Vito Sansevero
e45f67dbf5
library-concept.spec.mjs compares the Prompt library shelf with the immutable concept on the concept-shaped fixture: heading, toolbar, caption, grid and the six cards (art, title row, prompt block, meta, tags, footer) at 1600x1100 and 390x844 in both themes and motion settings, the 3-column grid at 1800x1100, and the behaviors in both documents (title and Explore history open history, the footer send carries the positive only, copy toasts "Prompt copied.", the "neon" search and the Landscapes category). A mutation test proves the comparison notices a footer padding change and a removed variants chip. mapLibraryConcept applies only recorded deviations: D14, D16, D23 (title min-width, singular variant chip, spoken rating, the two-line clamp the concept's samples exceed at 390px), D35, D36, D40 and the new D42 (tag chips in the store's alphabetical order). Each re-laid-out part is checked against the concept page with that deviation applied at runtime. Corrections the comparison demonstrated: - the fixture creates the concept's prompts last to first, so Recently added lists them in the concept's order; - the shelf no longer ends with the legacy "Current prompt library" link; Overview's legacy cards keep it, as with "Current gallery". The shared inventory gains the Library section, and reflow/withoutItems move into concept-inventory.mjs for both comparison specs.
471 lines
17 KiB
Python
471 lines
17 KiB
Python
"""Concept-shaped real data for every concept comparison (D30 of #249).
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The approved concept's six prompts (docs/concepts/workspace/app.js:71-178) and its
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24 sample images (app.js:180-239) become real rows and real PNG files in this
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server's disposable database and output folder, so production renders the
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concept's shapes from its own stores: the same titles, texts, categories, tags,
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ratings and negative names, the same image dimensions, filenames and seeds, and
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the same sidebar counts (24 images, 6 prompts, 15 tags). Nothing is mocked.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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import json
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import math
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import os
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from pathlib import Path
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import shutil
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import time
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from types import SimpleNamespace
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from typing import TYPE_CHECKING
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from uuid import UUID, uuid5
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from PIL import Image, ImageDraw
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from PIL.PngImagePlugin import PngInfo
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from utils.execution_contexts import (
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ContextBundle,
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PromptSnapshot,
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RecipeContext,
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TextEvidence,
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)
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from utils.execution_history_events import make_finish, make_start
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from utils.execution_outputs import OutputBundle, OutputReference, snapshot_output_roots
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from utils.pair_execution import EXECUTION_KEY
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if TYPE_CHECKING:
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from py.api import PromptManagerAPI
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@dataclass(frozen=True)
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class ConceptPrompt:
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id: str
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title: str
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category: str
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tags: tuple[str, ...]
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positive: str
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negatives: tuple[tuple[str, str], ...]
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color: tuple[str, str, str]
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rating: int
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# app.js:71-178, in concept order. Negatives are (name, text) pairs.
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CONCEPT_PROMPTS = (
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ConceptPrompt(
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"alpine",
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"Alpine observatory",
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"Landscapes",
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("cinematic", "blue_hour", "architecture"),
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"A remote alpine observatory at blue hour, warm light through glass, dramatic "
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"mountain peaks, cinematic composition, atmospheric mist, fine detail",
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(
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("Quality baseline", "blurry, low quality, low resolution"),
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(
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"Clean architecture",
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"blurry, low quality, low resolution, distorted architecture, "
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"watermark, text",
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),
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("No negative", ""),
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),
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("#14233c", "#9aaac3", "#d8d9dd"),
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5,
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),
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ConceptPrompt(
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"dunes",
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"Quiet geometry",
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"Architecture",
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("minimal", "warm_light", "editorial"),
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"Monolithic sandstone pavilion in a vast desert, sweeping dunes, warm "
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"terracotta, late afternoon sun, architectural photography, quiet geometry",
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(
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("Natural materials", "plastic, harsh shadows, noise, watermark"),
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(
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"Clean lines",
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"plastic, harsh shadows, noise, watermark, distorted geometry",
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),
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),
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("#6c4948", "#ddad91", "#efe0c5"),
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4,
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),
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ConceptPrompt(
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"forest",
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"Somewhere in the pines",
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"Landscapes",
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("forest", "mist", "peaceful"),
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"A quiet cedar cabin above a still forest lake, towering evergreens, soft "
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"morning mist, muted jade and moss, a small amber window, cinematic still",
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(("Soft atmosphere", "oversaturated, blurry, low quality, text"),),
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("#163b3d", "#6f9691", "#c0cfc1"),
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5,
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),
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ConceptPrompt(
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"city",
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"After the rain",
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"Environments",
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("neon", "night", "cinematic"),
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"Rain-soaked city street at night, blue neon and rose reflections, deep "
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"perspective, cinematic science fiction, solitary architecture, detailed "
