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("veravira/hermes")
prompt = "Photo of a woman wearing a brown coat and black underwear, standing on a red carpet. She is holding a brown purse in her hand and looking to her right. The woman is wearing a black bra and is the main focus of the image. The background is blurry, and the lighting is soft, creating a sense of focus on the woman. The image is in the style of a fashion photo shoot."
image = pipe(prompt).images[0]Hermes
- Prompt
- Photo of a woman wearing a brown coat and black underwear, standing on a red carpet. She is holding a brown purse in her hand and looking to her right. The woman is wearing a black bra and is the main focus of the image. The background is blurry, and the lighting is soft, creating a sense of focus on the woman. The image is in the style of a fashion photo shoot.
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
Trigger words
You should use HERMES 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('veravira/hermes', 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 veravira/hermes
Base model
black-forest-labs/FLUX.1-dev