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Clover-Image-Tiny
neonforestmist/Clover-Image-Tiny
Clover-Image-Tiny is a text-to-image model from neonforestmist. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as creativeml-openrail-m.
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At a glance
- Task
- Text-to-Image
- Library
- diffusers
- License
- creativeml-openrail-m
- Access
- Public
- Created
- Jul 14, 2026
- Updated
- Sep 7, 2026
- SHA
- f26a3449
Try a prompt
Base models
Model card
From the Hugging Face model README
<h1 align="center">Clover Image Tiny</h1>
</details>
<details>
<summary>iPhone integration, Core ML resources, and style downloads</summary>
Models
Use the regular model to generate an image from text. Use Inpaint HQ to edit a masked region of an existing image. Both run locally after the model and dependencies are downloaded.
| Clover Image Tiny | Clover Inpaint HQ |
|---|---|
 |  |
| Text → image. Compact SD 1.4-class generation with a 323.4M-parameter denoiser. | Image + mask + text → edit. SD 1.5-class inpainting with Clover's shared components. A larger, separate checkpoint. |
The regular Diffusers package is about 1.67 GB, including its text encoder, VAE, and safety checker. The 323.4M count describes the denoiser, not the complete pipeline. Inpainting uses a separate checkpoint; neither download includes the Python environment. The hosted demo runs remotely; local Python and Core ML workflows run on your hardware after setup.
Examples
Selected outputs from the regular model, with their original prompts.
| Moonlit greenhouse | Blue flowers | Stained-glass night |
|---|---|---|
 | |  |
| “a tiny glass greenhouse glowing in a moonlit garden” | “A bouquet of blue flowers” | “A stain glass window of a starry night” |
Inpainting example
Inpainting regenerates the white area of a mask. Black marks the area to preserve. This published before-and-after example uses the prompt “add blue sunglasses.”
| Before | After the masked edit |
|---|---|
 | |
Inpaint HQ provides SD 1.5-class masked editing. For exact preservation outside the edited region, composite the result through the original binary mask. Its full-size inpainting denoiser and shared components are documented in the inpainting model card.
Styles
Optional LoRA adapters change the model's visual style. Below is the same greenhouse prompt with the base model, Monet, Pointillism, and Watercolor Anime.
| Clover | Monet | Pointillism | Watercolor Anime |
|---|---|---|---|
 |  |  |  |
| Base model | Get Monet | Get Pointillism | Get Watercolor Anime |
Model facts
| Regular Clover model | What it means |
|---|---|
| 323.4M denoiser parameters | BK-SDM-Tiny architecture; denoiser count only |
| About 1.67 GB of model files | Includes the text encoder, VAE, and packaged safety checker; allow extra space for dependencies and caches |
| 512 × 512 native output | Native output resolution |
| Diffusers + separate Core ML exports | Python integration and a path to on-device Apple apps |
Evaluation
Compact model, practical generation speed
Clover combines a 323.4M-parameter denoiser with 1.024-second mean generation in the published NVIDIA A10G test. It retains the compact architecture while adding Clover's distillation pass, style adapters, and separate Core ML deployment options.
| Measured on NVIDIA A10G | Clover Image Tiny |
|---|---|
| Mean generation time ↓ | 1.024 s/image |
| Peak CUDA memory ↓ | 2,233 MB |
| Denoiser parameters ↓ | 323.4M |
Test settings: 16 prompts, 512 × 512, 30 DDIM steps, guidance 7.5, after warm-up. Lower values mean less time, memory, or denoiser storage—not automatically better images. These are GPU results, not older-device or iPhone timings.
Clover runs in the same approximate one-second range as BK-SDM-Tiny-2M and Segmind Tiny-SD in this test. Other references lead individual latency, memory, and CLIP metrics; the comparison does not establish an overall quality or speed lead.
