added stuff

This commit is contained in:
Saifeddine ALOUI 2024-05-31 01:03:19 +02:00
parent 3a2bc02c1c
commit 8b833fb615
7 changed files with 387 additions and 20 deletions

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@ -1,5 +1,5 @@
# =================== Lord Of Large Language Multimodal Systems Configuration file =========================== # =================== Lord Of Large Language Multimodal Systems Configuration file ===========================
version: 106 version: 108
binding_name: null binding_name: null
model_name: null model_name: null
model_variant: null model_variant: null
@ -31,7 +31,17 @@ app_custom_logo: ""
discussion_prompt_separator: "!@>" discussion_prompt_separator: "!@>"
start_header_id_template: "!@>" start_header_id_template: "!@>"
end_header_id_template: ": " end_header_id_template: ": "
separator_template: "\n" separator_template: "\n"
start_user_header_id_template: "!@>"
end_user_header_id_template: ": "
end_user_message_id_template: ""
start_ai_header_id_template: "!@>"
end_ai_header_id_template: ": "
end_ai_message_id_template: ""
system_message_template: "system" system_message_template: "system"
seed: -1 seed: -1
@ -156,6 +166,10 @@ openai_tts_voice: "alloy"
enable_sd_service: false enable_sd_service: false
sd_base_url: http://localhost:7860 sd_base_url: http://localhost:7860
# Image generation service
enable_fooocus_service: false
fooocus_base_url: http://localhost:7860
# diffuser # diffuser
diffusers_offloading_mode: sequential_cpu_offload # sequential_cpu_offload diffusers_offloading_mode: sequential_cpu_offload # sequential_cpu_offload
diffusers_model: PixArt-alpha/PixArt-Sigma-XL-2-1024-MS diffusers_model: PixArt-alpha/PixArt-Sigma-XL-2-1024-MS

