README.md

# dee-unlearning-tiny-sd

**Model family:** Stable Diffusion | **Base:** SG161222/Realistic_Vision_V4.0 (Diffusers 0.19.0.dev0)

This repository packages the inference components (VAE, UNet, tokenizer, text encoder, scheduler config) that instantiate a `StableDiffusionPipeline` tuned for lightweight experimentation with deep unlearning ideas. All large binaries are stored under Git LFS (`*.bin` and other model artifact extensions as configured in `.gitattributes`).

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## Model summary

- **Architecture:** `StableDiffusionPipeline` with `UNet2DConditionModel`, `CLIPTextModel`, `AutoencoderKL`, and `DPMSolverMultistepScheduler`.
- **Scheduler:** DPMSolver++ (multistep) configured with `num_train_timesteps=1000`, `steps_offset=1`, and the default `epsilon` prediction type that aligns with the diffusion formulation used in Realistic Vision.
- **Intended behavior:** Generate photorealistic samples guided by text prompts. The “tiny” name reflects a focus on a compact deployment bundle rather than a new generative architecture.

## Usage

1. Install dependencies (tested with `diffusers==0.19.0.dev0`, `transformers`, `torch`, `accelerate`, `safetensors`).
2. Load the pipeline with the provided components.

```python
from diffusers import StableDiffusionPipeline
from transformers import CLIPTokenizer, CLIPTextModel
from diffusers import UNet2DConditionModel, AutoencoderKL, DPMSolverMultistepScheduler

pipeline = StableDiffusionPipeline(
    text_encoder=CLIPTextModel.from_pretrained("path/to/text_encoder"),
    tokenizer=CLIPTokenizer.from_pretrained("path/to/tokenizer"),
    unet=UNet2DConditionModel.from_pretrained("path/to/unet"),
    vae=AutoencoderKL.from_pretrained("path/to/vae"),
    scheduler=DPMSolverMultistepScheduler.from_config("path/to/scheduler"),
)
pipeline.to("cuda")
prompt = "A cinematic portrait of a futuristic astronaut exploring a coral reef"
with torch.autocast("cuda"):
    image = pipeline(prompt, num_inference_steps=25, guidance_scale=7.5).images[0]
```

Replace each `from_pretrained` call with the relative path inside this repository (e.g., `"text_encoder"`). Exported weights follow the standard Diffusers layout, so you can also load the entire pipeline from disk with `StableDiffusionPipeline.load_from_directory(...)` if you prefer a single root.

## Known limitations

- Not evaluated on a public benchmark: quality, bias, and safety metrics are unknown beyond the original Realistic Vision baseline.
- Outputs inherit the biases of the base dataset, which can include underrepresentation of marginalized groups and the tendency to hallucinate architecture or people.
- Prompts that contradict physics, are highly abstract, or request disallowed content may fail or produce unpredictable imagery.
- Fine-tuning past the provided weights may require additional safety mitigations depending on your dataset.

## Opportunities

1. **Research experimentation:** Use this compact bundle to investigate targeted unlearning strategies or dataset pruning without re-downloading massive checkpoints.
2. **Edge deployment:** Swap in a smaller scheduler or reduce `num_inference_steps` to explore speed/quality trade-offs for on-device sampling.
3. **Controlled generation:** Attach additional conditioning (CLIP embeddings, ControlNet) to the pipeline for downstream applications such as assistive art tools, conditional rendering, or creative assistants.

## Safety considerations

- Follow established safety best practices when generating faces, political imagery, or NSFW prompts; the pipeline does not include a safety checker.
- Monitor outputs for deceptive or fabricated content before deployment in public-facing products.
- Don’t use the model to impersonate real people, create harmful memes, or automate disinformation campaigns.

## Attribution & licensing

This work builds on the `SG161222/Realistic_Vision_V4.0` checkpoints and the Diffusers ecosystem. Verify and comply with the upstream license before redistributing or fine-tuning the weights.