feat: Add headless CLI mode with advanced generation options
- New CLI mode: generate images without launching the WebUI - Supports: models, LoRAs, styles, samplers, schedulers, CFG, steps - Fixed CLIP model compatibility (transformers vs ldm_patched signatures) - Fixed PyTorch 2.7 tensor conversion in model_sampling.py - Added comprehensive CLI documentation (README_CLI.md)
This commit is contained in:
Mohamed Elnabarawi
parent
ae05379cc9
commit
56fd7eb8fb
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@ -0,0 +1,84 @@
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# Fooocus CLI (Command Line Interface)
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This is a custom Command Line Interface for Fooocus, allowing you to generate images programmatically without launching the web browser. It supports advanced features like model selection, custom styles, LoRAs, and batch generation.
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## 🚀 Quick Start
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From the Fooocus directory, run:
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```bash
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python entry_with_update.py --prompt "a beautiful landscape" --output "landscape.png"
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```
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For Windows Standalone users (assuming you are in the install folder):
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```powershell
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.\python_embeded\python.exe Fooocus\entry_with_update.py --prompt "test"
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```
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## 🛠️ Usage & Arguments
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### Basic Usage
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```bash
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python entry_with_update.py --prompt "your prompt" --aspect-ratio "1024x1024"
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```
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### Advanced Usage
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```bash
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python entry_with_update.py ^
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--prompt "cyberpunk detective, neon rain" ^
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--negative-prompt "bright, sunny" ^
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--steps 30 ^
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--cfg-scale 7.0 ^
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--style "Fooocus V2" --style "Fooocus Cyberpunk" ^
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--base-model "juggernautXL_v8Rundiffusion.safetensors" ^
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--lora "sd_xl_offset_example-lora_1.0.safetensors:0.5" ^
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--image-number 2 ^
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--output "detective.png"
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```
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### Argument Reference
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| Category | Argument | Description | Default |
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| :--- | :--- | :--- | :--- |
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| **Core** | `--prompt` | **(Required)** The positive prompt text. | N/A |
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| | `--negative-prompt` | valid negative prompt text. | "" |
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| | `--output` | Output filename. Relative command run location. | N/A |
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| | `--seed` | Seed number for reproducibility. `-1` is random. | -1 |
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| | `--image-number` | Number of images to generate in a row. | 1 |
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| **Performance** | `--performance` | Preset: `Speed`, `Quality`, `Extreme Speed`. | Speed |
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| | `--steps` | Exact number of sampling steps (overrides performance). | N/A |
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| | `--aspect-ratio` | Dimensions (e.g., `1152x896`, `1024x1024`). | 1152x896 |
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| | `--cfg-scale` | Guidance scale (how strictly to follow prompt). | 7.0 |
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| | `--sharpness` | Image sharpness filter strength. | 2.0 |
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| | `--sampler` | Sampler method name. | dpmpp_2m_sde_gpu |
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| | `--scheduler` | Scheduler name. | karras |
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| **Models** | `--base-model` | Filename of the base checkpoint. | (Config Default) |
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| | `--refiner-model` | Filename of the refiner checkpoint. | (Config Default) |
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| | `--refiner-switch` | Step ratio to switch to refiner (0.0-1.0). | 0.5 |
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| | `--lora` | Load LoRA: `filename:weight`. Use flag multiple times. | N/A |
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| **Styles** | `--style` | Style name. Use flag multiple times. | (Fooocus Defaults) |
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## 🤖 AI Agent Integration
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If you want to teach an AI agent to use this tool, provide it with the following specification:
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### Tool: `generate_image_fooocus`
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**Description:** Generates images locally using Fooocus via CLI.
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**Execution:** `d:\Fooocus_win64_2-5-0\fooocus-cli.bat`
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**Parameters:**
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* `prompt`: String (Required)
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* `negative_prompt`: String
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* `output`: String (Filename)
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* `aspect_ratio`: String (e.g., "1024x1024")
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* `base_model`: String (Checkpoint filename)
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* `style`: List[String] (Style names)
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* `lora`: List[String] (Format "name:weight")
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* `steps`: Integer
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* `cfg_scale`: Float
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**Notes:**
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* Output is saved relative to `d:\Fooocus_win64_2-5-0\Fooocus\` if a relative path is given in python, but the batch wrapper usually handles CWD. Absolute paths are recommended for the `--output` argument to ensure files are saved exactly where intended.
