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README.md
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url: videos/1742525503397__000001250_1.webp
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---
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url: videos/1742525503397__000001250_1.webp
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---
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# PostMalone Lora for WanVideo2.1
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<Gallery />
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- First 2 videos are from [750 steps trained]
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- Middle 2 videos are from [1000 steps trained] which is available for download
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- Last 2 videos are from [1250 steps trained]
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## Trigger words
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You should use `malone` to trigger the video generation.
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## Using with Diffusers
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```py
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pip install git+https://github.com/huggingface/diffusers.git
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```
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```py
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import torch
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from diffusers.utils import export_to_video
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from diffusers import AutoencoderKLWan, WanPipeline
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from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
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# Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers
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model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
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vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
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pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
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flow_shift = 5.0 # 5.0 for 720P, 3.0 for 480P
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
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pipe.to("cuda")
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pipe.load_lora_weights("shauray/PostMalone_WanLora")
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pipe.enable_model_cpu_offload() #for low-vram environments
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prompt = "malone a man walking through texas"
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output = pipe(
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prompt=prompt,
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height=480,
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width=720,
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num_frames=81,
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guidance_scale=5.0,
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).frames[0]
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export_to_video(output, "output.mp4", fps=16)
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```
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## Download model
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Weights for this model are available in Safetensors format.
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[Download](/shauray/PostMalone_WanLora/tree/main) them in the Files & versions tab.
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