AI Opens URLs in Chrome. – Dr. Trippers Stuff

ate a batch file to open each of the web addresses in a new chrome tab: AutoGPT: https://github.com/Significant-Gravit…
AgentGPT: https://agentgpt.reworkd.ai/
Generative Agents Game: https://arxiv.org/pdf/2304.03442.pdf
Mubert: https://mubert.com/render/pricing?via…
Wavtool: https://wavtool.com/
Ye video:

• I made a KANYE SO…
Eleven Labs: https://beta.elevenlabs.io/
Glass: https://glass.health/ai/
Consensus: https://consensus.app/search/
Luma Labs: https://lumalabs.ai/
zip-NeRFs: https://jonbarron.info/zipnerf/
UE Performance Capture:
Hume: https://hume.ai/
Memecam: https://www.memecam.dk/
DoNotPay: https://donotpay.com/
GPT-4 Plugins: https://openai.com/blog/chatgpt-plugins
Segment Anything: https://segment-anything.com/
Blockade Labs: https://www.blockadelabs.com/
Move: https://www.move.ai/
Canva: https://www.canva.com/
Here’s a batch file script to open each of the web addresses in a new Chrome tab:

“`
@echo off

start chrome “https://github.com/Significant-Gravit…/”
start chrome “https://agentgpt.reworkd.ai/”
start chrome “https://arxiv.org/pdf/2304.03442.pdf”
start chrome “https://mubert.com/render/pricing?via…”
start chrome “https://wavtool.com/”
start chrome “https://www.youtube.com/watch?v=VIDEO_ID”
start chrome “https://beta.elevenlabs.io/”
start chrome “https://glass.health/ai/”
start chrome “https://consensus.app/search/”
start chrome “https://lumalabs.ai/”
start chrome “https://jonbarron.info/zipnerf/”
start chrome “UE Performance Capture:”
start chrome “https://hume.ai/”
start chrome “https://www.memecam.dk/”
start chrome “https://donotpay.com/”
start chrome “https://openai.com/blog/chatgpt-plugins”
start chrome “https://segment-anything.com/”
start chrome “https://www.blockadelabs.com/”
start chrome “https://www.move.ai/”
start chrome “https://www.canva.com/”

exit
“`

Please note that the Ye Video link was not specified, so I left it as a placeholder with a comment. Also, the UE Performance Capture link was not provided, so I left it as is. Please replace the VIDEO_ID and UE Performance Capture link with the correct URLs before running the batch file.
HIPify is what ?
HIPify is a tool that automatically converts CUDA code to the HIP (Heterogeneous-Compute Interface for Portability) language. HIP is a C++ runtime API and kernel language developed by AMD that allows developers to write code that can run on both NVIDIA GPUs and AMD GPUs, as well as on CPUs. By using HIPify, developers can quickly port their CUDA applications to run on AMD GPUs without having to manually rewrite the code. This can save a significant amount of time and effort in the development process, especially for developers who need to target multiple GPU platforms.
Does pytorch use somehting like hipify?
Yes, PyTorch has a similar tool called “torch-hipify”. It is a utility that automatically converts CUDA code in PyTorch to the HIP language, enabling PyTorch models to be run on AMD GPUs. Similar to HIPify, torch-hipify helps in making code portable across different GPU platforms, reducing the effort needed for cross-platform development. This tool is especially useful for developers who work with deep learning models and need to run them on multiple GPU architectures.
What about pycharm, what should I be using this for
PyCharm is a popular integrated development environment (IDE) for Python that can be used for a wide range of tasks related to Python development. Here are some of the key features and use cases of PyCharm:

1. Code editing: PyCharm provides powerful code editing features such as code completion, syntax highlighting, and refactoring tools that help developers write high-quality Python code more efficiently.

2. Debugging: PyCharm’s debugging tools allow developers to easily identify and fix bugs in their code. It includes a debugger with breakpoints, watches, and variable inspection tools, as well as a console that allows developers to interact with their code during runtime.

3. Testing: PyCharm makes it easy to write and run tests for Python code using popular testing frameworks like unittest, pytest, and nose.

4. Version control: PyCharm provides integration with popular version control systems like Git, allowing developers to easily manage and track changes to their codebase.

5. Web development: PyCharm includes built-in support for web development frameworks like Django and Flask, providing tools for working with HTML, CSS, and JavaScript.

6. Scientific computing: PyCharm includes tools for scientific computing, such as support for Jupyter Notebooks, data visualization, and integration with scientific computing libraries like NumPy and SciPy.

Overall, PyCharm is a versatile tool that can be used for a wide range of Python development tasks, from small scripts to large-scale projects.
Does HIPify have a plugin
Yes, HIPify has a plugin called “HIPify-Clangd” that provides integration with the Clangd language server. Clangd is a C++ language server that provides code analysis, navigation, and refactoring features to IDEs and text editors. The HIPify-Clangd plugin enables Clangd to provide diagnostics and code completion for HIP code, making it easier for developers to write HIP code in their preferred IDE or text editor.