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"atmosphere",
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(
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("Clean atmosphere", "blurry, low resolution, watermark, oversaturated"),
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(
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"Less signage",
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"blurry, low resolution, watermark, oversaturated, text, signs",
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),
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),
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("#1a2143", "#6869a1", "#cf8fbc"),
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4,
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),
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ConceptPrompt(
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"lunar",
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"The long way home",
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"Illustration",
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("space", "minimal", "concept_art"),
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"An isolated lunar research outpost, a luminous planet above the horizon, "
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"sweeping pale terrain, quiet science fiction, finely detailed concept art",
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(("Quality baseline", "blurry, low quality, watermark"),),
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("#19283b", "#7398ac", "#e2d3c4"),
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5,
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),
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ConceptPrompt(
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"coast",
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"Between sea and sky",
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"Landscapes",
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("coast", "soft_light", "peaceful"),
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"A solitary lighthouse on a rugged coastal cliff, sea mist, glints of morning "
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"light on turquoise water, atmospheric perspective, fine art landscape",
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(("Natural light", "oversaturated, flat lighting, watermark, text"),),
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("#255565", "#8cbcc2", "#e8d9bf"),
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4,
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),
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)
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# app.js:220-227: the image at flat index n has the n % 6 dimensions; with six
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# prompts per round that is the prompt's own index.
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CONCEPT_DIMENSIONS = (
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(1536, 1152),
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(1024, 1536),
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(1280, 1280),
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(1600, 900),
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(1152, 1536),
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(1536, 1024),
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)
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CONCEPT_IMAGE_ROUNDS = 4
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CONCEPT_RUN_COUNT = 4
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# The concept's Model fact (app.js:408).
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CONCEPT_MODEL = "illustrative-model.safetensors"
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MAX_EXTRA_IMAGES = 120
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# Unlinked, seedless images (D11 "W × H · PNG") newer than the concept's, for D3 checks.
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MAX_SEEDLESS_IMAGES = 2
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SEEDLESS_SIZE = (1536, 1152)
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SESSION = "5c1f9a7e-3f0b-4a55-9d0e-2b8c4f6a7d10"
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_TABLES = (
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"execution_run_outputs",
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"execution_output_capture",
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"execution_run_contexts",
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"execution_context_capture",
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"execution_runs",
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"execution_contexts",
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"execution_prompt_snapshots",
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"execution_context_texts",
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"image_favorites",
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"image_prompt_pairs",
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"prompt_pair_tags",
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"prompt_pairs",
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"generated_images",
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"prompt_tags",
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"tags",
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"prompt_library_state",
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"prompts",
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)
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def concept_starred(prompt_index: int, round_index: int) -> bool:
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"""The concept's favorites (app.js ``starred``): each other prompt's first image."""
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return round_index == 0 and prompt_index % 2 == 0
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def concept_seed(prompt_index: int, round_index: int) -> int:
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"""The concept's sample seed (app.js:185)."""
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return 42001 + prompt_index * 100 + round_index
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def concept_filename(prompt: ConceptPrompt, round_index: int) -> str:
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"""The concept's sample filename (app.js:187)."""
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return f"{prompt.id}_{round_index + 1:05}.png"
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def _graph(positive: str, negative: str, seed: int) -> dict:
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"""A ComfyUI API prompt with the concept's facts (app.js:406-408).
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The recipe is a Prompt Manager pair node, so with its embedded execution record
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the inspector reports both roles as execution evidence, which the panel badges
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"Execution recorded" (D21). One sampler keeps the seed unambiguous.
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"""
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return {
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"1": {
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"class_type": "PromptManagerPair",
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"inputs": {"text": positive, "negative_text": negative, "clip": ["2", 1]},
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},
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"2": {
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"class_type": "CheckpointLoaderSimple",
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"inputs": {"ckpt_name": CONCEPT_MODEL},
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},
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"3": {
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"class_type": "KSampler",
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"inputs": {
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"model": ["2", 0],
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"positive": ["1", 0],
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"negative": ["1", 1],
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"seed": seed,
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"steps": 28,
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"cfg": 6.5,
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"sampler_name": "dpmpp_2m",
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"scheduler": "karras",
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},
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},
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"4": {"class_type": "SaveImage", "inputs": {"images": ["3", 0]}},
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}
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def _execution_record(positive: str, negative: str) -> dict:
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"""The pair node's embedded execution evidence (utils/pair_execution.py)."""
