Instructions to use mvrdock/sd1.4-electronics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mvrdock/sd1.4-electronics with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mvrdock/sd1.4-electronics", 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 Settings
- Draw Things
- DiffusionBee
Commit History
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Create README.md
Murdock AubryVerified signatureThis model is part of the research work described in "FeatureFusion: Merging Diffusion Models Through Representation Correlations" by Murdock Aubry and James Bona-Landry. <h1> Model Description </h1> <h2>Overview</h2> This model is an electronics specialist based on the Stable Diffusion 1.4 architecture. <br> <h2>Model Details</h2> Base Model: CompVis/stable-diffusion-v1-4 Type: Specialist Specialization: Electronics Training Data: Electronics shard Model Architecture: UNet-based diffusion model <h2>Limitations</h2> The model has the same limitations as the base Stable Diffusion model Best performance is achieved when prompts relate to the model's specialization May produce unexpected results for concepts outside its training distribution <h1>Training</h1> <h2>Training Procedure</h2> Training Data: Pick-a-Pic v1 Training Method: Finetuning of the UNet component while keeping text encoder and VAE frozen <h2>Hyperparameters:</h2> Optimizer: AdamW Learning rate: 1e-6 Schedule…704e84c -
Create README.md
Murdock AubryVerified signatureThis model is part of the research work described in "FeatureFusion: Merging Diffusion Models Through Representation Correlations" by Murdock Aubry and James Bona-Landry. <h1> Model Description </h1> <h2>Overview</h2> This model is an electronics specialist based on the Stable Diffusion 1.4 architecture. <br> <h2>Model Details</h2> Base Model: CompVis/stable-diffusion-v1-4 Type: Specialist Specialization: Electronics Training Data: Electronics shard Model Architecture: UNet-based diffusion model <h2>Limitations</h2> The model has the same limitations as the base Stable Diffusion model Best performance is achieved when prompts relate to the model's specialization May produce unexpected results for concepts outside its training distribution <h1>Training</h1> <h2>Training Procedure</h2> Training Data: Pick-a-Pic v1 Training Method: Finetuning of the UNet component while keeping text encoder and VAE frozen <h2>Hyperparameters:</h2> Optimizer: AdamW Learning rate: 1e-6 Schedule…4956f71 -
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