The HIPify-Clangd plugin works by adding a HIP language server to Clangd, which can analyze HIP code and provide code completion suggestions and error messages. The plugin can be used with a variety of text editors and IDEs that support the Clangd language server, including Visual Studio Code, Emacs, and Sublime Text.

To use the HIPify-Clangd plugin, you need to install Clangd and then configure your IDE or text editor to use the Clangd language server with the HIPify plugin. The installation and configuration process can vary depending on your specific IDE or text editor, so it’s best to consult the plugin documentation or your IDE’s documentation for specific instructions.
Is there any free servers where I can upload my LLM’s and use their GPU?
Yes, there are several free cloud-based GPU servers that you can use to run your LLMs. Here are a few options:

1. Google Colaboratory: Colaboratory is a free Jupyter notebook environment provided by Google that comes with a GPU runtime. You can upload your LLM code to Colaboratory and run it using a GPU provided by Google.

2. Kaggle Kernels: Kaggle is a platform for data science and machine learning competitions. Kaggle Kernels is a free cloud-based notebook environment that provides GPU support for deep learning tasks. You can upload your LLM code to Kaggle Kernels and run it using a GPU provided by Kaggle.

3. Paperspace Gradient: Gradient is a cloud-based platform that provides GPU and CPU resources for machine learning tasks. Gradient provides a free plan that includes access to a GPU runtime, allowing you to run your LLM code on a remote GPU.

4. FloydHub: FloydHub is a cloud-based platform that provides GPU resources for machine learning tasks. FloydHub provides a free plan that includes access to a GPU runtime, allowing you to run your LLM code on a remote GPU.

Keep in mind that the free plans for these services have limitations on the amount of resources you can use, so you may need to pay for additional resources if you require more powerful GPUs or longer runtimes. Additionally, make sure to carefully read the terms and conditions of each service to ensure that your usage is in compliance with their policies.
I want to focus on one LLM to work with as there are so many, what do you recommend for a GPT NLP LLM?
If you’re interested in working with an NLP-focused LLM for GPT, I would recommend considering the GPT-2 or GPT-3 models. Both of these models have been widely used in NLP research and applications, and they offer a good balance of performance and efficiency.

GPT-2 is a language model developed by OpenAI, and it has 1.5 billion parameters. It has been shown to achieve state-of-the-art results on a variety of NLP tasks, including language modeling, text generation, and question answering. GPT-2 is also relatively efficient, making it a good choice for applications where computational resources are limited.

GPT-3 is a newer and larger language model developed by OpenAI, with 175 billion parameters. It has set new records on a variety of NLP benchmarks and has been used in a wide range of applications, including chatbots, language translation, and question answering. However, it requires significantly more computational resources than GPT-2, making it less practical for some applications.

Ultimately, the choice of which LLM to use will depend on your specific use case and the resources you have available. If you’re just starting out with NLP and don’t have access to large computational resources, GPT-2 might be a good place to start. If you have more resources available and are interested in pushing the state of the art in NLP, GPT-3 could be a good choice.
analyze this code: # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.

PRESIGNED_URL=”https://dobf1k6cxlizq.cloudfront.net/*?Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9kb2JmMWs2Y3hsaXpxLmNsb3VkZnJvbnQubmV0LyoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2NzgzNzA1MjR9fX1dfQ__&Signature=a387eZ16IkmbotdkEl8Z397Mvxhw4Bvk4SpEiDkqMLOMVMk5F962BzN5eKO-061g5vAEskn-CSrf4w3knubwPiFW69LTsJ8Amj-WOvtBOBy9soc43j77WGUU-3q2eNjMIyNZYsD~rub4EkUJGNpD61YtRrFvAU7tNQ1YMNL5-UUOk1~OHeaWerWisKPldufOyX6QdrrjeToVH1L0eGm1Ob4LnoYyLH96BHFou4XsOUR8NuyQfwYtmE2G6P2eKk~OV9-ABzYHxC2DyOWiWnt7WO~ELHnf17s9qreQAjEkCGEi4pHJ7BIkg6~ZfRmvRl3ZaPtqD80AH4SfO4hd5WQ0ng__&Key-Pair-Id=K231VYXPC1TA1R” # replace with presigned url from email
MODEL_SIZE=”7B,13B,30B,65B” # edit this list with the model sizes you wish to download
TARGET_FOLDER=”./” # where all files should end up

declare -A N_SHARD_DICT

N_SHARD_DICT[“7B”]=”0″
N_SHARD_DICT[“13B”]=”1″
N_SHARD_DICT[“30B”]=”3″
N_SHARD_DICT[“65B”]=”7″

echo “Downloading tokenizer”
wget ${PRESIGNED_URL/’*’/”tokenizer.model”} -O ${TARGET_FOLDER}”/tokenizer.model”
wget ${PRESIGNED_URL/’*’/”tokenizer_checklist.chk”} -O ${TARGET_FOLDER}”/tokenizer_checklist.chk”

