Diffusers
Safetensors
OrbitQuantComponentArtifact
orbitquant
quantized
diffusion-transformer
8-bit precision
Instructions to use WaveCut/Z-Image-Turbo-OrbitQuant-W2A3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/Z-Image-Turbo-OrbitQuant-W2A3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/Z-Image-Turbo-OrbitQuant-W2A3", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Requantize W2A3 with mixed-width low-bit protection
Browse files- README.md +2 -16
- SHA256SUMS +9 -9
- assets/image_generation_comparison_matrix.webp +2 -2
- benchmark/summary.json +75 -61
- model.safetensors +2 -2
- model_index.json +1 -1
- orbitquant_codebooks.safetensors +2 -2
- orbitquant_manifest.json +494 -298
- prompts.json +3 -3
- quantization_config.json +9 -1
README.md
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Install OrbitQuant and the Hugging Face runtime dependencies:
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```bash
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pip install "orbitquant[hf,kernels]>=0.
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```
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The optimized native kernel package is provisioned automatically at first
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model load: OrbitQuant derives the runtime variant (torch minor and CUDA
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version for CUDA, the torch stable ABI for CPU, plus OS and architecture),
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downloads the matching prebuilt wheel from the
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[OrbitQuant kernels release](https://github.com/iamwavecut/OrbitQuant/releases/tag/kernels-v1)
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with checksum verification, and caches it under `~/.cache/orbitquant/kernels`.
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Without a matching variant, CUDA inference falls back to the Triton packed
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path. Run `orbitquant kernels-install` to provision explicitly. For ComfyUI,
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the [ComfyUI-OrbitQuant](https://github.com/iamwavecut/ComfyUI-OrbitQuant)
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node pack provisions the same kernels from its install hook.
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Download this model repository as an OrbitQuant artifact, then load the source Diffusers pipeline with the quantized component patched in:
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```python
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- Method: `orbitquant`
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- Bits: `W2A3`
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- Runtime mode: `auto_fused`
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- Activation kernel backend: `auto`
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- Activation normalization epsilon: `1e-10`
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- Calibration data: none
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- Text encoders and VAE: left in source precision by default
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## Observed Quality
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**Warning:** The OrbitQuant W2A3 outputs show severe visual collapse across all ten native comparison prompts. Use W3A3 or W4A4 for this model.
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## Visual Comparison
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The following assets are stored in this artifact and compare the BF16 base generation against the OrbitQuant generation with the same prompt and seed.
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Install OrbitQuant and the Hugging Face runtime dependencies:
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```bash
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pip install "orbitquant[hf,kernels]>=0.4.0"
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```
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Download this model repository as an OrbitQuant artifact, then load the source Diffusers pipeline with the quantized component patched in:
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```python
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- Method: `orbitquant`
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- Bits: `W2A3`
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- Module weight-width counts: `W2: 44, W3: 110, W4: 84`
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- Runtime mode: `auto_fused`
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- Activation kernel backend: `auto`
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- Activation normalization epsilon: `1e-10`
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- Calibration data: none
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- Text encoders and VAE: left in source precision by default
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## Visual Comparison
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The following assets are stored in this artifact and compare the BF16 base generation against the OrbitQuant generation with the same prompt and seed.
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667324a633b4e4d92931ca523943c093da46c24472d3f7dc315782ca57847585 model.safetensors
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cffb8406914ef65f5bb4df0c893e680c39f4c99ab4d6629f4457f61797ee33b3 model_index.json
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e7737934089114db3107ba8c16e8c6a882f259fc97d02352cf5f4fb1b0656fe9 orbitquant_codebooks.safetensors
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b11e2c4632b3d563c553eb33f8fc9162638ff19ea7c195a1b1e2cfe5dab69b17 orbitquant_manifest.json
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e1724c9394bf83ea05da6b457b8072d2bcb3d5a533dafce916da8deb517d3825 orbitquant_rotations.safetensors
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f48aca6d4d681d1ed10dcce8ad43fedb965b4a6a4b11003eb5e37ea53e4eb526 prompts.json
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6b778e6789a13a9e44c795571fc7d524d775730b602b634b87ba6c511d21e784 quantization_config.json
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assets/image_generation_comparison_matrix.webp
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benchmark/summary.json
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{
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"status": "native_validation_complete",
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"source_model_id": "Tongyi-MAI/Z-Image-Turbo",
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"source_revision": "f332072aa78be7aecdf3ee76d5c247082da564a6",
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"weight_bits": 2,
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"activation_bits": 3,
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"activation_kernel_backend": "auto",
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"activation_eps": 1e-10,
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"quantization_device": "cuda",
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"native_smoke": {
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"proof_source": "native_visual_pack_v2",
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"comparison_asset_path": "assets/image_generation_comparison_matrix.webp",
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"paired_prompt_seed_count": 10,
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"native_settings": [
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"published_summary": "compact",
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"raw_generation_records": "local-only"
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"comparison_prompt_pack": "image_visual_v2",
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"observed_quality": "The OrbitQuant W2A3 outputs show severe visual collapse across all ten native comparison prompts. Use W3A3 or W4A4 for this model."
