Upload miro-only-vqa weights

.gitattributes

@@ -47,3 +47,4 @@ miro-only-hpsv2/teaser.jpg filter=lfs diff=lfs merge=lfs -text
 miro-only-image-reward/teaser.jpg filter=lfs diff=lfs merge=lfs -text
 miro-only-pickscore/teaser.jpg filter=lfs diff=lfs merge=lfs -text
 miro-only-sciscore/teaser.jpg filter=lfs diff=lfs merge=lfs -text
+miro-only-vqa/teaser.jpg filter=lfs diff=lfs merge=lfs -text

miro-only-vqa/README.md

@@ -0,0 +1,166 @@
+---
+license: mit
+library_name: miro-t2i
+tags:
+  - text-to-image
+  - diffusion
+  - flow-matching
+  - miro
+  - reward-conditioning
+pipeline_tag: text-to-image
+---
+
+# VQAScore-only specialist (paper baseline)
+
+![Qualitative samples from MIRO](teaser.jpg)
+
+<sub>Qualitative samples from the released MIRO checkpoint — same gallery as the
+teaser of the [project page](https://nicolas-dufour.github.io/miro/).</sub>
+
+Single-reward baseline: trained with **only** VQAScore.
+
+This checkpoint accompanies the paper
+**MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency**
+(Dufour, Degeorge, Ghosh, Kalogeiton, Picard — ICML 2026).
+
+| | |
+|---|---|
+| **Paper** | <https://arxiv.org/abs/2510.25897> |
+| **Project page** | <https://nicolas-dufour.github.io/miro/> |
+| **Code** | <https://github.com/nicolas-dufour/miro> |
+| **Parameters** | 352.5M |
+| **Resolution** | 256×256 (SDXL VAE latent space) |
+| **Architecture** | RIN flow-matching backbone, FLAN-T5-XL text conditioning |
+| **Training data** | [CC12M](https://huggingface.co/datasets/pixparse/cc12m-wds) + [LAION Aesthetics v2 4.5](https://huggingface.co/datasets/laion/aesthetics_v2_4.5) (6.0+ aesthetic subset) |
+| **Reward signals** | `vqa_score` |
+| **Weights** | `model.safetensors`, **fp32** (EMA master weights — ready for finetuning) |
+
+## Usage
+
+```python
+import torch
+from miro import MiroPipeline
+
+pipe = MiroPipeline.from_pretrained(
+    "nicolas-dufour/miro-ablations", variant="miro-only-vqa",
+)
+pipe = pipe.to("cuda", torch.float16)
+
+prompt = (
+    "Photography closeup portrait of an adorable rusty broken­down steampunk "
+    "robot covered in budding vegetation, surrounded by tall grass, misty "
+    "futuristic sci­fi forest environment."
+)
+image = pipe(prompt, num_inference_steps=50, guidance_scale=7.0)[0]
+image.save("out.png")
+```
+
+### Reward conditioning
+
+MIRO conditions the flow model on a vector of reward targets in addition to the
+text prompt. By default every reward is requested at its maximum (`1.0`); you
+can override individual axes to bias generation toward a particular trade-off:
+
+```python
+image = pipe(
+    "a chest x-ray showing pneumonia",
+    reward_targets={
+        "clip_score": 1.0,        # strict prompt alignment
+        "aesthetic_score": 0.3,   # de-prioritise prettiness
+        "sciscore_score": 1.0,    # prioritise scientific accuracy
+        # any reward not listed defaults to 1.0
+    },
+    negative_reward_targets={
+        # zeros by default; what to push the unconditional branch toward
+    },
+    guidance_scale=7.0,
+)[0]
+```
+
+The seven reward dimensions are:
+
+| Reward | Normalised range | What it measures |
+|---|---|---|
+| `clip_score` | ~[0, 1] | CLIP text–image alignment |
+| `aesthetic_score` | ~[0, 1] | LAION aesthetic-quality predictor |
+| `image_reward_score` | ~[0, 1] | ImageReward (general preference model) |
+| `pick_a_score_score` | ~[0, 1] | PickScore (human preference) |
+| `hpsv2_score` | ~[0, 1] | HPSv2 (human preference v2) |
+| `vqa_score` | ~[0, 1] | VQAScore (compositional faithfulness) |
+| `sciscore_score` | ~[0, 1] | SciScore (scientific-image plausibility) |
+
+## Reported benchmarks
+
+The paper reports the following headline numbers for the **main MIRO** model
+(this repo's `nicolas-dufour/miro`):
+
+| Metric | MIRO (350M) | FLUX-dev (12B) |
+|---|---|---|
+| GenEval (overall) | **75** (with inference-time reward tuning) / 68 (default) | 67 |
+| Inference compute | **1×** | ~370× |
+| Aesthetic-metric convergence vs. baseline pretraining | **19×** faster | — |
+
+Per-variant scores (GenEval, FID, individual reward scores) for the eight
+ablations are reported in the paper's ablation tables. Please refer to
+[arXiv:2510.25897](https://arxiv.org/abs/2510.25897) for the full breakdown.
+
+## Training compute and data
+
+- **Default hardware**: 2 nodes × 8 H100 GPUs (16× H100, `16-mixed` precision)
+- **Optimiser**: LAMB, lr 1e-3 (5k warmup → cosine decay), weight decay 1e-2
+- **Batch size**: 1024 globally (64 per GPU on 16× H100), gradient-clip 2.0
+- **Steps**: 500 k (≈ ~29 epochs over the enriched training set)
+- **Wall-clock on 16× H100**: ~52 hours (≈ 2.65 train it/s sustained)
+- **8-GPU fallback**: 1 node × 8 H100 with `trainer.accumulate_grad_batches=2`,
+  measured at **≈ 1.45 train it/s** → ~96 hours (~4 days) end-to-end.
+  Requires `trainer.strategy.static_graph=false` and
+  `trainer.strategy.find_unused_parameters=true` to play well with the
+  self-conditioning skip in the loss; both flags are set automatically by
+  `miro/slurm/launch_multicad_synth_8gpu.py`.
+- **Data**: [CC12M](https://huggingface.co/datasets/pixparse/cc12m-wds) +
+  [LAION Aesthetics v2 4.5](https://huggingface.co/datasets/laion/aesthetics_v2_4.5)
+  filtered to `aesthetic_score >= 6.0` (the higher-quality subset), encoded to
+  SDXL VAE latents at 256 resolution. Each sample is paired with seven reward
+  scores and FLAN-T5-XL embeddings of both the original and a synthetic
+  caption, computed by
+  [`miro/data/preprocess_data.py`](https://github.com/nicolas-dufour/miro/blob/main/data/preprocess_data.py).
+
+## Limitations and intended use
+
+This checkpoint is a research artifact released to reproduce and build on the
+MIRO paper. Known limitations:
+
+- **Resolution**: 256×256 only. Higher-resolution outputs require upscaling.
+- **Domain**: trained on web-scraped image–caption pairs (CC12M + LAION
+  Aesthetics 6.0). Inherits the biases of those datasets — including
+  under-representation of many cultures, languages, and concepts, and the
+  presence of stereotypes. Generations may reflect or amplify these biases.
+- **Reward-model biases**: the seven reward predictors used during training
+  encode their own biases (e.g. aesthetic and human-preference models reflect
+  the taste of their annotator pools). Conditioning on these rewards inherits
+  and can sharpen those biases.
+- **Not for safety-critical use**: outputs are not factual and the SciScore
+  reward does not guarantee scientific accuracy.
+- **No safety filter** is shipped with the model; users deploying it in
+  user-facing settings should add their own.
+
+The model is released under the MIT license; the SDXL VAE and FLAN-T5-XL
+encoder it depends on at inference time are loaded from
+[`stabilityai/sdxl-vae`](https://huggingface.co/stabilityai/sdxl-vae) and
+[`google/flan-t5-xl`](https://huggingface.co/google/flan-t5-xl) and are
+subject to their respective licenses.
+
+## Citation
+
+```bibtex
+@inproceedings{dufour2026miro,
+  title     = {{MIRO}: {M}ult{I}-{R}eward c{O}nditioned pretraining improves {T2I} quality and efficiency},
+  author    = {Dufour, Nicolas and Degeorge, Lucas and Ghosh, Arijit and Kalogeiton, Vicky and Picard, David},
+  booktitle = {International Conference on Machine Learning (ICML)},
+  year      = {2026}
+}
+```
+
+## License
+
+MIT — see <https://github.com/nicolas-dufour/miro/blob/main/LICENSE>.

