Add custom nodes, Civitai loras (LFS), and vast.ai setup script

Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

custom_nodes/comfyui-image-saver/saver/test_saver.py

@@ -0,0 +1,197 @@
+import os
+import itertools
+import json
+import tempfile
+import shutil
+import pytest
+from PIL import Image
+import piexif
+import piexif.helper
+from .saver import save_image
+
+def get_default_workflow():
+    """Read the default workflow from the JSON file."""
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    default_workflow_path = os.path.join(current_dir, "default_workflow.json")
+    with open(default_workflow_path, 'r') as f:
+        return json.load(f)
+
+
+def get_large_workflow(padding_size: int):
+    """Create a large workflow by duplicating the default workflow until it's at least 500KB."""
+    default_workflow = get_default_workflow()
+    large_workflow = default_workflow.copy()
+    large_workflow["padding"] = "x" * padding_size
+    workflow_size = len(json.dumps(large_workflow)) / 1024  # Size in KB
+    print(f"Large workflow size: {workflow_size:.2f} KB")
+    return large_workflow
+
+
+@pytest.fixture(
+    params=list(itertools.product(
+        ["simple", "default", "large", "huge"],  # workflow_type
+        [True, False]                            # embed_workflow
+    )),
+    ids=lambda param: f"workflow-{param[0]}_embed-{param[1]}"
+)
+def setup_test_env(request):
+    """Setup test environment with temp directory and test image, parameterized by workflow type."""
+    temp_dir = tempfile.mkdtemp()
+
+    test_image = Image.new('RGB', (100, 100), color='red')
+
+    a111_params = """
+beautiful scenery nature glass bottle landscape, purple galaxy bottle, low key
+Negative prompt: (worst quality, low quality, bad quality:1.3), embedding:ng_deepnegative_v1_75t, embedding:EasyNegative, embedding:badhandv4
+Steps: 30, Sampler: DPM++ 2M SDE, CFG scale: 7.0, Seed: 42, Size: 512x512, Model: , Version: ComfyUI,
+Civitai resources: [
+    {"modelName":"Deep Negative V1.x","versionName":"V1 75T","weight":1.0,"air":"urn:air:sd1:embedding:civitai:4629@5637"},
+    {"modelName":"EasyNegative","versionName":"EasyNegative_pt","weight":1.0,"air":"urn:air:sd1:embedding:civitai:7808@9536"},
+    {"modelName":"badhandv4","versionName":"badhandv4","weight":1.0,"air":"urn:air:other:embedding:civitai:16993@20068"}]
+"""
+
+    prompt = {"prompt": "test prompt", "negative_prompt": "test negative prompt"}
+
+    workflow_type, embed_workflow = request.param
+
+    if workflow_type == "simple":
+        extra_pnginfo = {"workflow": {"version": "1.0", "nodes": []}}
+    elif workflow_type == "default":
+        default_workflow = get_default_workflow()
+        extra_pnginfo = {"workflow": default_workflow}
+    elif workflow_type == "large":
+        large_workflow = get_large_workflow(524288 )
+        extra_pnginfo = {"workflow": large_workflow}
+        # Check the size for debugging purposes
+        workflow_size = len(json.dumps(large_workflow)) / 1024  # Size in KB
+        print(f"Large workflow size: {workflow_size:.2f} KB")
+    elif workflow_type == "huge":
+        huge_workflow = get_large_workflow(2097152)
+        extra_pnginfo = {"workflow": huge_workflow}
+        # Check the size for debugging purposes
+        workflow_size = len(json.dumps(huge_workflow)) / 1024  # Size in KB
+        print(f"Large workflow size: {workflow_size:.2f} KB")
+
+    yield temp_dir, test_image, a111_params, prompt, extra_pnginfo, workflow_type, embed_workflow
+
+    shutil.rmtree(temp_dir)
+
+@pytest.mark.parametrize(
+    "optimize",
+    [True, False],
+    ids=["optimize", "no-optimize"]
+)
+def test_save_png(setup_test_env, optimize):
+    """Test that complete metadata is correctly saved and can be retrieved for PNG format."""
+    temp_dir, test_image, a111_params, prompt, extra_pnginfo, workflow_type, embed_workflow = setup_test_env
+    image_path = os.path.join(temp_dir, f"test_with_workflow_{workflow_type}.png")
+    save_image(test_image, image_path, "png", 100, True, optimize, a111_params, prompt, extra_pnginfo, embed_workflow)
+    saved_image = Image.open(image_path)
+    try:
+        assert saved_image.info.get("parameters") == a111_params
+        if embed_workflow:
+            assert json.loads(saved_image.info.get("prompt")) == prompt
+            assert json.loads(saved_image.info.get("workflow")) == extra_pnginfo["workflow"]
+        else:
+            assert set(saved_image.info.keys()) == {"parameters"}, "PNG should not contain prompt or workflow data"
+    finally:
+        saved_image.close()
+
+def test_save_jpeg(setup_test_env):
+    """Test that metadata is correctly saved and can be retrieved for JPEG format."""
+    temp_dir, test_image, a111_params, prompt, extra_pnginfo, workflow_type, embed_workflow = setup_test_env
+    jpeg_path = os.path.join(temp_dir, f"test_{workflow_type}.jpeg")
+    save_image(test_image, jpeg_path, "jpeg", 90, False, False, a111_params, prompt, extra_pnginfo, embed_workflow)
+    saved_image = Image.open(jpeg_path)
+    try:
+        exif_dict = piexif.load(saved_image.info["exif"])
