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 black and white animated scene unfolds with an anthropomorphic
+  goat surrounded by musical notes and symbols, suggesting a playful environment.
+  Mickey Mouse appears, leaning forward in curiosity as the goat remains still. The
+  goat then engages with Mickey, who bends down to converse or react. The dynamics
+  shift as Mickey grabs the goat, potentially in surprise or playfulness, amidst a
+  minimalistic background. The scene captures the evolving relationship between the
+  two characters in a whimsical, animated setting, emphasizing their interactions
+  and emotions
+widget:
+- text: A black and white animated scene unfolds with an anthropomorphic goat surrounded
+    by musical notes and symbols, suggesting a playful environment. Mickey Mouse appears,
+    leaning forward in curiosity as the goat remains still. The goat then engages
+    with Mickey, who bends down to converse or react. The dynamics shift as Mickey
+    grabs the goat, potentially in surprise or playfulness, amidst a minimalistic
+    background. The scene captures the evolving relationship between the two characters
+    in a whimsical, animated setting, emphasizing their interactions and emotions
+  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/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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+oid sha256:23ee7012c754ddabe91b25152bfc2357613e145c9a50f09de317206ef2861aff
+size 76072152