Text-to-Image
Diffusers
Safetensors
English
Portuguese
StableDiffusionXLPipeline
stable-diffusion-xl
imageflow
Instructions to use jeffvedd/Imageflow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jeffvedd/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("jeffvedd/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
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("jeffvedd/Imageflow", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]🌊 Imageflow.1.1.1
Text-to-Image model by jeffvedd
Base: stabilityai/stable-diffusion-xl-base-1.0
Framework: 🤗 Diffusers
🚀 Uso Rápido
from diffusers import StableDiffusionXLPipeline
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
'jeffvedd/Imageflow',
torch_dtype=torch.float16,
use_safetensors=True,
).to('cuda')
image = pipe(
prompt='A futuristic city at night, neon lights, ultra detailed',
negative_prompt='blurry, low quality',
num_inference_steps=30,
guidance_scale=7.5,
).images[0]
image.save('output.png')
⚙️ Parâmetros Recomendados
| Parâmetro | Valor |
|---|---|
guidance_scale |
7.0 – 9.0 |
num_inference_steps |
25 – 40 |
resolution |
1024×1024 |
Autor: jeffvedd · Treinado em: Kaggle GPU
- Downloads last month
- -
Model tree for jeffvedd/Imageflow
Base model
stabilityai/stable-diffusion-xl-base-1.0