diff --git a/.gitattributes b/.gitattributes index f5c6761c08b265abd125d02833c1bdcb177d92a4..31c8cd3e0e52e5e5ff037cf84d2f2b5a9ddee8d4 100644 --- a/.gitattributes +++ b/.gitattributes @@ -38,3 +38,4 @@ assets/clover-image-tiny-paired-contact-sheet.png filter=lfs diff=lfs merge=lfs assets/clover-image-tiny-banner.png filter=lfs diff=lfs merge=lfs -text examples/prompt-gallery/**/*.png filter=lfs diff=lfs merge=lfs -text examples/normal-to-lora/*.png filter=lfs diff=lfs merge=lfs -text +benchmarks/**/*.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 15324a462fc373bc61701126b44fdcb1db3433a0..4324a0c8df812818e7c16698ff094b7d49ac44ea 100644 --- a/README.md +++ b/README.md @@ -61,6 +61,10 @@ Windows, or Linux. **323,384,964 denoiser parameters · about 1.67 GB · 4–100 inference steps · PyTorch/Diffusers** +Clover Image Tiny is intentionally a compact, local-first 512×512 model rather +than a frontier-scale checkpoint. Its denoiser has 323,384,964 parameters; the +small-model comparison below gives that scale practical runtime context. + Clover Image Tiny is the public PyTorch/Diffusers checkpoint release behind these examples. Its output has a recognizable, playful **DALL·E mini-ish** character. That is a visual description, not a claim of @@ -74,7 +78,24 @@ The demo exposes prompt, negative prompt, seed, guidance, dimensions, scheduler, and 4–100 conventional Diffusers inference steps. It creates one image per request and keeps the packaged safety checker enabled. -## Examples +## Contents + +1. [Examples](#1-examples) +2. [Small-model benchmark](#2-small-model-benchmark) +3. [iPhone and Core ML](#3-iphone-and-core-ml) +4. [Run locally](#4-run-locally) +5. [Generation controls](#5-generation-controls) +6. [Hardware and operating systems](#6-hardware-and-operating-systems) +7. [Python API](#7-python-api) +8. [About this release](#8-about-this-release) +9. [Quality and known behavior](#9-quality-and-known-behavior) +10. [Safety](#10-safety) +11. [Training lineage and data](#11-training-lineage-and-data) +12. [Citation](#12-citation) +13. [Licenses](#13-licenses) +14. [Reproducibility and artifact identity](#14-reproducibility-and-artifact-identity) + +## 1. Examples ### Prompt gallery @@ -112,7 +133,29 @@ seed 1469, and the same 50-step configuration: ![Clover Image Tiny local MPS library example](assets/clover-image-tiny-local-mps-library-seed-1469.png) -## iPhone and Core ML +## 2. Small-model benchmark + +Clover is compared with its pinned BK-SDM-Tiny-2M base and two public +same-family references using 16 prompts, identical seeds, 512×512 output, 30 +DDIM steps, guidance 7.5, and a shared NVIDIA A10G runtime. The measurement is +an engineering comparison, not a human-preference leaderboard. + +| Model | U-Net parameters | Mean latency | Peak CUDA | Mean CLIP cosine | +|---|---:|---:|---:|---:| +| [Clover Image Tiny](https://huggingface.co/neonforestmist/Clover-Image-Tiny) | 323.4M | 1.024 s | 2,233 MB | 0.3195 | +| [BK-SDM-Tiny-2M](https://huggingface.co/nota-ai/bk-sdm-tiny-2m) | 323.4M | 1.027 s | 2,230 MB | 0.3246 | +| [Segmind Tiny-SD](https://huggingface.co/segmind/tiny-sd) | 323.4M | 1.028 s | 1,649 MB | 0.3345 | +| [BK-SDM-v2-Tiny](https://huggingface.co/nota-ai/bk-sdm-v2-tiny) | 326.8M | 0.957 s | 2,067 MB | 0.3303 | + +CLIP cosine is only a prompt-adherence proxy. It is not a human-quality score, +FID, safety evaluation, or evidence that these models are interchangeable. +The complete protocol, machine-readable results, and generated examples are in +[`benchmarks/text-to-image/`](benchmarks/text-to-image/) and the +[full benchmark report](benchmarks/text-to-image/results/clover-small-model-comparison-20260825/REPORT.md). + +![Four-prompt small-model