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}
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{
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"activation_bits": 3,
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"activation_eps": 1e-10,
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"activation_kernel_backend": "auto",
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"adaln_seconds": 0.14151337649673223,
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"codebook_version": 2,
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"device_transfer_seconds": 1.3492279867641628,
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"module_device_transfer_count": 1,
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"orbitquant_seconds": 1.1136700571514666,
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"quantization_device": "cuda",
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"quantization_elapsed_seconds": 2.619350762106478,
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"quantization_staging_mode": "component",
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"runtime_mode": "auto_fused",
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"source_model_id": "Tongyi-MAI/Z-Image-Turbo",
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"source_revision": "f332072aa78be7aecdf3ee76d5c247082da564a6",
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"status": "metrics_recorded",
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"synchronize_per_module": false,
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"target_policy": "z_image",
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"weight_bits": 2,
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"weight_quantization_backend": "triton_cuda",
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"metrics": {
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"original": {
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"records": 10,
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"latest_metrics": {
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"generated_samples": 1,
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"peak_vram_bytes": 23267429888,
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"wall_time_seconds": 6.092661592178047
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}
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},
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"orbitquant": {
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"records": 10,
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"latest_metrics": {
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"generated_samples": 1,
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"wall_time_seconds": 7.162837381940335,
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"peak_vram_bytes": 13385215488
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}
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}
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},
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"native_smoke": {
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"proof_format": "orbitquant-native-smoke-v1",
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"comparison_asset_path": "assets/image_generation_comparison_matrix.webp",
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"paired_prompt_seed_count": 10,
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"paired_prompt_seed_keys": [
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"z-image-native",
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],
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],
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"splits": {
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"prompt_ids": [
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|
| 107 |
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|
| 109 |
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|
| 110 |
+
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|
| 111 |
+
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|
| 112 |
+
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|
| 113 |
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| 114 |
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|
| 115 |
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| 116 |
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[
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| 123 |
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|
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|
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[
|
| 128 |
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|
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| 130 |
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|
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|
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|
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[
|
| 138 |
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|
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|
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[
|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
[
|
| 148 |
