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Model card: all released DMAD weights (MiniMax-H3, Wan2.1, SDXL, ImageNet-64) and per-family licenses

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README.md CHANGED
@@ -39,10 +39,15 @@ tags:
39
  <img src="https://huggingface.co/ZhengmingYu/DMAD/resolve/main/assets/teaser.gif" alt="Videos generated by the 4-step DMAD student of MiniMax-H3 (video only; every clip also has generated audio)">
40
  </p>
41
 
42
- This repository holds the **4-step DMAD students of [MiniMax-H3](https://huggingface.co/MiniMaxAI/MiniMax-H3)
43
- (33B, text-to-audio-video)**: rank-128 LoRAs on the H3 transformer that turn the 50-step teacher into a 4-step
44
- generator of 1344x768 video with native stereo audio. The inference code is in the
45
- [code repository](https://github.com/Yzmblog/DMAD).
 
 
 
 
 
46
 
47
  | File | Checkpoint | Size |
48
  |------|------------|------|
@@ -53,8 +58,6 @@ LoRA layout: Diffusers keys (`<module>.lora.down.weight` = A `[128, in]`, `<modu
53
  `[out, 128]`) over `attn.to_q/to_k/to_v/to_out.0`, `ff.net.0.proj`, `ff.net.2` of all 50 transformer blocks and the 2
54
  token-refiner blocks (312 modules). alpha = rank = 128. The safetensors metadata repeats this.
55
 
56
- ## Usage
57
-
58
  The inference code lives in the [code repository](https://github.com/Yzmblog/DMAD): `inference.py` with the sampler
59
  the paper used (re-noise step rule) and a Diffusers-pipeline example; its README covers the environment. Sampling
60
  settings: 4 steps, time shift 12 (video) and 2 (audio), no classifier-free guidance, 124 frames at 24 fps.
@@ -67,6 +70,63 @@ python inference.py --model-dir models/MiniMax-H3 --lora ckpt/minimax_h3/dmad_mi
67
  --prompt-file prompts/dmad_sweater.txt --seed 42 --output-dir outputs/dmad_sweater
68
  ```
69
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
70
  ## Citation
71
 
72
  ```bibtex
@@ -82,6 +142,14 @@ python inference.py --model-dir models/MiniMax-H3 --lora ckpt/minimax_h3/dmad_mi
82
 
83
  ## License
84
 
85
- These checkpoints are Model Derivatives of MiniMax H3 and are distributed under the
86
- [MiniMax H3 Community License Agreement](LICENSE). The weights were
87
- modified from MiniMax-H3 by LoRA fine-tuning. Videos produced with them are AI-generated.
 
 
 
 
 
 
 
 
 
39
  <img src="https://huggingface.co/ZhengmingYu/DMAD/resolve/main/assets/teaser.gif" alt="Videos generated by the 4-step DMAD student of MiniMax-H3 (video only; every clip also has generated audio)">
40
  </p>
41
 
42
+ This repository holds the DMAD students of the paper: 4-step students of **[MiniMax-H3](https://huggingface.co/MiniMaxAI/MiniMax-H3)**
43
+ (33B, text-to-audio-video) and of **Wan2.1-T2V** (1.3B and 14B), 4- and 1-step students of **SDXL**, and 1-step
44
+ students of the **EDM ImageNet-64** teacher. The inference and training code is in the
45
+ [code repository](https://github.com/Yzmblog/DMAD) (`train/h3`, `train/wan`, `train/image`).
46
+
47
+ ## MiniMax-H3 (text-to-audio-video)
48
+
49
+ Rank-128 LoRAs on the H3 transformer that turn the 50-step teacher into a 4-step generator of 1344x768 video with
50
+ native stereo audio.
51
 
52
  | File | Checkpoint | Size |
53
  |------|------------|------|
 
58
  `[out, 128]`) over `attn.to_q/to_k/to_v/to_out.0`, `ff.net.0.proj`, `ff.net.2` of all 50 transformer blocks and the 2
59
  token-refiner blocks (312 modules). alpha = rank = 128. The safetensors metadata repeats this.
60
 
 
 
61
  The inference code lives in the [code repository](https://github.com/Yzmblog/DMAD): `inference.py` with the sampler
62
  the paper used (re-noise step rule) and a Diffusers-pipeline example; its README covers the environment. Sampling
63
  settings: 4 steps, time shift 12 (video) and 2 (audio), no classifier-free guidance, 124 frames at 24 fps.
 