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@ -419,29 +419,29 @@ class LollmsApplication(LoLLMsCom):
trace_exception(ex) trace_exception(ex)
self.warning(f"Couldn't load Motion control") self.warning(f"Couldn't load Motion control")
if self.config.active_tti_service == "diffusers": if self.config.active_tti_service == "diffusers" and type(self.tti)!=LollmsDiffusers:
from lollms.services.diffusers.lollms_diffusers import LollmsDiffusers from lollms.services.diffusers.lollms_diffusers import LollmsDiffusers
self.tti = LollmsDiffusers(self) self.tti = LollmsDiffusers(self)
elif self.config.active_tti_service == "autosd": elif self.config.active_tti_service == "autosd" and type(self.tti)!=LollmsSD:
from lollms.services.sd.lollms_sd import LollmsSD from lollms.services.sd.lollms_sd import LollmsSD
self.tti = LollmsSD(self) self.tti = LollmsSD(self)
elif self.config.active_tti_service == "dall-e": elif self.config.active_tti_service == "dall-e" and type(self.tti)!=LollmsDalle:
from lollms.services.dalle.lollms_dalle import LollmsDalle from lollms.services.dalle.lollms_dalle import LollmsDalle
self.tti = LollmsDalle(self, self.config.dall_e_key) self.tti = LollmsDalle(self, self.config.dall_e_key)
elif self.config.active_tti_service == "midjourney": elif self.config.active_tti_service == "midjourney" and type(self.tti)!=LollmsMidjourney:
from lollms.services.midjourney.lollms_midjourney import LollmsMidjourney from lollms.services.midjourney.lollms_midjourney import LollmsMidjourney
self.tti = LollmsMidjourney(self, self.config.midjourney_key) self.tti = LollmsMidjourney(self, self.config.midjourney_key)
if self.config.active_tts_service == "openai_tts": if self.config.active_tts_service == "openai_tts" and type(self.tts)!=LollmsOpenAITTS:
from lollms.services.open_ai_tts.lollms_openai_tts import LollmsOpenAITTS from lollms.services.open_ai_tts.lollms_openai_tts import LollmsOpenAITTS
self.tts = LollmsOpenAITTS(self, self.config.openai_tts_model, self.config.openai_tts_voice, self.config.openai_tts_key) self.tts = LollmsOpenAITTS(self, self.config.openai_tts_model, self.config.openai_tts_voice, self.config.openai_tts_key)
elif self.config.active_tts_service == "xtts" and self.xtts: elif self.config.active_tts_service == "xtts" and self.xtts:
self.tts = self.xtts self.tts = self.xtts
if self.config.active_stt_service == "openai_whisper": if self.config.active_stt_service == "openai_whisper" and type(self.stt)!=LollmsOpenAIWhisper:
from lollms.services.openai_whisper.lollms_openai_whisper import LollmsOpenAIWhisper from lollms.services.openai_whisper.lollms_openai_whisper import LollmsOpenAIWhisper
self.stt = LollmsOpenAIWhisper(self, self.config.openai_whisper_model, self.config.openai_whisper_key) self.stt = LollmsOpenAIWhisper(self, self.config.openai_whisper_model, self.config.openai_whisper_key)
elif self.config.active_stt_service == "whisper": elif self.config.active_stt_service == "whisper" and type(self.stt)!=LollmsWhisper :
from lollms.services.whisper.lollms_whisper import LollmsWhisper from lollms.services.whisper.lollms_whisper import LollmsWhisper
self.stt = LollmsWhisper(self, self.config.whisper_model) self.stt = LollmsWhisper(self, self.config.whisper_model)
@ -771,11 +771,19 @@ class LollmsApplication(LoLLMsCom):
""" """
start_header_id_template = self.config.start_header_id_template start_header_id_template = self.config.start_header_id_template
end_header_id_template = self.config.end_header_id_template end_header_id_template = self.config.end_header_id_template
separator_template = self.config.separator_template separator_template = self.config.separator_template
start_user_header_id_template = self.config.start_user_header_id_template
end_user_header_id_template = self.config.end_user_header_id_template
end_user_message_id_template = self.config.end_user_message_id_template
start_ai_header_id_template = self.config.start_ai_header_id_template
end_ai_header_id_template = self.config.end_ai_header_id_template
end_ai_message_id_template = self.config.end_ai_message_id_template
system_message_template = self.config.system_message_template system_message_template = self.config.system_message_template
if self.personality.callback is None: if self.personality.callback is None:
self.personality.callback = partial(self.process_chunk, client_id=client_id) self.personality.callback = partial(self.process_chunk, client_id=client_id)
# Get the list of messages # Get the list of messages
@ -1069,13 +1077,13 @@ class LollmsApplication(LoLLMsCom):
# Tokenize the message content # Tokenize the message content
if self.config.use_model_name_in_discussions: if self.config.use_model_name_in_discussions:
if message.model: if message.model:
msg = f"{separator_template}{start_header_id_template}{message.sender}({message.model}){end_header_id_template}" + message.content.strip() msg = f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}({message.model}){end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
else: else:
msg = f"{separator_template}{start_header_id_template}{message.sender}{end_header_id_template}" + message.content.strip() msg = f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
message_tokenized = self.model.tokenize(msg) message_tokenized = self.model.tokenize(msg)
else: else:
message_tokenized = self.model.tokenize( message_tokenized = self.model.tokenize(
f"{separator_template}{start_header_id_template}{message.sender}{end_header_id_template}" + message.content.strip() f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
) )
# Check if adding the message will exceed the available space # Check if adding the message will exceed the available space
if tokens_accumulated + len(message_tokenized) > available_space-n_tokens: if tokens_accumulated + len(message_tokenized) > available_space-n_tokens:
@ -1099,13 +1107,13 @@ class LollmsApplication(LoLLMsCom):
if self.config.use_model_name_in_discussions: if self.config.use_model_name_in_discussions:
if message.model: if message.model:
msg = f"{separator_template}{start_header_id_template}{message.sender}({message.model}){end_header_id_template}" + message.content.strip() msg = f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}({message.model}){end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
else: else:
msg = f"{separator_template}{start_header_id_template}{message.sender}{end_header_id_template}" + message.content.strip() msg = f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
message_tokenized = self.model.tokenize(msg) message_tokenized = self.model.tokenize(msg)
else: else:
message_tokenized = self.model.tokenize( message_tokenized = self.model.tokenize(
f"{separator_template}{start_header_id_template}{message.sender}{end_header_id_template}" + message.content.strip() f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
) )
# Add the tokenized message to the full_message_list # Add the tokenized message to the full_message_list

