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fix(metadata): trace conditioning provenance for prompts

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This commit is contained in:
Will Miao
2026-04-23 14:41:54 +08:00
parent 2eef629821
commit ebdbb36271
3 changed files with 494 additions and 38 deletions
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214
tests/metadata_collector/test_metadata_collector.py
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@@ -177,6 +177,220 @@ def test_attention_bias_clip_text_encode_prompts_are_collected(metadata_registry
assert prompt_results["negative_prompt"] == "low quality"
def test_conditioning_provenance_recovers_combined_controlnet_prompts(
metadata_registry, monkeypatch
):
import types
prompt_graph = {
"encode_wd": {
"class_type": "CLIPTextEncode",
"inputs": {"text": "wd14 tags", "clip": ["clip", 0]},
},
"encode_manual": {
"class_type": "CLIPTextEncode",
"inputs": {"text": "manual tags", "clip": ["clip", 0]},
},
"encode_neg": {
"class_type": "CLIPTextEncode",
"inputs": {"text": "low quality", "clip": ["clip", 0]},
},
"combine": {
"class_type": "ConditioningCombine",
"inputs": {
"conditioning_1": ["encode_wd", 0],
"conditioning_2": ["encode_manual", 0],
},
},
"controlnet": {
"class_type": "ControlNetApplyAdvanced",
"inputs": {
"positive": ["combine", 0],
"negative": ["encode_neg", 0],
},
},
"sampler": {
"class_type": "KSampler",
"inputs": {
"seed": 123,
"steps": 20,
"cfg": 7.0,
"sampler_name": "Euler",
"scheduler": "karras",
"denoise": 1.0,
"positive": ["controlnet", 0],
"negative": ["controlnet", 1],
"latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
},
},
}
prompt = SimpleNamespace(original_prompt=prompt_graph)
wd_conditioning = object()
manual_conditioning = object()
negative_conditioning = object()
combined_conditioning = object()
controlnet_positive = object()
controlnet_negative = object()
monkeypatch.setattr(metadata_processor, "standalone_mode", False)
metadata_registry.start_collection("prompt-provenance")
metadata_registry.set_current_prompt(prompt)
metadata_registry.record_node_execution(
"encode_wd", "CLIPTextEncode", {"text": "wd14 tags"}, None
)
metadata_registry.update_node_execution(
"encode_wd", "CLIPTextEncode", [(wd_conditioning,)]
)
metadata_registry.record_node_execution(
"encode_manual", "CLIPTextEncode", {"text": "manual tags"}, None
)
metadata_registry.update_node_execution(
"encode_manual", "CLIPTextEncode", [(manual_conditioning,)]
)
metadata_registry.record_node_execution(
"encode_neg", "CLIPTextEncode", {"text": "low quality"}, None
)
metadata_registry.update_node_execution(
"encode_neg", "CLIPTextEncode", [(negative_conditioning,)]
)
metadata_registry.record_node_execution(
"combine",
"ConditioningCombine",
{
"conditioning_1": wd_conditioning,
"conditioning_2": manual_conditioning,
},
None,
)
metadata_registry.update_node_execution(
"combine", "ConditioningCombine", [(combined_conditioning,)]
)
metadata_registry.record_node_execution(
"controlnet",
"ControlNetApplyAdvanced",
{
"positive": combined_conditioning,
"negative": negative_conditioning,
},
None,
)
metadata_registry.update_node_execution(
"controlnet",
"ControlNetApplyAdvanced",
[(controlnet_positive, controlnet_negative)],
)
metadata_registry.record_node_execution(
"sampler",
"KSampler",
{
"seed": 123,
"steps": 20,
"cfg": 7.0,
"sampler_name": "Euler",
"scheduler": "karras",
"denoise": 1.0,
"positive": controlnet_positive,
"negative": controlnet_negative,
"latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
},
None,
)
metadata = metadata_registry.get_metadata("prompt-provenance")
params = MetadataProcessor.extract_generation_params(metadata)
assert params["prompt"] == "wd14 tags, manual tags"
assert params["negative_prompt"] == "low quality"
def test_conditioning_provenance_recovers_kj_set_get_prompts(
metadata_registry, monkeypatch
):
import types
prompt_graph = {
"encode_pos": {
"class_type": "CLIPTextEncode",
"inputs": {"text": "from set node", "clip": ["clip", 0]},
},
"set_positive": {
"class_type": "SetNode",
"inputs": {"CONDITIONING": ["encode_pos", 0], "name": "positive"},
},
"get_positive": {
"class_type": "GetNode",
"inputs": {"name": "positive"},
},
"sampler": {
"class_type": "KSampler",
"inputs": {
"seed": 123,
"steps": 20,
"cfg": 7.0,
"sampler_name": "Euler",
"scheduler": "karras",
"denoise": 1.0,
"positive": ["get_positive", 0],
"negative": ["encode_pos", 0],
"latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
},
},
}
prompt = SimpleNamespace(original_prompt=prompt_graph)
original_conditioning = object()
get_conditioning = object()
monkeypatch.setattr(metadata_processor, "standalone_mode", False)
metadata_registry.start_collection("prompt-kj-get")
metadata_registry.set_current_prompt(prompt)
metadata_registry.record_node_execution(
"encode_pos", "CLIPTextEncode", {"text": "from set node"}, None
)
metadata_registry.update_node_execution(
"encode_pos", "CLIPTextEncode", [(original_conditioning,)]
)
metadata_registry.record_node_execution(
"set_positive",
"SetNode",
{"CONDITIONING": original_conditioning, "name": "positive"},
None,
)
metadata_registry.record_node_execution(
"get_positive", "GetNode", {"name": "positive"}, None
)
metadata_registry.update_node_execution(
"get_positive", "GetNode", [(get_conditioning,)]
)
metadata_registry.record_node_execution(
"sampler",
"KSampler",
{
"seed": 123,
"steps": 20,
"cfg": 7.0,
"sampler_name": "Euler",
"scheduler": "karras",
"denoise": 1.0,
"positive": get_conditioning,
"negative": original_conditioning,
"latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
},
None,
)
metadata = metadata_registry.get_metadata("prompt-kj-get")
params = MetadataProcessor.extract_generation_params(metadata)
assert params["prompt"] == "from set node"
assert params["negative_prompt"] == "from set node"
def test_sampler_custom_advanced_recovers_prompt_text_through_guidance_nodes(metadata_registry, monkeypatch):
import types