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record = {
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"text": positive,
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"negative_text": negative,
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"positive_text": positive,
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"negative_effective_text": negative,
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"prepend_text": "",
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"append_text": "",
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}
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return {"version": 1, "nodes": {"1": {"records": [record]}}}
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def _snapshot(text: str) -> PromptSnapshot:
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"""A recorded prompt snapshot whose raw and effective texts are ``text``."""
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def known(value: str) -> TextEvidence:
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return TextEvidence(value, "recorded", "runtime_return", None)
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return PromptSnapshot(known(text), known(text), known(""), known(""))
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def write_concept_png(path: Path, prompt_index: int, round_index: int) -> dict:
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"""Write the concept's sample image ``round_index`` of prompt ``prompt_index``.
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Returns its facts: variant, negative, seed and dimensions.
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"""
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prompt = CONCEPT_PROMPTS[prompt_index]
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index = round_index * len(CONCEPT_PROMPTS) + prompt_index
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variant = round_index % len(prompt.negatives)
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negative = prompt.negatives[variant][1]
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seed = concept_seed(prompt_index, round_index)
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width, height = size = CONCEPT_DIMENSIONS[index % len(CONCEPT_DIMENSIONS)]
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sky, ground, light = prompt.color
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info = PngInfo()
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info.add_text("prompt", json.dumps(_graph(prompt.positive, negative, seed)))
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info.add_text(
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EXECUTION_KEY, json.dumps(_execution_record(prompt.positive, negative))
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)
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with Image.new("RGB", size, sky) as image:
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draw = ImageDraw.Draw(image)
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draw.rectangle((0, height * 2 // 3, width, height), fill=ground)
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radius = min(width, height) // 9
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left, top = width * 2 // 3, height // 6
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draw.ellipse((left, top, left + radius, top + radius), fill=light)
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image.save(path, pnginfo=info)
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return {
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"variant": variant,
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"negative": negative,
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"seed": seed,
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"width": width,
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"height": height,
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}
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def _clear(api: PromptManagerAPI, root: Path) -> None:
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with api.db.model.get_connection() as conn:
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for table in _TABLES:
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conn.execute(f"DELETE FROM {table}")
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for name in ("output", "input"):
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shutil.rmtree(root / name)
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(root / name).mkdir()
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def _record_run(
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api: PromptManagerAPI,
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*,
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epoch: str,
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item: int,
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context: RecipeContext,
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output: OutputReference,
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observed_at: int,
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) -> str:
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"""One finished execution with its recipe context and its one output."""
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job = str(uuid5(UUID(SESSION), f"concept-job-{item}"))
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start = make_start(SESSION, ((item, job, {}, {}), item), epoch, observed_at)
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api.db.write_execution_start(start)
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finish = make_finish(
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SESSION,
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item,
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epoch,
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SimpleNamespace(messages=[("execution_success", {"prompt_id": job})]),
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observed_at + 10,
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)
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api.db.write_execution_finish(finish)
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api.db.write_execution_contexts(
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start.run_id, epoch, ContextBundle((context,), "complete", "captured")
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)
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api.db.write_execution_outputs_for_finish(
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finish, OutputBundle((output,), "complete", "reported")
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)
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return start.run_id
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def _write_seedless_png(path: Path) -> None:
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"""A plain PNG with no metadata: no prompt, no seed, no link."""
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with Image.new("RGB", SEEDLESS_SIZE, "#2b3442") as image:
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image.save(path)
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def _bounded(name: str, value: int, maximum: int) -> None:
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if isinstance(value, bool) or not isinstance(value, int):
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raise TypeError(f"{name} must be an integer")
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if not 0 <= value <= maximum:
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raise ValueError(f"{name} is out of range")
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def seed_workspace_concept(
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api: PromptManagerAPI,
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root: Path,
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*,
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extra_images: int = 0,
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seedless_images: int = 0,
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) -> dict:
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"""Replace this server's disposable rows and files with the concept's data.
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``extra_images`` continues the concept's rounds (same prompts, dimensions and
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seed rule) after its 24 images, for bounded-rendering and Load more checks.
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``seedless_images`` adds unlinked PNGs without metadata, newest of all.
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"""
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_bounded("extra_images", extra_images, MAX_EXTRA_IMAGES)
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_bounded("seedless_images", seedless_images, MAX_SEEDLESS_IMAGES)
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_clear(api, root)
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db = api.db
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epoch = db.get_execution_epoch()
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prompts = []
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# The concept lists its prompts most recent first (renderLibrary under Recently
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# added keeps the array order), so they are created last to first.
