Instructions to use BarrenWardo/CatVTON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BarrenWardo/CatVTON with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BarrenWardo/CatVTON", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Delete DensePose
Browse files
DensePose/Base-DensePose-RCNN-FPN.yaml
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VERSION: 2
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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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BACKBONE:
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NAME: "build_resnet_fpn_backbone"
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RESNETS:
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OUT_FEATURES: ["res2", "res3", "res4", "res5"]
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FPN:
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IN_FEATURES: ["res2", "res3", "res4", "res5"]
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ANCHOR_GENERATOR:
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SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
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ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
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RPN:
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IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
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PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
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PRE_NMS_TOPK_TEST: 1000 # Per FPN level
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# Detectron1 uses 2000 proposals per-batch,
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# (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
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# which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
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POST_NMS_TOPK_TRAIN: 1000
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POST_NMS_TOPK_TEST: 1000
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DENSEPOSE_ON: True
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ROI_HEADS:
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NAME: "DensePoseROIHeads"
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IN_FEATURES: ["p2", "p3", "p4", "p5"]
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NUM_CLASSES: 1
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ROI_BOX_HEAD:
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NAME: "FastRCNNConvFCHead"
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NUM_FC: 2
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POOLER_RESOLUTION: 7
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POOLER_SAMPLING_RATIO: 2
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POOLER_TYPE: "ROIAlign"
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ROI_DENSEPOSE_HEAD:
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NAME: "DensePoseV1ConvXHead"
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POOLER_TYPE: "ROIAlign"
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NUM_COARSE_SEGM_CHANNELS: 2
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DATASETS:
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TRAIN: ("densepose_coco_2014_train", "densepose_coco_2014_valminusminival")
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TEST: ("densepose_coco_2014_minival",)
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SOLVER:
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IMS_PER_BATCH: 16
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BASE_LR: 0.01
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STEPS: (60000, 80000)
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MAX_ITER: 90000
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WARMUP_FACTOR: 0.1
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INPUT:
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MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
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DensePose/densepose_rcnn_R_50_FPN_s1x.yaml
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
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MODEL:
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WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
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RESNETS:
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DEPTH: 50
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SOLVER:
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MAX_ITER: 130000
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STEPS: (100000, 120000)
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DensePose/model_final_162be9.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:b8a7382001b16e453bad95ca9dbc68ae8f2b839b304cf90eaf5c27fbdb4dae91
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size 255757821
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