model card update

README.md

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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
+
+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.