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@ -1,5 +1,5 @@
# =================== Lord Of Large Language Multimodal Systems Configuration file =========================== # =================== Lord Of Large Language Multimodal Systems Configuration file ===========================
version: 106 version: 108
binding_name: null binding_name: null
model_name: null model_name: null
model_variant: null model_variant: null
@ -31,7 +31,17 @@ app_custom_logo: ""
discussion_prompt_separator: "!@>" discussion_prompt_separator: "!@>"
start_header_id_template: "!@>" start_header_id_template: "!@>"
end_header_id_template: ": " end_header_id_template: ": "
separator_template: "\n" separator_template: "\n"
start_user_header_id_template: "!@>"
end_user_header_id_template: ": "
end_user_message_id_template: ""
start_ai_header_id_template: "!@>"
end_ai_header_id_template: ": "
end_ai_message_id_template: ""
system_message_template: "system" system_message_template: "system"
seed: -1 seed: -1
@ -156,6 +166,10 @@ openai_tts_voice: "alloy"
enable_sd_service: false enable_sd_service: false
sd_base_url: http://localhost:7860 sd_base_url: http://localhost:7860
# Image generation service
enable_fooocus_service: false
fooocus_base_url: http://localhost:7860
# diffuser # diffuser
diffusers_offloading_mode: sequential_cpu_offload # sequential_cpu_offload diffusers_offloading_mode: sequential_cpu_offload # sequential_cpu_offload
diffusers_model: PixArt-alpha/PixArt-Sigma-XL-2-1024-MS diffusers_model: PixArt-alpha/PixArt-Sigma-XL-2-1024-MS

