612 lines
22 KiB
Python
612 lines
22 KiB
Python
import json
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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def _save_jpeg_with_user_comment(path, comment: str, prefix: bytes = b"ASCII\x00\x00\x00"):
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exif = Image.Exif()
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if prefix.startswith(b"UNICODE"):
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payload = comment.encode("utf-16le")
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else:
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payload = comment.encode("utf-8")
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exif[37510] = prefix + payload
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Image.new("RGB", (32, 24), (20, 40, 60)).save(path, exif=exif)
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def _save_png_with_prompt(path, prompt, workflow=None):
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pnginfo = PngInfo()
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pnginfo.add_text("prompt", json.dumps(prompt))
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if workflow is not None:
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pnginfo.add_text("workflow", json.dumps(workflow))
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Image.new("RGB", (32, 24), (20, 40, 60)).save(path, pnginfo=pnginfo)
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def _simple_prompt_graph(positive="original positive prompt", negative="original negative prompt"):
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return {
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"1": {"class_type": "SaveImage", "inputs": {"images": ["2", 0]}},
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"2": {"class_type": "VAEDecode", "inputs": {"samples": ["3", 0]}},
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"3": {
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"class_type": "KSampler",
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"inputs": {
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"positive": ["4", 0],
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"negative": ["5", 0],
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"seed": 111,
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"steps": 12,
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"cfg": 5.5,
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"sampler_name": "euler",
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"scheduler": "normal",
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},
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},
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"4": {"class_type": "CLIPTextEncode", "inputs": {"text": positive}},
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"5": {"class_type": "CLIPTextEncode", "inputs": {"text": negative}},
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}
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def _upscale_prompt_graph(reader_image="003.png"):
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return {
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"22": {
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"class_type": "FaceDetailer",
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"inputs": {
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"image": ["24", 0],
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"positive": ["26", 0],
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"negative": ["27", 0],
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},
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},
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"24": {
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"class_type": "UltimateSDUpscale",
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"inputs": {
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"image": ["40", 0],
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"positive": ["26", 0],
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"negative": ["27", 0],
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"seed": 222,
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"steps": 9,
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"cfg": 1.5,
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"sampler_name": "Euler",
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"scheduler": "normal",
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"tile_width": 768,
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"seam_fix_mode": "half tile",
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},
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},
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"26": {"class_type": "CLIPTextEncode", "inputs": {"text": ["37", 0]}},
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"27": {"class_type": "CLIPTextEncode", "inputs": {"text": ["38", 0]}},
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"36": {"class_type": "SmartMetadataReader", "inputs": {"image": reader_image}},
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"37": {"class_type": "ShowText|pysssss", "inputs": {"text": ["36", 2]}},
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"38": {"class_type": "ShowText|pysssss", "inputs": {"text": ["36", 3]}},
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"40": {"class_type": "LoadImage", "inputs": {"image": "source.png"}},
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"90": {
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"class_type": "GeminiChatNode",
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"inputs": {"prompt": "unused Gemini prompt template"},
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},
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"91": {"class_type": "ShowText|pysssss", "inputs": {"text": ["90", 0]}},
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"99": {"class_type": "PreviewImage", "inputs": {"images": ["22", 0]}},
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}
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def _upscale_workflow(reader_image="003.png"):
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return {
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"nodes": [
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{
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"id": 36,
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"type": "SmartMetadataReader",
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"inputs": {"image": reader_image},
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"outputs": [
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{"name": "image"},
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{"name": "mask"},