<details> <summary>Full four-model comparison: latency, memory, and prompt alignment</summary>| Model | U-Net parameters ↓ | Loaded pipeline parameters ↓ | Mean latency ↓ | Peak CUDA memory ↓ | Mean CLIP cosine ↑ |
|---|---|---|---|---|---|
| Clover Image Tiny | 323.4M | 834.1M | 1.024 s | 2,233 MB | 0.3195 |
| BK-SDM-Tiny-2M | 323.4M | 834.1M | 1.027 s | 2,230 MB | 0.3246 |
| Segmind Tiny-SD | 323.4M | 530.1M | 1.028 s | 1,649 MB | 0.3345 |
| BK-SDM-v2-Tiny | 326.8M | 750.9M | 0.957 s | 2,067 MB | 0.3303 |
↓ Lower is better for size, latency, and memory; ↑ higher is better for CLIP prompt alignment. Bold marks the best result in each column, including ties. Parameter counts describe footprint, not image quality.
CLIP cosine measures prompt alignment, not overall visual quality. The latency gaps between Clover, BK-SDM-Tiny-2M, and Segmind Tiny-SD are below 0.4% and should be read as near parity, not an established speed advantage.
</details>Benchmark protocol, all outputs, and raw results.
Usage notes
Can I use Clover offline? Yes. Download the model and dependencies first, then use
--local-files-only with the local runner. No hosted generation service is required.
Does it work on a Mac? The Python path supports Apple silicon through PyTorch MPS. NVIDIA systems use CUDA; CPU inference is also supported, but slower.
Is normal Clover the same as Inpaint HQ? They are separate checkpoints. Normal Clover creates images from text. Inpaint HQ takes an image, a mask, and a prompt, and uses a larger denoiser.
What are its limits? Hands, faces, readable text, precise counts, and complex relationships can be unreliable. Examples are selected outputs; results vary with prompts and settings. The packaged safety checker in the Python runner and hosted demo is imperfect.
Model lineage, license, and research details
Clover starts from BK-SDM-Tiny-2M and adds a recorded 500-step distillation pass against Stable Diffusion 1.4. It retains the compact architecture; its U-Net weights are refreshed.
Model weights use CreativeML Open RAIL-M and the example code uses Apache-2.0. See the component license ledger for the full terms and inherited obligations.
Data provenance · All nine prompt examples
Full gallery
<Gallery />Technical documentation
Setup instructions, comparisons, training records, licenses, and checkpoint identifiers are retained below. The Documentation link above opens the standalone reference.
<details> <summary>Model profile and relationship to BK-SDM-Tiny</summary>1.1 Model profile
| Profile | Value |
|---|---|
| Published PyTorch/Diffusers package | ~1.67 GB |
| Denoiser / U-Net | 323,384,964 parameters · ~646.8 MB |
| Text encoder | ~246.1 MB |
| VAE | ~167.3 MB |
| Safety checker | ~608.0 MB |
| Native output | 512×512 |
| Inference range | 4–100 steps |
| Published formats | PyTorch/Diffusers + Core ML adapters |
The package figure includes the text encoder, VAE, tokenizer/configuration, and the enabled upstream safety checker; it is therefore larger than the denoiser alone. Component sizes are approximate runtime-footprint context, not a claim that every byte is active in every backend.
Model origin
Clover keeps BK-SDM-Tiny-2M's U-Net architecture and parameter count. The additional Clover training changes the U-Net weights; the tokenizer, text encoder, and VAE retain the pinned upstream components.
