Instructions to use Muapi/infundibulum-insertion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/infundibulum-insertion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cocktailpeanut/pony-diffusion-v6-xl", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/infundibulum-insertion") 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
Add 精液漏斗/infundibulum insertion
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- README.md +6 -6
- infundibulum-insertion.safetensors +2 -2
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README.md
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license: openrail++
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library_name: diffusers
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base_model:
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tags:
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- lora
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**Base model**:
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**Trained words**: infundibulum insertion
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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",
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"lora_model": "
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license: openrail++
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library_name: diffusers
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base_model: cocktailpeanut/pony-diffusion-v6-xl
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tags:
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- lora
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- text-to-image
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- pony
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pipeline_tag: text-to-image
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---
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**Base model**: Pony
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**Trained words**: infundibulum insertion
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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",
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"lora_model": "infundibulum-insertion",
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"lora_strength": 1.0,
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"width": 1024,
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"height": 1024,
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infundibulum-insertion.safetensors
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Git LFS Details
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