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{"name": "positive"},
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{"name": "negative"},
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{"name": "seed"},
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{"name": "steps"},
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{"name": "cfg"},
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{"name": "width"},
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{"name": "height"},
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{"name": "model_name"},
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{"name": "filename"},
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{"name": "setting"},
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],
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},
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{
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"id": 91,
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"type": "ShowText|pysssss",
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"inputs": {"text": ["90", 0]},
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"widgets_values": ["wrong unused prompt"],
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},
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]
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}
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def test_read_metadata_extracts_png_text_chunks_and_dimensions(tmp_path):
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from smart_metadata_reader.metadata_reader import read_metadata
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image_path = tmp_path / "sample.png"
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prompt_graph = {
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"1": {
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"class_type": "CLIPTextEncode",
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"inputs": {"text": "positive trigger"},
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}
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}
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workflow_graph = {
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"nodes": [
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{
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"id": 1,
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"type": "CLIPTextEncode",
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"inputs": {"text": "positive trigger"},
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}
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]
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}
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pnginfo = PngInfo()
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pnginfo.add_text("prompt", json.dumps(prompt_graph))
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pnginfo.add_text("workflow", json.dumps(workflow_graph))
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pnginfo.add_text(
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"parameters",
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(
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"positive\n"
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"Negative prompt: negative\n"
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"Steps: 20, Sampler: Euler, CFG scale: 7, Seed: 123, Size: 512x768"
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),
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)
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Image.new("RGBA", (64, 32), (255, 0, 0, 128)).save(
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image_path, pnginfo=pnginfo
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)
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bundle = read_metadata(image_path)
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assert bundle.filename == "sample.png"
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assert bundle.width == 64
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assert bundle.height == 32
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assert json.loads(bundle.prompt_raw) == prompt_graph
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assert json.loads(bundle.workflow_raw) == workflow_graph
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assert "Negative prompt: negative" in bundle.parameters_raw
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assert bundle.source_format == "ComfyUI prompt/workflow"
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def test_load_image_and_mask_returns_batched_image_and_mask_shapes(tmp_path):
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from smart_metadata_reader.image_io import load_image_and_mask
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image_path = tmp_path / "transparent.png"
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Image.new("RGBA", (10, 12), (10, 20, 30, 128)).save(image_path)
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image, mask, width, height = load_image_and_mask(image_path)
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assert width == 10
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assert height == 12
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assert tuple(image.shape) == (1, 12, 10, 3)
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assert tuple(mask.shape) == (1, 12, 10)
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def test_parse_standard_a1111_parameters():
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from smart_metadata_reader.a1111_parser import parse_a1111_parameters
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parameters = (
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"masterpiece, character trigger\n"
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"Negative prompt: blurry, low quality\n"
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"Steps: 25, Sampler: Euler a, CFG scale: 7.5, Seed: 12345, "
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"Size: 768x1024, Model: demo_model"
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)
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result = parse_a1111_parameters(parameters)
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assert result.positive == "masterpiece, character trigger"
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assert result.negative == "blurry, low quality"
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assert result.steps == 25
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assert result.sampler_name == "Euler a"
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assert result.cfg == 7.5
