Image-to-Image
Diffusers
Safetensors
image-decomposition
layered-image-editing
diffusion
flux
lora
transparent-rgba
Instructions to use SynLayers/synlayers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SynLayers/synlayers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("SynLayers/synlayers") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
Upload demo/infer/run_caption_bbox_infer.py with huggingface_hub
Browse files
demo/infer/run_caption_bbox_infer.py
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@@ -31,12 +31,11 @@ def resolve_default_bbox_model() -> str:
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candidates = [
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PROJECT_ROOT if (PROJECT_ROOT / "config.json").exists() and (PROJECT_ROOT / "tokenizer_config.json").exists() else None,
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PROJECT_ROOT / "Bbox-caption-8b",
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Path("/project/llmsvgen/share/data/kmw_layered_checkpoint/Bbox-caption-8b"),
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]
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for candidate in candidates:
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if candidate and candidate.exists():
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return str(candidate)
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return
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CAPTION_BBOX_PROMPT_TOP_LEFT = (
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candidates = [
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PROJECT_ROOT if (PROJECT_ROOT / "config.json").exists() and (PROJECT_ROOT / "tokenizer_config.json").exists() else None,
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PROJECT_ROOT / "Bbox-caption-8b",
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]
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for candidate in candidates:
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if candidate and candidate.exists():
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return str(candidate)
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return "SynLayers/Bbox-caption-8b"
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CAPTION_BBOX_PROMPT_TOP_LEFT = (
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