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@ -40,6 +40,25 @@ args_parser.parser.add_argument("--always-download-new-model", action='store_tru
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args_parser.parser.add_argument("--rebuild-hash-cache", help="Generates missing model and LoRA hashes.",
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type=int, nargs="?", metavar="CPU_NUM_THREADS", const=-1)
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# CLI Arguments
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args_parser.parser.add_argument("--prompt", type=str, default=None, help="Positive prompt for CLI mode.")
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args_parser.parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt for CLI mode.")
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args_parser.parser.add_argument("--seed", type=int, default=-1, help="Seed for CLI mode.")
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args_parser.parser.add_argument("--output", type=str, default=None, help="Output filename for CLI mode.")
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args_parser.parser.add_argument("--performance", type=str, default="Speed", choices=["Speed", "Quality", "Extreme Speed"], help="Performance mode for CLI mode.")
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args_parser.parser.add_argument("--aspect-ratio", type=str, default="1152\u00d7896", help="Aspect ratio for CLI mode.")
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args_parser.parser.add_argument("--base-model", type=str, default=None, help="Base model filename.")
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args_parser.parser.add_argument("--refiner-model", type=str, default=None, help="Refiner model filename.")
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args_parser.parser.add_argument("--refiner-switch", type=float, default=0.5, help="Refiner switch point (0.0-1.0).")
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args_parser.parser.add_argument("--lora", action='append', help="LoRA definition in format 'filename:weight'. Can be specified multiple times.")
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args_parser.parser.add_argument("--style", action='append', help="Style name. Can be specified multiple times.")
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args_parser.parser.add_argument("--cfg-scale", type=float, default=7.0, help="CFG Scale (default 7.0).")
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args_parser.parser.add_argument("--sampler", type=str, default="dpmpp_2m_sde_gpu", help="Sampler name.")
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args_parser.parser.add_argument("--scheduler", type=str, default="karras", help="Scheduler name.")
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args_parser.parser.add_argument("--sharpness", type=float, default=2.0, help="Image sharpness (default 2.0).")
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args_parser.parser.add_argument("--image-number", type=int, default=1, help="Number of images to generate (default 1).")
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args_parser.parser.add_argument("--steps", type=int, default=-1, help="Number of steps (overrides performance).")
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args_parser.parser.set_defaults(
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disable_cuda_malloc=True,
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in_browser=True,
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15
launch.py
15
launch.py
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@ -27,9 +27,9 @@ TRY_INSTALL_XFORMERS = False
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def prepare_environment():
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torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121")
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torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu128")
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torch_command = os.environ.get('TORCH_COMMAND',
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f"pip install torch==2.1.0 torchvision==0.16.0 --extra-index-url {torch_index_url}")
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f"pip install torch==2.7.1 torchvision==0.22.1 --extra-index-url {torch_index_url}")
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requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
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print(f"Python {sys.version}")
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@ -149,4 +149,15 @@ config.default_base_model_name, config.checkpoint_downloads = download_models(
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config.update_files()
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init_cache(config.model_filenames, config.paths_checkpoints, config.lora_filenames, config.paths_loras)
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if args.prompt is not None:
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try:
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from modules.cli_worker import run_cli
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run_cli(args)
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sys.exit(0)
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except Exception as e:
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print(f"[CLI] Error: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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from webui import *
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@ -58,7 +58,7 @@ class ModelSamplingDiscrete(torch.nn.Module):
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else:
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betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
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alphas = 1. - betas
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alphas_cumprod = torch.cumprod(alphas, dim=0)
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alphas_cumprod = torch.cumprod(torch.tensor(alphas, dtype=torch.float32), dim=0)
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timesteps, = betas.shape
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self.num_timesteps = int(timesteps)
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"""
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Fooocus CLI Worker
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This module handles the programmatic generation of images using the Fooocus pipeline.
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It is invoked by launch.py when CLI arguments are detected.
