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("fofr/flux-jwst")
prompt = "a beautiful JWST landscape astrophotography photo of snowy mountains, distant village, with a beautiful nebula"
image = pipe(prompt).images[0]Flux JWST
- Prompt
- a beautiful JWST landscape astrophotography photo of snowy mountains, distant village, with a beautiful nebula
- Prompt
- a dynamic and beautiful JWST landscape astrophotography photo of snowy mountains, distant village, with a beautiful nebula
Run on Replicate:
https://replicate.com/fofr/flux-jwst
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
Trigger words
You should use JWST to trigger the image generation.
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.float16).to('cuda')
pipeline.load_lora_weights('fofr/flux-jwst', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
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Model tree for fofr/flux-jwst
Base model
black-forest-labs/FLUX.1-dev