| Aspect | BK-SDM-Tiny-2M | Clover Image Tiny |
|---|---|---|
| Training lineage | Its model card reports 50,000 distillation-pretraining iterations on 2,256,472 LAION-Aesthetics pairs | Inherits the pinned aad3e0e8 checkpoint, then adds a separately audited 500-step Clover distillation run |
| Teacher and objectives | SD v1.4 compression lineage with output and feature KD | Re-distilled against the exact pinned SD v1.4 teacher using equal-weight diffusion-target, teacher-output, and six-point normalized feature losses |
| Clover calibration data | — | Deterministically selected 1,000-pair PD3M set; license-filtered to CC0/Public Domain Mark items with a checksummed deletion audit and manifest |
| Trainable scope | Upstream release | U-Net only; teacher, text encoder, and VAE remained frozen |
| U-Net architecture | Block-removed U-Net, ~323.4M parameters | Same topology and ~323.4M parameters; weights are modified |
| Product target | Efficient compressed Stable Diffusion baseline | Low-latency consumer products: desktop-local generation, edge deployment, and Core ML phone inference |
| Runtime envelope | Original compact baseline | Same parameter count and effectively the same measured latency/memory class—no denoiser-size penalty for the Clover refresh |
| Release additions | Upstream BK-SDM model | Reproducible checkpoint identity, local runner, benchmark evidence, Core ML resources, style LoRAs, inpainting companion, and iOS integration |
iPhone and Core ML
The companion Clover Image Tiny iOS project follows Apple platform conventions and exposes prompt, negative prompt, steps, guidance, seed, image count, scheduler, random generator, and compute target. Its model picker downloads SHA-256-verified, immutable Core ML resources from Hugging Face; the large weights are not committed to the Xcode project or bundled into the app.
The base Core ML resources are published separately:
Each trained style is available both as a standard Diffusers LoRA and as its own Core ML picker download:
| Style | Diffusers LoRA | Core ML LoRA model |
|---|---|---|
| Monet | clover-image-tiny-monet-lora | clover-image-tiny-monet-lora-coreml |
| Pointillism | clover-image-tiny-pointillism-lora | clover-image-tiny-pointillism-lora-coreml |
| Watercolor Anime | clover-image-tiny-watercolor-anime-lora | clover-image-tiny-watercolor-anime-lora-coreml |
See COREML.md for conversion details and
training/README.md for the pinned LoRA jobs.
3. 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 ↓ | Loaded pipeline parameters ↓ | Mean latency ↓ | Peak CUDA memory ↓ | Mean CLIP cosine ↑ |
|---|---|---|---|---|---|
| Clover Image Tiny | 323.4M | 834.1M | 1.024 s | 2,233 MB | 0.3195 |
| BK-SDM-Tiny-2M | 323.4M | 834.1M | 1.027 s | 2,230 MB | 0.3246 |
| Segmind Tiny-SD | 323.4M | 530.1M | 1.028 s | 1,649 MB | 0.3345 |
| BK-SDM-v2-Tiny | 326.8M | 750.9M | 0.957 s | 2,067 MB | 0.3303 |
↓ Lower is better for size, latency, and memory; ↑ higher is better for CLIP prompt alignment. Bold marks the best result in each column, including ties. Parameter counts describe footprint, not image quality.
Clover's measured strengths are its joint-smallest denoiser (323.4M parameters) and roughly one-second generation (1.024 s/image) in this test. Its latency is within 0.4% of BK-SDM-Tiny-2M and Segmind Tiny-SD; that small gap is not an established speed advantage. BK-SDM-v2-Tiny is faster here, while Segmind Tiny-SD uses less memory and has the highest CLIP score.
The table keeps denoiser size and loaded pipeline size separate. The former is the most useful apples-to-apples model comparison; the latter includes the text encoder, VAE, and other loaded components and is runtime context rather than a download-size metric.
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/ and the
full benchmark report.
5. Run locally
Download once, then generate offline with the bundled runner. Python 3.11 and 3.12 are supported.
5.1 macOS — Apple silicon
mkdir clover-image-tiny-local
cd clover-image-tiny-local
python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install "huggingface-hub==0.36.2"
hf download "neonforestmist/Clover-Image-Tiny" --local-dir model
python -m pip install -r model/requirements.txt
python model/examples/generate.py \
--model model \
--device mps \
--local-files-only \
--prompt "a tiny glass greenhouse glowing in a moonlit garden, detailed photography" \
--negative-prompt "blurry, distorted, low detail" \
--steps 50 \
--guidance-scale 7.5 \
--scheduler pndm \
--seed 1337 \
--output clover-image-tiny.png
open clover-image-tiny.png
Use python3.11 instead if that is the installed supported Python.
