output_folder.mkdir(exist_ok=True, parents=True)
+ # Default output path within the standard Lollms outputs structure
+ self.output_folder = app.lollms_paths.personal_outputs_path / name
+
+ # Ensure the output directory exists
+ self.output_folder.mkdir(exist_ok=True, parents=True)
-
-
- def generate(self,
- positive_prompt: str,
- negative_prompt: str = "",
- duration=30,
- generation_engine=None,
- output_path = None) -> List[Dict[str, str]]:
+ def generate(self,
+ prompt: str,
+ negative_prompt: str = "",
+ duration_s: float = 10.0,
+ seed: Optional[int] = None,
+ # Add other common TTM parameters as needed by specific models
+ # e.g., model_name: Optional[str] = None,
+ # e.g., tempo_bpm: Optional[int] = None,
+ # e.g., genre: Optional[str] = None,
+ output_dir: Optional[str | Path] = None,
+ output_file_name: Optional[str] = None
+ ) -> List[Dict[str, str]]:
"""
- Generates images based on the given positive and negative prompts.
+ Generates audio based on the given text prompt.
+
+ This method must be implemented by subclasses to perform the actual text-to-music generation.
Args:
- positive_prompt (str): The positive prompt describing the desired image.
- negative_prompt (str, optional): The negative prompt describing what should be avoided in the image. Defaults to an empty string.
+ prompt (str): The positive prompt describing the desired music.
+ negative_prompt (str, optional): A prompt describing elements to avoid in the music. Defaults to "".
+ duration_s (float, optional): The desired duration of the generated audio in seconds. Defaults to 10.0.
+ seed (int, optional): A seed for reproducibility. If None, a random seed may be used. Defaults to None.
+ output_dir (str | Path, optional): Directory to save the output file(s). If None, uses self.output_folder.
+ output_file_name (str, optional): Desired name for the output file (without extension).
+ If None, a unique name will be generated.
Returns:
- List[Dict[str, str]]: A list of dictionaries containing image paths, URLs, and metadata.
+ List[Dict[str, str]]: A list of dictionaries, each containing details about a generated audio file.
+ Expected keys might include 'path' (local file path), 'url' (if served),
+ 'prompt', 'duration_s', 'seed', 'format', etc.
"""
- pass
+ # Base implementation does nothing - subclasses must override this
+ raise NotImplementedError("Subclasses must implement the 'generate' method.")
- def generate_from_samples(self, positive_prompt: str, samples: List[str], negative_prompt: str = "") -> List[Dict[str, str]]:
- """
- Generates images based on the given positive prompt and reference images.
-
- Args:
- positive_prompt (str): The positive prompt describing the desired image.
- images (List[str]): A list of paths to reference images.
- negative_prompt (str, optional): The negative prompt describing what should be avoided in the image. Defaults to an empty string.
-
- Returns:
- List[Dict[str, str]]: A list of dictionaries containing image paths, URLs, and metadata.
- """
- pass
+ # Optional: Add methods for other TTM functionalities like music continuation, variation, etc.
+ # def generate_continuation(self, audio_path: str | Path, prompt: str, ...):
+ # pass
+ #
+ # def generate_variation(self, audio_path: str | Path, prompt: str, ...):
+ # pass
@staticmethod
def verify(app: LollmsApplication) -> bool:
"""
- Verifies if the TTM service is available.
+ Verifies if the TTM service's dependencies are met or if it's ready to use (e.g., API key configured).
+
+ This base implementation returns True. Subclasses should override this
+ to perform actual checks (e.g., check for installed libraries, API connectivity).
Args:
app (LollmsApplication): The instance of the main Lollms application.
Returns:
- bool: True if the service is available, False otherwise.
+ bool: True if the service is considered available/verified, False otherwise.
"""
return True
@staticmethod
def install(app: LollmsApplication) -> bool:
"""
- Installs the necessary components for the TTM service.
+ Installs the necessary components or dependencies for the TTM service.
+
+ This base implementation returns True. Subclasses should override this
+ to perform actual installation steps (e.g., pip install required packages).
Args:
app (LollmsApplication): The instance of the main Lollms application.
Returns:
- bool: True if the installation was successful, False otherwise.
+ bool: True if the installation was successful (or not needed), False otherwise.
"""
return True
-
- @staticmethod
+
+ @staticmethod
def get(app: LollmsApplication) -> 'LollmsTTM':
"""
- Returns the LollmsTTM class.
+ Returns the LollmsTTM class type itself.
+
+ Used for discovery or instantiation purposes within the Lollms framework.
Args:
app (LollmsApplication): The instance of the main Lollms application.
Returns:
- LollmsTTM: The LollmsTTM class.
+ LollmsTTM: The LollmsTTM class type.
"""
return LollmsTTM
+
+# Example of how a specific TTM implementation might inherit (Conceptual)
+# class MySpecificTTM(LollmsTTM):
+# def __init__(self, app: LollmsApplication, service_config: TypedConfig, output_folder: Optional[str | Path] = None):
+# super().__init__("my_specific_ttm", app, service_config, output_folder)
+# # Initialize specific model, API client, etc.
