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83 lines
2.0 KiB
83 lines
2.0 KiB
9 months ago
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from swarms import Agent, Anthropic, tool
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# Model
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llm = Anthropic(
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temperature=0.1,
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)
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# Tools
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@tool
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def text_to_video(task: str):
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"""
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Converts a given text task into an animated video.
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Args:
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task (str): The text task to be converted into a video.
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Returns:
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str: The path to the exported GIF file.
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"""
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import torch
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from diffusers import (
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AnimateDiffPipeline,
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MotionAdapter,
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EulerDiscreteScheduler,
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)
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from diffusers.utils import export_to_gif
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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device = "cuda"
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dtype = torch.float16
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step = 4 # Options: [1,2,4,8]
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repo = "ByteDance/AnimateDiff-Lightning"
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ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
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base = ( # Choose to your favorite base model.
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"emilianJR/epiCRealism"
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)
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adapter = MotionAdapter().to(device, dtype)
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adapter.load_state_dict(
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load_file(hf_hub_download(repo, ckpt), device=device)
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)
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pipe = AnimateDiffPipeline.from_pretrained(
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base, motion_adapter=adapter, torch_dtype=dtype
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).to(device)
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pipe.scheduler = EulerDiscreteScheduler.from_config(
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pipe.scheduler.config,
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timestep_spacing="trailing",
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beta_schedule="linear",
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)
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output = pipe(
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prompt=task, guidance_scale=1.0, num_inference_steps=step
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)
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out = export_to_gif(output.frames[0], "animation.gif")
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return out
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# Agent
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agent = Agent(
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agent_name="Devin",
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system_prompt=(
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"Autonomous agent that can interact with humans and other"
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" agents. Be Helpful and Kind. Use the tools provided to"
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" assist the user. Return all code in markdown format."
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),
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llm=llm,
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max_loops="auto",
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autosave=True,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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interactive=True,
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tools=[text_to_video],
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)
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# Run the agent
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out = agent("Create a vide of a girl coding AI wearing hijab")
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print(out)
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