metadata

license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
  - en
tags:
  - flux
  - diffusers
  - lora
  - replicate
base_model: black-forest-labs/FLUX.1-dev
pipeline_tag: text-to-image
instance_prompt: Vaginov
widget:
  - text: >-
      HD vintage color raw photo of a solo Vaginov reading out a poem to trees
      at a park in Peterhof, detailed realistic crisp lifelike amateur
      photography, HD 16mm color movie about the 1920s USSR, text: \A Journey
      into Chaos\, mysterious mythic creatures from Greek mythology like nymphs
      and fauns and satyrs peek from behind trees to hear the poems he reads,
      moderately wrinkled weathered lined skin texture with pores and
      imperfections, intricate details, analog camera, raw photo, film grain
    output:
      url: images/example_1g0hs7ig5.png

Vaginovflux

Prompt
HD vintage color raw photo of a solo Vaginov reading out a poem to trees at a park in Peterhof, detailed realistic crisp lifelike amateur photography, HD 16mm color movie about the 1920s USSR, text: \A Journey into Chaos\, mysterious mythic creatures from Greek mythology like nymphs and fauns and satyrs peek from behind trees to hear the poems he reads, moderately wrinkled weathered lined skin texture with pores and imperfections, intricate details, analog camera, raw photo, film grain

Prompt
HD vintage color raw cartoon illustration of a solo Vaginov arguing with an angry centaur stomping his hooves at an office of the Soviet writer's union,

Trained on Replicate using:

https://replicate.com/ostris/flux-dev-lora-trainer/train

Trigger words

You should use Vaginov 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('AlekseyCalvin/VaginovFlux', 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