Instructions to use KeepNoob/Pokemon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KeepNoob/Pokemon with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("KeepNoob/Pokemon", 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
Upload files
Browse files- logs/train_example/events.out.tfevents.1711183606.autodl-container-a62d4480bf-b8f4adf9.5028.0 +3 -0
- logs/train_example/events.out.tfevents.1711195963.autodl-container-a62d4480bf-b8f4adf9.53390.0 +3 -0
- model_index.json +12 -0
- samples/.ipynb_checkpoints/0001-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/0500-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/1000-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/10000-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/1500-checkpoint.png +0 -0
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- samples/.ipynb_checkpoints/2500-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/4000-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/5500-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/6500-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/7000-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/8000-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/8500-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/9000-checkpoint.png +0 -0
- samples/.ipynb_checkpoints/9500-checkpoint.png +0 -0
- samples/0001.png +0 -0
- samples/0500.png +0 -0
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- samples/8000.png +0 -0
- samples/8500.png +0 -0
- samples/9000.png +0 -0
- samples/9500.png +0 -0
- scheduler/scheduler_config.json +19 -0
- unet/config.json +51 -0
- unet/diffusion_pytorch_model.safetensors +3 -0
logs/train_example/events.out.tfevents.1711183606.autodl-container-a62d4480bf-b8f4adf9.5028.0
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"_class_name": "DDPMPipeline",
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"scheduler": [
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scheduler/scheduler_config.json
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"time_embedding_type": "positional",
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"up_block_types": [
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"UpBlock2D",
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"AttnUpBlock2D",
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"UpBlock2D",
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"UpBlock2D",
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"UpBlock2D",
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"UpBlock2D"
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"upsample_type": "conv"
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unet/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ffd6181f5fd38afe85e537767a4b7d3d1bd2be2d244264330f7eedb43f9c5005
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size 454741108
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