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Image-to-Embroidery Public Model Package
Public model package for the image-to-embroidery research prototype. This refreshed package contains the latest available neural checkpoint and the current M2.91 professional-candidate research configuration.
This repository contains both:
- a Transformers-compatible custom PyTorch model that can be loaded with
AutoModel.from_pretrained(..., trust_remote_code=True); - the original project checkpoint and planner policy artifacts used by the full DST/PES export pipeline.
Version and status
- Latest trained neural checkpoint:
checkpoints/model14B_gentle_continue_geometry_final.pt - Direct Transformers weights:
model.safetensors(converted from the model14B checkpoint) - Promoted geometry baseline:
checkpoints/best_model13_multiformat_all_vector_continuity.pt - Latest planner research stage:
M2.91 professional candidate generation - Public Hub repository:
LLYszs070206/image-to-embroidery-private
Important: M2.91 is a planner/candidate-generation strategy, not a new .pt neural checkpoint. It is included under research/ so the newest research direction travels with the latest callable model.
Use and attribution
This is a public, research-only release. Read MODEL_LICENSE.md
before downloading or using it. The release claimant is LLYszs070206; cite
CITATION.cff and preserve AUTHOR_DECLARATION.md
and SHA256SUMS.txt when redistributing permitted reports.
Files
config.json
preprocessor_config.json
model.safetensors
configuration_embroidery.py
modeling_embroidery.py
image_processing_embroidery.py
checkpoints/model14B_gentle_continue_geometry_final.pt
checkpoints/best_model13_multiformat_all_vector_continuity.pt
checkpoints/m2_edge_policy.json
checkpoints/m2_edge_policy_m2_1_globalrepair20.json
configs/relation_planner.yaml
model_index.json
research/configs/m2_89_hard_failure_aware_selector.json
research/configs/m2_90_professional_texture_objective.json
research/configs/m2_91_professional_candidate_generation.json
research/docs/m2_89_hard_failure_aware_selector.md
research/docs/m2_90_professional_texture_objective.md
research/docs/m2_91_professional_candidate_generation.md
research/evidence/*
Direct Transformers Usage
from PIL import Image
from transformers import AutoImageProcessor, AutoModel
repo_id = "LLYszs070206/image-to-embroidery-private"
processor = AutoImageProcessor.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)
model.eval()
image = Image.open("your_image.png").convert("RGB")
inputs = processor(image, return_tensors="pt")
outputs = model(**inputs)
print(outputs.logits.shape) # [1, 20, 256, 256]
maps = model.post_process(outputs.logits)
mask = maps["mask"]
density = maps["density"]
path_order = maps["path_order"]
The 20 output channels are:
mask, density, axis_x, axis_y, axis_confidence, boundary, centerline,
stitch_type_no_stitch, stitch_type_running, stitch_type_satin, stitch_type_fill,
entry_endpoint_heatmap, exit_endpoint_heatmap, path_order,
segment_mask, segment_boundary, segment_order,
stitch_trace, near_connect, jump_endpoint
Recommended Local Download
hf download <your-namespace>/image-to-embroidery-private `
--include "checkpoints/*" "configs/*" "model_index.json" `
--local-dir hf_downloads/image-to-embroidery-private
The model repository is public and does not require authentication to download.
Distribution note: this repository publishes the model and runtime code. Any third-party images, DST/PES files, or source datasets used during research are not automatically relicensed by this repository; redistribute them only when their original license permits it.
One-command image to DST/PES
The repository also includes the local runtime needed by the exporter:
runtime/infer_model3_portrait_hybrid.py
runtime/planner/graph_tsp.py
runtime/planner/geometry_priors.py
runtime/planner/relation_cost.py
checkpoints/m2_edge_policy_m2_1_globalrepair20.json
Install the runtime dependencies, then run from the downloaded repository root:
python -m pip install -r requirements.txt
python run_image_to_dst.py inputs/your_image.png `
--output-dir outputs/your_image_m2_1
The command enables the model14B checkpoint, Graph-TSP ordering, M2 hard-safe
edge filtering, mask-safe connectors, continuity maps, and safe-connect repair.
It writes .dst, .pes, preview PNGs, and summary.json into the output
directory. Use --cpu if CUDA is unavailable.
M2.91 research files are included under research/. M2.91 is currently a
candidate-generation/selection experiment; the one-command conservative
runtime uses the validated M2.1 decode policy until M2.91's higher execution
cost is reduced.
Recommended Inference Flags
Use these artifacts with the local project repository:
python infer_model3_portrait_hybrid.py inputs/your_image.png `
--checkpoint hf_downloads/image-to-embroidery-private/checkpoints/best_model13_multiformat_all_vector_continuity.pt `
--output-dir outputs/your_image_m2_1 `
--planner-config hf_downloads/image-to-embroidery-private/configs/relation_planner.yaml `
--geometry-planner `
--model-path-order `
--use-continuity-planner `
--serpentine-fill `
--graph-tsp-planner `
--mask-safe-connectors `
--m2-edge-policy hf_downloads/image-to-embroidery-private/checkpoints/m2_edge_policy.json `
--m2-edge-policy-top-k 4 `
--m2-hard-safe-filter `
--m2-safe-min-inside-fraction 0.96 `
--m2-jump-aware-weight 0.25 `
--m2-offmask-weight 0.5 `
--m2-visible-weight 1.0 `
--m2-trim-weight 0.25 `
--safe-connect-repair `
--safe-connect-repair-max-mm 20.0 `
--safe-connect-repair-min-inside-fraction 0.90 `
--safe-connect-repair-global-mask
Current Status
- Main uploaded image model: the latest available model14B gentle-continue geometry checkpoint,
published as
model.safetensorsfor direct Transformers loading. - Previous promoted model13 vector-continuity checkpoint is retained under
checkpoints/for traceability. - Current promoted M2 decode setting: M2.1 global safe-connect repair20.
- M2.89 remains the conservative hard-safety deployment baseline.
- M2.90 is the safer professional-texture selector.
- M2.91 is the latest professional candidate-generation experiment. It improves professional-preview and texture signals, but its execution cost is higher, so it is not the conservative default yet.
The direct Transformers model returns dense geometry/planning maps. Full DST/PES export still uses the local project pipeline because DST writing depends on the graph planner, M2 policy, and pyembroidery exporter.
This package does not include training datasets.
- Downloads last month
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