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"""
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import os
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import random
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import torch
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import modules.default_pipeline as pipeline
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import modules.async_worker as worker
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import modules.flags as flags
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import modules.config
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from modules.util import get_enabled_loras
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from modules.sdxl_styles import apply_style
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from modules.private_logger import log
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def run_cli(args):
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prompt = args.prompt
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negative_prompt = args.negative_prompt
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seed = args.seed
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performance = args.performance
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aspect_ratio = args.aspect_ratio
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# 1. Configuration & Models
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# Styles
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if args.style:
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styles = args.style
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else:
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styles = ["Fooocus V2", "Fooocus Photograph", "Fooocus Enhance"]
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# Models
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base_model = args.base_model if args.base_model else modules.config.default_base_model_name
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refiner_model = args.refiner_model if args.refiner_model else modules.config.default_refiner_model_name
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# LoRAs (convert 'name:weight' to (enabled, name, weight))
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if args.lora:
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loras = []
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for l in args.lora:
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try:
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if ':' in l:
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parts = l.rsplit(':', 1)
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name = parts[0]
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weight = float(parts[1])
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else:
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name = l
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weight = 1.0
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loras.append((True, name, weight))
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except Exception as e:
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print(f"[CLI] Warning: Could not parse LoRA '{l}': {e}")
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else:
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loras = modules.config.default_loras
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print(f"\n[CLI] Settings:")
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print(f" Prompt: {prompt}")
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print(f" Negative: {negative_prompt}")
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print(f" Styles: {styles}")
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print(f" Models: Base='{base_model}', Refiner='{refiner_model}'")
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print(f" LoRAs: {len(loras)} enabled")
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print(f" Performance: {performance}")
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# Refresh Pipeline
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try:
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pipeline.refresh_everything(
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refiner_model_name=refiner_model,
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base_model_name=base_model,
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loras=get_enabled_loras(loras),
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vae_name=modules.config.default_vae
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)
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except Exception as e:
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print(f"[CLI] Error loading models: {e}")
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return
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# 2. Process Prompts
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positive_basic_workloads = [prompt]
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negative_basic_workloads = [negative_prompt]
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for s in styles:
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if s == "Fooocus V2":
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continue
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try:
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p, n, _ = apply_style(s, positive=prompt)
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positive_basic_workloads += p
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negative_basic_workloads += n
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except Exception as e:
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print(f"[CLI] Warning: Failed to apply style '{s}': {e}")
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# 3. Expansion
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if "Fooocus V2" in styles:
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seed_for_expansion = seed if seed != -1 else random.randint(0, 2**32 - 1)
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expansion = pipeline.final_expansion(prompt, seed_for_expansion)
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print(f"[CLI] Expansion: {expansion}")
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positive_prompts = positive_basic_workloads + [expansion]
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else:
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expansion = ""
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positive_prompts = positive_basic_workloads
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# 4. Encoding
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print("[CLI] Encoding prompts...")
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c = pipeline.clip_encode(texts=positive_prompts, pool_top_k=len(positive_basic_workloads))
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uc = pipeline.clip_encode(texts=negative_basic_workloads, pool_top_k=len(negative_basic_workloads))
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# 5. Parameters
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perf_obj = flags.Performance(performance)
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steps = perf_obj.steps()
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if args.steps > 0:
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steps = args.steps
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switch = int(steps * args.refiner_switch)
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try:
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if '\u00d7' in aspect_ratio:
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w_h = aspect_ratio.split('\u00d7')
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elif 'x' in aspect_ratio:
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w_h = aspect_ratio.split('x')
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else:
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w_h = aspect_ratio.split(',') # fallback? no, probably just error out to default
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width = int(w_h[0])
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height = int(w_h[1])
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except:
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width, height = 1152, 896
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print(f"[CLI] Resolution: {width}x{height}, Steps: {steps}, Sampler: {args.sampler}")
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# Initialize PatchSettings
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from modules.patch import PatchSettings, patch_settings
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pid = os.getpid()
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patch_settings[pid] = PatchSettings(
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sharpness=args.sharpness,
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adm_scaler_end=0.3,
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positive_adm_scale=1.5,
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negative_adm_scale=0.8,
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controlnet_softness=0.25,
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adaptive_cfg=7.0
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)
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# 6. Generation Loop
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def callback(step, x0, x, total_steps, y):
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print(f" Step {step}/{total_steps}", end="\r")
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total_images = args.image_number
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print(f"[CLI] Generating {total_images} image(s)...")