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assert result.seed == 12345
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assert result.width == 768
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assert result.height == 1024
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assert result.model_name == "demo_model"
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assert result.status_message == "A1111 parameters fallback"
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assert result.partial_result["source_format"] == "A1111 parameters"
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def test_parse_a1111_preserves_multiline_positive_and_negative_prompts():
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from smart_metadata_reader.a1111_parser import parse_a1111_parameters
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parameters = (
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"masterpiece, best quality\n"
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"character trigger, detailed eyes\n"
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"Negative prompt: low quality\n"
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"bad hands, extra fingers\n"
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"Steps: 30, Sampler: DPM++ 2M, CFG scale: 6, Seed: 999, Size: 512x768"
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)
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result = parse_a1111_parameters(parameters)
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assert result.positive == "masterpiece, best quality\ncharacter trigger, detailed eyes"
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assert result.negative == "low quality\nbad hands, extra fingers"
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assert result.steps == 30
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assert result.seed == 999
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def test_parse_a1111_preserves_lora_triggers_nsfw_and_user_tags():
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from smart_metadata_reader.a1111_parser import parse_a1111_parameters
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parameters = (
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"<lora:role_lora:0.8>, role_lora_trigger, nsfw, nude, user handwritten tag\n"
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"Negative prompt: low quality\n"
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"Steps: 20, Sampler: Euler, CFG scale: 7, Seed: 101, Size: 640x960"
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)
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result = parse_a1111_parameters(parameters)
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assert result.positive == (
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"<lora:role_lora:0.8>, role_lora_trigger, nsfw, nude, user handwritten tag"
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)
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assert result.negative == "low quality"
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def test_parse_a1111_without_negative_prompt_keeps_positive_and_empty_negative():
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from smart_metadata_reader.a1111_parser import parse_a1111_parameters
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parameters = (
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"solo, dramatic lighting\n"
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"Steps: 12, Sampler: Euler, CFG scale: 4.5, Seed: 42, Size: 320x448"
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)
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result = parse_a1111_parameters(parameters)
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assert result.positive == "solo, dramatic lighting"
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assert result.negative == ""
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assert result.steps == 12
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assert result.seed == 42
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def test_parse_a1111_missing_partial_parameters_uses_defaults():
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from smart_metadata_reader.a1111_parser import parse_a1111_parameters
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result = parse_a1111_parameters("simple prompt only")
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assert result.positive == "simple prompt only"
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assert result.negative == ""
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assert result.seed == -1
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assert result.steps == 0
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assert result.cfg == 0.0
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assert result.width == 0
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assert result.height == 0
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assert result.model_name == ""
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def test_parse_a1111_scheduler_and_model_hash_metadata():
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from smart_metadata_reader.a1111_parser import parse_a1111_parameters
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parameters = (
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"positive text\n"
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"Negative prompt: negative text\n"
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"Steps: 25, Sampler: DPM++ 2M, Scheduler: Karras, CFG scale: 7.25, "
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"Seed: 123, Size: 768x1024, Model hash: abc123, Model: forge_model"
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)
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result = parse_a1111_parameters(parameters)
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assert result.scheduler == "Karras"
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assert result.model_name == "forge_model"
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assert result.partial_result["model_hash"] == "abc123"
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def test_parse_comfyui_ultimate_sd_upscale_sampler_like_node():
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from smart_metadata_reader.metadata_reader import parse_metadata_bundle
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from smart_metadata_reader.models import MetadataBundle
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prompt = {
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"1": {
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"class_type": "CheckpointLoaderSimple",