comparison](benchmarks/text-to-image/results/clover-small-model-comparison-20260825/contact-sheet.png) + +## 3. iPhone and Core ML The SwiftUI app in [`Clover-iOS`](Clover-iOS/) follows Apple platform conventions and exposes prompt, negative prompt, steps, guidance, seed, image @@ -137,7 +180,7 @@ own Core ML picker download: See [`COREML.md`](COREML.md) for conversion details and [`training/README.md`](training/README.md) for the pinned LoRA jobs. -### Inpainting track +### 3.1 Inpainting track The 9-channel SD 1.4-class inpainting adaptation is trained and packaged separately: [`neonforestmist/Clover-Image-Tiny-Inpaint`](https://huggingface.co/neonforestmist/Clover-Image-Tiny-Inpaint). @@ -153,12 +196,12 @@ diverse free-form and object-like masks. It improved held-out masked MAE by 96-pixel mask-context crop; the runtime composites through the exact mask so unmasked pixels remain unchanged. -## Run locally +## 4. Run locally Download once, then generate offline with the bundled runner. Python 3.11 and 3.12 are supported. -### macOS — Apple silicon +### 4.1 macOS — Apple silicon ~~~bash mkdir clover-image-tiny-local @@ -189,7 +232,7 @@ open clover-image-tiny.png Use `python3.11` instead if that is the installed supported Python. -### Windows — PowerShell +### 4.2 Windows — PowerShell ~~~powershell mkdir clover-image-tiny-local @@ -221,7 +264,7 @@ Invoke-Item .\clover-image-tiny.png Use `py -3.11` if needed. With `--device auto`, the runner selects an available NVIDIA CUDA GPU and otherwise uses CPU. -### Linux +### 4.3 Linux ~~~bash mkdir clover-image-tiny-local @@ -250,7 +293,7 @@ python model/examples/generate.py \ uses CPU. After the first download, `--local-files-only` prevents network access during generation. -## Generation controls +## 5. Generation controls The command above is ready to copy. Change these flags to explore the model: @@ -281,7 +324,7 @@ outputs are never overwritten. Run `python model/examples/generate.py --help` for the complete CLI reference. -## Hardware and operating systems +## 6. Hardware and operating systems | System | Automatic backend | Precision | Current evidence | |---|---|---|---| @@ -301,7 +344,7 @@ image in 18.21 seconds with fp16 MPS. Its process-lifetime maximum RSS was 631,341,056 bytes. This is a measured point, not a minimum-RAM claim. No Core ML package is required for the Python path. -## Python API +## 7. Python API ~~~python import torch @@ -337,7 +380,7 @@ image.save("clover-image-tiny.png") Seeded generation is repeatable within the selected runtime. Different devices, dtypes, kernels, and dependency builds can produce different pixels. -## About this release +## 8. About this release Clover Image Tiny is a conventional knowledge-distillation checkpoint trained for 500 optimizer steps on an exact licensed 1,000-pair calibration set. The @@ -356,7 +399,7 @@ reproducible Core ML conversion and iPhone app source. The downloadable Core ML artifacts and style adapters are versioned in the separate repositories linked above. -## Quality and known behavior +## 9. Quality and known behavior - The included gallery demonstrates recognizable subjects across colorful scenes, products, food, an animal, a landscape, and an interior. @@ -368,7 +411,7 @@ above. controlled benchmark or broad human-preference study. - Resolution and batch size multiply memory use. -## Safety +## 10. Safety The upstream safety checker is packaged and enabled in both the supported runner and hosted demo. A flagged output may be returned as a black placeholder; @@ -381,7 +424,7 @@ outputs before sharing them. Do not use the model for consequential decisions, identity claims, medical or legal conclusions, harassment, exploitation, illegal activity, or uses prohibited by CreativeML