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|
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|
| 150 |
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|
| 151 |
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|
| 152 |
[
|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
[
|
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|
| 162 |
[
|
| 163 |
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|
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|
| 166 |
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|
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|
| 188 |
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|
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|
| 190 |
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|
| 191 |
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|
| 192 |
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|
| 193 |
+
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|
| 194 |
+
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|
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|
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|
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|
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[
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| 206 |
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| 208 |
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|
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|
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[
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| 211 |
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|
| 212 |
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|
| 214 |
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[
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| 216 |
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| 217 |
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|
| 219 |
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[
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| 221 |
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| 222 |
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| 223 |
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| 224 |
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| 225 |
[
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| 226 |
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| 228 |
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|
| 229 |
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| 230 |
[
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| 231 |
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| 232 |
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| 233 |
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| 234 |
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| 236 |
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| 237 |
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| 239 |
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| 241 |
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| 245 |
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| 266 |
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model_index.json
CHANGED
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|
| 21 |
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| 22 |
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|
| 2875 |
+
"layers.23.attention.to_v": 3,
|
| 2876 |
+
"layers.23.attention.to_out.0": 3,
|
| 2877 |
+
"layers.23.feed_forward.w1": 3,
|
| 2878 |
+
"layers.23.feed_forward.w2": 3,
|
| 2879 |
+
"layers.23.feed_forward.w3": 3,
|
| 2880 |
+
"layers.24.attention.to_v": 3,
|
| 2881 |
+
"layers.24.attention.to_out.0": 3,
|
| 2882 |
+
"layers.24.feed_forward.w1": 3,
|
| 2883 |
+
"layers.24.feed_forward.w2": 3,
|
| 2884 |
+
"layers.24.feed_forward.w3": 3,
|
| 2885 |
+
"layers.25.attention.to_v": 3,
|
| 2886 |
+
"layers.25.attention.to_out.0": 3,
|
| 2887 |
+
"layers.25.feed_forward.w1": 3,
|
| 2888 |
+
"layers.25.feed_forward.w2": 3,
|
| 2889 |
+
"layers.25.feed_forward.w3": 3,
|
| 2890 |
+
"layers.26.attention.to_q": 4,
|
| 2891 |
+
"layers.26.attention.to_k": 4,
|
| 2892 |
+
"layers.26.attention.to_v": 4,
|
| 2893 |
+
"layers.26.attention.to_out.0": 4,
|
| 2894 |
+
"layers.26.feed_forward.w1": 4,
|
| 2895 |
+
"layers.26.feed_forward.w2": 4,
|
| 2896 |
+
"layers.26.feed_forward.w3": 4,
|
| 2897 |
+
"layers.27.attention.to_q": 4,
|
| 2898 |
+
"layers.27.attention.to_k": 4,
|
| 2899 |
+
"layers.27.attention.to_v": 4,
|
| 2900 |
+
"layers.27.attention.to_out.0": 4,
|
| 2901 |
+
"layers.27.feed_forward.w1": 4,
|
| 2902 |
+
"layers.27.feed_forward.w2": 4,
|
| 2903 |
+
"layers.27.feed_forward.w3": 4,
|
| 2904 |
+
"layers.28.attention.to_q": 4,
|
| 2905 |
+
"layers.28.attention.to_k": 4,
|
| 2906 |
+
"layers.28.attention.to_v": 4,
|
| 2907 |
+
"layers.28.attention.to_out.0": 4,
|
| 2908 |
+
"layers.28.feed_forward.w1": 4,
|
| 2909 |
+
"layers.28.feed_forward.w2": 4,
|
| 2910 |
+
"layers.28.feed_forward.w3": 4,
|
| 2911 |
+
"layers.29.attention.to_q": 4,
|
| 2912 |
+
"layers.29.attention.to_k": 4,
|
| 2913 |
+
"layers.29.attention.to_v": 4,
|
| 2914 |
+
"layers.29.attention.to_out.0": 4,
|
| 2915 |
+
"layers.29.feed_forward.w1": 4,
|
| 2916 |
+