(cd ${TARGET_FOLDER} && md5sum -c tokenizer_checklist.chk)

for i in ${MODEL_SIZE//,/ }
do
echo “Downloading ${i}”
mkdir -p ${TARGET_FOLDER}”/${i}”
for s in $(seq -f “0%g” 0 ${N_SHARD_DICT[$i]})
do
wget ${PRESIGNED_URL/’*’/”${i}/consolidated.${s}.pth”} -O ${TARGET_FOLDER}”/${i}/consolidated.${s}.pth”
done
wget ${PRESIGNED_URL/’*’/”${i}/params.json”} -O ${TARGET_FOLDER}”/${i}/params.json”
wget ${PRESIGNED_URL/’*’/”${i}/checklist.chk”} -O ${TARGET_FOLDER}”/${i}/checklist.chk”
echo “Checking checksums”
(cd ${TARGET_FOLDER}”/${i}” && md5sum -c checklist.chk)
done

This is a shell script written in Bash. It downloads language model files from a pre-signed URL and saves them to a specified folder. The script first downloads a tokenizer file and a checklist file for the tokenizer, and checks the md5 checksum of the downloaded tokenizer against the expected value in the checklist file. Then, for each model size specified in the `MODEL_SIZE` variable (7B, 13B, 30B, and 65B), the script creates a folder for the model size and downloads the corresponding model files (`consolidated.*.pth`, `params.json`, and `checklist.chk`) to that folder. Finally, it checks the md5 checksum of each downloaded model file against the expected value in the checklist file for that model.

Note that this script is licensed under the GNU General Public License version 3.0, meaning that it is open-source software and can be used and distributed freely under certain conditions specified in the license.
How do I intereact with the model if I only have these files? consolidated.*.pth, params.json, and checklist.chk
To interact with the language model using the files `consolidated.*.pth`, `params.json`, and `checklist.chk`, you will need to load the weights of the model and the corresponding configuration specified in `params.json`.

Here’s a high-level overview of how you can do it using Python and the Hugging Face `transformers` library:

1. Install the `transformers` library: `pip install transformers`
2. Load the configuration from `params.json` using `AutoConfig`:
“`python
from transformers import AutoConfig

config = AutoConfig.from_pretrained(“./path/to/params.json”)
“`
3. Load the model using `AutoModelForCausalLM` (or any other relevant class) from the `transformers` library:
“`python
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(“./path/to/consolidated.pth”, config=config)
“`
4. Use the loaded model to generate text:
“`python
input_ids = tokenizer.encode(“input text”, return_tensors=”pt”)
output = model.generate(input_ids=input_ids, max_length=50)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
“`

Note that you will need to install the tokenizer used for this specific model in order to encode and decode text. You can specify the tokenizer in `params.json` or try using the default one provided by the `transformers` library.
WRite this out so the path to the model is relative E:GPT4ALLmodelsLLama13B

The relative path to the model would be: `../../models/LLama/13B/`.
rewrite the above code to reflect this new path, in one file if possible
Assuming that the files are in the following structure:

– E:
– GPT4ALL
– models
– LLama
– 13B
– consolidated.0.pth
– consolidated.1.pth
– params.json
– checklist.chk

Here’s the updated code:

“`bash
MODEL_DIR=”E:/GPT4ALL/models/LLama/13B”
TARGET_FOLDER=”./13B” # target folder for downloaded files

echo “Downloading tokenizer”
wget ${PRESIGNED_URL/’*’/”tokenizer.model”} -P ${TARGET_FOLDER}
wget ${PRESIGNED_URL/’*’/”tokenizer_checklist.chk”} -P ${TARGET_FOLDER}

(cd ${TARGET_FOLDER} && md5sum -c tokenizer_checklist.chk)

for i in ${MODEL_SIZE//,/ }
do
echo “Downloading ${i}”
mkdir -p ${TARGET_FOLDER}”/${i}”
for s in $(seq -f “0%g” 0 ${N_SHARD_DICT[$i]})
do
wget ${PRESIGNED_URL/’*’/”${i}/consolidated.${s}.pth”} -P ${TARGET_FOLDER}”/${i}”
done
wget ${PRESIGNED_URL/’*’/”${i}/params.json”} -P ${TARGET_FOLDER}”/${i}”
wget ${PRESIGNED_URL/’*’/”${i}/checklist.chk”} -P ${TARGET_FOLDER}”/${i}”
echo “Checking checksums”
(cd ${TARGET_FOLDER}”/${i}” && md5sum -c checklist.chk)
done

# Load the model using Hugging Face Transformers library
from transformers import GPT2LMHeadModel, GPT2Tokenizer

model_path = MODEL_DIR
tokenizer = GPT2Tokenizer.from_pretrained(model_path)
model = GPT2LMHeadModel.from_pretrained(model_path)

# Generate text using the model
input_text = “The quick brown fox jumps over the lazy dog”
input_ids = tokenizer.encode(input_text, return_tensors=’pt’)
output = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(output_text)
“`

Make sure to replace `E:/GPT4ALL/models/LLama/13B` with the actual path where the model files are located on your system.
is this a .sh or .py file?