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"inputs": {"ckpt_name": "base_model.safetensors"},
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},
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"2": {
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"class_type": "LoraLoader",
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"inputs": {
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"model": ["1", 0],
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"clip": ["1", 1],
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"lora_name": "upscale_detail.safetensors",
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"strength_model": 0.5,
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"strength_clip": 0.75,
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},
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},
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"3": {"class_type": "VAELoader", "inputs": {"vae_name": "main_vae.safetensors"}},
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"4": {"class_type": "CLIPTextEncode", "inputs": {"text": "role_lora_trigger, 1girl, nsfw"}},
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"5": {"class_type": "CLIPTextEncode", "inputs": {"text": "low quality"}},
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"6": {
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"class_type": "UltimateSDUpscaleNoUpscale",
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"inputs": {
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"model": ["2", 0],
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"clip": ["2", 1],
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"vae": ["3", 0],
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"positive": ["4", 0],
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"negative": ["5", 0],
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"seed": 221,
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"steps": 9,
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"cfg_scale": 1.5,
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"sampler": "Euler",
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"scheduler_name": "normal",
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"tile_width": 768,
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"seam_fix_mode": "half tile",
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"upscale_model": ["9", 0],
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},
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},
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"7": {"class_type": "SaveImage", "inputs": {"images": ["6", 0]}},
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"9": {"class_type": "UpscaleModelLoader", "inputs": {"model_name": "4x.pth"}},
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}
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bundle = MetadataBundle(
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filename="usdu.png",
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width=4800,
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height=6400,
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prompt_raw=json.dumps(prompt),
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workflow_raw=None,
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parameters_raw=None,
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source_format="ComfyUI prompt/workflow",
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)
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result = parse_metadata_bundle(bundle)
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assert result.positive == "role_lora_trigger, 1girl, nsfw"
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assert result.negative == "low quality"
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assert result.seed == 221
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assert result.steps == 9
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assert result.cfg == 1.5
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assert result.sampler_name == "Euler"
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assert result.scheduler == "normal"
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assert result.model_name == "base_model.safetensors"
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assert result.vae_name == "main_vae.safetensors"
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assert [lora.lora_name for lora in result.loras] == ["upscale_detail.safetensors"]
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assert "UltimateSDUpscaleNoUpscale 6" in result.debug_trace
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assert "tile_width" not in result.positive
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assert result.status_message == "OK"
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def test_failed_comfyui_parse_includes_output_chain_diagnostics_in_setting():
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from smart_metadata_reader.metadata_reader import parse_metadata_bundle
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from smart_metadata_reader.models import MetadataBundle
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prompt = {
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"1": {"class_type": "UnsupportedGenerator", "inputs": {"image": ["3", 0]}},
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"2": {"class_type": "SaveImage", "inputs": {"images": ["1", 0]}},
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"3": {"class_type": "LoadImage", "inputs": {"image": "source.png"}},
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}
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bundle = MetadataBundle(
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filename="unsupported.png",
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width=512,
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height=768,
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prompt_raw=json.dumps(prompt),
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workflow_raw=json.dumps({"nodes": []}),
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parameters_raw=None,
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source_format="ComfyUI prompt/workflow",
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)
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result = parse_metadata_bundle(bundle)
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assert result.status_message == "FAILED"
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assert "no supported sampler candidate found" in result.debug_trace
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assert "SaveImage 2.images <- UnsupportedGenerator 1" in result.debug_trace
