Mochi docs #9934
Mochi docs #9934
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
| prompt = "Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k." | ||
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| with torch.autocast("cuda", torch.bfloat16, cache_enabled=False): | ||
| frames = pipe(prompt, num_frames=84).frames[0] |
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num_frames=84 should be num_frames=85, (14 * 6 + 1) like mentioned here
| <Tip> | ||
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| Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. | ||
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(One line above this) Only FlowMatchEulerDiscreteScheduler has invert_sigmas, so anything else wouldn't work as of now as I understand it
| pipe.enable_vae_tiling() | ||
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| prompt = "Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k." | ||
| frames = pipe(prompt, num_frames=84).frames[0] |
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same thing, num_frames=85
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Comments from @Ednaordinary are already great, so let's resolve them. Maybe we could add a section on how to reproduce some of their videos generated with the original inference code and params? I think most people would be interested in that.
Additionally, it seems like we should suggest using a maximum sequence length of 256?
#9769 (comment)
Already the case:
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@DN6 is this ready to be merged? Cc: @a-r-r-o-w as well |
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| Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. | ||
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| <Tip> | ||
| Decoding the latents in full precision is very memory intensive. You will need at least 70GB VRAM to generate the 163 frames | ||
| in this example. To reduce memory, either reduce the number of frames or run the decoding step in `torch.bfloat16` | ||
| </Tip> |
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Even if we use enable_model_cpu_offload(), we would need 70GBs?
| <Tip> | |
| Decoding the latents in full precision is very memory intensive. You will need at least 70GB VRAM to generate the 163 frames | |
| in this example. To reduce memory, either reduce the number of frames or run the decoding step in `torch.bfloat16` | |
| </Tip> | |
| <Tip> | |
| Decoding the latents in full precision is very memory intensive. You will need at least 70GB VRAM to generate the 163 frames | |
| in this example. To reduce memory, either reduce the number of frames or run the decoding step in `torch.bfloat16`. | |
| </Tip> |
* update * update * update * update * update --------- Co-authored-by: Sayak Paul <[email protected]>
* update * update * update * update * update --------- Co-authored-by: Sayak Paul <[email protected]>
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What does this PR do?
Update Mochi docs
Fixes # (issue)
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