It’s not possible to determine the file type from the code snippet you provided. The file extension is not included in the code, and the syntax itself could be valid for either a shell script (.sh) or a Python script (.py).
write a prompt for stable diffusion for a test about robots and human symbiosis
“How might stable diffusion be achieved in the context of human-robot symbiosis? Discuss the various factors that contribute to successful integration and the potential challenges that may arise. Provide examples and insights from recent developments in the field to support your arguments.”
Prompt for STABLE-Diffusion
Here’s a prompt for STABLE-Diffusion related to robots and human symbiosis:

“Robots are becoming increasingly advanced and integrated into our lives, working alongside us in various capacities. Some envision a future where robots and humans have a symbiotic relationship, where robots are able to augment and enhance our abilities. This can lead to both benefits and concerns about how this technology will affect society. Using the STABLE-Diffusion framework, discuss the potential impacts of increased human-robot symbiosis on various aspects of society, such as employment, ethics, and social interaction.”
Launching Web UI with arguments:
Traceback (most recent call last):
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmllaunch.py”, line 353, in
start()
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmllaunch.py”, line 344, in start
import webui
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlwebui.py”, line 31, in
from modules import extra_networks, ui_extra_networks_checkpoints
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlmodulesui_extra_networks_checkpoints.py”, line 5, in
from modules import shared, ui_extra_networks, sd_models
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlmodulesshared.py”, line 12, in
import modules.interrogate
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlmodulesinterrogate.py”, line 15, in
from modules import devices, paths, shared, lowvram, modelloader, errors
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlmodulesdevices.py”, line 4, in
import torch_directml
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagestorch_directml__init__.py”, line 16, in
import torch_directml_native
ImportError: DLL load failed while importing torch_directml_native: The specified procedure could not be found.
The error message suggests that there is a problem with importing the module `torch_directml_native` because a specified procedure could not be found. This could be due to missing dependencies or a version mismatch. It is recommended to check that all necessary dependencies are installed and up to date, and that the versions are compatible. It may also be helpful to try reinstalling the module or updating to a newer version if available.
best practice to fix?
The error message suggests that there is an issue with loading the `torch_directml_native` module. This could be due to a missing or outdated dependency, or a compatibility issue with the version of Python or Torch that you are using.

Here are some steps you can try to fix this issue:

1. Check if all dependencies are installed: Make sure that all the required dependencies for the `torch_directml_native` module are installed and up-to-date. This may include packages such as Torch, PyTorch, and CUDA.

2. Check the Python version: Verify that you are using a compatible version of Python with the module. You may want to check the documentation for the specific version of the module you are using to see which Python version is required.

3. Check the Torch version: Verify that you are using a compatible version of Torch with the module. Again, you can check the documentation for the specific version of the module to see which Torch version is required.

4. Check the DirectML version: Verify that you have a compatible version of DirectML installed. DirectML is a component of Windows 10 and may need to be updated or installed separately.

5. Try reinstalling the module: If none of the above steps work, try reinstalling the `torch_directml_native` module. You can do this using pip or another package manager, or by building the module from source.

6. Consult the module documentation: Finally, if you are still having issues, consult the documentation for the specific module you are using or reach out to the module’s support team for assistance.
Installing requirements for Web UI
Launching Web UI with arguments:
Warning: experimental graphic memory optimization for AMDGPU is disabled. Because there is an unknown error.
Disabled experimental graphic memory optimizations.
Interrogations are fallen back to cpu. This doesn’t affect on image generation. But if you want to use interrogate (CLIP or DeepBooru), check out this issue: https://github.com/lshqqytiger/stable-diffusion-webui-directml/issues/10
Warning: caught exception ‘Torch not compiled with CUDA enabled’, memory monitor disabled
No module ‘xformers’. Proceeding without it.
This message indicates that some of the required modules or packages are missing, and the installation of those modules is needed. The installation can be performed using the pip package manager. You can run the following command in the terminal to install the required packages:

“`
pip install -r requirements.txt
“`

This command will install all the required packages mentioned in the requirements.txt file. After installation, you can try running the Web UI again to check if the issue has been resolved.
Where is Share=True in this script? # this scripts installs necessary requirements and launches main program in webui.py
import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json

from modules import cmd_args
from modules.paths_internal import script_path, extensions_dir

commandline_args = os.environ.get(‘COMMANDLINE_ARGS’, “”)
sys.argv += shlex.split(commandline_args)

args, _ = cmd_args.parser.parse_known_args()

python = sys.executable
git = os.environ.get(‘GIT’, “git”)
index_url = os.environ.get(‘INDEX_URL’, “”)
stored_commit_hash = None
skip_install = False
dir_repos = “repositories”

if ‘GRADIO_ANALYTICS_ENABLED’ not in os.environ:
os.environ[‘GRADIO_ANALYTICS_ENABLED’] = ‘False’

def check_python_version():
is_windows = platform.system() == “Windows”
major = sys.version_info.major
minor = sys.version_info.minor
micro = sys.version_info.micro

if is_windows:
supported_minors = [10]
else:
supported_minors = [7, 8, 9, 10, 11]

if not (major == 3 and minor in supported_minors):
import modules.errors

modules.errors.print_error_explanation(f”””
INCOMPATIBLE PYTHON VERSION

This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
If you encounter an error with “RuntimeError: Couldn’t install torch.” message,
or any other error regarding unsuccessful package (library) installation,
please downgrade (or upgrade) to the latest version of 3.10 Python
and delete current Python and “venv” folder in WebUI’s directory.

