Instructions to use Muapi/ev-ganin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/ev-ganin with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/ev-ganin") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Update README metadata for HF classification
Browse files
README.md
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**Base model**: Flux.1 D
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**Trained words**:
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## 🧠 Usage (Python)
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headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
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payload = {
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"prompt": "masterpiece, best quality, 1girl, looking at viewer",
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"model_id": [{"model": "civitai:
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"width": 1024,
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"height": 1024,
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"num_images": 1
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**Base model**: Flux.1 D
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**Trained words**: Ev Ganin II style
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## 🧠 Usage (Python)
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headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
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payload = {
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"prompt": "masterpiece, best quality, 1girl, looking at viewer",
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"model_id": [{"model": "civitai:1287604@1243084", "weight": 1.0}],
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"width": 1024,
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"height": 1024,
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"num_images": 1
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