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ComfyUI workflow for 8 GB GPUs (5 s); pick-by-GPU table; --low-vram on 8 GB / GPU-size presets

Browse files

Adds minimax_h3/workflows/dmad_h3_4step_5s_8gb_guitar.{json,mp4} and its screenshot; model card: 8 GB section, workflow-by-GPU table, inference.py --low-vram on 8 GB GPUs.

.gitattributes CHANGED
@@ -38,3 +38,5 @@ minimax_h3/workflows/dmad_h3_4step_15s_podcast.mp4 filter=lfs diff=lfs merge=lfs
38
  minimax_h3/workflows/dmad_h3_8step_15s_wok.mp4 filter=lfs diff=lfs merge=lfs -text
39
  minimax_h3/workflows/screenshot_4step_15s.png filter=lfs diff=lfs merge=lfs -text
40
  minimax_h3/workflows/screenshot_8step_15s.png filter=lfs diff=lfs merge=lfs -text
 
 
 
38
  minimax_h3/workflows/dmad_h3_8step_15s_wok.mp4 filter=lfs diff=lfs merge=lfs -text
39
  minimax_h3/workflows/screenshot_4step_15s.png filter=lfs diff=lfs merge=lfs -text
40
  minimax_h3/workflows/screenshot_8step_15s.png filter=lfs diff=lfs merge=lfs -text
41
+ minimax_h3/workflows/dmad_h3_4step_5s_8gb_guitar.mp4 filter=lfs diff=lfs merge=lfs -text
42
+ minimax_h3/workflows/screenshot_4step_5s_8gb.png filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -62,7 +62,7 @@ token-refiner blocks (312 modules). alpha = rank = 128. The safetensors metadata
62
 
63
  The inference code lives in the [code repository](https://github.com/Yzmblog/DMAD): `inference.py` with the sampler
64
  the paper used (re-noise step rule) and a Diffusers-pipeline example; its README covers the environment. Sampling
65
- settings: 4 steps, time shift 12 (video) and 2 (audio), no classifier-free guidance, 124 frames at 24 fps. `--low-vram` runs it on a 16 GB GPU.
66
 
67
  ```bash
68
  git clone https://github.com/Yzmblog/DMAD.git && cd DMAD # code + environment setup (see its README)
@@ -87,12 +87,22 @@ wget -P /path/to/ComfyUI/models/loras https://huggingface.co/ZhengmingYu/DMAD/re
87
  wget -P /path/to/ComfyUI/models/loras https://huggingface.co/ZhengmingYu/DMAD/resolve/main/minimax_h3/dmad_minimax_h3_4step_lora_critic_comfyui.safetensors
88
  ```
89
 
90
- ### Ready-to-run workflows (16 GB GPU and up)
91
 
92
- Two complete text-to-audio-video workflows in [`minimax_h3/workflows`](https://huggingface.co/ZhengmingYu/DMAD/tree/main/minimax_h3/workflows)
93
- that make 15 s of 1344x768 video with stereo audio on a 16 GB GPU or larger. Load the `.json`, or drag the example `.mp4` (it
94
- embeds the workflow) into ComfyUI; missing models are offered for download from links stored in the workflow. Both
95
- sample with stock nodes (`lcm` + `simple`, `full_critic` LoRA); the ComfyUI-DMAD nodes are not needed.
 
 
 
 
 
 
 
 
 
 
96
 
97
  | | 4 steps | 8 steps |
98
  |---|---|---|
@@ -126,8 +136,28 @@ with the PyTorch allocator capped at 24 or 14 GiB; a consumer GPU is slower, and
126
  card's desktop and CUDA context): ComfyUI's dynamic VRAM keeps less of the model resident and streams more of it from
127
  system memory, and the videos are bit-identical to the 24 GiB runs. About 46 GiB of system memory is in use, so 64 GB
128
  of RAM is recommended. Outside ComfyUI, `inference.py --low-vram` in the
129
- [code repository](https://github.com/Yzmblog/DMAD) makes 5 s and 15 s videos in under 14 GiB of GPU memory, bit-identical
130
- to its larger-GPU runs.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
131
 
