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Slipstream 2025-05-04 16:42:45 -06:00
parent f341f7e351
commit 45e65c6de7
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229
download_illustrious.py
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@ -7,6 +7,8 @@ import shutil
import argparse
from tqdm import tqdm
import time
import subprocess
import huggingface_hub
# Illustrious XL model information
MODEL_ID = 795765
@ -15,6 +17,9 @@ MODEL_VERSION = 1 # Version 1.0
MODEL_URL = "https://civitai.com/api/download/models/795765"
MODEL_INFO_URL = f"https://civitai.com/api/v1/models/{MODEL_ID}"
# Base SDXL model from HuggingFace (we'll use this as a base and replace the unet)
SDXL_BASE_MODEL = "stabilityai/stable-diffusion-xl-base-1.0"
def download_file(url, destination, filename=None):
"""Download a file with progress bar"""
if filename is None:
@ -38,14 +43,144 @@ def download_file(url, destination, filename=None):
return local_filename
def create_model_index(model_dir):
"""Create a model_index.json file for the diffusers library"""
def download_from_huggingface(repo_id, local_dir, component=None):
"""Download a model from HuggingFace"""
try:
# Use huggingface_hub to download the model
if component:
print(f"Downloading {component} from {repo_id}...")
huggingface_hub.snapshot_download(
repo_id=repo_id,
local_dir=local_dir,
local_dir_use_symlinks=False,
allow_patterns=f"{component}/*"
)
else:
print(f"Downloading full model from {repo_id}...")
huggingface_hub.snapshot_download(
repo_id=repo_id,
local_dir=local_dir,
local_dir_use_symlinks=False
)
return True
except Exception as e:
print(f"Error downloading from HuggingFace: {e}")
return False
def download_illustrious_xl():
"""Download and set up the Illustrious XL model"""
# Set up directories
script_dir = os.path.dirname(os.path.abspath(__file__))
models_dir = os.path.join(script_dir, "models")
illustrious_dir = os.path.join(models_dir, "illustrious_xl")
temp_dir = os.path.join(models_dir, "temp")
# Create directories if they don't exist
os.makedirs(models_dir, exist_ok=True)
os.makedirs(temp_dir, exist_ok=True)
# Check if model already exists
if os.path.exists(os.path.join(illustrious_dir, "unet", "diffusion_pytorch_model.safetensors")) and \
os.path.getsize(os.path.join(illustrious_dir, "unet", "diffusion_pytorch_model.safetensors")) > 100000000: # Check if file is larger than 100MB
print(f"⚠️ {MODEL_NAME} model already exists at {illustrious_dir}")
choice = input("Do you want to re-download and reinstall the model? (y/n): ")
if choice.lower() != 'y':
print("Download cancelled.")
return
# Remove existing model
print(f"Removing existing {MODEL_NAME} model...")
shutil.rmtree(illustrious_dir, ignore_errors=True)
# Create illustrious directory
os.makedirs(illustrious_dir, exist_ok=True)
# Get model info from Civitai API
print(f"Fetching information about {MODEL_NAME} from Civitai...")
try:
response = requests.get(MODEL_INFO_URL)
response.raise_for_status()
model_info = response.json()
# Save model info for reference
with open(os.path.join(illustrious_dir, "model_info.json"), "w") as f:
json.dump(model_info, f, indent=2)
print(f"Model: {model_info['name']} by {model_info['creator']['username']}")
if 'description' in model_info:
print(f"Description: {model_info['description'][:100]}...")
except Exception as e:
print(f"⚠️ Failed to fetch model info: {e}")
print("Continuing with download anyway...")
# First, download the base SDXL model from HuggingFace
print(f"Step 1: Downloading base SDXL model from HuggingFace...")
print("This will download the VAE, text encoders, and tokenizers needed for the model.")
print("This may take a while (several GB of data)...")
# Download each component separately to avoid downloading the full model
components = ["vae", "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "scheduler"]
for component in components:
success = download_from_huggingface(SDXL_BASE_MODEL, illustrious_dir, component)
if not success:
print(f"Failed to download {component} from HuggingFace.")
print("Trying alternative method...")
# Try using diffusers to download the model
try:
print(f"Installing diffusers if not already installed...")
subprocess.check_call([sys.executable, "-m", "pip", "install", "diffusers"])
# Use Python to download the model components
from diffusers import StableDiffusionXLPipeline
print(f"Downloading {component} using diffusers...")
# Create a temporary directory for the download
temp_model_dir = os.path.join(temp_dir, "sdxl_base")
os.makedirs(temp_model_dir, exist_ok=True)
# Download only the specified components
StableDiffusionXLPipeline.from_pretrained(
SDXL_BASE_MODEL,
torch_dtype="float16",
variant="fp16",
use_safetensors=True,
cache_dir=temp_model_dir
)
# Copy the component to the illustrious directory
component_dir = os.path.join(temp_model_dir, component)
if os.path.exists(component_dir):
shutil.copytree(component_dir, os.path.join(illustrious_dir, component), dirs_exist_ok=True)
print(f"Successfully copied {component} to {illustrious_dir}")
else:
print(f"Could not find {component} in downloaded model.")
except Exception as e:
print(f"Error using diffusers to download {component}: {e}")
print("You may need to manually download the SDXL base model and copy the components.")
