Historic_Color_Dev

Prompt

HST style autochrome photo, analog camera, blemished extremely elaborate mind-bogglingly fine objects within objects and textures within objects within textures within objects within subjects within textures within objects within textures within subjects within life within objects within textures, film photo, androgynous communist diverse deities amid peaceful revolution within revolt within reform within revolution, consolidate an inset progression of co-extending inspiring psychedelicate psychonautical images similar to poetic news cycle coverage, fearless spirits smitten with despairlessness, crisp, detailed timelessness, of future solarpunk utopian transurbanities, sublime global disassembly of capitalism, postcapitalist society, inspired by Walter Benjamin's Theses on the Philosophy of History, photorealistic reportage

Prompt

HST style autochrome photo, analog camera, blemished extremely elaborate mind-bogglingly fine objects within objects and textures within objects within textures within objects within subjects within textures within objects within textures within subjects within life within objects within textures, film photo, androgynous communist diverse deities amid peaceful revolution within revolt within reform within revolution, consolidate an inset progression of co-extending inspiring psychedelicate psychonautical images similar to poetic news cycle coverage, fearless spirits smitten with despairlessness, crisp, detailed timelessness, of future solarpunk utopian transurbanities, sublime global disassembly of capitalism, postcapitalist society, inspired by Walter Benjamin's Theses on the Philosophy of History, photorealistic reportage

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Trained on Replicate using:

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

Trigger words

You should use HST 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/historic_color_dev', 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