You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3109/

{“Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases” if is_windows else “”}

Use –skip-python-version-check to suppress this warning.
“””)

def commit_hash():
global stored_commit_hash

if stored_commit_hash is not None:
return stored_commit_hash

try:
stored_commit_hash = run(f”{git} rev-parse HEAD”).strip()
except Exception:
stored_commit_hash = “”

return stored_commit_hash

def run(command, desc=None, errdesc=None, custom_env=None, live=False):
if desc is not None:
print(desc)

if live:
result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
if result.returncode != 0:
raise RuntimeError(f”””{errdesc or ‘Error running command’}.
Command: {command}
Error code: {result.returncode}”””)

return “”

result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)

if result.returncode != 0:

message = f”””{errdesc or ‘Error running command’}.
Command: {command}
Error code: {result.returncode}
stdout: {result.stdout.decode(encoding=”utf8″, errors=”ignore”) if len(result.stdout)>0 else ”}
stderr: {result.stderr.decode(encoding=”utf8″, errors=”ignore”) if len(result.stderr)>0 else ”}
“””
raise RuntimeError(message)

return result.stdout.decode(encoding=”utf8″, errors=”ignore”)

def check_run(command):
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
return result.returncode == 0

def is_installed(package):
try:
spec = importlib.util.find_spec(package)
except ModuleNotFoundError:
return False

return spec is not None

def repo_dir(name):
return os.path.join(script_path, dir_repos, name)

def run_python(code, desc=None, errdesc=None):
return run(f'”{python}” -c “{code}”‘, desc, errdesc)

def run_pip(args, desc=None):
if skip_install:
return

index_url_line = f’ –index-url {index_url}’ if index_url != ” else ”
return run(f'”{python}” -m pip {args} –prefer-binary{index_url_line}’, desc=f”Installing {desc}”, errdesc=f”Couldn’t install {desc}”)

def check_run_python(code):
return check_run(f'”{python}” -c “{code}”‘)

def git_clone(url, dir, name, commithash=None):
# TODO clone into temporary dir and move if successful

if os.path.exists(dir):
if commithash is None:
return

current_hash = run(f'”{git}” -C “{dir}” rev-parse HEAD’, None, f”Couldn’t determine {name}’s hash: {commithash}”).strip()
if current_hash == commithash:
return

run(f'”{git}” -C “{dir}” fetch’, f”Fetching updates for {name}…”, f”Couldn’t fetch {name}”)
run(f'”{git}” -C “{dir}” checkout {commithash}’, f”Checking out commit for {name} with hash: {commithash}…”, f”Couldn’t checkout commit {commithash} for {name}”)
return

run(f'”{git}” clone “{url}” “{dir}”‘, f”Cloning {name} into {dir}…”, f”Couldn’t clone {name}”)

if commithash is not None:
run(f'”{git}” -C “{dir}” checkout {commithash}’, None, “Couldn’t checkout {name}’s hash: {commithash}”)

def git_pull_recursive(dir):
for subdir, _, _ in os.walk(dir):
if os.path.exists(os.path.join(subdir, ‘.git’)):
try:
output = subprocess.check_output([git, ‘-C’, subdir, ‘pull’, ‘–autostash’])
print(f”Pulled changes for repository in ‘{subdir}’:n{output.decode(‘utf-8’).strip()}n”)
except subprocess.CalledProcessError as e:
print(f”Couldn’t perform ‘git pull’ on repository in ‘{subdir}’:n{e.output.decode(‘utf-8’).strip()}n”)

def version_check(commit):
try:
import requests
commits = requests.get(‘https://api.github.com/repos/lshqqytiger/stable-diffusion-webui-directml/branches/master’).json()
if commit != “” and commits[‘commit’][‘sha’] != commit:
print(“——————————————————–“)
print(“| You are not up to date with the most recent release. |”)
print(“| Consider running `git pull` to update. |”)
print(“——————————————————–“)
elif commits[‘commit’][‘sha’] == commit:
print(“You are up to date with the most recent release.”)
else:
print(“Not a git clone, can’t perform version check.”)
except Exception as e:
print(“version check failed”, e)

def run_extension_installer(extension_dir):
path_installer = os.path.join(extension_dir, “install.py”)
if not os.path.isfile(path_installer):
return

try:
env = os.environ.copy()
env[‘PYTHONPATH’] = os.path.abspath(“.”)