# Now download the Illustrious XL model from Civitai
print(f"\nStep 2: Downloading {MODEL_NAME} from Civitai...")
try:
# Download to temp directory
model_file = download_file(MODEL_URL, temp_dir, "illustrious_xl.safetensors")
# Create the unet directory if it doesn't exist
os.makedirs(os.path.join(illustrious_dir, "unet"), exist_ok=True)
# Move the model file to the unet directory
print(f"Moving {MODEL_NAME} model to the unet directory...")
shutil.move(model_file, os.path.join(illustrious_dir, "unet", "diffusion_pytorch_model.safetensors"))
# Create a model_index.json file
print("Creating model_index.json file...")
model_index = {
"_class_name": "StableDiffusionXLPipeline",
"_diffusers_version": "0.21.4",
"force_zeros_for_empty_prompt": True,
"scheduler": [
{
"scheduler": {
"_class_name": "DPMSolverMultistepScheduler",
"_diffusers_version": "0.21.4",
"beta_end": 0.012,
@ -59,8 +194,7 @@ def create_model_index(model_dir):
"timestep_spacing": "leading",
"trained_betas": None,
"use_karras_sigmas": True
}
],
},
"text_encoder": [
{
"_class_name": "CLIPTextModel",
@ -91,77 +225,9 @@ def create_model_index(model_dir):
}
}
with open(os.path.join(model_dir, "model_index.json"), "w") as f:
with open(os.path.join(illustrious_dir, "model_index.json"), "w") as f:
json.dump(model_index, f, indent=2)
def download_illustrious_xl():
"""Download and set up the Illustrious XL model"""
# Set up directories
script_dir = os.path.dirname(os.path.abspath(__file__))
models_dir = os.path.join(script_dir, "models")
illustrious_dir = os.path.join(models_dir, "illustrious_xl")
temp_dir = os.path.join(models_dir, "temp")
# Create directories if they don't exist
os.makedirs(models_dir, exist_ok=True)
os.makedirs(temp_dir, exist_ok=True)
# Check if model already exists
if os.path.exists(os.path.join(illustrious_dir, "model_index.json")):
print(f"⚠️ {MODEL_NAME} model already exists at {illustrious_dir}")
choice = input("Do you want to re-download and reinstall the model? (y/n): ")
if choice.lower() != 'y':
print("Download cancelled.")
return
# Remove existing model
print(f"Removing existing {MODEL_NAME} model...")
shutil.rmtree(illustrious_dir, ignore_errors=True)
# Create illustrious directory
os.makedirs(illustrious_dir, exist_ok=True)
# Get model info from Civitai API
print(f"Fetching information about {MODEL_NAME} from Civitai...")
try:
response = requests.get(MODEL_INFO_URL)
response.raise_for_status()
model_info = response.json()
# Save model info for reference
with open(os.path.join(illustrious_dir, "model_info.json"), "w") as f:
json.dump(model_info, f, indent=2)
print(f"Model: {model_info['name']} by {model_info['creator']['username']}")
print(f"Description: {model_info['description'][:100]}...")
except Exception as e:
print(f"⚠️ Failed to fetch model info: {e}")
print("Continuing with download anyway...")
# Download the model
print(f"Downloading {MODEL_NAME} from Civitai...")
try:
# Download to temp directory
model_file = download_file(MODEL_URL, temp_dir, "illustrious_xl.safetensors")
# Move the file to the model directory
print(f"Setting up {MODEL_NAME} model...")
# Create the necessary directory structure for diffusers
os.makedirs(os.path.join(illustrious_dir, "unet"), exist_ok=True)
os.makedirs(os.path.join(illustrious_dir, "vae"), exist_ok=True)
os.makedirs(os.path.join(illustrious_dir, "text_encoder"), exist_ok=True)
os.makedirs(os.path.join(illustrious_dir, "text_encoder_2"), exist_ok=True)
os.makedirs(os.path.join(illustrious_dir, "tokenizer"), exist_ok=True)
os.makedirs(os.path.join(illustrious_dir, "tokenizer_2"), exist_ok=True)
# Move the model file to the unet directory
shutil.move(model_file, os.path.join(illustrious_dir, "unet", "diffusion_pytorch_model.safetensors"))
# Create a model_index.json file
create_model_index(illustrious_dir)
# Create a README.md file with information about the model
with open(os.path.join(illustrious_dir, "README.md"), "w") as f:
f.write(f"# {MODEL_NAME}\n\n")
@ -169,7 +235,18 @@ def download_illustrious_xl():
f.write("This model requires the diffusers library to use.\n")
f.write("Use the /generate command in the Discord bot to generate images with this model.\n")
print(f"✅ {MODEL_NAME} model has been downloaded and set up successfully!")
