---
title: DiffusionDB SD 1.5 LoRA — Inference
emoji: ⚙️
colorFrom: gray
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
license: creativeml-openrail-m
models:
  - whosouravsharma/diffusiondb-sd15-lora
---

# Inference backend

Generation backend for
[`diffusiondb-sd15-lora`](https://huggingface.co/spaces/whosouravsharma/diffusiondb-sd15-lora).
Loads Stable Diffusion 1.5 plus the LoRA adapter and exposes a single
`/generate` endpoint. The minimal interface is for debugging; the real client
is the UI Space.

**This is the Space that needs a GPU.** `t4-small` renders a 512×512 image in
a few seconds; CPU takes minutes.

```python
from gradio_client import Client

client = Client("whosouravsharma/diffusiondb-sd15-lora-inference")
image, seed = client.predict(
    "a steampunk owl inside a glass jar, intricate detail",  # prompt
    "",       # negative_prompt
    25,       # steps
    7.5,      # guidance
    1.0,      # lora_scale — 0 renders vanilla SD 1.5
    -1,       # seed, -1 for random
    api_name="/generate",
)
```

## Files

- `app.py` — Gradio app exposing `/generate`
- `src/pipeline.py` — model loading and generation, no Gradio dependency
- `src/infer.py` — CLI: `python3 -m src.infer "a prompt"`
- `src/tracing.py` — optional LangSmith tracing + structured logging
- `scripts/compare_checkpoints.py` — contact sheet across checkpoints/scales

## Observability

Every generation logs one structured line — seed, steps, guidance,
lora_scale, device, latency, ms/step — with no configuration needed.

LangSmith tracing is opt-in. Unset, `src/tracing.py` no-ops and the package
is never imported, so the serving path is unchanged:

```bash
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=<key>
export LANGSMITH_PROJECT=diffusiondb-sd15-lora-inference
```

On the Space, set `LANGSMITH_API_KEY` as a **secret** and the other two as
plain **variables**.

Traces carry the generation parameters, the resolved device/dtype/checkpoint,
and latency — but never the image itself: LangSmith stores run I/O as JSON,
and a base64 512×512 PNG is ~400 KB per run, which gets truncated and makes
the trace view unusable. Set `LANGSMITH_LOG_IMAGES=1` to attach a ~10 KB WEBP
thumbnail instead (`LANGSMITH_THUMBNAIL_PX` controls the size).

A generation that arrives from the UI Space is not a trace of its own. The
UI sends `langsmith-trace` and `baggage` headers with its `gradio_client`
call; `app.py` reads them off the Gradio request and continues that trace,
so the `sd15_lora_generate` run here lands as a child of the UI's
`ui_generate` run instead of in a separate tree. The visitor location the
UI puts in baggage rides along, so it appears on this run too — the
backend never sees the visitor directly, since the call reaches it
server-to-server from the UI Space. A direct API call sends no such
headers and simply starts its own trace.

## Comparing checkpoints

Fixed seed, fixed prompts, every (checkpoint, lora_scale) combination tiled
into one labelled PNG — the direct way to see what the adapter changed:

```bash
python3 -m scripts.compare_checkpoints \
    --checkpoints base checkpoint-4240 \
    --scales 0.0 0.5 1.0 --seed 42 --out sweep.png
```

With tracing on, the sweep is one parent run with a child run per image.

## Environment

| Variable | Default | Effect |
| --- | --- | --- |
| `BASE_MODEL` | `stable-diffusion-v1-5/stable-diffusion-v1-5` | base checkpoint |
| `LORA_REPO` | `whosouravsharma/diffusiondb-sd15-lora` | adapter repo |
| `LORA_CHECKPOINT` | `checkpoint-4240` | `base` skips the adapter |
| `ATTENTION_SLICING` | `auto` | `auto` = MPS only; `true`/`off` to force |
| `LOG_LEVEL` | `INFO` | log verbosity |

Weights are pulled from the model repo on first request, not baked into the
image, so switching checkpoints is a `LORA_CHECKPOINT` env change. The fp16
variant is used on GPU, so the cold-start download is ~2 GB rather than ~4.

**Batch seeds:** image *i* of a batch is generated with `seed + i`, so any
single image is reproducible on its own at that seed with `--count 1`.