Instructions to use seawolf2357/photo-jo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use seawolf2357/photo-jo 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("seawolf2357/photo-jo") prompt = "A man jogging by the riverbank a man at jo" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
photo-jo

- Prompt
- A man jogging by the riverbank a man at jo

- Prompt
- A man lecturing at a university a man at jo

- Prompt
- A person in a bustling cafe a man at jo
Trigger words
You should use a man at jo to trigger the image generation.
Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('seawolf2357/photo-jo', weight_name='photo-jo')
image = pipeline('A man jogging by the riverbank a man at jo').images[0]
image.save("my_image.png")
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
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Model tree for seawolf2357/photo-jo
Base model
black-forest-labs/FLUX.1-dev