Instructions to use valhalla/sdxl-inpaint-ema with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use valhalla/sdxl-inpaint-ema with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("valhalla/sdxl-inpaint-ema", torch_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
50k
Browse files- config.json +2 -2
- diffusion_pytorch_model.safetensors +1 -1
config.json
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{
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"_class_name": "UNet2DConditionModel",
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"_diffusers_version": "0.21.0.dev0",
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"_name_or_path": "checkpoint-
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"act_fn": "silu",
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"addition_embed_type": "text_time",
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"addition_embed_type_num_heads": 64,
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"num_attention_heads": null,
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"num_class_embeds": null,
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"only_cross_attention": false,
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"optimization_step":
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"out_channels": 4,
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"power": 0.6666666666666666,
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"projection_class_embeddings_input_dim": 2816,
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{
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"_class_name": "UNet2DConditionModel",
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"_diffusers_version": "0.21.0.dev0",
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"_name_or_path": "checkpoint-50000/unet_ema",
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"act_fn": "silu",
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"addition_embed_type": "text_time",
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"addition_embed_type_num_heads": 64,
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"num_attention_heads": null,
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"num_class_embeds": null,
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"only_cross_attention": false,
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"optimization_step": 50000,
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"out_channels": 4,
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"power": 0.6666666666666666,
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"projection_class_embeddings_input_dim": 2816,
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diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 5135178560
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version https://git-lfs.github.com/spec/v1
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oid sha256:183a10e938e8d8938765c020fd4fddaba894c6d88878890e40024be9dbd4d8ce
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size 5135178560
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