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Merge pull request #1121 from mmartial/loader
Add Load Image Metadata node for reusing generation settings
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pixelpaws
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# Load Image Metadata (LoraManager)
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Load a source image and reuse its prompts, local models, LoRAs, and sampling settings.
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The node lives under **Lora Manager → loaders**. Restart ComfyUI after installing
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this change and refresh the page. This Python node needs no Vue widget build.
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## Wiring a checkpoint workflow
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1. Upload/select an image in **Load Image Metadata (LoraManager)**.
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2. Convert `ckpt_name` on **Checkpoint Loader (LoraManager)** to an input and
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connect `model_name`. Leave its randomization control fixed.
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3. Connect the checkpoint's MODEL and CLIP to **Lora Loader (LoraManager)**.
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Connect the metadata node's `lora_stack` to that loader. Leave its LoRA widget
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empty unless you intentionally want additional LoRAs.
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4. Connect the LoRA loader's CLIP to two CLIP Text Encode nodes. Connect metadata
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`positive` and `negative` to their text inputs, and their conditioning outputs
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to KSampler. Connect the LoRA loader's MODEL to KSampler.
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5. Convert KSampler's seed, steps, cfg, sampler_name, scheduler, and denoise
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widgets to inputs and connect the corresponding metadata outputs.
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6. For text-to-image, connect width/height to an appropriate Empty Latent node.
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For img2img, encode the `image` output with the appropriate VAE instead.
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7. Connect KSampler's samples and the checkpoint's VAE to VAE Decode, then Save Image.
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8. Connect `readable_report` to a text display node for prompts, sampling settings,
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model/LoRA names, local resolution status and warnings. The original `report`
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output remains notes followed by formatted JSON; it is not a pure JSON string.
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`model_name`, `sampler_name`, and `scheduler` use COMBO outputs for converted
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dropdown inputs in current ComfyUI. `model_name` contains the matching local
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checkpoint or diffusion-model filename. The report identifies the resolved type;
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connect it to the appropriate loader. Lookup searches both categories regardless
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of how the original metadata labels the model.
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For a diffusion-model workflow, connect `model_name` to **Unet Loader
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(LoraManager)** and select the correct text encoder(s), VAE, latent node and
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architecture-specific conditioning separately. These settings do not reconstruct
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an entire workflow or guarantee pixel-identical reproduction.
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## Selection and overrides
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`prefer_saved_image_metadata` is enabled by default. It prefers the saved
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A1111-style generation parameters (including ComfyUI exports in that format)
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over the workflow. The report identifies this source; `sampler_node_id` is
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ignored in this mode when valid saved parameters are available. If saved
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parameters are absent or malformed, the node tries workflow metadata and
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reports any parsing failure.
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Disable the flag to prefer workflow extraction. Only active samplers are
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eligible: muted/bypassed sampler nodes and samplers inside muted/bypassed
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subgraph instances are excluded. This uses the saved UI workflow's mode flags
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when available, including nested subgraphs, and any modes in the API graph.
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Explicitly selecting an inactive sampler produces an error report and the
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usual saved-parameter/default recovery; it never extracts that inactive stage.
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With one supported active sampler, leave `sampler_node_id` blank. With several, enter
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its original node ID. Reports list candidate IDs when selection is ambiguous.
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Native subgraphs in API prompt metadata use colon-qualified paths: `1481:1783`
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means node 1783 inside subgraph instance 1481. Nested paths such as `10:20:30`
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are supported; slash notation (`1481/1783`) is also accepted. A container ID
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(`1481`) or leaf ID (`1783`) is accepted only if it identifies one sampler.
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An exact sampler ID takes precedence over abbreviated matching.
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Selection follows that sampler's graph, rather than mixing branches. Supported
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sampling nodes include KSampler, KSamplerAdvanced and SamplerCustomAdvanced with
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standard RandomNoise, CFGGuider/BasicGuider, BasicScheduler and KSamplerSelect
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components. BasicGuider's CFG is 1; its architecture-specific lack of negative
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conditioning is reported. Known Image Saver parameter/selector outputs and
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rgthree seed values can be read without executing those nodes.
