Trained via Ostris'
ai-toolkit on 50 high-resolution scans of 1910s/1920s posters & artworks by the great Soviet
poet, artist, & Marxist activist Vladimir Mayakovsky.
For this training experiment, we first spent many days rigorously translating the textual elements (slogans, captions, titles, inset poems, speech fragments, etc), with form/signification/rhymes intact, throughout every image subsequently used for training.
These translated textographic elements were, furthermore, re-placed by us into their original visual contexts, using fonts matched up to the sources.
We then manually composed highly detailed paragraph-long captions, wherein we detailed both the graphic and the textual content of each piece, its layout, as well as the most intuitive/intended apprehension of each composition.
This version of the resultent LoRA was trained on our custom Schnell-based checkpoint (Historic Color 2), available
here in fp8 Safetensors and
here in Diffusers format.
To produce this earlier checkpoint (the full 5000 run is available in
another repo), the training went on for 3600 steps at a DiT Learning Rate of .00002, batch 1, with the ademamix8bit optimizer, and both text encoders trained alongside the DiT!
No synthetic data was used for the training, nor any auto-generated captions! Everything was manually and attentively pre-curated with a deep respect for the sources used.
Check out our
translations of Mayakovsky's verse-works, adapted from a proto-Soviet song-tongue into a Worldish one...
And found, along with many other poets' songs and tomes...
Over
at SilverAgePoets.com!
1from diffusers import AutoPipelineForText2Image
2import torch
3
4pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
5pipeline.load_lora_weights('AlekseyCalvin/Mayakovsky_Posters_2_3600st', weight_name='lora.safetensors')
6image = pipeline('your prompt').images[0]
For more details, including weighting, merging and fusing LoRAs, check the
documentation on loading LoRAs in diffusers