70
  --prompt-file prompts/dmad_sweater.txt --seed 42 --output-dir outputs/dmad_sweater
71
  ```
72
 
73
+ ## Wan2.1 (text-to-video)
74
+
75
+ Full generators (EMA, spectral norm folded in) in the `.pth` format of
76
+ [`train/wan`](https://github.com/Yzmblog/DMAD/tree/main/train/wan): 4 steps, 480p, 81 frames, no classifier-free
77
+ guidance.
78
+
79
+ | File | Model | Size |
80
+ |------|-------|------|
81
+ | `wan2.1/dmad_wan2pt1_1pt3B.pth` | Wan2.1-T2V-1.3B student, iteration 17k | 2.8 GB |
82
+ | `wan2.1/dmad_wan2pt1_14B.pth` | Wan2.1-T2V-14B student, iteration 19.5k | 29 GB |
83
+
84
+ ```bash
85
+ hf download ZhengmingYu/DMAD --include "wan2.1/*" --local-dir ckpt
86
+ bash experiments/dmad/sample.sh 1.3B ckpt/wan2.1/dmad_wan2pt1_1pt3B.pth my_prompts.json outputs/samples # in train/wan
87
+ ```
88
+
89
+ ## SDXL (text-to-image)
90
+
91
+ UNet state dicts in fp16 (spectral norm folded in) that load into the standard SDXL pipeline; see
92
+ [`train/image`](https://github.com/Yzmblog/DMAD/tree/main/train/image) for the sampling code.
93
+
94
+ | File | Model | Size |
95
+ |------|-------|------|
96
+ | `sdxl/dmad_sdxl_4step_unet_fp16.bin` | 4-step student (backward simulation), iteration 17k | 5.1 GB |
97
+ | `sdxl/dmad_sdxl_1step_unet_fp16.bin` | 1-step student (ODE init), iteration 49k | 5.1 GB |
98
+ | `sdxl/dmad_sdxl_1step_frozencritic_unet_fp16.bin` | 1-step student (ODE init, frozen critic backbone), iteration 17.5k | 5.1 GB |
99
+
100
+ ```python
101
+ import torch
102
+ from diffusers import DiffusionPipeline, LCMScheduler, UNet2DConditionModel
103
+ from huggingface_hub import hf_hub_download
104
+
105
+ base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
106
+ unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16)
107
+ unet.load_state_dict(torch.load(hf_hub_download("ZhengmingYu/DMAD", "sdxl/dmad_sdxl_4step_unet_fp16.bin"), map_location="cuda"))
108
+ pipe = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
109
+ pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
110
+ image = pipe(prompt="a photo of a cat", num_inference_steps=4, guidance_scale=0, timesteps=[999, 749, 499, 249]).images[0]
111
+ # 1-step models: num_inference_steps=1, timesteps=[399]
112
+ ```
113
+
114
+ ## ImageNet-64 (class-conditional)
115
+
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+ 1-step EMA generators of the EDM ImageNet-64 teacher, one folder per critic setting of the paper, in the
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+ `checkpoint_model_<iteration>/pytorch_model_ema.bin` layout that `train/image`'s evaluation reads directly.
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+
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+ | Folder | Setting | Size |
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+ |--------|---------|------|
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+ | `imagenet64/dmad_imagenet_gaproute/` | teacher-UNet critic + gap routing, iteration 568k | 1.2 GB |
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+ | `imagenet64/dmad_imagenet_pgcritic/` | pretrained-feature critic, iteration 108k | 1.2 GB |
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+ | `imagenet64/dmad_imagenet_frozencritic/` | frozen teacher-UNet critic + gap routing, iteration 221k | 1.2 GB |
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+
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+ ```bash
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+ hf download ZhengmingYu/DMAD --include "imagenet64/dmad_imagenet_gaproute/*" --local-dir ckpt
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+ python main/edm/test_folder_edm.py --folder ckpt/imagenet64/dmad_imagenet_gaproute --run_once ... # in train/image
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+ ```
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+
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  ## Citation
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132
  ```bibtex
 
142
 
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  ## License
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+ Each family of weights is a derivative of its base model and is distributed under that model's license:
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+
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+ * `minimax_h3/`: Model Derivatives of MiniMax H3, under the [MiniMax H3 Community License Agreement](LICENSE)
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+ (see also [NOTICE](NOTICE)). The weights were modified from MiniMax-H3 by LoRA fine-tuning.
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+ * `wan2.1/`: derived from Wan2.1-T2V-1.3B / 14B, under the [Apache License 2.0](LICENSE_Wan2.1).
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+ * `sdxl/`: derived from Stable Diffusion XL base 1.0, under the
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+ [CreativeML Open RAIL++-M License](LICENSE_SDXL), including its use-based restrictions.
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+ * `imagenet64/`: derived from the NVIDIA EDM ImageNet-64 model, under
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+ [CC BY-NC-SA 4.0](LICENSE_EDM) (non-commercial).
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+
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+ Images and videos produced with these weights are AI-generated.