README.md

metadata

license: bigscience-bloom-rail-1.0
tags:
  - stable-diffusion
  - diffusion
model-index:
  - name: bloom-560m-RLHF-SD2-prompter
    results: []
datasets:
  - Gustavosta/Stable-Diffusion-Prompts
widget:
  - text: '<s>Prompt: '
inference:
  parameters:
    eos_token_id: 2
    max_length: 128
    do_sample: true

BLOOM-560m RLHF SD2 Prompter Aesthetic

This is a further finetuned version of crumb/bloom-560m-RLHF-SD2-prompter to optimize for aesthetic score with models from https://github.com/crowsonkb/simulacra-aesthetic-models instead of me hand scoring each image

donate so i can do this on real hardware : https://github.com/aicrumb/aicrumb/blob/main/README.md

Example usage

# Install libraries needed to run the models
!pip install transformers diffusers accelerate -qq

# Import the libraries
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
from transformers import pipeline
import torch

# This is the model that the transformer was finetuned to generate prompts for
model_id = "stabilityai/stable-diffusion-2-base"

# Use the Euler scheduler here
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, revision="fp16", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

# Load the transformer model
prompt_pipe = pipeline("text-generation", model="crumb/bloom-560m-RLHF-SD2-prompter")
prompt = "cool landscape"

# Auto-complete prompt
prompt = "<s>Prompt: " + prompt + ","
extended_prompt = prompt_pipe(prompt, do_sample=True, max_length=42)[0]['generated_text']
extended_prompt = extended_prompt[10:]
print("Prompt is now: ", extended_prompt)

# Generate image
image = pipe(extended_prompt).images[0]  

image.save("output.png")
image