5.2 Windows — PowerShell
mkdir clover-image-tiny-local
cd clover-image-tiny-local
py -3.12 -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install "huggingface-hub==0.36.2"
hf download "neonforestmist/Clover-Image-Tiny" --local-dir model
python -m pip install -r model\requirements.txt
python model\examples\generate.py `
--model model `
--device auto `
--local-files-only `
--prompt "a tiny glass greenhouse glowing in a moonlit garden, detailed photography" `
--negative-prompt "blurry, distorted, low detail" `
--steps 50 `
--guidance-scale 7.5 `
--scheduler pndm `
--seed 1337 `
--output clover-image-tiny.png
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.
5.3 Linux
mkdir clover-image-tiny-local
cd clover-image-tiny-local
python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install "huggingface-hub==0.36.2"
hf download "neonforestmist/Clover-Image-Tiny" --local-dir model
python -m pip install -r model/requirements.txt
python model/examples/generate.py \
--model model \
--device auto \
--local-files-only \
--prompt "a tiny glass greenhouse glowing in a moonlit garden, detailed photography" \
--negative-prompt "blurry, distorted, low detail" \
--steps 50 \
--seed 1337 \
--output clover-image-tiny.png
--device auto selects CUDA when PyTorch can see an NVIDIA GPU and otherwise
uses CPU. After the first download, --local-files-only prevents network
access during generation.
6. Generation controls
The command above is ready to copy. Change these flags to explore the model:
| Flag | Accepted values | Default | What it controls |
|---|---|---|---|
--prompt | Non-empty text | Required | What to generate |
--negative-prompt | Text, or empty | Empty | Details to discourage; the starter commands and live demo use blurry, distorted, low detail |
--steps | 4–100 | 50 | Diffusion iterations; more steps take longer and do not guarantee a better image |
--guidance-scale | 0.0–20.0 | 7.5 | How strongly the image follows the prompt |
--scheduler | pndm, ddim, euler, euler-a, dpmpp-2m | pndm | Sampling method |
--width | 256–768, divisible by 64 | 512 | Output width |
--height | 256–768, divisible by 64 | 512 | Output height |
--num-images | 1–4 | 1 | Images generated in one run |
--seed | 0–(2⁶³−1) | 1337 | Repeatable starting seed |
--device | auto, cuda, mps, cpu | auto | Compute backend |
--local-files-only | Flag | Off | Require an already-downloaded local model |
The reference configuration is 50-step PNDM, guidance 7.5, 512×512, one
image, seed 1337, and an empty negative prompt. The live demo pre-fills
blurry, distorted, low detail; the local runner leaves the field empty unless
you pass the flag.
For multiple images, the first uses the requested filename and later images use
numbered names such as clover-image-tiny-02.png. Seeds advance from the
requested seed. A JSON sidecar beside the first PNG records every resolved
setting, output filename, seed, checksum, and safety result. Existing planned
outputs are never overwritten.
Run python model/examples/generate.py --help for the complete CLI reference.
7. Hardware and operating systems
| System | Automatic backend | Precision | Current evidence |
|---|---|---|---|
| iPhone (see current app requirements) | Core ML | mixed/compiled | GitHub project and chunked download path linked above |
| Apple-silicon Mac | MPS | fp16 | Measured locally on an M4 Pro |
| Windows/Linux with NVIDIA | CUDA | fp16 | Supported code path; performance not measured |
| CPU-only macOS/Windows/Linux | CPU | fp32 | Supported code path; performance not measured |
| Windows AMD/DirectML | — | — | No packaged DirectML path |
Keep at least 2 GB free for the model alone and additional room for the Python
environment and caches; no formal total-install minimum has been measured.
