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fix(metadata): recover prompts through scheduled guidance

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Will Miao
2026-04-10 21:36:42 +08:00
parent 4ac78f8aa8
commit 39643eb2bc
2 changed files with 124 additions and 1 deletions
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11
py/metadata_collector/metadata_processor.py
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@@ -595,6 +595,15 @@ class MetadataProcessor:
if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
else:
positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
# Generic guider nodes often expose separate positive/negative inputs.
positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", max_depth=10)
if not positive_node_id:
positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", max_depth=10)
if not negative_node_id:
negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")

114
tests/metadata_collector/test_metadata_collector.py
View File

@@ -177,6 +177,120 @@ def test_attention_bias_clip_text_encode_prompts_are_collected(metadata_registry
assert prompt_results["negative_prompt"] == "low quality"
def test_sampler_custom_advanced_recovers_prompt_text_through_guidance_nodes(metadata_registry, monkeypatch):
import types
prompt_graph = {
"encode_pos": {
"class_type": "CLIPTextEncodeAttentionBias",
"inputs": {
"text": "A low-angle, medium close-up portrait of her.",
"clip": ["clip", 0],
},
},
"encode_neg": {
"class_type": "CLIPTextEncodeAttentionBias",
"inputs": {
"text": " This low quality greyscale unfinished sketch is inaccurate and flawed. The image is very blurred and lacks detail with excessive chromatic aberrations and artifacts. The image is overly saturated with excessive bloom. It has a toony aesthetic with bold outlines and flat colors. ",
"clip": ["clip", 0],
},
},
"scheduled_cfg_guidance": {
"class_type": "ScheduledCFGGuidance",
"inputs": {
"model": ["model", 0],
"positive": ["encode_pos", 0],
"negative": ["encode_neg", 0],
"cfg": 2.6,
"start_percent": 0.0,
"end_percent": 0.62,
},
},
"sampler": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"noise": types.SimpleNamespace(seed=174),
"guider": ["scheduled_cfg_guidance", 0],
"sampler": ["sampler_select", 0],
"sigmas": ["scheduler", 0],
"latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 128, 128))},
},
},
"sampler_select": {
"class_type": "KSamplerSelect",
"inputs": {"sampler_name": "multistep/deis_2m"},
},
"scheduler": {
"class_type": "BasicScheduler",
"inputs": {"steps": 20, "scheduler": "power_shift", "denoise": 1.0},
},
}
prompt = SimpleNamespace(original_prompt=prompt_graph)
pos_conditioning = object()
neg_conditioning = object()
monkeypatch.setattr(metadata_processor, "standalone_mode", False)
metadata_registry.start_collection("prompt-guidance")
metadata_registry.set_current_prompt(prompt)
metadata_registry.record_node_execution(
"encode_pos",
"CLIPTextEncodeAttentionBias",
{"text": "A low-angle, medium close-up portrait of her."},
None,
)
metadata_registry.update_node_execution(
"encode_pos", "CLIPTextEncodeAttentionBias", [(pos_conditioning,)]
)
metadata_registry.record_node_execution(
"encode_neg",
"CLIPTextEncodeAttentionBias",
{
"text": " This low quality greyscale unfinished sketch is inaccurate and flawed. The image is very blurred and lacks detail with excessive chromatic aberrations and artifacts. The image is overly saturated with excessive bloom. It has a toony aesthetic with bold outlines and flat colors. ",
},
None,
)
metadata_registry.update_node_execution(
"encode_neg", "CLIPTextEncodeAttentionBias", [(neg_conditioning,)]
)
metadata_registry.record_node_execution(
"scheduled_cfg_guidance",
"ScheduledCFGGuidance",
{
"positive": pos_conditioning,
"negative": neg_conditioning,
"cfg": 2.6,
},
None,
)
metadata_registry.record_node_execution(
"sampler",
"SamplerCustomAdvanced",
{
"noise": types.SimpleNamespace(seed=174),
"guider": {
"positive": pos_conditioning,
"negative": neg_conditioning,
},
"sampler": ["sampler_select", 0],
"sigmas": ["scheduler", 0],
"latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 128, 128))},
},
None,
)
metadata = metadata_registry.get_metadata("prompt-guidance")
params = MetadataProcessor.extract_generation_params(metadata)
assert params["prompt"] == "A low-angle, medium close-up portrait of her."
assert (
params["negative_prompt"]
== " This low quality greyscale unfinished sketch is inaccurate and flawed. The image is very blurred and lacks detail with excessive chromatic aberrations and artifacts. The image is overly saturated with excessive bloom. It has a toony aesthetic with bold outlines and flat colors. "
)
def test_metadata_registry_caches_and_rehydrates(populated_registry):
registry = populated_registry["registry"]
prompt = populated_registry["prompt"]