OpenRAIL-M. -## Training lineage and data +## 11. Training lineage and data - Clover fine-tuning data: exactly 1,000 accepted image-caption pairs from `Spawning/PD3M@2a5eb24a8dccf245acd8e56341761aee06da0bdf` @@ -401,7 +444,7 @@ for all foundational pretraining is not available to this project. See `DATA_PROVENANCE.md` for the portable manifest identity and `MODEL_DATA_LICENSES.md` for the complete component ledger. -## Citation +## 12. Citation If Clover Image Tiny is useful in your work, please cite the model release: @@ -414,7 +457,7 @@ If Clover Image Tiny is useful in your work, please cite the model release: } ``` -## Licenses +## 13. Licenses The model weights are a derivative under **CreativeML OpenRAIL-M**. The example runner and packaging code are under **Apache-2.0**. Dataset and item-level terms @@ -426,7 +469,7 @@ request for display in this public model repository. It is not benchmark evidence, its panel-generation provenance is not claimed, and this package does not grant a downstream reuse license for it. -## Reproducibility and artifact identity +## 14. Reproducibility and artifact identity | Field | Value | |---|---| diff --git a/benchmark_text_to_image.py b/benchmark_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..f06659ee0f8134094efde83d9d8292e8fea14654 --- /dev/null +++ b/benchmark_text_to_image.py @@ -0,0 +1,283 @@ +#!/usr/bin/env python3 +"""Run a reproducible small-model text-to-image comparison on Modal. + +The benchmark is intentionally modest: it compares prompt adherence and +runtime behavior under one shared recipe. It is not a human-preference study, +FID benchmark, or claim of overall image quality. +""" + +from __future__ import annotations + +import json +import os +import sys +import time +from pathlib import Path + +import modal + +APP_NAME = "clover-image-tiny-text-to-image-benchmark" +OUTPUT_VOLUME_NAME = "clover-image-tiny-text-to-image-benchmark-output" +CACHE_VOLUME_NAME = "clover-image-tiny-text-to-image-benchmark-cache" +OUTPUT_ROOT = Path("/outputs") +CACHE_ROOT = Path("/cache") +CLIP_MODEL_ID = "openai/clip-vit-base-patch32" + +MODELS = { + "clover": "neonforestmist/Clover-Image-Tiny", + "base_bk_sdm_tiny_2m": "nota-ai/bk-sdm-tiny-2m", + "segmind_tiny_sd": "segmind/tiny-sd", + "bk_sdm_v2_tiny": "nota-ai/bk-sdm-v2-tiny", +} + +PROMPTS = [ + "a glass of red wine", + "a tiny glass greenhouse glowing in a moonlit garden", + "daisies in a blue ceramic pot", + "snowy mountains under a cloudy sky", + "a desert with a big moon in the sky", + "a bouquet of blue flowers", + "a stained-glass window of a starry night", + "an origami heart on textured paper", + "an anime boy with light blue hair and eyes", + "a red bicycle beside a yellow cottage", + "a small blue cat beside a quiet garden stream", + "a bowl of colorful fruit on a wooden table", + "an astronaut riding a horse in space", + "a lighthouse during a storm", + "a cozy cabin in a snowy forest", + "a vintage train station at sunset", +] + +SEEDS = [1337 + index for index in range(len(PROMPTS))] +STEPS = 30 +GUIDANCE_SCALE = 7.5 +WIDTH = 512 +HEIGHT = 512 + +image = ( + modal.Image.debian_slim(python_version="3.11") + .pip_install( + "accelerate==1.14.0", + "diffusers==0.39.0", + "huggingface_hub==0.36.2", + "numpy==2.2.6", + "pillow==12.3.0", + "safetensors==0.8.0", + "torch==2.7.0", + "torchvision==0.22.0", + "transformers==4.57.6", + ) +) + +output_volume = modal.Volume.from_name(OUTPUT_VOLUME_NAME, create_if_missing=True) +cache_volume = modal.Volume.from_name(CACHE_VOLUME_NAME, create_if_missing=True) +app = modal.App( + APP_NAME, + image=image, + volumes={str(OUTPUT_ROOT): output_volume, str(CACHE_ROOT): cache_volume}, +) + + +def _module_parameters(module: object) -> int: + if module is None