Instructions to use AmrutaMuthal/mero_sd2_controlnet_inpaint_masked_loss_wt_parts_ellipse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AmrutaMuthal/mero_sd2_controlnet_inpaint_masked_loss_wt_parts_ellipse with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AmrutaMuthal/mero_sd2_controlnet_inpaint_masked_loss_wt_parts_ellipse", 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
Upload model
Browse files- config.json +1 -1
- diffusion_pytorch_model.safetensors +1 -1
config.json
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{
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"_class_name": "ControlNetModel",
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"_diffusers_version": "0.31.0.dev0",
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"_name_or_path": "/
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"act_fn": "silu",
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"addition_embed_type": null,
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"addition_embed_type_num_heads": 64,
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{
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"_class_name": "ControlNetModel",
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"_diffusers_version": "0.31.0.dev0",
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"_name_or_path": "/home2/amrutamuthal/controlnet/ellipse/checkpoint/130000/checkpoint-130000/controlnet",
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"act_fn": "silu",
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"addition_embed_type": null,
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"addition_embed_type_num_heads": 64,
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diffusion_pytorch_model.safetensors
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size 1456953560
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