Create README.md

README.md

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+---
+base_model:
+- THUDM/CogVideoX1.5-5b
+datasets: finetrainers/crush-smol
+library_name: diffusers
+license: other
+license_link: https://huggingface.co/THUDM/CogVideoX1.5-5b/blob/main/LICENSE
+widget:
+- text: >-
+    PIKA_CRUSH A red toy car is being crushed by a large hydraulic press, which is flattening objects as if they were under a hydraulic press.
+  output:
+    url: final-3000-0-2-PIKA_CRUSH-A-red-toy-car-.mp4
+- text: >-
+    PIKA_CRUSH A large metal cylinder is seen pressing down on a pile of colorful jelly beans, flattening them as if they were under a hydraulic press.
+  output:
+    url: final-3000-0-2-PIKA_CRUSH-A-large-metal-.mp4
+- text: >-
+    PIKA_CRUSH A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.
+  output:
+    url: final-3000-1-2-PIKA_CRUSH-A-large-metal-.mp4
+tags:
+- text-to-video
+- diffusers-training
+- diffusers
+- cogvideox
+- cogvideox-diffusers
+- template:sd-lora
+---
+
+<Gallery />
+
+This is a LoRA fine-tune of the [THUDM/CogVideoX1.5-5b](https://huggingface.co/THUDM/CogVideoX1.5-5b) model on the
+[finetrainers/crush-smol](https://huggingface.co/datasets/finetrainers/crush-smol) dataset.
+
+Code: https://github.com/a-r-r-o-w/finetrainers
+
+> [!IMPORTANT]
+> This is an experimental checkpoint and its poor generalization is well-known.
+
+Inference code:
+
+```py
+from diffusers import CogVideoXTransformer3DModel, DiffusionPipeline 
+from diffusers.utils import export_to_video
+import torch 
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "THUDM/CogVideoX1.5-5b", torch_dtype=torch.bfloat16
+).to("cuda")
+pipeline.load_lora_weights("finetrainers/CogVideoX-1.5-crush-smol-v0", adapter_name="cogvideox-lora")
+pipeline.set_adapters("cogvideox-lora", 0.9)
+
+prompt = """
+PIKA_CRUSH A red toy car is being crushed by a large hydraulic press, which is flattening objects as if they were under a hydraulic press.
+"""
+negative_prompt = "inconsistent motion, blurry motion, worse quality, degenerate outputs, deformed outputs"
+
+video = pipeline(
+    prompt=prompt, 
+    negative_prompt=negative_prompt, 
+    num_frames=81, 
+    height=480,
+    width=768,
+    num_inference_steps=50
+).frames[0]
+export_to_video(video, "output.mp4", fps=25)
+```
+
+Training logs are available on WandB [here](https://wandb.ai/aryanvs/finetrainers-cogvideox?nw=nwuseraryanvs).