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Detail Daemon's underlying sampler name is recovered, but its sampling effects
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are explicitly unsupported. Other custom model/conditioning nodes can still
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require defaults or overrides. If a requested stage cannot be read and global
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image parameters are used instead, the report explicitly says those parameters
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cannot verify the selected stage. Subgraph traversal requires the expanded API
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prompt; UI-workflow-only subgraph definitions are not expanded or executed.
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Extraction errors do not stop this node. If an API prompt uses unsupported
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samplers, the node first tries the image's saved generation parameters. Any
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remaining unavailable or invalid extracted fields use the SDXL starter defaults;
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valid extracted fields are preserved. `readable_report` starts with **❌ ERROR**
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and explains each recovery or substitution. This also applies to existing nodes
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saved with `missing_settings=strict`; that legacy option no longer blocks
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extraction recovery. New nodes default to `use_defaults`.
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The report uses emoji section markers (🖼️ image, 📦 model, ⚙️ sampling, 🧩 LoRAs,
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➕/➖ prompts) and ❌/⚠️/ℹ️ status markers. It is plain text, so colors depend on the
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connected display node. Missing/ambiguous local files still appear in
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`missing_files`. An empty model output requires selecting a local model manually.
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Invalid explicit overrides and unreadable image files remain execution errors.
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`overrides_json` replaces extracted values, for example:
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```json
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{
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"scheduler": "normal",
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"model_name": "portraits/model.safetensors",
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"seed": 12345,
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"loras": [["styles/ink.safetensors", 0.7, 0.3]]
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}
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```
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Supported keys: `positive`, `negative`, `model_name`, `seed`,
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`steps`, `cfg`, `sampler_name`, `scheduler`, `width`, `height`, `denoise`, `loras`.
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LoRA entries are `[name, model_strength, clip_strength]`; `"loras": []` explicitly
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clears the extracted stack. Legacy `checkpoint_name` and `unet_name` override
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keys remain accepted as aliases for `model_name`; supply only one model key.
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Exact relative or absolute local
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business paths disambiguate duplicate basenames. Matching falls back to a unique
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filename or extensionless filename, then an exact unique catalog `file_name` or
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`model_name` alias. Version dots are preserved when stripping known file
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extensions. It never downloads or fuzzy-matches models, and stale entries whose
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files no longer exist are excluded.
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Images with no metadata automatically use a bottle-inspired SDXL starter preset,
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even with an existing saved `strict` setting: a glass-bottle/galaxy landscape
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prompt, negative `text, watermark`, seed 0, 20 steps, CFG 7, Euler/normal,
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1024×1024 and denoise 1, with no LoRAs. These settings are clearly identified as
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synthetic defaults in both reports. Source image pixels and mask are unchanged.
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Overrides take precedence. The node selects `sd_xl_base_1.0.safetensors` only
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when uniquely indexed; otherwise choose an SDXL checkpoint manually or supply
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`model_name`. Malformed or unsupported metadata also recovers with an explicit ERROR report.
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## Supported metadata and limits
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- PNG API prompt metadata; JPEG/WebP EXIF parameter comments; ComfyUI WebP
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`prompt:`/`workflow:` EXIF fields.
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- Standard KSampler, core checkpoint/UNet/LoRA loaders, LoRA Manager checkpoint,
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UNet, LoRA/text loaders and LoRA stacks. LoRA application order and separate
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model/CLIP strengths are preserved, including intentional repeated entries.
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Different LoRA chains on model and prompt CLIP branches require an explicit
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stack override rather than being silently merged.
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- Literal CLIPTextEncode text and supported primitive value connections. Prompt
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polarity comes from sampler wiring, never from words such as “ugly”.
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- A1111/Forge generation text with explicit sampler alias mappings. Recognized
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LoRA directives become stack entries and are removed from prompt text. Literal
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tags in ComfyUI encoder text remain literal; graph loaders determine its stack.
