End of training

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README.md

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+---
+library_name: diffusers
+license: creativeml-openrail-m
+tags:
+- 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]