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assert "Failure Reason:" in result.setting
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assert "final output nodes found: SaveImage 2" in result.setting
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def test_read_metadata_extracts_ascii_and_unicode_exif_user_comment(tmp_path):
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from smart_metadata_reader.metadata_reader import read_metadata
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ascii_path = tmp_path / "ascii.jpeg"
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unicode_path = tmp_path / "unicode.jpeg"
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_save_jpeg_with_user_comment(ascii_path, "ASCII prompt\nSteps: 9, Seed: 1")
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_save_jpeg_with_user_comment(
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unicode_path,
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"UNICODE prompt\nSteps: 10, Seed: 2",
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prefix=b"UNICODE\x00",
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)
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ascii_bundle = read_metadata(ascii_path)
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unicode_bundle = read_metadata(unicode_path)
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assert ascii_bundle.user_comment_raw == "ASCII prompt\nSteps: 9, Seed: 1"
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assert unicode_bundle.user_comment_raw == "UNICODE prompt\nSteps: 10, Seed: 2"
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assert ascii_bundle.source_format == "EXIF UserComment"
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assert unicode_bundle.source_format == "EXIF UserComment"
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def test_exif_user_comment_civitai_metadata_fallback(tmp_path):
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from smart_metadata_reader.metadata_reader import parse_metadata_bundle, read_metadata
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positive = (
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"A green tree frog wearing a tiny straw hat sitting on a lily pad with "
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"a fishing rod made from a twig and spider silk."
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)
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resources = [
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{
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"type": "checkpoint",
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"modelVersionId": 2442439,
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"modelName": "Z Image Turbo",
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"modelVersionName": "Turbo",
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},
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{
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"type": "lora",
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"weight": 0.5,
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"modelVersionId": 2460437,
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"modelName": "Midjourney Luneva Cinematic Lora",
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"modelVersionName": "LORA: R128-5000step",
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},
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{
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"type": "lora",
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"weight": 0.8,
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"modelVersionId": 2515203,
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"modelName": "[ZIT] Detail Slider",
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"modelVersionName": "v1.0",
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},
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]
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civitai_metadata = {
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"workflow": "txt2img",
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"cfgScale": 1,
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"steps": 9,
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"seed": 2214950711,
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"prompt": positive,
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"resources": [
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{"modelVersionId": 2442439, "strength": 1, "type": "Checkpoint"},
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{"modelVersionId": 2460437, "strength": 0.5, "type": "LORA"},
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{"modelVersionId": 2515203, "strength": 0.8, "type": "LORA"},
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],
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}
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user_comment = (
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f"{positive}\n"
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"Steps: 9, Sampler: Euler, CFG scale: 1, Seed: 2214950711, "
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"Size: 832x1216, Model type: Z-Image Turbo, "
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f"Civitai resources: {json.dumps(resources)}, "
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f"Civitai metadata: {json.dumps(civitai_metadata)}"
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)
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image_path = tmp_path / "civitai.jpeg"
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_save_jpeg_with_user_comment(image_path, user_comment, prefix=b"UNICODE\x00")
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bundle = read_metadata(image_path)
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result = parse_metadata_bundle(bundle)
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assert bundle.user_comment_raw.startswith("A green tree frog")
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assert result.positive == positive
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assert result.negative == ""
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assert result.seed == 2214950711
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assert result.steps == 9
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assert result.cfg == 1.0
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assert result.sampler_name == "Euler"
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assert result.width == 832
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assert result.height == 1216
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assert result.model_name == "Z Image Turbo"
