readme: ---
  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 
  
  **COLAB DEMO INCLUDING STABLE DIFFUSION: https://colab.research.google.com/github/aicrumb/doohickey/blob/main/rlhf_prompt_tuner.ipynb**
  
  Using RLHF (Reinforcement Learning from Human Feedback) to finetune [mrm8488/bloom-560m-finetuned-sd-prompts](https://hf.co/mrm8488/bloom-560m-finetuned-sd-prompts) further for SD2.0
  
  ```
  batch_size = 16
  learning_rate = 0.001 # this is why I didn't have to spend _forever_ on it
  ```
  
  Generate extension with "\<s>Prompt: " and whatever your normal prompt is.
  
  I did this myself. I sat down and just ranked images for so long. It's gone through a couple iterations. Only the biases and layernorm weights were trained. The commit messages are a MESS. **First iteration of this project**
  
  donate so i can do this on real hardware : https://github.com/aicrumb/aicrumb/blob/main/README.md
  
  ## Example usage
  
  ```python
  # 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
  ```
  *Prompt is now:   cool landscape, concept art*
  ![](https://cdn.discordapp.com/attachments/1010693530181718146/1047831482808406067/image.png)
  
  *Prompt is now:   cool landscape, concept art, sharp focus, digital painting*
  ![](https://cdn.discordapp.com/attachments/1010693530181718146/1047832480335536249/image.png)
  
  short additions, they work though I guess (results vary)
  
  It's also very good at generating prompts by itself, with just the "Prompt:" prompt.
  
  *\<s>Prompt: 1 0 th century, highly detailed, concept art, cinematic lighting, unreal engine, trending on artstation, artstation hd, artstation hq, very very detailed*
  ![](https://cdn.discordapp.com/attachments/1010693530181718146/1047843202050310174/image.png)
  
  Further testing to be done in this area (automated training with aesthetic predicting models, larger data collection about prompt scores, better training in general)
  
  Also, enjoy this graphic I had to make myself because I kept being indecisive of the reward methodology ![](https://cdn.discordapp.com/attachments/1010693530181718146/1047846272096292925/image.png)