132
  ## Wan2.1 (text-to-video)
133
 
 
62
 
63
  The inference code lives in the [code repository](https://github.com/Yzmblog/DMAD): `inference.py` with the sampler
64
  the paper used (re-noise step rule) and a Diffusers-pipeline example; its README covers the environment. Sampling
65
+ settings: 4 steps, time shift 12 (video) and 2 (audio), no classifier-free guidance, 124 frames at 24 fps. `--low-vram` runs it on an 8 GB GPU (5 s; 15 s needs 12 GB; `--low-vram 8 / 12 / 16 / 24` picks the settings for your GPU, same output).
66
 
67
  ```bash
68
  git clone https://github.com/Yzmblog/DMAD.git && cd DMAD # code + environment setup (see its README)
 
87
  wget -P /path/to/ComfyUI/models/loras https://huggingface.co/ZhengmingYu/DMAD/resolve/main/minimax_h3/dmad_minimax_h3_4step_lora_critic_comfyui.safetensors
88
  ```
89
 
90
+ ### Ready-to-run workflows (8 GB GPU and up)
91
 
92
+ Complete text-to-audio-video workflows in [`minimax_h3/workflows`](https://huggingface.co/ZhengmingYu/DMAD/tree/main/minimax_h3/workflows):
93
+ two that make 15 s of 1344x768 video with stereo audio on a 16 GB GPU or larger, and a 5 s one for 8 GB GPUs. Load the
94
+ `.json`, or drag the example `.mp4` (it embeds the workflow) into ComfyUI; missing models are offered for download from
95
+ links stored in the workflow. All sample with stock nodes (`lcm` + `simple`, `full_critic` LoRA); the ComfyUI-DMAD nodes
96
+ are not needed.
97
+
98
+ | GPU | workflow | video |
99
+ |---|---|---|
100
+ | 24 GB | [`dmad_h3_4step_15s_podcast.json`](https://huggingface.co/ZhengmingYu/DMAD/resolve/main/minimax_h3/workflows/dmad_h3_4step_15s_podcast.json) (4 steps) or [`dmad_h3_8step_15s_wok.json`](https://huggingface.co/ZhengmingYu/DMAD/resolve/main/minimax_h3/workflows/dmad_h3_8step_15s_wok.json) (8 steps) | 15 s |
101
+ | 16 GB, 22 GB | the same two workflows, unchanged (see below) | 15 s |
102
+ | 8 GB | [`dmad_h3_4step_5s_8gb_guitar.json`](https://huggingface.co/ZhengmingYu/DMAD/resolve/main/minimax_h3/workflows/dmad_h3_4step_5s_8gb_guitar.json) (see below) | 5 s |
103
+
104
+ The 24 GB and 16 GB rows share the workflow files: ComfyUI keeps as much of the model on the GPU as fits and streams
105
+ the rest from system memory, so the same workflow adapts to the card (and gives the same video).
106
 