miro-only-vqa/config.json

@@ -0,0 +1,69 @@
+{
+  "network": {
+    "data_size": 32,
+    "data_dim": 512,
+    "num_input_channels": 4,
+    "num_latents": 256,
+    "latents_dim": 1024,
+    "label_dim": 2048,
+    "num_cond_tokens": 77,
+    "num_processing_layers": 4,
+    "num_blocks": 4,
+    "patch_size": 2,
+    "read_write_heads": 16,
+    "compute_heads": 32,
+    "latent_mlp_multiplier": 4,
+    "data_mlp_multiplier": 4,
+    "compute_dropout": 0,
+    "rw_stochastic_depth": 0,
+    "compute_stochastic_depth": 0,
+    "concat_cond_token_to_latents": false,
+    "use_cond_rin_block": true,
+    "num_text_registers": 16,
+    "coherence_keys": [
+      "vqa_score"
+    ],
+    "coherence_dropout": 0.0,
+    "use_self_conditioning": true
+  },
+  "preconditioning": {
+    "num_latents": 256,
+    "latents_dim": 1024,
+    "do_normalization": true,
+    "sigma_data": 0.5,
+    "do_gradnorm_reweighting": true,
+    "logvar_channels": 128,
+    "logvar_mlp_layers": 0
+  },
+  "data_preprocessing": {
+    "input_key_mean": "vae_embeddings_mean_256",
+    "input_key_std": "vae_embeddings_std_256",
+    "output_key_root": "x_0",
+    "vae_sample": true,
+    "channel_wise_normalisation": true,
+    "model_type": "sdxl"
+  },
+  "postprocessing": {
+    "channel_wise_normalisation": true,
+    "model_type": "sdxl"
+  },
+  "scheduler": {
+    "start": 1,
+    "end": 0,
+    "clip_min": 1e-09
+  },
+  "coherence_keys": [
+    "vqa_score"
+  ],
+  "sampler_defaults": {
+    "num_steps": 50,
+    "guidance_scale": 7.0,
+    "sigma_data": 0.5
+  },
+  "data_resolution": 32,
+  "img_resolution": 256,
+  "max_text_len": 77,
+  "model_type": "sdxl",
+  "vae_repo": "stabilityai/sdxl-vae",
+  "text_encoder_repo": "google/flan-t5-xl"
+}

miro-only-vqa/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:892a409c1b58bf1a602043d3c003e9f6519f1ec64a9d63fde552c08e521b867f
+size 1410184229

miro-only-vqa/uncond_embedding.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c18c7b19cb2a19a559949ff090c08f6c74dd2502a203c4ac2a93d06dd232d9d7
+size 8320