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Model: crumb/bloom-560m-RLHF-SD2-prompter-aesthetic
Source: Original Platform

.gitattributes

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README.md

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+---
+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
+---
+
+# The RAT (RLHF-Aesthetic Tuned model for prompt synthesis)
+
+**COLAB DEMO INCLUDING STABLE DIFFUSION: https://colab.research.google.com/github/aicrumb/doohickey/blob/main/rlhf_prompt_tuner.ipynb**
+
+This is a further finetuned version of [crumb/bloom-560m-RLHF-SD2-prompter](https://hf.co/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
+
+trained at bs=32, lr=0.0001, only tuning biases and layernorm weights
+
+## 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-aesthetic")
+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
+```
+
+## Limitations
+Aesthetic scoring models have been shown to have very large biases, and one I noticed is it really likes images of women no matter the actual quality, so those were optimized for more than other things.
+
+Also it fell into the trap of rlhf models, it gets kinda same-ey, so if you don't like the general "stable diffusion, trending on artstation" look this might not be for you.

config.json

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+{
+  "_name_or_path": "crumb/bloom-560m-RLHF-SD2-prompter-aesthetic",
+  "apply_residual_connection_post_layernorm": false,
+  "architectures": [
+    "BloomForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "attention_softmax_in_fp32": true,
+  "bias_dropout_fusion": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_dropout": 0.0,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "masked_softmax_fusion": true,
+  "model_type": "bloom",
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 24,
+  "offset_alibi": 100,
+  "pad_token_id": 3,
+  "pretraining_tp": 1,
+  "seq_length": 2048,
+  "skip_bias_add": true,
+  "skip_bias_add_qkv": false,
+  "slow_but_exact": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.24.0",
+  "unk_token_id": 0,
+  "use_cache": false,
+  "vocab_size": 250880
+}

model.safetensors

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special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "name_or_path": "crumb/bloom-560m-RLHF-SD2-prompter",
+  "pad_token": "<pad>",
+  "padding_side": "left",
+  "special_tokens_map_file": null,
+  "tokenizer_class": "BloomTokenizer",
+  "unk_token": "<unk>"
+}