Text-to-Image
Diffusers
Safetensors
English
Portuguese
StableDiffusionXLPipeline
stable-diffusion-xl
imageflow
Instructions to use BinaryLight1011/Imageflow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BinaryLight1011/Imageflow with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BinaryLight1011/Imageflow", 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
Jefferson de Azevedo commited on
Add Model Card — Imageflow.1.1.1
Browse files
README.md
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---
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license: creativeml-openrail-m
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language:
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- en
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- pt
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tags:
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- text-to-image
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- stable-diffusion-xl
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- diffusers
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- imageflow
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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pipeline_tag: text-to-image
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---
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# 🌊 Imageflow.1.1.1
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**Text-to-Image** model by [Jeffvelox](https://huggingface.co/Jeffvelox)
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Base: `stabilityai/stable-diffusion-xl-base-1.0`
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Framework: 🤗 Diffusers
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## 🚀 Uso Rápido
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```python
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from diffusers import StableDiffusionXLPipeline
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import torch
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pipe = StableDiffusionXLPipeline.from_pretrained(
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'Jeffvelox/Imageflow.1.1.1',
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torch_dtype=torch.float16,
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use_safetensors=True,
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).to('cuda')
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image = pipe(
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prompt='A futuristic city at night, neon lights, ultra detailed',
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negative_prompt='blurry, low quality',
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num_inference_steps=30,
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guidance_scale=7.5,
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).images[0]
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image.save('output.png')
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```
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## ⚙️ Parâmetros Recomendados
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| Parâmetro | Valor |
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|-----------|-------|
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| `guidance_scale` | 7.0 – 9.0 |
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| `num_inference_steps` | 25 – 40 |
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| `resolution` | 1024×1024 |
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**Autor:** Jeffvelox · **Treinado em:** Kaggle GPU
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