"layers.29.feed_forward.w2": 4,
|
| 2917 |
+
"layers.29.feed_forward.w3": 4
|
| 2918 |
+
},
|
| 2919 |
"checksums": {
|
| 2920 |
+
"assets/image_generation_comparison_matrix.webp": "8f4235baa433900bfaf3ac5160af91c0ed68c25fffa02958f19d0a1b9dc3e421",
|
| 2921 |
+
"benchmark/summary.json": "119fadb02071c014a9c51c1108a54e20fa3532b0eaa39c2a57b788787ecbd38c",
|
| 2922 |
+
"model.safetensors": "667324a633b4e4d92931ca523943c093da46c24472d3f7dc315782ca57847585",
|
| 2923 |
+
"model_index.json": "cffb8406914ef65f5bb4df0c893e680c39f4c99ab4d6629f4457f61797ee33b3",
|
| 2924 |
+
"orbitquant_codebooks.safetensors": "e7737934089114db3107ba8c16e8c6a882f259fc97d02352cf5f4fb1b0656fe9",
|
| 2925 |
"orbitquant_rotations.safetensors": "e1724c9394bf83ea05da6b457b8072d2bcb3d5a533dafce916da8deb517d3825",
|
| 2926 |
+
"prompts.json": "f48aca6d4d681d1ed10dcce8ad43fedb965b4a6a4b11003eb5e37ea53e4eb526",
|
| 2927 |
+
"quantization_config.json": "6b778e6789a13a9e44c795571fc7d524d775730b602b634b87ba6c511d21e784"
|
| 2928 |
}
|
| 2929 |
}
|
prompts.json
CHANGED
|
@@ -49,19 +49,19 @@
|
|
| 49 |
"id": "cyrillic-text-rendering",
|
| 50 |
"title": "08 Russian Constructivist typography",
|
| 51 |
"category": "russian_typography",
|
| 52 |
-
"prompt": "A richly detailed Russian Constructivist science-fiction poster with the exact Cyrillic headline \"
|
| 53 |
},
|
| 54 |
{
|
| 55 |
"id": "style-heavy",
|
| 56 |
"title": "09 Japanese typography and mixed style",
|
| 57 |
"category": "japanese_typography",
|
| 58 |
-
"prompt": "An elaborate Japanese art magazine cover combining Edo woodblock printing with a futuristic Tokyo skyline, with the exact vertical title \"
|
| 59 |
},
|
| 60 |
{
|
| 61 |
"id": "occlusion-reflection",
|
| 62 |
"title": "10 Chinese typography, reflection, occlusion",
|
| 63 |
"category": "chinese_typography_reflection_occlusion",
|
| 64 |
-
"prompt": "A luxurious Chinese retro-futurist department-store window at night with the exact gold title \"
|
| 65 |
}
|
| 66 |
]
|
| 67 |
}
|
|
|
|
| 49 |
"id": "cyrillic-text-rendering",
|
| 50 |
"title": "08 Russian Constructivist typography",
|
| 51 |
"category": "russian_typography",
|
| 52 |
+
"prompt": "A richly detailed Russian Constructivist science-fiction poster with the exact Cyrillic headline \"\u041a\u0412\u0410\u041d\u0422\u041e\u0412\u0410\u042f \u041e\u0420\u0411\u0418\u0422\u0410\", the exact subtitle \"\u041c\u041e\u0421\u041a\u0412\u0410 2049\", and a small exact stamp \"\u041a\u0412\u0410\u041d\u0422\u041e\u0412\u0410\u041d\u0418\u0415\"; diagonal red and black geometry, cream paper, cosmonaut portrait, orbital diagrams, halftone grain, folded corners, layered ink, museum archival photograph, all letters crisp and correctly ordered"
|
| 53 |
},
|
| 54 |
{
|
| 55 |
"id": "style-heavy",
|
| 56 |
"title": "09 Japanese typography and mixed style",
|
| 57 |
"category": "japanese_typography",
|
| 58 |
+
"prompt": "An elaborate Japanese art magazine cover combining Edo woodblock printing with a futuristic Tokyo skyline, with the exact vertical title \"\u91cf\u5b50\u306e\u8ecc\u9053\" and the exact subtitle \"\u6771\u4eac\u306e\u672a\u6765\"; giant indigo waves curl around glass towers, red-crowned cranes cross a gold moon, tiny pedestrians and trains fill the lower streets, visible washi fibers, layered spot colors, precise Japanese glyphs, balanced editorial layout"
|
| 59 |
},
|
| 60 |
{
|
| 61 |
"id": "occlusion-reflection",
|
| 62 |
"title": "10 Chinese typography, reflection, occlusion",
|
| 63 |
"category": "chinese_typography_reflection_occlusion",
|
| 64 |
+
"prompt": "A luxurious Chinese retro-futurist department-store window at night with the exact gold title \"\u91cf\u5b50\u8f68\u9053\" and the exact red subtitle \"\u672a\u6765\u4e4b\u57ce\"; a curved chrome robot is partly occluded by peonies and blue-and-white porcelain, the calligraphy and neon street must appear coherently reflected across its body, multiple glass layers, silk textures, passing bicycles, cinematic rain, fine product-photography detail"
|
| 65 |
}
|
| 66 |
]
|
| 67 |
}
|
quantization_config.json
CHANGED
|
@@ -15,6 +15,8 @@
|
|
| 15 |
"target_policy": "z_image",
|
| 16 |
"adaln_policy": "int4_rtn",
|
| 17 |
"adaln_group_size": 64,
|
|
|
|
|
|
|
| 18 |
"modules_to_not_convert": [],
|
| 19 |
"modules_dtype_dict": {},
|
| 20 |
"artifact_format_version": 1,
|
|
@@ -23,5 +25,11 @@
|
|
| 23 |
"packed_matmul_block_m": 64,
|
| 24 |
"packed_matmul_block_n": 64,
|
| 25 |
"packed_matmul_block_k": 128,
|
| 26 |
-
"packed_matmul_num_warps": 4
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 27 |
}
|
|
|
|
| 15 |
"target_policy": "z_image",
|
| 16 |
"adaln_policy": "int4_rtn",
|
| 17 |
"adaln_group_size": 64,
|
| 18 |
+
"modules_to_convert": [],
|
| 19 |
+
"modules_to_use_adaln": [],
|
| 20 |
"modules_to_not_convert": [],
|
| 21 |
"modules_dtype_dict": {},
|
| 22 |
"artifact_format_version": 1,
|
|
|
|
| 25 |
"packed_matmul_block_m": 64,
|
| 26 |
"packed_matmul_block_n": 64,
|
| 27 |
"packed_matmul_block_k": 128,
|
| 28 |
+
"packed_matmul_num_warps": 4,
|
| 29 |
+
"weight_row_tile_size": 256,
|
| 30 |
+
"w4a4_int8_weight_cache": false,
|
| 31 |
+
"lowbit_boundary_protection": "auto",
|
| 32 |
+
"lowbit_protected_blocks": 4,
|
| 33 |
+
"lowbit_protected_bits": 4,
|
| 34 |
+
"lowbit_interior_protection": true
|
| 35 |
}
|