End of training

README.md

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+---
+base_model: genmo/mochi-1-preview
+library_name: diffusers
+license: apache-2.0
+instance_prompt: A pristine snowglobe featuring a winter scene sits peacefully. The
+  globe violently explodes, sending glass, water, and glittering fake snow in all
+  directions. The scene is captured with high-speed photography.
+widget:
+- text: A pristine snowglobe featuring a winter scene sits peacefully. The globe violently
+    explodes, sending glass, water, and glittering fake snow in all directions. The
+    scene is captured with high-speed photography.
+  output:
+    url: final_video_0.mp4
+tags:
+- text-to-video
+- diffusers-training
+- diffusers
+- lora
+- mochi-1-preview
+- mochi-1-preview-diffusers
+- template:sd-lora
+---
+
+<!-- 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. -->
+
+
+# Mochi-1 Preview LoRA Finetune
+
+<Gallery />
+
+## Model description
+
+This is a lora finetune of the Mochi-1 preview model `genmo/mochi-1-preview`.
+
+The model was trained using [CogVideoX Factory](https://github.com/a-r-r-o-w/cogvideox-factory) - a repository containing memory-optimized training scripts for the CogVideoX and Mochi family of models using [TorchAO](https://github.com/pytorch/ao) and [DeepSpeed](https://github.com/microsoft/DeepSpeed). The scripts were adopted from [CogVideoX Diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/cogvideo/train_cogvideox_lora.py).
+
+## Download model
+
+[Download LoRA](sayakpaul/mochi-lora-dissolve/tree/main) in the Files & Versions tab.
+
+## Usage
+
+Requires the [🧨 Diffusers library](https://github.com/huggingface/diffusers) installed.
+
+```py
+from diffusers import MochiPipeline
+from diffusers.utils import export_to_video
+import torch 
+
+pipe = MochiPipeline.from_pretrained("genmo/mochi-1-preview")
+pipe.load_lora_weights("CHANGE_ME")
+pipe.enable_model_cpu_offload()
+
+with torch.autocast("cuda", torch.bfloat16):
+    video = pipe(
+        prompt="CHANGE_ME",
+        guidance_scale=6.0,
+        num_inference_steps=64,
+        height=480,
+        width=848,
+        max_sequence_length=256,
+        output_type="np"
+    ).frames[0]
+export_to_video(video)
+```
+
+For more details, including weighting, merging and fusing LoRAs, check the [documentation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) on loading LoRAs in diffusers.
+
+
+
+## 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]

pytorch_lora_weights.safetensors

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+size 76072152