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@ -206,3 +206,153 @@ if __name__ == "__main__":
# Print the HTML output # Print the HTML output
print(html_output) print(html_output)
# Core function to search for PDFs on arXiv and download them to a specified directory
def search_and_rank(llm, search_subject:str, score_threshold:float, query: str, max_results: Optional[int] = 5, sort_by: Optional[str] = 'relevance', start_date: Optional[str] = None, end_date: Optional[str] = None, author: Optional[str] = None, client: Optional[Any] = None) -> (str, Dict[str, Any]):
try:
if client is None:
download_to = Path("./pdf_search")
else:
download_to = client.discussion.discussion_folder / "pdf_search"
# Construct the search URL with additional parameters
url = f'http://export.arxiv.org/api/query?search_query={query}&start=0&max_results={max_results}&sortBy={sort_by}'
if start_date:
url += f'&startDate={start_date}'
if end_date:
url += f'&endDate={end_date}'
if author:
url += f'&author={author}'
response = requests.get(url)
response.raise_for_status()
# Parse the response
soup = BeautifulSoup(response.content, 'xml')
entries = soup.find_all('entry')
# Create the directory if it doesn't exist
download_to.mkdir(parents=True, exist_ok=True)
# Extract PDF URLs and additional information
html_output = "<html><body>"
report_content = ""
pdf_info = {}
for entry in entries:
pdf_url = entry.id.text.replace('abs', 'pdf') + '.pdf'
pdf_name = pdf_url.split('/')[-1]
pdf_path = download_to / pdf_name
if client is None:
local_url = f'/discussions/pdf_search/{pdf_name}'
else:
local_url = discussion_path_to_url(pdf_path)
# Extract additional information
title = entry.title.text
authors = ', '.join(author.find('name').text for author in entry.find_all('author'))
affiliations = ', '.join(affiliation.text for affiliation in entry.find_all('affiliation'))
abstract = entry.summary.text
published_date = entry.published.text
journal_ref = entry.find('journal_ref').text if entry.find('journal_ref') else 'N/A'
# Use the LLM to rate the relevance of the text example compared to the search prompt
relevance_score = llm.fast_gen(f"Rate the relevance of the following text compared to the prompt on a scale of 0 to 1. Only respond with a number between 0 and 1:\nPrompt: {search_subject}\nText: {abstract}\nRelevance score:")
# Extract the relevance score from the generated text
try:
score = float(relevance_score.strip())
except ValueError:
score = 0.0 # Default score in case of parsing issues
if score<score_threshold: # drop irrelevant scores
continue
# Create the output tuple
result = (f"Relevance Score: {score}", {"score": score})
# Write abstract and additional information to text file
abstract_path = download_to / f"{pdf_name}_abstract.txt"
with abstract_path.open('w', encoding='utf-8') as abstract_file:
abstract_file.write(f"Title: {title}\n")
abstract_file.write(f"Authors: {authors}\n")
abstract_file.write(f"Affiliations: {affiliations}\n")
abstract_file.write(f"Abstract: {abstract}\n")
abstract_file.write(f"Published Date: {published_date}\n")
abstract_file.write(f"Journal/Conference: {journal_ref}\n")
abstract_file.write(f"Relevance score: {score}\n")
# Download PDF
pdf_response = requests.get(pdf_url)
with pdf_path.open('wb') as pdf_file:
pdf_file.write(pdf_response.content)
# Append to HTML output
html_output += f"""
<div>
<h2>{title}</h2>
<p><strong>Authors:</strong> {authors}</p>
<p><strong>Affiliations:</strong> {affiliations}</p>
<p><strong>Abstract:</strong> {abstract}</p>
<p><strong>Published Date:</strong> {published_date}</p>
<p><strong>Journal/Conference:</strong> {journal_ref}</p>
<p><strong>Relevance score:</strong> {score}</p>
<p><a href="{pdf_url}" target="_blank">PDF Link</a></p>
<p><a href="{local_url}" target="_blank">Local PDF</a></p>
</div>
"""
# Append to report content
report_content += f"""
Title: {title}
Authors: {authors}
Affiliations: {affiliations}
Abstract: {abstract}
Published Date: {published_date}
Journal/Conference: {journal_ref}
Relevance score: {score}
PDF Link: {pdf_url}
Local PDF: {local_url}
------------------------
"""
# Append to pdf_info dict
pdf_info[pdf_name] = {
"title": title,
"authors": authors,
"affiliations": affiliations,
"abstract": abstract,
"published_date": published_date,
"journal_ref": journal_ref,
"pdf_url": pdf_url,
"local_url": local_url
}
# Save the report to a text file
report_path = download_to / "pdf_search_report.txt"
with report_path.open('w', encoding='utf-8') as report_file:
report_file.write(report_content)
html_output += "</body></html>"
return html_output, pdf_info
except Exception as e:
return trace_exception(e), {}
# Metadata function
def search_and_rank_function(llm, score_threshold:float, client: Optional[Any] = None):
return {
"function_name": "arxiv_pdf_search", # The function name in string
"function": partial(arxiv_pdf_search, llm=llm, client=client, score_threshold= score_threshold), # The function to be called with partial to preset client
"function_description": "Searches for PDFs on arXiv based on a query, downloads them to a specified directory, rates them, sort them by rating and returns a HTML string containing article details and links, along with a dictionary containing detailed information about each PDF.", # Description of the function
"function_parameters": [ # The set of parameters
{"name": "search_subject", "type": "str", "description": "The search subject."},
{"name": "query", "type": "str", "description": "The search query for arXiv."},
{"name": "max_results", "type": "int", "description": "The maximum number of results to return. (Optional)", "optional": True, "default": 5},
{"name": "sort_by", "type": "str", "description": "The sorting criteria for the search results (e.g., relevance, lastUpdatedDate). (Optional)", "optional": True, "default": "relevance"},
{"name": "start_date", "type": "str", "description": "The start date for the search results in the format YYYY-MM-DD. (Optional)", "optional": True},
{"name": "end_date", "type": "str", "description": "The end date for the search results in the format YYYY-MM-DD. (Optional)", "optional": True},
{"name": "author", "type": "str", "description": "The author name for the search results. (Optional)", "optional": True},
]
}