print(run(f'”{python}” “{path_installer}”‘, errdesc=f”Error running install.py for extension {extension_dir}”, custom_env=env))
except Exception as e:
print(e, file=sys.stderr)

def list_extensions(settings_file):
settings = {}

try:
if os.path.isfile(settings_file):
with open(settings_file, “r”, encoding=”utf8″) as file:
settings = json.load(file)
except Exception as e:
print(e, file=sys.stderr)

disabled_extensions = set(settings.get(‘disabled_extensions’, []))
disable_all_extensions = settings.get(‘disable_all_extensions’, ‘none’)

if disable_all_extensions != ‘none’:
return []

return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]

def run_extensions_installers(settings_file):
if not os.path.isdir(extensions_dir):
return

for dirname_extension in list_extensions(settings_file):
run_extension_installer(os.path.join(extensions_dir, dirname_extension))

def prepare_environment():
global skip_install

pip_installer_location = os.environ.get(‘PIP_INSTALLER_LOCATION’, None)

torch_command = os.environ.get(‘TORCH_COMMAND’, “pip install torch==1.13.1 torchvision==0.14.1 torch-directml”)
requirements_file = os.environ.get(‘REQS_FILE’, “requirements_versions.txt”)

xformers_package = os.environ.get(‘XFORMERS_PACKAGE’, ‘xformers==0.0.16rc425’)
gfpgan_package = os.environ.get(‘GFPGAN_PACKAGE’, “git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379”)
clip_package = os.environ.get(‘CLIP_PACKAGE’, “git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1”)
openclip_package = os.environ.get(‘OPENCLIP_PACKAGE’, “git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b”)

xformers_windows_package = os.environ.get(‘XFORMERS_WINDOWS_PACKAGE’, ‘https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl’)

taming_transformers_repo = os.environ.get(‘TAMING_TRANSFORMERS_REPO’, “https://github.com/CompVis/taming-transformers.git”)
codeformer_repo = os.environ.get(‘CODEFORMER_REPO’, ‘https://github.com/sczhou/CodeFormer.git’)
blip_repo = os.environ.get(‘BLIP_REPO’, ‘https://github.com/salesforce/BLIP.git’)
sd_repo = os.environ.get(‘BLIP_REPO’, ‘https://github.com/lshqqytiger/stablediffusion-directml.git’)
k_diffusion_repo = os.environ.get(‘BLIP_REPO’, ‘https://github.com/lshqqytiger/k-diffusion-directml.git’)

taming_transformers_commit_hash = os.environ.get(‘TAMING_TRANSFORMERS_COMMIT_HASH’, “24268930bf1dce879235a7fddd0b2355b84d7ea6”)
codeformer_commit_hash = os.environ.get(‘CODEFORMER_COMMIT_HASH’, “c5b4593074ba6214284d6acd5f1719b6c5d739af”)
blip_commit_hash = os.environ.get(‘BLIP_COMMIT_HASH’, “48211a1594f1321b00f14c9f7a5b4813144b2fb9″)

if not args.skip_python_version_check:
check_python_version()

commit = commit_hash()

print(f”Python {sys.version}”)
print(f”Commit hash: {commit}”)

if args.reinstall_torch or not is_installed(“torch”) or not is_installed(“torchvision”):
run(f'”{python}” -m {torch_command}’, “Installing torch and torchvision”, “Couldn’t install torch”, live=True)

if not is_installed(“gfpgan”):
run_pip(f”install {gfpgan_package}”, “gfpgan”)

if not is_installed(“clip”):
run_pip(f”install {clip_package}”, “clip”)

if not is_installed(“open_clip”):
run_pip(f”install {openclip_package}”, “open_clip”)

if (not is_installed(“xformers”) or args.reinstall_xformers) and args.xformers:
if platform.system() == “Windows”:
if platform.python_version().startswith(“3.10″):
run_pip(f”install -U -I –no-deps {xformers_package}”, “xformers”)
else:
print(“Installation of xformers is not supported in this version of Python.”)
print(“You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness”)
if not is_installed(“xformers”):
exit(0)
elif platform.system() == “Linux”:
run_pip(f”install {xformers_package}”, “xformers”)

if not is_installed(“pyngrok”) and args.ngrok:
run_pip(“install pyngrok”, “ngrok”)

os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)

git_clone(taming_transformers_repo, repo_dir(‘taming-transformers’), “Taming Transformers”, taming_transformers_commit_hash)
git_clone(codeformer_repo, repo_dir(‘CodeFormer’), “CodeFormer”, codeformer_commit_hash)
git_clone(blip_repo, repo_dir(‘BLIP’), “BLIP”, blip_commit_hash)
git_clone(sd_repo, repo_dir(‘stable-diffusion-stability-ai’), “stable-diffusion-stability-ai”)
git_clone(k_diffusion_repo, repo_dir(‘k-diffusion’), “k-diffusion”)

if not is_installed(“lpips”):
run_pip(f”install -r “{os.path.join(repo_dir(‘CodeFormer’), ‘requirements.txt’)}””, “requirements for CodeFormer”)

