Instructions to use SeanZheng/Distill-WAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SeanZheng/Distill-WAM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SeanZheng/Distill-WAM", 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
galaxea-a1-nyush-relation-approach-cup-flashwam-step40: lingbot_norm_stat.json
Browse files
galaxea-a1-nyush-relation-approach-cup-flashwam-step40/lingbot_norm_stat.json
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{
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"q01": [
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-0.009920889511704445,
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-0.13945214450359344,
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-0.12064539894461632,
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-0.11070928439497948,
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0.755806097984314,
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0.0531192272901535
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],
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"q99": [
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0.4581281143426896,
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0.09143948964774616,
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0.23990689590573314,
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0.30121618926525123,
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0.6139286923408509,
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0.2442081540822989,
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0.9999809265136719,
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0.9051401370763799
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],
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"n_frames": 66318,
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"action_dim": 8
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}
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