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telcom/
dee-unlearning-tiny-sd

Name: dee-unlearning-tiny-sd
Rating: 2.5 (19 reviews)

StableDiffusionPipeline

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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).

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

Install dependencies (tested with diffusers==0.19.0.dev0, transformers, torch, accelerate, safetensors).
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

Research experimentation: Use this compact bundle to investigate targeted unlearning strategies or dataset pruning without re-downloading massive checkpoints.
Edge deployment: Swap in a smaller scheduler or reduce num_inference_steps to explore speed/quality trade-offs for on-device sampling.
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.

telcom/dee-unlearning-tiny-sd