dee-unlearning-tiny-sd

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.

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.