+        user_comment = piexif.helper.UserComment.load(exif_dict["Exif"][piexif.ExifIFD.UserComment])
+        assert user_comment == a111_params
+
+        if embed_workflow:
+            if workflow_type == "simple" or workflow_type == "default":
+                assert "0th" in exif_dict, "Expected workflow data in EXIF"
+                # verify that prompt and workflow data are in EXIF
+                expected_keys = {piexif.ImageIFD.Make, piexif.ImageIFD.Model}
+                found_keys = set(exif_dict["0th"].keys()) & expected_keys
+                assert len(found_keys) > 0, "Expected workflow or prompt data in EXIF"
+
+                if piexif.ImageIFD.Make in exif_dict["0th"]:
+                    make_data = exif_dict["0th"][piexif.ImageIFD.Make]
+                    make_str = make_data.decode('utf-8')
+                    # Check that workflow matches
+                    if make_str.startswith("workflow:"):
+                        make_str = make_str[len("workflow:"):]
+                    saved_workflow = json.loads(make_str)
+                    original_workflow = extra_pnginfo["workflow"]
+
+                    assert saved_workflow == original_workflow, "Saved workflow content doesn't match original"
+
+                if piexif.ImageIFD.Model in exif_dict["0th"]:
+                    model_data = exif_dict["0th"][piexif.ImageIFD.Model]
+                    model_str = model_data.decode('utf-8')
+                    # Check that "prompt" matches
+                    if model_str.startswith("prompt:"):
+                        model_str = model_str[len("prompt:"):]
+                    saved_prompt = json.loads(model_str)
+                    assert saved_prompt == prompt, "Saved prompt content doesn't match original"
+            else:
+                # When workflow_type is "large", verify that the workflow is too large to embed
+                if "0th" in exif_dict:
+                    assert not any(k in exif_dict["0th"] for k in (piexif.ImageIFD.Make, piexif.ImageIFD.Model)), "JPEG should not contain prompt or workflow data"
+        else:
+            # When embed_workflow is False, verify no prompt or workflow in EXIF
+            if "0th" in exif_dict:
+                assert not any(k in exif_dict["0th"] for k in (piexif.ImageIFD.Make, piexif.ImageIFD.Model)), "JPEG should not contain prompt or workflow data"
+    finally:
+        saved_image.close()
+
+@pytest.mark.parametrize(
+    "lossless,quality",
+    [(True, 100), (False, 90)],
+    ids=["lossless-max", "lossy-90"]
+)
+def test_save_webp(setup_test_env, lossless, quality):
+    """Test that metadata is correctly saved and can be retrieved for lossless WebP format."""
+    temp_dir, test_image, a111_params, prompt, extra_pnginfo, workflow_type, embed_workflow = setup_test_env
+    iamge_path = os.path.join(temp_dir, f"test_lossless_{workflow_type}.webp")
+    save_image(test_image, iamge_path, "webp", quality, lossless, False, a111_params, prompt, extra_pnginfo, embed_workflow)
+    saved_image = Image.open(iamge_path)
+    try:
+        # Verify a111_params is correctly stored in EXIF UserComment
+        exif_dict = piexif.load(saved_image.info["exif"])
+        user_comment = piexif.helper.UserComment.load(exif_dict["Exif"][piexif.ExifIFD.UserComment])
+        assert user_comment == a111_params
+
+        if embed_workflow:
+            assert "0th" in exif_dict, "Expected workflow data in EXIF"
+            # When embed_workflow is True, verify that prompt and workflow data are in EXIF
+            expected_keys = {piexif.ImageIFD.Make, piexif.ImageIFD.Model}
+            found_keys = set(exif_dict["0th"].keys()) & expected_keys
+            assert len(found_keys) > 0, "Expected workflow or prompt data in EXIF"
+
+            if piexif.ImageIFD.Make in exif_dict["0th"]:
+                make_data = exif_dict["0th"][piexif.ImageIFD.Make]
+                make_str = make_data.decode('utf-8')
+                # Check that workflow matches
+                if make_str.startswith("workflow:"):
+                    make_str = make_str[len("workflow:"):]
+                saved_workflow = json.loads(make_str)
+                original_workflow = extra_pnginfo["workflow"]
+
+                assert saved_workflow == original_workflow, "Saved workflow content doesn't match original"
+
+            if piexif.ImageIFD.Model in exif_dict["0th"]:
+                model_data = exif_dict["0th"][piexif.ImageIFD.Model]
+                model_str = model_data.decode('utf-8')
+                # Check that "prompt" matches
+                if model_str.startswith("prompt:"):
+                    model_str = model_str[len("prompt:"):]
+                saved_prompt = json.loads(model_str)
+                assert saved_prompt == prompt, "Saved prompt content doesn't match original"
+        else:
+            # When embed_workflow is False, verify no prompt or workflow in EXIF
+            if "0th" in exif_dict:
+                assert not any(k in exif_dict["0th"] for k in (piexif.ImageIFD.Make, piexif.ImageIFD.Model)), "WEBP should not contain prompt or workflow data"
+    finally:
+        saved_image.close()