Instructions to use kwagh20ite/finetuned_stable_diffusion_batch2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kwagh20ite/finetuned_stable_diffusion_batch2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kwagh20ite/finetuned_stable_diffusion_batch2", 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
- Local Apps
- Draw Things
- DiffusionBee
Upload folder using huggingface_hub
#6
by kwagh20ite - opened
- config.json +31 -0
- diffusion_pytorch_model.bin +3 -0
config.json
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{
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"_class_name": "AutoencoderKL",
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"_diffusers_version": "0.17.0.dev0",
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"_name_or_path": "stabilityai/stable-diffusion-2-1",
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"act_fn": "silu",
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"block_out_channels": [
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128,
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256,
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512,
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512
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],
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"down_block_types": [
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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"DownEncoderBlock2D"
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],
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"in_channels": 3,
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"latent_channels": 4,
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"layers_per_block": 2,
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"norm_num_groups": 32,
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"out_channels": 3,
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"sample_size": 768,
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"scaling_factor": 0.18215,
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"up_block_types": [
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"UpDecoderBlock2D",
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"UpDecoderBlock2D",
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"UpDecoderBlock2D",
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"UpDecoderBlock2D"
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]
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}
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diffusion_pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:7cfdd672df17db3283633acb3721afc7735927293c2d3bd2bf64939a6dcd950e
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size 167407857
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