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22398c5213 | ||
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1a4cfbfd7e | ||
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eac4e73b47 |
@@ -58,10 +58,10 @@
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- `Dumas H3 Prompt Curator`
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- Inputs: `action_prompt`, `anatomy_guard`, `subject_count_guard`, optional `anchor`, optional `soundscape`, optional `bgm`, optional `ref_1` through `ref_9`
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- Outputs: `prompt`, `ref_image_1` through `ref_image_9`, `reference_count`, `debug`, `anchor`, `sounds`, `bgm`, `original_ref_1` through `original_ref_9`, `reference_description`
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- Outputs: `prompt`, `ref_image_1` through `ref_image_9`, `reference_count`, `debug`, `anchor`, `sounds`, `bgm`, `original_ref_1` through `original_ref_9`
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- Builds one standalone MiniMax H3 prompt from your final action text plus structured character/location references.
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- The action text can mention references by character/location name, alias, `<Picture N>`, or `<refN>`. Only mentioned references are emitted, and the output images are compacted/renumbered so skipped inputs do not leave gaps.
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- Extra component outputs expose the cleaned anchor, sounds, BGM, selected structured references in compacted order, and the compiled reference-description block used inside the prompt.
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- Extra component outputs expose the cleaned anchor, sounds, BGM, and each selected original reference image in compacted order.
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- Adds curated reference context, anatomy guard text, optional subject-count guard text, anchor/style text, `overall_soundscape:` text, and `background_music:` text while respecting MiniMax H3's reference-generation shape: one prompt plus up to nine reference images.
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- `Dumas H3 Shot Length`
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@@ -1277,8 +1277,6 @@ def curate_h3_prompt(
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prompt = prompt[: _H3_PROMPT_MAX_CHARS - 3].rstrip() + "..."
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images = [_reference_image(ref) for _slot, ref in selected]
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images.extend([None] * (_H3_PROMPT_REF_SLOTS - len(images)))
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selected_refs = [ref for _slot, ref in selected]
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selected_refs.extend([None] * (_H3_PROMPT_REF_SLOTS - len(selected_refs)))
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debug = (
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f"Selected {len(selected)} reference(s): "
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+ ", ".join(
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@@ -1296,8 +1294,7 @@ def curate_h3_prompt(
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anchor_text,
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soundscape_text,
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bgm_text,
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*selected_refs[:_H3_PROMPT_REF_SLOTS],
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reference_description,
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*images[:_H3_PROMPT_REF_SLOTS],
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)
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@@ -2689,8 +2686,7 @@ class DumasH3PromptCuratorNode:
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("STRING",)
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+ ("IMAGE",) * _H3_PROMPT_REF_SLOTS
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+ ("INT", "STRING", "STRING", "STRING", "STRING")
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+ (_REFERENCE_TYPE,) * _H3_PROMPT_REF_SLOTS
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+ ("STRING",)
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+ ("IMAGE",) * _H3_PROMPT_REF_SLOTS
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)
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RETURN_NAMES = (
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"prompt",
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@@ -2717,7 +2713,6 @@ class DumasH3PromptCuratorNode:
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"original_ref_7",
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"original_ref_8",
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"original_ref_9",
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"reference_description",
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)
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FUNCTION = "curate_prompt"
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CATEGORY = "Dumas/MiniMax"
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@@ -502,14 +502,9 @@ class DumasImageNodeTests(unittest.TestCase):
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self.assertEqual(result[12], "grounded handheld thriller")
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self.assertEqual(result[13], "steady rain")
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self.assertEqual(result[14], "low suspense music")
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self.assertEqual(result[15]["name"], "Dave")
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self.assertIs(result[15]["image"], dave_image)
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self.assertEqual(result[16]["name"], "Coffee Shop")
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self.assertIs(result[16]["image"], cafe_image)
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self.assertIs(result[15], dave_image)
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self.assertIs(result[16], cafe_image)
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self.assertIsNone(result[17])
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self.assertIn("<Picture 1> Dave", result[24])
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self.assertIn("<Picture 2> Coffee Shop", result[24])
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self.assertIn("Location context for <Picture 2> Coffee Shop", result[24])
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def test_h3_prompt_curator_renumbers_explicit_reference_tags(self):
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node = self.image_nodes.DumasH3PromptCuratorNode()
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@@ -647,11 +642,10 @@ class DumasImageNodeTests(unittest.TestCase):
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def test_h3_prompt_curator_uses_documented_reference_limits(self):
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node = self.image_nodes.DumasH3PromptCuratorNode()
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self.assertEqual(len(node.RETURN_TYPES), 25)
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self.assertEqual(len(node.RETURN_TYPES), 24)
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self.assertEqual(node.RETURN_NAMES[1:10], tuple(f"ref_image_{i}" for i in range(1, 10)))
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self.assertEqual(node.RETURN_NAMES[12:15], ("anchor", "sounds", "bgm"))
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self.assertEqual(node.RETURN_NAMES[15:24], tuple(f"original_ref_{i}" for i in range(1, 10)))
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self.assertEqual(node.RETURN_NAMES[24], "reference_description")
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def test_normalize_reference_upgrades_generic_summary_with_socket_picture_id(self):
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image = FakeTensorBatch()
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