Text-to-Video
Diffusers
Safetensors
English
MotifVideoPipeline
image-to-video
video-generation
diffusion-transformer
Instructions to use Motif-Technologies/Motif-Video-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Motif-Technologies/Motif-Video-2B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Motif-Technologies/Motif-Video-2B", dtype=torch.bfloat16, device_map="cuda") prompt = "A vibrant blue jay perches gracefully on a slender branch, its feathers shimmering in the soft morning light. The bird's keen eyes scan the surroundings, capturing the essence of the tranquil forest. It flutters its wings briefly, showcasing the intricate patterns of blue, white, and black on its plumage. The background reveals a lush canopy of green leaves, with rays of sunlight filtering through, creating a dappled effect on the forest floor. The blue jay then tilts its head, emitting a melodious call that echoes through the serene woodland, adding a touch of magic to the peaceful scene." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Commit History
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attn to cross_attn key conversion (#27)
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diffusers integration (#23)
- Update diffusers integration (83dc22d9db931b0d98183ee86be4414690192817) - Delete inference.py (15d68f9137246214d1b2afc84e52cc2d90bf2636) - Update README.md (d2cb24cdd7482c46ac0c3842d73c29b9e227c042)7acae4b -
docs: add RTX 4090 benchmark + GPU arch list for SageAttention build (#22)
- docs: add RTX 4090 benchmark + GPU arch list for SageAttention build (2302f6bd85ac3271b69db57d0ddb9853448a18b4)637b205
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docs: split Memory-efficient Inference and GGUF+SageAttention into sub READMEs (#20)
- docs: split Memory-efficient Inference and GGUF+SageAttention into sub READMEs (e9a061b3abf9db713413ddef437adf948fd2364c) - docs: minor README formatting adjustment (9a9a0f47e6835678d0930df94814234ee60af452)190632e -
docs: ComfyUI release + section update (#18)
- docs: add ComfyUI release to News + update ComfyUI section (b790e27480a8df0a0366aac876accfc88dcaa77e)191ad8c -
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fix: readme anchor links for HF renderer
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feat: GGUF + SageAttention guide — DPMSolver++ default, benchmark, README update (#17)
- feat: GGUF + SageAttention guide — DPMSolver++ default, benchmark, README update (922a8b78f59f5ad9d0d114f4390d5a1b224e1f06) - Upload assets/sage_compare_BF16.webp with huggingface_hub (b1f39ad47ba4b4fc30f0936e9d2375b0e6e48594) - Upload assets/sage_compare_Q4_K_M.webp with huggingface_hub (81665a89a57553481cbe2db7573e5cab87a1a212) - Upload assets/sage_compare_Q5_K_M.webp with huggingface_hub (c188e4526e859d2f477688719ced04ca804b0f8c) - Upload assets/sage_compare_Q8_0.webp with huggingface_hub (0ca01ed010c3983fb8809d4ad0f81030ff2ef4b6)ed9bedb
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Add FP8 weight quantization guide to README (#9)
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Enable Flash Attention by trimming prompt embedding padding (#8)
- feat: trim prompt_embeds padding for batch=1 to enable Flash Attention (d1a46b18645d57467e630bf664f2e9f34cb92d6d) - feat: move prompt_embeds trim to __call__ for correct CFG alignment (eabf2fae153ad8ecc6d9837c55fefdc8d025bddc) - feat: skip encoder_attention_mask in guider when mask is None (2134e9d09511f37876026af8d08e9f8ea20fa850) - chore: verify batch>1 preserves original attention_mask path (item 4) (8ca961d2d0ce0acbb589012dc8bee4d266b0b1bd) - chore: verify I2V compatibility with encoder_attention_mask=None (item 5) (9717caf550404f4d91c8cadf65e0adcb3c018161) - docs: add Flash Attention section to README (4b01b7406caf46637b3c541e1beb47f58cd4c2a2) - fix: trim pos/neg independently instead of max alignment (b67e1aacb2ad744282f9fcc364ce8706070087c0) - chore: remove debug prints and Flash Attention README section (20f9f0572b691df5ffde509bd72b4d9c83741380)3a95e87 -
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chore: remove .plans from tracking (internal working docs)
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chore: close low-vram-inference plan (MM-1091)
All checklist items completed. HF pushed, Jira closed. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>0ed7f12 -
feat: enable_model_cpu_offload() for 24GB GPUs + README update
- Fix T5Gemma2Model device mismatch in pipeline_motif_video.py (accelerate hook on parent, but .encoder child called directly) - Add Memory-efficient Inference section to README (expandable_segments:True + peak ~19GB on nvidia-smi) - Update Requirements: 30GB+ for default, 24GB with offload - Remove Scheduler shift from Recommended Settings (auto-configured) - Add ComfyUI coming soon section Measured on H200: allocated 14.66GB, nvidia-smi peak ~19GB with expandable_segments:True (vs ~25GB without). Jira: MM-1091 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>160bda0
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Tech report coming soon
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Initial release
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initial commit
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