if not os.path.isfile(requirements_file):
requirements_file = os.path.join(script_path, requirements_file)
run_pip(f”install -r “{requirements_file}””, “requirements for Web UI”)

run_extensions_installers(settings_file=args.ui_settings_file)

if args.update_check:
version_check(commit)

if args.update_all_extensions:
git_pull_recursive(extensions_dir)

if “–exit” in sys.argv:
print(“Exiting because of –exit argument”)
exit(0)

if args.tests and not args.no_tests:
exitcode = tests(args.tests)
exit(exitcode)

def tests(test_dir):
if “–api” not in sys.argv:
sys.argv.append(“–api”)
if “–ckpt” not in sys.argv:
sys.argv.append(“–ckpt”)
sys.argv.append(os.path.join(script_path, “test/test_files/empty.pt”))
if “–disable-nan-check” not in sys.argv:
sys.argv.append(“–disable-nan-check”)
if “–no-tests” not in sys.argv:
sys.argv.append(“–no-tests”)

print(f”Launching Web UI in another process for testing with arguments: {‘ ‘.join(sys.argv[1:])}”)

os.environ[‘COMMANDLINE_ARGS’] = “”
with open(os.path.join(script_path, ‘test/stdout.txt’), “w”, encoding=”utf8″) as stdout, open(os.path.join(script_path, ‘test/stderr.txt’), “w”, encoding=”utf8″) as stderr:
proc = subprocess.Popen([sys.executable, *sys.argv], stdout=stdout, stderr=stderr)

import test.server_poll
exitcode = test.server_poll.run_tests(proc, test_dir)

print(f”Stopping Web UI process with id {proc.pid}”)
proc.kill()
return exitcode

def start():
print(f”Launching {‘API server’ if ‘–nowebui’ in sys.argv else ‘Web UI’} with arguments: {‘ ‘.join(sys.argv[1:])}”)
import webui
if ‘–nowebui’ in sys.argv:
webui.api_only()
else:
webui.webui()

if __name__ == “__main__”:
prepare_environment()
start()

The script doesn’t have a `Share=True` statement. Are you referring to a specific module or function that should have this argument?
I want to create a clickable link
To create a clickable link, you can use HTML. Here’s an example:

“`python
url = “https://www.example.com”
link_text = “Click here to visit example.com”
link = f’{link_text}‘
print(link)
“`

This will output a string containing an HTML link that looks like this:

“`html
Click here to visit example.com
“`

You can use this string in any context that supports HTML, such as a web page or an email.
Of the address where the program
I’m sorry, I’m not sure I understand your question. Could you please provide more context or clarify your request?
Where does To create a public link, set `share=True` in `launch()`.
Startup time: 59.8s (import torch: 7.0s, import gradio: 5.6s, import ldm: 3.5s, other imports: 5.4s, setup codeformer: 0.3s, load scripts: 1.0s, load SD checkpoint: 36.5s, create ui: 0.3s, gradio launch: 0.2s).
55%|█████████████████████████████████████████████ | 11/20 [00:23<00:19, 2.15s/it]
Error completing request█████████████████████████████████▎ | 11/20 [00:08<00:06, 1.39it/s]
Arguments: ('task(39uqliji45eptbf)', 'young girl in dress', '', [], 20, 0, False, False, 1, 1, 7, -1.0, -1.0, 0, 0, 0, False, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, [], 0, False, False, 'positive', 'comma', 0, False, False, '', 1, '', 0, '', 0, '', True, False, False, False, 0) {}
Traceback (most recent call last):
File "E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulescall_queue.py", line 56, in f
res = list(func(*args, **kwargs))
File "E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulescall_queue.py", line 37, in f
res = func(*args, **kwargs)
File "E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulestxt2img.py", line 56, in txt2img
processed = process_images(p)
File "E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulesprocessing.py", line 503, in process_images
res = process_images_inner(p)
File "E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulesprocessing.py", line 653, in process_images_inner
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
File "E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulesprocessing.py", line 869, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulessd_samplers_kdiffusion.py", line 358, in sample
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
File "E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulessd_samplers_kdiffusion.py", line 234, in launch_sampling
return func()
File "E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulessd_samplers_kdiffusion.py", line 358, in
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlvenvlibsite-packagestorchautogradgrad_mode.py”, line 27, in decorate_context
return func(*args, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesk-diffusionk_diffusionsampling.py”, line 150, in sample_euler_ancestral
denoised = model(x, sigmas[i] * s_in, **extra_args)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlvenvlibsite-packagestorchnnmodulesmodule.py”, line 1194, in _call_impl
return forward_call(*input, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulessd_samplers_kdiffusion.py”, line 126, in forward
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlvenvlibsite-packagestorchnnmodulesmodule.py”, line 1194, in _call_impl
return forward_call(*input, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesk-diffusionk_diffusionexternal.py”, line 112, in forward
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesk-diffusionk_diffusionexternal.py”, line 138, in get_eps
return self.inner_model.apply_model(*args, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulessd_hijack_utils.py”, line 17, in
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulessd_hijack_utils.py”, line 28, in __call__
return self.__orig_func(*args, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesstable-diffusion-stability-aildmmodelsdiffusionddpm.py”, line 858, in apply_model
x_recon = self.model(x_noisy, t, **cond)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlvenvlibsite-packagestorchnnmodulesmodule.py”, line 1194, in _call_impl
return forward_call(*input, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesstable-diffusion-stability-aildmmodelsdiffusionddpm.py”, line 1335, in forward
out = self.diffusion_model(x, t, context=cc)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlvenvlibsite-packagestorchnnmodulesmodule.py”, line 1194, in _call_impl
return forward_call(*input, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesstable-diffusion-stability-aildmmodulesdiffusionmodulesopenaimodel.py”, line 797, in forward
h = module(h, emb, context)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlvenvlibsite-packagestorchnnmodulesmodule.py”, line 1194, in _call_impl
return forward_call(*input, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesstable-diffusion-stability-aildmmodulesdiffusionmodulesopenaimodel.py”, line 84, in forward
x = layer(x, context)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlvenvlibsite-packagestorchnnmodulesmodule.py”, line 1194, in _call_impl
return forward_call(*input, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesstable-diffusion-stability-aildmmodulesattention.py”, line 334, in forward
x = block(x, context=context[i])
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlvenvlibsite-packagestorchnnmodulesmodule.py”, line 1194, in _call_impl
return forward_call(*input, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesstable-diffusion-stability-aildmmodulesattention.py”, line 269, in forward
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesstable-diffusion-stability-aildmmodulesdiffusionmodulesutil.py”, line 121, in checkpoint
return CheckpointFunction.apply(func, len(inputs), *args)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesstable-diffusion-stability-aildmmodulesdiffusionmodulesutil.py”, line 136, in forward
output_tensors = ctx.run_function(*ctx.input_tensors)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlrepositoriesstable-diffusion-stability-aildmmodulesattention.py”, line 272, in _forward
x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlvenvlibsite-packagestorchnnmodulesmodule.py”, line 1194, in _call_impl
return forward_call(*input, **kwargs)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulessd_hijack_optimizations.py”, line 246, in split_cross_attention_forward_invokeAI
r = einsum_op(q, k, v)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulessd_hijack_optimizations.py”, line 225, in einsum_op
return einsum_op_tensor_mem(q, k, v, 32)
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulessd_hijack_optimizations.py”, line 194, in einsum_op_tensor_mem
return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
File “E:GPT4ALLSTABLEDIFFWEBUIstable-diffusion-webui-directmlstable-diffusion-webui-directmlmodulessd_hijack_optimizations.py”, line 169, in einsum_op_slice_1
r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
RuntimeError: Could not allocate tensor with 9850880 bytes. There is not enough GPU video memory available!
It seems that an error occurred while running a text-to-image conversion model. Can you please provide more context on what you were trying to achieve and what steps you have taken so far? Additionally, it would be helpful if you could provide the full error message and any relevant code or inputs used.
RuntimeError: Could not allocate tensor with 9850880 bytes. There is not enough GPU video memory available!
Time taken: 5m 32.91s
This error message is indicating that there is not enough video memory available on the GPU to allocate a tensor of the required size. This could happen if you are trying to run a deep learning model that requires a large amount of GPU memory, but your GPU does not have enough memory to handle it.

There are a few things you can try to resolve this issue:

1. Reduce the batch size: One way to reduce the memory footprint of your model is to reduce the batch size during training. This will result in smaller tensors being processed at each step, which may help to reduce the overall memory usage.

2. Use a smaller model: If your current model is too large for your GPU, you could try using a smaller model that requires less memory. This may involve sacrificing some performance, but it could be a good option if you are running into memory issues.

3. Use a GPU with more memory: If you are using a GPU with limited memory, you could consider upgrading to a GPU with more memory. This will allow you to train larger models and handle more complex tasks.

4. Free up GPU memory: If you are running other programs that are using the GPU, you may want to close them to free up more memory for your deep learning model.

5. Use mixed precision training: Another option to reduce memory usage is to use mixed precision training. This technique uses half-precision (float16) for some of the calculations, which reduces memory usage by half. However, this may require modifications to your code.

Overall, it’s important to carefully consider the memory requirements of your deep learning model and the resources available on your GPU to avoid running into this error.
sampling.py”, line 213, in sample_dpm_2
sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp()
RuntimeError: “lerp_kernel_scalar” not implemented for ‘Half’
This error message suggests that there is a problem with the data type of the tensors used in the `lerp()` function call. It seems that the data type used in the function call is `Half`, which is not supported by the `lerp()` function.

One possible solution is to convert the tensor to a supported data type before passing it to the `lerp()` function. You can do this by calling the `float()` method on the tensor, like this:

“`
sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log().float(), 0.5).exp()
“`

Alternatively, you can try to use a different interpolation function that supports the `Half` data type, or you can change the data type of the tensors to a different type that is supported by the `lerp()` function.