Larger images and batches need more memory; lower --width, --height, or
--num-images if necessary.
The measured Mac reference used a 24 GB Apple M4 Pro and completed one 512×512 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.
8. Python API
import torch
from diffusers import DiffusionPipeline, PNDMScheduler
model_id = "neonforestmist/Clover-Image-Tiny"
if torch.cuda.is_available():
device = "cuda"
elif torch.backends.mps.is_available():
device = "mps"
else:
device = "cpu"
dtype = torch.float16 if device in {"cuda", "mps"} else torch.float32
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype)
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(device)
generator_device = "cuda" if device == "cuda" else "cpu"
generator = torch.Generator(device=generator_device).manual_seed(1337)
image = pipe(
prompt="a tiny greenhouse glowing in a moonlit garden",
negative_prompt="blurry, distorted, low detail",
num_inference_steps=50,
guidance_scale=7.5,
height=512,
width=512,
generator=generator,
).images[0]
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.
9. 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. This was a real U-Net optimization run—not a repackaging operation. Its final cursor records 4,000 microsteps and 4,000 sample presentations, with finite training rows and nonzero gradients throughout.
The model was initialized from
nota-ai/bk-sdm-tiny-2m@aad3e0e8ba61b7cb9f64869dc4e586f8ad9d3665
and distilled with a frozen
CompVis/stable-diffusion-v1-4@133a221b8aa7292a167afc5127cb63fb5005638b
teacher. It is a genuinely modified checkpoint, but it was not trained from
random initialization.
9.1 Clover distillation recipe
| Training field | Recorded value |
|---|---|
| Student initialization | nota-ai/bk-sdm-tiny-2m@aad3e0e8ba61b7cb9f64869dc4e586f8ad9d3665 |
| Frozen teacher | CompVis/stable-diffusion-v1-4@133a221b8aa7292a167afc5127cb63fb5005638b |
| Trainable parameters | Clover U-Net only; text encoder, VAE, and teacher frozen |
| Resolution | 512×512 |
| Optimization | 500 AdamW steps · effective batch 8 · learning rate 1e-5 |
| Precision | bfloat16 autocast with float32 master weights |
| Objective | 1.0 × diffusion + 1.0 × teacher output + 1.0 × normalized feature KD |
| Feature transfer | Six source-audited BK-Tiny ↔ SD v1.4 internal feature mappings |
| Reproducibility | Seed 1337 · atomic checkpoints every 50 steps · exact resume proven at step 100 |
| Training hardware | One NVIDIA A100-SXM4-80GB |
Each objective contributed something complementary: the diffusion term retained
the standard epsilon-prediction task, output KD pulled the compact student
toward the full teacher's denoising prediction, and feature KD aligned internal
representations at six explicitly mapped points across the down, attention, and
up paths. Teacher execution used no_grad; no teacher, CLIP text-encoder, or
VAE gradients were accumulated. This gives Clover a targeted weight refresh
without increasing its U-Net parameter count or abandoning standard Stable
Diffusion/Diffusers compatibility.
This repository contains the PyTorch/Diffusers checkpoint. Core ML artifacts, style adapters, and the companion iOS project are versioned separately and linked above.
10. Quality and known behavior
- The included gallery demonstrates recognizable subjects across colorful scenes, products, food, an animal, a landscape, and an interior.
- Individual results vary by prompt, seed, scheduler, and step count. More steps increase runtime but do not guarantee a better result.
- Hands, anatomy, exact counts and relationships, and readable text can be difficult.
- The small-model comparison is an engineering benchmark with a CLIP prompt-adherence proxy, not a controlled human-preference study.
- Resolution and batch size multiply memory use.
11. 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;
the JSON sidecar records nsfw_content_detected so the result is not silent.