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- A1111 `Automatic`/absent schedules do not reliably identify a ComfyUI schedule.
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The node substitutes `normal` and reports the missing information as an ERROR;
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an explicit override can select a different schedule.
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- UI-workflow-only fallback supports known core widget layouts, with a report
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warning. Saved widgets can differ from executed values (for example a seed
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randomized after generation). Custom widget layouts are not guessed.
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- KSamplerAdvanced partial/noise settings require an explicit denoise override;
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this is an intentional approximation, not a reconstruction of those controls.
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- Distinct SDXL/Flux encoder prompts, combined/regional/zeroed conditioning,
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arbitrary custom nodes, dynamic wildcards and unsupported custom sampling components are
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not automatically reconstructed. Supply explicit overrides or retain the
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original workflow for those cases.
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- Width/height come from a recognized latent source or fall back to source-image
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dimensions; resized/upscaled images can therefore need dimension overrides.
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- VAE, text encoder choice, CLIP skip, ControlNet and architecture-specific
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conditioning still need the appropriate nodes. No embedded code is executed
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and no external metadata service is contacted.
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LoRA Manager must have indexed the required models. Library resolution includes
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its configured extra folders and preserves business paths through symlinks.
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## Extraction without a local catalog
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The parser extracts names before attempting local resolution. In recovery mode,
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`report` includes `source_resources` with original model names, LoRA names and
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strengths, and embedded resource hashes even when none are installed. The model
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output sockets remain empty and the resolved stack excludes missing files.
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Combined sampler labels such as `Euler a SGM Uniform`, `Euler Normal` and
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`er_sde simple` are split into sampler and scheduler. Multiline parameter blocks
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and their nested JSON resource lists are supported. If prompt LoRA tags are
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absent, one hash-name entry and one weighted resource can be matched offline;
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multiple entries require an explicit mapping rather than guessing from order.
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A single resource also disambiguates duplicated identical prompt tags.
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The `Model` field in A1111-style metadata does not distinguish checkpoints from
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standalone diffusion models. The node searches both indexed categories by name,
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then reports the matched type. Local model type and filename cannot be verified
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without an indexed library. Multiple equally good matches are reported as
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ambiguous; specify a relative path through `model_name` to disambiguate.
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## “Image contains no supported generation metadata”
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For older versions, this means extraction failed before any library lookup.
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The current node uses the starter preset when metadata is entirely absent. The error identifies the
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actual server file, its format, byte size and metadata keys. PNG text chunks are
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read both before and after pixel data. If no generation metadata remains, upload
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the original saved file: clipboard copies and re-encoded/exported images may
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lose it. `use_defaults` supplies replacement settings; it does not recover the
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original prompts or seed.
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## Missing local resources
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`missing_files` is a text output listing unresolved checkpoints/UNets and LoRAs.
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LoRA entries include both model and CLIP weights and the resolution failure.
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It is empty when all requested resources resolve. Missing and ambiguous LoRAs
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are excluded from `lora_stack`, including in strict mode, so downstream loaders
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receive only resolved files. Valid entries keep their original order and weights.
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Unresolved model-name sockets are empty: select a model manually or override its
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name before connecting that socket to a loader.
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## Output layout and upgrade
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The outputs start with `image`, `mask`, `positive`, `negative`, **`model_name`**,
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**`lora_stack`**, **`lora_stack_text`**, followed by the sampling settings and reports.
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`lora_stack_text` lists each resolved stack path with model and CLIP weights in
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application order. It is empty for an empty stack; unresolved files appear only
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in `missing_files`, with their requested weights.
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This replaces the former separate checkpoint/UNet sockets and renames `lost_list`
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to `missing_files`. Restart ComfyUI, refresh, and recreate existing instances of
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this node; reconnect the model and stack outputs to avoid stale saved slot indices.
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Sampling and report output indices remain unchanged. No Vue build is required.
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