Instructions to use mhnakif/comfy2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mhnakif/comfy2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mhnakif/comfy2", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| """Tests for path_utils – asset category resolution.""" | |
| import os | |
| import tempfile | |
| from pathlib import Path | |
| from unittest.mock import patch | |
| import pytest | |
| from app.assets.services.path_utils import get_asset_category_and_relative_path | |
| def fake_dirs(): | |
| """Create temporary input, output, and temp directories.""" | |
| with tempfile.TemporaryDirectory() as root: | |
| root_path = Path(root) | |
| input_dir = root_path / "input" | |
| output_dir = root_path / "output" | |
| temp_dir = root_path / "temp" | |
| models_dir = root_path / "models" / "checkpoints" | |
| for d in (input_dir, output_dir, temp_dir, models_dir): | |
| d.mkdir(parents=True) | |
| with patch("app.assets.services.path_utils.folder_paths") as mock_fp: | |
| mock_fp.get_input_directory.return_value = str(input_dir) | |
| mock_fp.get_output_directory.return_value = str(output_dir) | |
| mock_fp.get_temp_directory.return_value = str(temp_dir) | |
| with patch( | |
| "app.assets.services.path_utils.get_comfy_models_folders", | |
| return_value=[("checkpoints", [str(models_dir)])], | |
| ): | |
| yield { | |
| "input": input_dir, | |
| "output": output_dir, | |
| "temp": temp_dir, | |
| "models": models_dir, | |
| } | |
| class TestGetAssetCategoryAndRelativePath: | |
| def test_input_file(self, fake_dirs): | |
| f = fake_dirs["input"] / "photo.png" | |
| f.touch() | |
| cat, rel = get_asset_category_and_relative_path(str(f)) | |
| assert cat == "input" | |
| assert rel == "photo.png" | |
| def test_output_file(self, fake_dirs): | |
| f = fake_dirs["output"] / "result.png" | |
| f.touch() | |
| cat, rel = get_asset_category_and_relative_path(str(f)) | |
| assert cat == "output" | |
| assert rel == "result.png" | |
| def test_temp_file(self, fake_dirs): | |
| """Regression: temp files must be categorised, not raise ValueError.""" | |
| f = fake_dirs["temp"] / "GLSLShader_output_00004_.png" | |
| f.touch() | |
| cat, rel = get_asset_category_and_relative_path(str(f)) | |
| assert cat == "temp" | |
| assert rel == "GLSLShader_output_00004_.png" | |
| def test_temp_file_in_subfolder(self, fake_dirs): | |
| sub = fake_dirs["temp"] / "sub" | |
| sub.mkdir() | |
| f = sub / "ComfyUI_temp_tczip_00004_.png" | |
| f.touch() | |
| cat, rel = get_asset_category_and_relative_path(str(f)) | |
| assert cat == "temp" | |
| assert os.path.normpath(rel) == os.path.normpath("sub/ComfyUI_temp_tczip_00004_.png") | |
| def test_model_file(self, fake_dirs): | |
| f = fake_dirs["models"] / "model.safetensors" | |
| f.touch() | |
| cat, rel = get_asset_category_and_relative_path(str(f)) | |
| assert cat == "models" | |
| def test_unknown_path_raises(self, fake_dirs): | |
| with pytest.raises(ValueError, match="not within"): | |
| get_asset_category_and_relative_path("/some/random/path.png") | |