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for prompt in reversed(CONCEPT_PROMPTS):
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first_name, first_text = prompt.negatives[0]
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receipt = db.create_library_recipe(
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prompt.positive,
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first_text,
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expected_epoch=epoch,
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parent_metadata={
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"title": prompt.title,
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"category": prompt.category,
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"tags": list(prompt.tags),
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"rating": prompt.rating,
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},
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)
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positive_id = receipt["positive"]["id"]
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pair_ids = [receipt["pair"]["id"]]
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db.update_prompt_pair_metadata(pair_ids[0], title=first_name)
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for name, text in prompt.negatives[1:]:
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pair_ids.append(db.save_prompt_pair(prompt.positive, text, title=name))
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# Real queue-hook counting (D23): four runs, the last one now.
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db.record_prompt_use(positive_id, times=CONCEPT_RUN_COUNT)
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prompts.insert(
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0, {"id": prompt.id, "positive_id": positive_id, "pair_ids": pair_ids}
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)
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folder = root / "output" / "concepts"
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folder.mkdir()
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roots = snapshot_output_roots("posix", str(root / "output"), str(root / "temp"))
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# The API package needs the host stubs the server installs before importing it.
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from py.api.images import _favorite_relative_key, _favorite_root_key
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favorite_root = _favorite_root_key(list(api._get_all_output_dirs())[0])
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total = len(CONCEPT_PROMPTS) * CONCEPT_IMAGE_ROUNDS + extra_images
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rounds = math.ceil(total / len(CONCEPT_PROMPTS))
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now = time.time()
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images, runs = [], []
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for round_index in range(rounds):
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for prompt_index, prompt in enumerate(CONCEPT_PROMPTS):
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index = round_index * len(CONCEPT_PROMPTS) + prompt_index
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if index >= total:
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break
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name = concept_filename(prompt, round_index)
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path = folder / name
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facts = write_concept_png(path, prompt_index, round_index)
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variant, negative, seed = facts["variant"], facts["negative"], facts["seed"]
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# The concept lists images newest first in this order.
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stamp = now - 60 - index * 60
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os.utime(path, (stamp, stamp))
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owner = prompts[prompt_index]
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image_id = db.link_image_to_prompt(owner["positive_id"], str(path))
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pair_id = owner["pair_ids"][variant]
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db.link_image_to_pair(pair_id, image_id, {"source": "concept fixture"})
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if concept_starred(prompt_index, round_index):
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db.set_image_favorite(
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favorite_root, _favorite_relative_key(f"concepts/{name}")
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)
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context = RecipeContext(
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"3", _snapshot(prompt.positive), _snapshot(negative), "inferred"
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)
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reference = OutputReference(
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"4", 0, "output", "concepts", name, "posix", roots.output_key
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)
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runs.append(
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_record_run(
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api,
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epoch=epoch,
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item=index,
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context=context,
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output=reference,
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observed_at=int(stamp * 1000),
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)
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)
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images.append(
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{
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"filename": name,
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"image_id": image_id,
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"pair_id": pair_id,
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"prompt": prompt.id,
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"variant": variant,
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"seed": seed,
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"path": f"concepts/{name}",
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"width": facts["width"],
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"height": facts["height"],
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}
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)
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seedless = []
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for number in range(seedless_images):
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name = f"seedless_{number + 1:05}.png"
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path = folder / name
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_write_seedless_png(path)
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stamp = now - 10 - number * 10
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os.utime(path, (stamp, stamp))
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seedless.append({"filename": name, "path": f"concepts/{name}"})
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with db.model.get_connection() as conn:
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counts = conn.execute(
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"SELECT COUNT(*), MIN(run_count), MAX(run_count) FROM prompts "
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"WHERE id IN (SELECT prompt_id FROM prompt_library_state)"
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).fetchone()
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tags = [row[0] for row in conn.execute("SELECT name FROM tags ORDER BY id")]
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if tuple(counts) != (len(CONCEPT_PROMPTS), CONCEPT_RUN_COUNT, CONCEPT_RUN_COUNT):
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raise RuntimeError(f"Concept fixture run counts are not real: {tuple(counts)}")
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api._generate_gallery_thumbnails_sync(api._get_all_output_dirs(), (150, 150))
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api.invalidate_gallery_cache()
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return {
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"epoch": epoch,
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# The newest file's mtime (epoch seconds), for the D17 caption date.
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"newest_modified": now - 10 if seedless_images else now - 60,
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"seedless": seedless,
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"prompts": prompts,
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"images": images,
|
||
"runs": runs,
|
||
"tags": tags,
|
||
}
|