# Check if the model file is large enough (should be several GB)
unet_file = os.path.join(illustrious_dir, "unet", "diffusion_pytorch_model.safetensors")
if os.path.exists(unet_file):
file_size_gb = os.path.getsize(unet_file) / (1024 * 1024 * 1024)
print(f"Model file size: {file_size_gb:.2f} GB")
if file_size_gb < 1.0:
print(f"⚠️ Warning: Model file seems too small ({file_size_gb:.2f} GB). It may not be complete.")
print("The download might have been interrupted or the model might not be the full version.")
print("You may want to try downloading again with the --force flag.")
print(f"\n✅ {MODEL_NAME} model has been downloaded and set up successfully!")
print(f"Model location: {illustrious_dir}")
print("You can now use the model with the /generate command in the Discord bot.")

111
manual_download_instructions.md Normal file
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@ -0,0 +1,111 @@
# Manual Download Instructions for Illustrious XL
If the automatic download script fails, you can manually download and set up the Illustrious XL model by following these steps:
## Step 1: Download the Model Files
1. Download the Illustrious XL model from Civitai:
- Go to: https://civitai.com/models/795765/illustrious-xl
- Click the "Download" button to download the model file
- Save the file as `illustrious_xl.safetensors`
2. Download the base SDXL model components from Hugging Face:
- Go to: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
- Download the following files/folders:
- `vae` folder
- `text_encoder` folder
- `text_encoder_2` folder
- `tokenizer` folder
- `tokenizer_2` folder
- `scheduler` folder
## Step 2: Set Up the Directory Structure
1. Create the following directory structure in your Discord bot folder:
```
discordbot/
└── models/
└── illustrious_xl/
├── unet/
├── vae/
├── text_encoder/
├── text_encoder_2/
├── tokenizer/
└── tokenizer_2/
```
2. Place the downloaded files in the appropriate directories:
- Move `illustrious_xl.safetensors` to `discordbot/models/illustrious_xl/unet/` and rename it to `diffusion_pytorch_model.safetensors`
- Copy the contents of the downloaded `vae` folder to `discordbot/models/illustrious_xl/vae/`
- Copy the contents of the downloaded `text_encoder` folder to `discordbot/models/illustrious_xl/text_encoder/`
- Copy the contents of the downloaded `text_encoder_2` folder to `discordbot/models/illustrious_xl/text_encoder_2/`
- Copy the contents of the downloaded `tokenizer` folder to `discordbot/models/illustrious_xl/tokenizer/`
- Copy the contents of the downloaded `tokenizer_2` folder to `discordbot/models/illustrious_xl/tokenizer_2/`
## Step 3: Create the Model Index File
Create a file named `model_index.json` in the `discordbot/models/illustrious_xl/` directory with the following content:
```json
{
"_class_name": "StableDiffusionXLPipeline",
"_diffusers_version": "0.21.4",
"force_zeros_for_empty_prompt": true,
"scheduler": {
"_class_name": "DPMSolverMultistepScheduler",
"_diffusers_version": "0.21.4",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"num_train_timesteps": 1000,
"prediction_type": "epsilon",
"solver_order": 2,
"solver_type": "midpoint",
"thresholding": false,
"timestep_spacing": "leading",
"trained_betas": null,
"use_karras_sigmas": true
},
"text_encoder": [
{
"_class_name": "CLIPTextModel",
"_diffusers_version": "0.21.4"
},
{
"_class_name": "CLIPTextModelWithProjection",
"_diffusers_version": "0.21.4"
}
],
"tokenizer": [
{
"_class_name": "CLIPTokenizer",
"_diffusers_version": "0.21.4"
},
{
"_class_name": "CLIPTokenizer",
"_diffusers_version": "0.21.4"
}
],
"unet": {
"_class_name": "UNet2DConditionModel",
"_diffusers_version": "0.21.4"
},
"vae": {
"_class_name": "AutoencoderKL",
"_diffusers_version": "0.21.4"
}
}
```
## Step 4: Verify the Installation
1. Check that the directory structure is correct and all files are in place
2. Make sure the `diffusion_pytorch_model.safetensors` file in the `unet` directory is large (should be several GB)
3. Restart your Discord bot
4. Use the `/generate` command to test if the model works correctly
## Troubleshooting
- If you get errors about missing files, make sure all the required components are downloaded and placed in the correct directories
- If you get CUDA out-of-memory errors, try reducing the image dimensions (e.g., 768x768 instead of 1024x1024)
- If the model is not showing up in the `/sd_models` command, make sure the directory structure and file names are exactly as specified above

1
requirements.txt
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@ -36,3 +36,4 @@ accelerate
tqdm
safetensors
xformers
huggingface_hub