+# # self.model = ... load model based on service_config ...
+#
+# def generate(self, prompt: str, negative_prompt: str = "", duration_s: float = 10.0, seed: Optional[int] = None, ...) -> List[Dict[str, str]]:
+# # ... Actual implementation using self.model or an API ...
+# self.app.ShowBlockingMessage(f"Generating music for: {prompt}")
+# try:
+# # 1. Prepare parameters for the specific model/API
+# # 2. Call the generation function
+# # audio_data = self.model.generate(prompt=prompt, neg_prompt=negative_prompt, duration=duration_s, seed=seed, ...)
+# # 3. Determine output path and filename
+# output_path = Path(output_dir or self.output_folder)
+# output_path.mkdir(parents=True, exist_ok=True)
+# if output_file_name:
+# base_filename = output_file_name
+# else:
+# # Generate a unique filename (e.g., using timestamp or hash)
+# import time
+# base_filename = f"ttm_output_{int(time.time())}"
+#
+# # Assume generated format is WAV for example
+# full_output_path = output_path / f"{base_filename}.wav"
+#
+# # 4. Save the generated audio data to the file
+# # save_audio(audio_data, full_output_path) # Replace with actual saving logic
+#
+# # 5. Prepare the result dictionary
+# result = {
+# "path": str(full_output_path),
+# # "url": Optional URL if served by Lollms web server
+# "prompt": prompt,
+# "negative_prompt": negative_prompt,
+# "duration_s": duration_s, # Actual duration might differ slightly
+# "seed": seed,
+# "format": "wav"
+# }
+# self.app.HideBlockingMessage()
+# return [result]
+# except Exception as e:
+# self.app.HideBlockingMessage()
+# self.app.ShowError(f"Error generating music: {e}")
+# # Log the error properly
+# print(f"Error in MySpecificTTM.generate: {e}") # Use app.error or logging framework
+# return []
+#
+# @staticmethod
+# def verify(app: LollmsApplication) -> bool:
+# # Check if 'my_specific_library' is installed
+# try:
+# import my_specific_library
+# return True
+# except ImportError:
+# return False
+#
+# @staticmethod
+# def install(app: LollmsApplication) -> bool:
+# # Install 'my_specific_library'
+# return app.binding_pip_install("my_specific_library")
+#
+# @staticmethod
+# def get(app: LollmsApplication) -> 'LollmsTTM':
+# return MySpecificTTM
\ No newline at end of file
diff --git a/lollms/utilities.py b/lollms/utilities.py
index b5225f8..4437820 100644
--- a/lollms/utilities.py
+++ b/lollms/utilities.py
@@ -927,7 +927,148 @@ def find_first_available_file_index(folder_path, prefix, extension=""):
return available_number
+def find_first_available_file_path(folder_path, prefix, extension=""):
+ """
+ Finds the full path for the first available filename in a folder,
+ based on a prefix and an optional extension.
+ The numbering starts from 1 (e.g., prefix1.ext, prefix2.ext, ...).
+
+ Args:
+ folder_path (str or Path): The path to the folder.
+ The folder will be created if it doesn't exist.
+ prefix (str): The desired file prefix.
+ extension (str, optional): The desired file extension (including the dot, e.g., ".txt").
+ Defaults to "".
+
+ Returns:
+ Path: A Path object representing the first available file path
+ (e.g., /path/to/folder/prefix1.txt if it doesn't exist).
+ Returns None if the folder cannot be created or accessed.
+ """
+ try:
+ # Ensure folder_path is a Path object
+ folder = Path(folder_path)
+
+ # Create the folder if it doesn't exist
+ # os.makedirs(folder, exist_ok=True) # Using exist_ok=True prevents errors if it already exists
+ # Using Pathlib's equivalent:
+ folder.mkdir(parents=True, exist_ok=True)
+
+
+ available_number = 1
+ while True:
+ # Construct the potential file path using an f-string
+ potential_path = folder / f"{prefix}{available_number}{extension}"
+
+ # Check if this path already exists (works for files and directories)
+ if not potential_path.exists():
+ # If it doesn't exist, this is the first available path
+ return potential_path
+ else:
+ # If it exists, increment the number and try the next one
+ available_number += 1
+ except OSError as e:
+ print(f"Error accessing or creating folder {folder_path}: {e}")
+ return None
+ except Exception as e:
+ print(f"An unexpected error occurred in find_first_available_file_path: {e}")
+ return None
+
+def is_file_path(path_string: Union[str, Path, None]) -> bool:
+ """
+ Checks if a given string or Path object structurally resembles a file path.
+
+ This function performs basic checks:
+ 1. Is the input a non-empty string or Path?
+ 2. Does it contain path separators ('/' or '\\')?
+ 3. Does it likely have a filename component (doesn't end with a separator)?
+ 4. Does it potentially have an extension (contains a '.')?
+
+ It does NOT check if the path actually exists on the filesystem.
+ It aims to distinguish plausible file paths from simple strings, URLs (basic check),
+ or paths explicitly ending like directories.
