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---
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Image-to-Video finetuning - zhuhz22/try4
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import EulerDiscreteScheduler
import torch
from diffusers.utils import load_image, export_to_video
from svd.inference.pipline_CILsvd import StableVideoDiffusionCILPipeline
# set the start time M (sigma_max) for inference
scheduler = EulerDiscreteScheduler.from_pretrained(
"zhuhz22/try4",
subfolder="scheduler",
sigma_max=100
)
pipeline = StableVideoDiffusionCILPipeline.from_pretrained(
"zhuhz22/try4", scheduler=scheduler, torch_dtype=torch.float16, variant="fp16"
) # Note that set the default parameters, fps, motion_bucket_id
pipeline.enable_model_cpu_offload()
# demo
image = load_image("demo/a car parked in a parking lot with palm trees nearby,calm seas and skies..png")
image = image.resize((512,320))
generator = torch.manual_seed(42)
# analytic_path:
# if is video path, compute the initial noise automatically.
# if is tensor path, load
# if none, standard inference
analytic_path=None
frames = pipeline(
image,
height=image.height,
width=image.width,
num_frames=16,
fps=3,
motion_bucket_id=20,
decode_chunk_size=8,
generator=generator,
analytic_path=analytic_path
).frames[0]
export_to_video(frames, "generated.mp4", fps=7)
```
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |