Rename README.md to TESTE.md

README.md

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----
-pipeline_tag: text-to-video
-license: cc-by-nc-4.0
----
-
-![model example](https://i.imgur.com/1mrNnh8.png)
-
-# zeroscope_v2 576w
-A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. This model was trained from the [original weights](https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis) using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.<br />
-zeroscope_v2_567w is specifically designed for upscaling with [zeroscope_v2_XL](https://huggingface.co/cerspense/zeroscope_v2_XL) using vid2vid in the [1111 text2video](https://github.com/kabachuha/sd-webui-text2video) extension by [kabachuha](https://github.com/kabachuha). Leveraging this model as a preliminary step allows for superior overall compositions at higher resolutions in zeroscope_v2_XL, permitting faster exploration in 576x320 before transitioning to a high-resolution render. See some [example outputs](https://www.youtube.com/watch?v=HO3APT_0UA4) that have been upscaled to 1024x576 using zeroscope_v2_XL. (courtesy of [dotsimulate](https://www.instagram.com/dotsimulate/))<br />
-
-zeroscope_v2_576w uses 7.9gb of vram when rendering 30 frames at 576x320
-
-### Using it with the 1111 text2video extension
-
-1. Download files in the zs2_576w folder.
-2. Replace the respective files in the 'stable-diffusion-webui\models\ModelScope\t2v' directory.
-
-### Upscaling recommendations
-
-For upscaling, it's recommended to use [zeroscope_v2_XL](https://huggingface.co/cerspense/zeroscope_v2_XL) via vid2vid in the 1111 extension. It works best at 1024x576 with a denoise strength between 0.66 and 0.85. Remember to use the same prompt that was used to generate the original clip. <br />
-
-### Usage in 🧨 Diffusers
-
-Let's first install the libraries required:
-
-```bash
-$ pip install diffusers transformers accelerate torch
-```
-
-Now, generate a video:
-
-```py
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from diffusers.utils import export_to_video
-
-pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-
-prompt = "Darth Vader is surfing on waves"
-video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
-video_path = export_to_video(video_frames)
-```
-
-Here are some results:
-
-<table>
-    <tr>
-        Darth vader is surfing on waves.
-        <br>
-        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/darthvader_cerpense.gif"
-            alt="Darth vader surfing in waves."
-            style="width: 576;" />
-        </center></td>
-    </tr>
-</table>
-
-### Known issues
-
-Lower resolutions or fewer frames could lead to suboptimal output. <br />
-
-Thanks to [camenduru](https://github.com/camenduru), [kabachuha](https://github.com/kabachuha), [ExponentialML](https://github.com/ExponentialML), [dotsimulate](https://www.instagram.com/dotsimulate/), [VANYA](https://twitter.com/veryVANYA), [polyware](https://twitter.com/polyware_ai), [tin2tin](https://github.com/tin2tin)<br />

TESTE.md

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+A ROBO invades a university with books in his hands and builds a house in the middle of a lawn.