+
+ Args:
+ path_string: The string or Path object to check.
+
+ Returns:
+ True if the string looks like a file path, False otherwise.
+ """
+ # --- Basic Input Validation ---
+ if path_string is None:
+ return False
+
+ # Convert Path object to string for consistent processing
+ if isinstance(path_string, Path):
+ path_string = str(path_string)
+
+ if not isinstance(path_string, str):
+ # If it's not None, not a Path, and not a string, it's invalid input
+ return False
+
+ # Remove leading/trailing whitespace
+ path_string = path_string.strip()
+
+ # Empty string is not a valid file path
+ if not path_string:
+ return False
+
+ # --- Structural Checks ---
+
+ # Very basic check to filter out obvious URLs (can be expanded if needed)
+ if path_string.startswith(('http://', 'https://', 'ftp://', 'mailto:')):
+ return False
+
+ # Check if it ends with a path separator (more likely a directory)
+ # os.path.sep is the primary separator ('/' on Unix, '\' on Windows)
+ # os.path.altsep is the alternative ('/' on Windows)
+ ends_with_separator = path_string.endswith(os.path.sep)
+ if os.path.altsep: # Check altsep only if it exists (it's None on Unix)
+ ends_with_separator = ends_with_separator or path_string.endswith(os.path.altsep)
+
+ if ends_with_separator:
+ return False # Paths ending in separators usually denote directories
+
+ # Check for the presence of path separators within the string
+ has_separator = os.path.sep in path_string
+ if os.path.altsep:
+ has_separator = has_separator or os.path.altsep in path_string
+
+ # Use os.path.splitext to check for an extension
+ # It splits "path/to/file.txt" into ("path/to/file", ".txt")
+ # It splits "path/to/file" into ("path/to/file", "")
+ # It splits "path/.bashrc" into ("path/.bashrc", "") - important edge case!
+ # It splits "path/archive.tar.gz" into ("path/archive.tar", ".gz")
+ base, extension = os.path.splitext(path_string)
+
+ # A simple filename like "file.txt" is a valid relative path
+ # It won't have separators but will likely have an extension
+ has_extension = bool(extension) and extension != '.' # Ensure extension is not just a single dot
+
+ # Check if the part *before* the extension (or the whole string if no extension)
+ # contains a '.' which might indicate a hidden file like '.bashrc' when
+ # there are no separators. We need the base name for this.
+ filename = os.path.basename(path_string)
+ is_likely_hidden_file = filename.startswith('.') and '.' not in filename[1:] and not has_separator
+
+
+ # --- Decision Logic ---
+ # It looks like a file path if:
+ # 1. It contains separators (e.g., "folder/file", "folder/file.txt") OR
+ # 2. It has a valid extension (e.g., "file.txt") OR
+ # 3. It looks like a "hidden" file in the current directory (e.g., ".bashrc")
+ # AND it doesn't end with a separator (checked earlier).
+ if has_separator or has_extension or is_likely_hidden_file:
+ # Further refinement: Avoid matching just "." or ".."
+ if path_string == '.' or path_string == '..':
+ return False
+ return True
+ else:
+ # If it has no separators and no extension (e.g., "myfile"),
+ # it's ambiguous - could be a directory name or a file without extension.
+ # Let's default to False for this ambiguity unless separators are present.
+ return False
# Prompting tools
def detect_antiprompt(text:str, anti_prompts=["!@>"]) -> bool:
@@ -1544,3 +1685,51 @@ def remove_text_from_string(string: str, text_to_find:str):
string = string[:index]
return string
+
+
+def short_desc(text: str, max_length: int = 80) -> str:
+ """
+ Creates a shortened description of a text string, adding ellipsis if truncated.
+
+ Tries to break at a word boundary (space) if possible within the length limit.
+
+ Args:
+ text: The input string. Can be None.
+ max_length: The maximum desired length of the output string (including ellipsis).
+ Must be at least 4 to accommodate "...".
+
+ Returns:
+ The shortened string, or the original string if it's already short enough.
+ Returns an empty string if the input is None or not a string.
+ """
+ if text is None:
+ return ""
+
+ # Ensure input is treated as a string
+ if not isinstance(text, str):
+ try:
+ text = str(text)
+ except Exception:
+ return "[Invalid Input]" # Or return "" depending on desired behavior
+
+ # If text is already short enough, return it as is.
+ if len(text) <= max_length:
+ return text
+
+ # Ensure max_length is usable
+ if max_length < 4:
+ # Cannot add ellipsis, just truncate hard
+ return text[:max_length]
+
+ # Calculate the ideal truncation point before adding "..."
+ trunc_point = max_length - 3
+
+ # Find the last space character at or before the truncation point
+ last_space = text.rfind(' ', 0, trunc_point + 1) # Include trunc_point itself
+
+ if last_space != -1:
+ # Found a space, truncate there
+ return text[:last_space] + "..."
+ else:
+ # No space found in the initial part, hard truncate
+ return text[:trunc_point] + "..."
\ No newline at end of file