107
  | | 4 steps | 8 steps |
108
  |---|---|---|
 
136
  card's desktop and CUDA context): ComfyUI's dynamic VRAM keeps less of the model resident and streams more of it from
137
  system memory, and the videos are bit-identical to the 24 GiB runs. About 46 GiB of system memory is in use, so 64 GB
138
  of RAM is recommended. Outside ComfyUI, `inference.py --low-vram` in the
139
+ [code repository](https://github.com/Yzmblog/DMAD) makes 5 s videos in under 7 GiB and 15 s videos in 12 GiB of GPU memory,
140
+ bit-identical to its larger-GPU runs.
141
+
142
+ **8 GB GPUs (5 s).** [`dmad_h3_4step_5s_8gb_guitar.json`](https://huggingface.co/ZhengmingYu/DMAD/resolve/main/minimax_h3/workflows/dmad_h3_4step_5s_8gb_guitar.json) is the 4-step
143
+ workflow set up for 8 GB GPUs: 5 s instead of 15 s, the `H3 Memory Optimization` node in its lowest-memory setting
144
+ (`Attention memory mode` Lower VRAM (slower), `Activation chunk rows` 1024), and the `H3 AIMDO Residency Limiter` node of
145
+ the same pack (`VBAR residency budget` 0 blocks), which streams every transformer block from system memory. Models and
146
+ sampling are the same as above.
147
+
148
+ | | 4 steps, 5 s |
149
+ |---|---|
150
+ | Workflow | [`dmad_h3_4step_5s_8gb_guitar.json`](https://huggingface.co/ZhengmingYu/DMAD/resolve/main/minimax_h3/workflows/dmad_h3_4step_5s_8gb_guitar.json) |
151
+ | Example (workflow embedded) | [`dmad_h3_4step_5s_8gb_guitar.mp4`](https://huggingface.co/ZhengmingYu/DMAD/resolve/main/minimax_h3/workflows/dmad_h3_4step_5s_8gb_guitar.mp4), seed 1 |
152
+ | Output | 1344x768, 124 frames = 5 s at 24 fps, stereo audio |
153
+ | Peak GPU memory, 7 GiB cap (8 GB GPUs) | 7.7 GiB |
154
+ | Peak GPU memory, 5 GiB cap | 5.9 GiB |
155
+ | Time (either cap) | about 210 s, of which sampling 152 s |
156
+
157
+ The video is the same at both caps, bit for bit; the peaks include memory ComfyUI's dynamic VRAM allocates outside the
158
+ PyTorch allocator. About 45 GiB of system memory is in use (64 GB of RAM recommended). The memory settings change the
159
+ attention arithmetic, so the same prompt and seed do not reproduce the video the 16 GB+ settings make. 15 s does not
160
+ fit in 8 GB.
161
 
162
  ## Wan2.1 (text-to-video)
163
 
minimax_h3/workflows/dmad_h3_4step_5s_8gb_guitar.json ADDED
@@ -0,0 +1,1214 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ "## DMAD × MiniMax-H3 — 4-step text-to-audio-video (8 GB GPU)\n\n**Models** (put in `ComfyUI/models/...`):\n- `diffusion_models/minimax_h3_fl2va_pruned_int8_convrot.safetensors` — [Comfy-Org/MiniMax-H3](https://huggingface.co/Comfy-Org/MiniMax-H3)\n- `text_encoders/qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors` — same repo\n- `vae/minimax_h3_video_vae_fp16.safetensors`, `vae/minimax_h3_audio_vae_fp32.safetensors` — same repo\n- `loras/dmad_minimax_h3_4step_full_critic_comfyui.safetensors` — [ZhengmingYu/DMAD](https://huggingface.co/ZhengmingYu/DMAD/tree/main/minimax_h3)\n\n**Settings that matter:** LoRA strength 1.0 · shift 12 / audio 2 · sampler `lcm` + scheduler `simple` · no CFG.\n**Steps:** 4 = fastest; 8 or 12 give finer detail on fast motion. Avoid `euler` (softer).\n**Prompt:** describe the shot, then `overall_soundscape:` and `non_diegetic_music:` for the audio.\n**8 GB GPUs:** 124 frames = 5 s at 24 fps. The `H3 Memory Optimization` node (Lower VRAM attention, 1024-row\nchunks) and the `H3 AIMDO Residency Limiter` (0 blocks) of [H3-Optimizations](https://github.com/Zironic/H3-Optimizations)\n(install with ComfyUI-Manager) make it fit in 8 GB (it runs with 6 GB free); the model streams from system memory (64 GB RAM\nrecommended).\nFor 15 s use the 4- or 8-step 15 s workflows on a 16 GB GPU or larger.\n"
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