or not hasattr(module, "parameters"): + return 0 + return sum(parameter.numel() for parameter in module.parameters()) + + +def _load_pipeline(model_id: str): + import torch + from diffusers import DDIMScheduler, DiffusionPipeline + + pipe = DiffusionPipeline.from_pretrained( + model_id, + torch_dtype=torch.float16, + cache_dir=str(CACHE_ROOT / "huggingface"), + ) + # A single scheduler makes the comparison recipe explicit and portable. + pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) + pipe = pipe.to("cuda") + pipe.set_progress_bar_config(disable=True) + return pipe + + +@app.function( + gpu="A10G", + timeout=4 * 60 * 60, + cpu=8, + memory=32768, +) +def benchmark(*, output_name: str) -> str: + import torch + from PIL import Image + from transformers import CLIPModel, CLIPProcessor + + output_dir = OUTPUT_ROOT / output_name + if output_dir.exists(): + raise RuntimeError(f"Benchmark output already exists: {output_dir}") + output_dir.mkdir(parents=True) + (output_dir / "images").mkdir() + (output_dir / "prompts.json").write_text( + json.dumps( + { + "prompts": PROMPTS, + "seeds": SEEDS, + "recipe": { + "scheduler": "DDIMScheduler", + "steps": STEPS, + "guidance_scale": GUIDANCE_SCALE, + "width": WIDTH, + "height": HEIGHT, + "num_images_per_prompt": 1, + "negative_prompt": "", + }, + }, + indent=2, + ) + + "\n" + ) + + env = os.environ.copy() + env.update( + { + "HF_HOME": str(CACHE_ROOT / "huggingface"), + "HF_HUB_CACHE": str(CACHE_ROOT / "huggingface" / "hub"), + "TOKENIZERS_PARALLELISM": "false", + "PYTHONUNBUFFERED": "1", + } + ) + os.environ.update(env) + all_records: dict[str, dict] = {} + + for label, model_id in MODELS.items(): + print(f"Loading {label}: {model_id}", flush=True) + pipe = _load_pipeline(model_id) + unet_parameters = _module_parameters(pipe.unet) + total_parameters = sum( + _module_parameters(module) + for module in ( + getattr(pipe, "unet", None), + getattr(pipe, "text_encoder", None), + getattr(pipe, "vae", None), + getattr(pipe, "safety_checker", None), + ) + ) + + # Warm up kernels before measuring. The warmup image is discarded. + warmup_generator = torch.Generator(device="cuda").manual_seed(7) + with torch.inference_mode(): + pipe( + "a simple red apple", + num_inference_steps=4, + guidance_scale=GUIDANCE_SCALE, + height=HEIGHT, + width=WIDTH, + generator=warmup_generator, + ) + torch.cuda.synchronize() + torch.cuda.reset_peak_memory_stats() + + model_dir = output_dir / "images" / label + model_dir.mkdir() + records = [] + for index, (prompt, seed) in enumerate(zip(PROMPTS, SEEDS, strict=True)): + generator = torch.Generator(device="cuda").manual_seed(seed) + start = time.perf_counter() + with torch.inference_mode(): + result = pipe( + prompt, + negative_prompt="", + num_inference_steps=STEPS, + guidance_scale=GUIDANCE_SCALE, + height=HEIGHT, + width=WIDTH, + generator=generator, + ) + torch.cuda.synchronize() + elapsed = time.perf_counter() - start + filename = f"{index + 1:02d}.png" + result.images[0].save(model_dir / filename, format="PNG") + safety = getattr(result, "nsfw_content_detected", None) + records.append( + { + "index": index, + "prompt": prompt, + "seed": seed, + "filename": f"images/{label}/{filename}", + "latency_seconds": elapsed, + "nsfw_content_detected": safety[0] if isinstance(safety, list) else None, + } + ) + print(f"{label} {index + 1}/{len(PROMPTS)} {elapsed:.3f}s", flush=True) + + peak_memory_mb = torch.cuda.max_memory_allocated() / (1024 * 1024) + all_records[label] = { + "model_id": model_id, + "unet_parameters": unet_parameters, + "pipeline_parameters": total_parameters, + "mean_latency_seconds": sum(r["latency_seconds"] for r in records) / len(records), + "median_latency_seconds": sorted(r["latency_seconds"] for r