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assert [(lora.lora_name, lora.strength_model) for lora in result.loras] == [
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("Midjourney Luneva Cinematic Lora", 0.5),
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("[ZIT] Detail Slider", 0.8),
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]
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assert result.partial_result["source_format"] == "EXIF UserComment / Civitai metadata"
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assert result.status_message == "EXIF UserComment / Civitai metadata fallback"
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assert "NO_METADATA" not in result.setting
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def test_nested_smart_metadata_reader_outputs_resolve_referenced_image_prompts(tmp_path):
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from smart_metadata_reader.metadata_reader import parse_metadata_bundle, read_metadata
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original_path = tmp_path / "003.png"
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_save_png_with_prompt(original_path, _simple_prompt_graph())
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|
|
|
upscale_path = tmp_path / "upscaled.png"
|
|
_save_png_with_prompt(
|
|
upscale_path,
|
|
_upscale_prompt_graph(),
|
|
workflow=_upscale_workflow(),
|
|
)
|
|
|
|
result = parse_metadata_bundle(read_metadata(upscale_path))
|
|
|
|
assert result.positive == "original positive prompt"
|
|
assert result.negative == "original negative prompt"
|
|
assert result.status_message != "FAILED"
|
|
assert not (result.status_message == "OK" and result.positive == "" and result.negative == "")
|
|
assert "Resolved SmartMetadataReader positive output from referenced image 003.png" in result.debug_trace
|
|
assert "Resolved SmartMetadataReader negative output from referenced image 003.png" in result.debug_trace
|
|
|
|
|
|
def test_unconnected_showtext_workflow_cache_does_not_pollute_nested_reader_prompt(tmp_path):
|
|
from smart_metadata_reader.metadata_reader import parse_metadata_bundle, read_metadata
|
|
|
|
original_path = tmp_path / "003.png"
|
|
_save_png_with_prompt(original_path, _simple_prompt_graph())
|
|
upscale_path = tmp_path / "upscaled.png"
|
|
_save_png_with_prompt(
|
|
upscale_path,
|
|
_upscale_prompt_graph(),
|
|
workflow=_upscale_workflow(),
|
|
)
|
|
|
|
result = parse_metadata_bundle(read_metadata(upscale_path))
|
|
|
|
assert result.positive == "original positive prompt"
|
|
assert "wrong unused prompt" not in result.positive
|
|
assert "wrong unused prompt" not in result.negative
|
|
|
|
|
|
def test_nested_smart_metadata_reader_falls_back_to_current_output_contract_without_workflow_outputs(tmp_path):
|
|
from smart_metadata_reader.metadata_reader import parse_metadata_bundle, read_metadata
|
|
|
|
original_path = tmp_path / "003.png"
|
|
_save_png_with_prompt(original_path, _simple_prompt_graph())
|
|
upscale_path = tmp_path / "upscaled.png"
|
|
_save_png_with_prompt(upscale_path, _upscale_prompt_graph())
|
|
|
|
result = parse_metadata_bundle(read_metadata(upscale_path))
|
|
|
|
assert result.positive == "original positive prompt"
|
|
assert result.negative == "original negative prompt"
|
|
|
|
|
|
def test_nested_smart_metadata_reader_prefers_workflow_output_names_over_indices(tmp_path):
|
|
from smart_metadata_reader.metadata_reader import parse_metadata_bundle, read_metadata
|
|
|
|
original_path = tmp_path / "003.png"
|
|
_save_png_with_prompt(original_path, _simple_prompt_graph())
|
|
prompt = _upscale_prompt_graph()
|
|
prompt["37"]["inputs"]["text"] = ["36", 3]
|
|
prompt["38"]["inputs"]["text"] = ["36", 2]
|
|
workflow = _upscale_workflow()
|
|
workflow["nodes"][0]["outputs"][2] = {"name": "negative"}
|
|
workflow["nodes"][0]["outputs"][3] = {"name": "positive"}
|
|
upscale_path = tmp_path / "upscaled.png"
|
|
_save_png_with_prompt(upscale_path, prompt, workflow=workflow)
|
|
|
|
result = parse_metadata_bundle(read_metadata(upscale_path))
|
|
|
|
assert result.positive == "original positive prompt"
|
|
assert result.negative == "original negative prompt"
|
|
|
|
|
|
def test_nested_smart_metadata_reader_missing_image_returns_partial_unresolved(tmp_path):
|
|
from smart_metadata_reader.metadata_reader import parse_metadata_bundle, read_metadata
|
|
|
|
upscale_path = tmp_path / "upscaled.png"
|
|
_save_png_with_prompt(
|
|
upscale_path,
|
|
_upscale_prompt_graph(reader_image="missing.png"),
|
|
workflow=_upscale_workflow(reader_image="missing.png"),
|
|
)
|
|
|
|
result = parse_metadata_bundle(read_metadata(upscale_path))
|
|
|
|
assert result.positive == ""
|
|
assert result.negative == ""
|
|
assert result.status_message == "PARTIAL"
|
|
assert result.confidence <= 0.6
|
|
assert result.partial_result["unresolved"][0]["class_type"] == "SmartMetadataReader"
|
|
assert result.partial_result["unresolved"][0]["output_index"] == 2
|
|
assert result.partial_result["unresolved"][0]["resolved_output_role"] == "positive"
|
|
assert result.partial_result["unresolved"][0]["referenced_image_filename"] == "missing.png"
|
|
assert "referenced image not found" in result.partial_result["unresolved"][0]["reason"]
|
|
assert "Unresolved:" in result.setting
|
|
|
|
|
|
def test_nested_smart_metadata_reader_rejects_unsafe_image_paths(tmp_path):
|
|
from smart_metadata_reader.metadata_reader import parse_metadata_bundle, read_metadata
|
|
|
|
for unsafe_path in ("../secret.png", "C:\\Users\\xxx\\secret.png"):
|
|
upscale_path = tmp_path / f"upscaled-{unsafe_path[0].encode().hex()}.png"
|
|
_save_png_with_prompt(
|
|
upscale_path,
|
|
_upscale_prompt_graph(reader_image=unsafe_path),
|
|
workflow=_upscale_workflow(reader_image=unsafe_path),
|
|
)
|
|
|
|
result = parse_metadata_bundle(read_metadata(upscale_path))
|
|
|
|
assert result.positive == ""
|
|
assert result.status_message == "PARTIAL"
|
|
assert result.partial_result["unresolved"][0]["class_type"] == "SmartMetadataReader"
|
|
assert "unsafe referenced image path" in result.partial_result["unresolved"][0]["reason"]
|
|
|
|
|
|
def test_nested_smart_metadata_reader_respects_actual_connected_output_role(tmp_path):
|
|
from smart_metadata_reader.metadata_reader import parse_metadata_bundle, read_metadata
|
|
|
|
original_path = tmp_path / "003.png"
|
|
_save_png_with_prompt(original_path, _simple_prompt_graph())
|
|
prompt = _upscale_prompt_graph()
|
|
prompt["37"]["inputs"]["text"] = ["36", 3]
|
|
upscale_path = tmp_path / "upscaled.png"
|
|
_save_png_with_prompt(upscale_path, prompt, workflow=_upscale_workflow())
|
|
|
|
result = parse_metadata_bundle(read_metadata(upscale_path))
|
|
|
|
assert result.positive == "original negative prompt"
|
|
assert "SmartMetadataReader output negative used in positive chain" in result.debug_trace
|