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@ -3401,8 +3401,9 @@ The AI should respond in this format using data from actions_list:
start_header_id_template = self.config.start_header_id_template start_header_id_template = self.config.start_header_id_template
end_header_id_template = self.config.end_header_id_template end_header_id_template = self.config.end_header_id_template
system_message_template = self.config.system_message_template system_message_template = self.config.system_message_template
separator_template = self.config.separator_template
function_descriptions = [f"{start_header_id_template}{system_message_template}{end_header_id_template}If you need to call a function to fulfull the user request, use a function markdown tag with the function call as the following json format:", function_descriptions = [f"{start_header_id_template}{system_message_template}{end_header_id_template}If you need to call a function to fulfill the user request, use a function markdown tag with the function call as the following json format:",
"```function", "```function",
"{", "{",
'"function_name":the name of the function to be called,', '"function_name":the name of the function to be called,',
@ -3412,8 +3413,9 @@ The AI should respond in this format using data from actions_list:
"Only use available functions.", "Only use available functions.",
"You can call multiple functions in one generation.", "You can call multiple functions in one generation.",
"Each function call needs to be in a separate function markdown tag.", "Each function call needs to be in a separate function markdown tag.",
"Do not add status of the execution as it will be added automatically by the system.", f"Do not add status of the execution as it will be added automatically by the system.{separator_template}"
f"{start_header_id_template}Available functions{end_header_id_template}\n"] f"{start_header_id_template}Available functions{end_header_id_template}\n"]
for function in functions: for function in functions:
description = f"{function['function_name']}: {function['function_description']}\nparameters:{function['function_parameters']}" description = f"{function['function_name']}: {function['function_description']}\nparameters:{function['function_parameters']}"
function_descriptions.append(description) function_descriptions.append(description)

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@ -0,0 +1,179 @@
# 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
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"
if not PackageManager.check_package_installed("diffusers"):
PackageManager.install_or_update("diffusers")
if not PackageManager.check_package_installed("torch"):
check_and_install_torch(True)
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 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)
if not PackageManager.check_package_installed("diffusers"):
PackageManager.install_or_update("diffusers")
PackageManager.install_or_update("xformers")
def upgrade_diffusers(lollms_app:LollmsApplication):
PackageManager.install_or_update("diffusers")
PackageManager.install_or_update("xformers")
class LollmsFooocus(LollmsTTI):
has_controlnet = False
def __init__(
self,
app:LollmsApplication,
wm = "Artbot",
):
super().__init__(app)
self.ready = False
# Get the current directory
lollms_paths = app.lollms_paths
root_dir = lollms_paths.personal_path
self.wm = wm
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.red(" _ _ _ ______ ")
ASCIIColors.red("| | | | | | ___| ")
ASCIIColors.red("| | ___ | | |_ __ ___ ___ | |_ ___ ___ ___ ___ _ _ ___ ")
ASCIIColors.red("| | / _ \| | | '_ ` _ \/ __| | _/ _ \ / _ \ / _ \ / __| | | / __|")
ASCIIColors.red("| |___| (_) | | | | | | | \__ \ | || (_) | (_) | (_) | (__| |_| \__ \ ")
ASCIIColors.red("\_____/\___/|_|_|_| |_| |_|___/ \_| \___/ \___/ \___/ \___|\__,_|___/")
ASCIIColors.red(" ______ ")
ASCIIColors.red(" |______| ")
import torch
from diffusers import PixArtSigmaPipeline
self.model = PixArtSigmaPipeline.from_pretrained(
app.config.diffusers_model, torch_dtype=torch.float16, cache_dir=self.models_dir,
use_safetensors=True,
)
# Enable memory optimizations.
if app.config.diffusers_offloading_mode=="sequential_cpu_offload":
self.model.enable_sequential_cpu_offload()
elif app.coinfig.diffusers_offloading_mode=="model_cpu_offload":
self.model.enable_model_cpu_offload()
@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.diffusers.lollms_diffusers import LollmsFooocus
ASCIIColors.success("ok")
return LollmsFooocus
def paint(
self,
positive_prompt,
negative_prompt,
files=[],
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
from diffusers.utils.pil_utils import pt_to_pil
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}

View File

@ -767,7 +767,7 @@ class TasksLibrary:
end_header_id_template = self.config.end_header_id_template end_header_id_template = self.config.end_header_id_template
system_message_template = self.config.system_message_template system_message_template = self.config.system_message_template
separator_template = self.config.separator_template separator_template = self.config.separator_template
function_descriptions = [f"{start_header_id_template}information{end_header_id_template}If you need to call a function to fulfull the user request, use a function markdown tag with the function call as the following json format:", function_descriptions = [f"{start_header_id_template}information{end_header_id_template}If you need to call a function to fulfill the user request, use a function markdown tag with the function call as the following json format:",
"```function", "```function",
"{", "{",
'"function_name":the name of the function to be called,', '"function_name":the name of the function to be called,',