in records)[len(records) // 2], + "peak_cuda_allocated_mb": peak_memory_mb, + "images": records, + } + del pipe + torch.cuda.empty_cache() + + print("Scoring generated images with CLIP", flush=True) + processor = CLIPProcessor.from_pretrained( + CLIP_MODEL_ID, + cache_dir=str(CACHE_ROOT / "huggingface"), + ) + clip_model = CLIPModel.from_pretrained( + CLIP_MODEL_ID, + torch_dtype=torch.float16, + cache_dir=str(CACHE_ROOT / "huggingface"), + ).to("cuda") + clip_model.eval() + text_inputs = processor(text=PROMPTS, return_tensors="pt", padding=True).to("cuda") + with torch.inference_mode(): + text_features = clip_model.get_text_features(**text_inputs) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + + for label, summary in all_records.items(): + images = [Image.open(output_dir / record["filename"]).convert("RGB") for record in summary["images"]] + scores = [] + for image_item, prompt in zip(images, PROMPTS, strict=True): + image_inputs = processor(images=image_item, return_tensors="pt").to("cuda") + with torch.inference_mode(): + image_features = clip_model.get_image_features(**image_inputs) + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + prompt_index = PROMPTS.index(prompt) + scores.append(float((image_features @ text_features[prompt_index : prompt_index + 1].T).item())) + summary["clip_prompt_cosine_mean"] = sum(scores) / len(scores) + summary["clip_prompt_cosine_median"] = sorted(scores)[len(scores) // 2] + summary["clip_prompt_cosine_scores"] = scores + + report = { + "schema_version": 1, + "benchmark": "clover-image-tiny-small-model-comparison", + "status": "completed", + "hardware": "Modal NVIDIA A10G", + "public_identity_note": "The compute account identity is intentionally omitted.", + "protocol": { + "prompt_count": len(PROMPTS), + "resolution": f"{WIDTH}x{HEIGHT}", + "scheduler": "DDIMScheduler", + "steps": STEPS, + "guidance_scale": GUIDANCE_SCALE, + "negative_prompt": "", + "same_prompts_and_seeds": True, + "metric_note": "CLIP cosine is a prompt-adherence proxy, not a human-quality score or a broad benchmark.", + }, + "models": all_records, + } + (output_dir / "results.json").write_text(json.dumps(report, indent=2) + "\n") + output_volume.commit() + cache_volume.commit() + return str(output_dir) + + +@app.local_entrypoint() +def main(output_name: str = f"clover-small-model-comparison-{int(time.time())}") -> None: + result = benchmark.remote(output_name=output_name) + print(f"Benchmark output: {result}") diff --git a/benchmarks/text-to-image/README.md b/benchmarks/text-to-image/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3e63147353fb522d1aec5675a4df15fdc0e378be --- /dev/null +++ b/benchmarks/text-to-image/README.md @@ -0,0 +1,37 @@ +# Clover Image Tiny small-model comparison + +This benchmark compares Clover Image Tiny with its pinned BK-SDM-Tiny-2M base +and two public Diffusers checkpoints in the same broad SD-tiny family: + +- `neonforestmist/Clover-Image-Tiny` +- `nota-ai/bk-sdm-tiny-2m` +- `segmind/tiny-sd` +- `nota-ai/bk-sdm-v2-tiny` + +The comparison uses the same 16 prompts, seeds, 512×512 resolution, empty +negative prompt, 30 DDIM steps, guidance scale 7.5, and one NVIDIA A10G +runtime. It records U-Net and loaded-pipeline parameter counts, generation +latency, peak CUDA allocation, and CLIP image/text cosine similarity. + +CLIP cosine is used only as a prompt-adherence proxy. It is not a human +preference score, FID, a safety evaluation, or a claim that the models are +identical in training data, scheduler defaults, or intended use. + +Run it with: + +```bash +modal run benchmark_text_to_image.py +``` + +The Modal app writes a timestamped result directory to its private output +volume. Download a completed run with: + +```bash +modal volume