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for i in range(total_images):
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current_seed = seed
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if current_seed == -1:
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current_seed = random.randint(0, 2**32 - 1)
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elif i > 0:
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current_seed += 1 # Auto-increment seed
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print(f"\n[CLI] Image {i+1}/{total_images} (Seed: {current_seed})")
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with torch.no_grad():
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imgs = pipeline.process_diffusion(
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positive_cond=c,
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negative_cond=uc,
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steps=steps,
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switch=switch,
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width=width,
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height=height,
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image_seed=current_seed,
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callback=callback,
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sampler_name=args.sampler,
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scheduler_name=args.scheduler,
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latent=None,
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denoise=1.0,
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cfg_scale=args.cfg_scale,
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refiner_swap_method="joint"
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)
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# 7. Save
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log_task = {
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'log_positive_prompt': prompt,
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'log_negative_prompt': negative_prompt,
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'expansion': expansion,
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'styles': styles,
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'task_seed': current_seed,
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'base_model_name': base_model
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}
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for img_idx, img in enumerate(imgs):
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d = [('Prompt', 'prompt', prompt)]
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path = log(img, d, None, "png", log_task)
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print(f"[CLI] Saved to: {path}")
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if args.output:
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if total_images > 1:
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# Append index to filename if multiple images
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root, ext = os.path.splitext(args.output)
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final_output = f"{root}_{i+1}{ext}"
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else:
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final_output = args.output
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import shutil
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shutil.copy(path, final_output)
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print(f"[CLI] Copied to: {final_output}")
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print("\n[CLI] Generation complete!")
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@ -122,8 +122,28 @@ def patched_SDClipModel_forward(self, tokens):
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if tokens[x, y] == max_token:
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break
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outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask,
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output_hidden_states=self.layer == "hidden")
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try:
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outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask,
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output_hidden_states=self.layer == "hidden")
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except TypeError:
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# Fallback for ldm_patched.modules.clip_model.CLIPTextModel_
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# which uses a different signature than transformers.CLIPTextModel
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intermediate_output = self.layer_idx if self.layer == "hidden" else None
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# Note: ldm_patched expects positional input_tokens
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outputs = self.transformer(tokens, attention_mask=attention_mask,
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intermediate_output=intermediate_output)
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# Handle ldm_patched return type (tuple) vs transformers return type (BaseModelOutputWithPooling)
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if isinstance(outputs, tuple):
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class MockOutput:
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pass
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out_obj = MockOutput()
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out_obj.last_hidden_state = outputs[0]
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# output[1] is intermediate
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out_obj.hidden_states = {self.layer_idx: outputs[1]} if outputs[1] is not None else None
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out_obj.pooler_output = outputs[2]
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outputs = out_obj
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self.transformer.set_input_embeddings(backup_embeds)
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if self.layer == "last":
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@ -131,7 +151,12 @@ def patched_SDClipModel_forward(self, tokens):
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elif self.layer == "pooled":
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z = outputs.pooler_output[:, None, :]
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else:
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z = outputs.hidden_states[self.layer_idx]
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# For ldm_patched, we stored it in the dict with key layer_idx
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if isinstance(outputs.hidden_states, dict):
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z = outputs.hidden_states[self.layer_idx]
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else:
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z = outputs.hidden_states[self.layer_idx]
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if self.layer_norm_hidden_state:
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z = self.transformer.text_model.final_layer_norm(z)
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@ -428,6 +428,15 @@ Processed only for positive prompt.
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Applies a LoRA to the prompt. The LoRA file must be located in the `models/loras` directory.
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## Command Line Interface (CLI)
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Fooocus now supports a headless Command Line Interface for generating images without the Web UI.
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See [README_CLI.md](README_CLI.md) for detailed instructions.
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Quick Example:
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`python entry_with_update.py --prompt "astronaut in space" --output "space.png"`
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## Advanced Features
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[Click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117)
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Reference in New Issue