The checker is useful but not a complete moderation system and can miss harmful
content or over-filter benign content.
Applications should add controls appropriate to their audience and review 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.
12. Training lineage and data
- Clover fine-tuning data: exactly 1,000 accepted image-caption pairs from
Spawning/PD3M@2a5eb24a8dccf245acd8e56341761aee06da0bdf - Split: 973 train, 17 validation, and 10 test records
- Data gate:
CDLA-Permissive-2.0; accepted items retain CC0-1.0 or Public Domain Mark 1.0 provenance - Preprocessing: deterministic center crop and 512×512 JPEG conversion,
version
clover-pd3m-center-crop-512-jpeg95-v1 - Dataset-manifest SHA-256:
50c1249f1cb0d8d690a9acc451ca10c9432eb5a7f4e26f34acb5462096e72322
The set was chosen by a deterministic hash ordering from the pinned PD3M revision, then validated for license, dimensions, MIME type, source organization, payload integrity, and deletion-list status. The resulting shard, manifest, rejection log, selection statistics, and preprocessing recipe were all checksummed. This is a deliberately small calibration pass layered on top of BK-SDM-Tiny-2M's much larger inherited pretraining—not a claim that Clover learned general image generation from only 1,000 examples.
The 1,000 records describe the Clover fine-tuning run. The student and teacher already contain knowledge from larger upstream corpora. Their pinned model cards and weight licenses are disclosed, while complete item-level provenance 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.
14. 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
remain separate. Read LICENSE, LICENSE-MODEL-CREATIVEML-OPENRAIL-M.txt,
LICENSE-CODE, and MODEL_DATA_LICENSES.md before redistribution or use.
The legacy hero mosaic is user-supplied presentation artwork included by explicit 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.
15. Reproducibility and artifact identity
| Field | Value |
|---|---|
| Repository | neonforestmist/Clover-Image-Tiny |
| Release status | PUBLIC PYTORCH/DIFFUSERS CHECKPOINT RELEASE |
| Training experiment | clover-kd-20260712T050925Z-01KXABNHP0 |
| Optimizer step | 500 |
| Checkpoint SHA-256 | 4a5b99ff18478742528a0d31c97dcee939b166a51be858721d40ad5984110893 |
| Checkpoint-bundle SHA-256 | 384b6515f5f26838aea33ec9a941e06610a20764f0b8637c8b7b0667bfc0d447 |
| Resolved-config SHA-256 | 80cf9395d1f587dc0c1d440d9f5b55c55c20703187998509bb306d19d463f597 |
| Denoiser parameters | 323,384,964 |
| Package bytes | 1676086612 |
| Package files | 31 |
| Validated Stage B source-package checksums SHA-256 | d9a28d5fe6f5b675ee1b9db52e6d0493c8d3d357bb824eac590911acbd5c3ebc |
| Builder source commit | 9f5ce495fcb88238ec7fdc33204fa42ec9690c37 |
checksums.json covers every file in the immutable validated release package
at the recorded builder commit. Later model-card-only revisions are additionally
preserved by the Hugging Face Git history.
Citation
If you use Clover Image Tiny in your work, please cite the model release:
@software{lozadaperez2026cloverimagetiny,
author = {Lukas Lozada Perez},
title = {Clover Image Tiny: Compact Local Text-to-Image Diffusion},
year = {2026},
url = {https://huggingface.co/neonforestmist/Clover-Image-Tiny}
}
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
remain separate. Read LICENSE, LICENSE-MODEL-CREATIVEML-OPENRAIL-M.txt,
LICENSE-CODE, and MODEL_DATA_LICENSES.md before redistribution or use.
The hero mosaic is user-supplied presentation artwork included by explicit 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.
Created by Lukas Lozada Perez.
- Task
- Text-to-Image
- Library
- diffusers
- License
- creativeml-openrail-m
- Created
- Jul 14, 2026
- Updated
- Sep 7, 2026