get clover-image-tiny-text-to-image-benchmark-output \ + clover-small-model-comparison- \ + benchmarks/text-to-image/results +``` + +The public model card should include only the exported `results.json`, selected +example images, the protocol, and the benchmark caveats. Compute-account +identity is intentionally not part of the artifact. diff --git a/benchmarks/text-to-image/results/clover-small-model-comparison-20260825/REPORT.md b/benchmarks/text-to-image/results/clover-small-model-comparison-20260825/REPORT.md new file mode 100644 index 0000000000000000000000000000000000000000..37fcb522af8df153128f38a9bd6990ac91c01c31 --- /dev/null +++ b/benchmarks/text-to-image/results/clover-small-model-comparison-20260825/REPORT.md @@ -0,0 +1,53 @@ +# Clover Image Tiny — small-model comparison + +This is a same-recipe engineering comparison, not a human-preference or +state-of-the-art benchmark. It places Clover Image Tiny beside its pinned base +and two public Diffusers checkpoints with the same broad SD-tiny U-Net shape. + +## Protocol + +- 16 fixed prompts and seeds `1337`–`1352` +- 512 × 512 output, empty negative prompt +- DDIM scheduler, 30 steps, guidance scale 7.5 +- One image per prompt on the same NVIDIA A10G runtime +- CLIP image/text cosine similarity as a prompt-adherence proxy +- Latency measured after one warm-up generation per model + +CLIP cosine is not a human-quality score, FID, a safety evaluation, or proof +that one model is generally better. The models have different training data, +text encoders, and original release goals. + +## Results + +| Model | U-Net parameters | Loaded pipeline parameters | Mean latency (s) | Peak CUDA (MB) | Mean CLIP cosine | +|---|---:|---:|---:|---:|---:| +| [Clover Image Tiny](https://huggingface.co/neonforestmist/Clover-Image-Tiny) | 323,384,964 | 834,080,895 | 1.024 | 2,233.0 | 0.3195 | +| [BK-SDM-Tiny-2M](https://huggingface.co/nota-ai/bk-sdm-tiny-2m) | 323,384,964 | 834,080,895 | 1.027 | 2,230.0 | 0.3246 | +| [Segmind Tiny-SD](https://huggingface.co/segmind/tiny-sd) | 323,384,964 | 530,099,307 | 1.028 | 1,648.9 | 0.3345 | +| [BK-SDM-v2-Tiny](https://huggingface.co/nota-ai/bk-sdm-v2-tiny) | 326,825,604 | 750,867,307 | 0.957 | 2,067.1 | 0.3303 | + +The result is best read as scale and runtime context: Clover is a compact +512×512 model with a 323.4M-parameter denoiser, and its scores are in the same +range as these similarly sized references under this limited recipe. This is +not evidence that Clover wins a broad quality contest, nor that a larger model +would be unnecessary. + +## Visual examples + +![Four-prompt comparison across the four models](contact-sheet.png) + +The full generated set contains all 16 prompts for all four models under +`images/`. The exact machine-readable report is [`results.json`](results.json) +and the prompt/seed manifest is [`prompts.json`](prompts.json). + +## Reproduction + +From the Clover source repository: + +```bash +modal run benchmark_text_to_image.py \ + --output-name clover-small-model-comparison- +``` + +The benchmark intentionally omits the compute-account identity from its public +report and artifacts. diff --git a/benchmarks/text-to-image/results/clover-small-model-comparison-20260825/contact-sheet.png b/benchmarks/text-to-image/results/clover-small-model-comparison-20260825/contact-sheet.png new file mode 100644 index 0000000000000000000000000000000000000000..d17cb2e2f1fd6eccab29e5d455b2e2f7eff69bc8 --- /dev/null +++ b/benchmarks/text-to-image/results/clover-small-model-comparison-20260825/contact-sheet.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26405ad268cd1b25daccb7c01ae489038c064fcdf9bfbd2c1f113684d6120915 +size 901611 diff --git 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