import torch
from diffusers import LTXPipeline
from diffusers.utils import export_to_video

pipeline = LTXPipeline.from_pretrained(
    "Lightricks/LTX-Video", torch_dtype=torch.bfloat16
).to("cuda")

prompt = """
A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman 
with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The 
camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and 
natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be 
real-life footage
"""

video = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=768,
    height=512,
    num_frames=161,
    decode_timestep=0.03,
    decode_noise_scale=0.025,
    num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)

import torch
from diffusers import CogVideoXPipeline, CogVideoXTransformer3DModel
from diffusers.utils import export_to_video

pipeline = CogVideoXPipeline.from_pretrained(
  "THUDM/CogVideoX-2b",
  torch_dtype=torch.float16
).to("cuda")

prompt = """
A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over
a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, 
with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an 
oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at 
a playful environment. The scene captures the innocence and imagination of childhood, 
with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.
"""

video = pipeline(
  prompt=prompt,
  guidance_scale=6,
  num_inference_steps=50
).frames[0]
export_to_video(video, "output.mp4", fps=8)

# pip install ftfy
import torch
from diffusers import WanPipeline
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
from diffusers.utils import export_to_video

vae = AutoencoderKLWan.from_pretrained(
  "Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="vae", torch_dtype=torch.float32
)
pipeline = WanPipeline.from_pretrained(
  "Wan-AI/Wan2.1-T2V-14B-Diffusers", vae=vae, torch_dtype=torch.bfloat16
)
pipeline.scheduler = UniPCMultistepScheduler.from_config(
  pipeline.scheduler.config, flow_shift=5.0
)
pipeline.to("cuda")

pipeline.load_lora_weights("benjamin-paine/steamboat-willie-14b", adapter_name="steamboat-willie")
pipeline.set_adapters("steamboat-willie")

pipeline.enable_model_cpu_offload()

# use "steamboat willie style" to trigger the LoRA
prompt = """
steamboat willie style, golden era animation, The camera rushes from far to near in a low-angle shot, 
revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in 
for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. 
Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts 
dynamic shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
"""

output = pipeline(
  prompt=prompt,
  num_frames=81,
  guidance_scale=5.0,
).frames[0]
export_to_video(output, "output.mp4", fps=16)

# pip install ftfy
import torch
import numpy as np
from diffusers import AutoModel, WanPipeline
from diffusers.hooks.group_offloading import apply_group_offloading
from diffusers.utils import export_to_video, load_image
from transformers import UMT5EncoderModel

text_encoder = UMT5EncoderModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="text_encoder", torch_dtype=torch.bfloat16)
vae = AutoModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="vae", torch_dtype=torch.float32)
transformer = AutoModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)

# group-offloading
onload_device = torch.device("cuda")
offload_device = torch.device("cpu")
apply_group_offloading(text_encoder,
    onload_device=onload_device,
    offload_device=offload_device,
    offload_type="block_level",
    num_blocks_per_group=4
)
transformer.enable_group_offload(
    onload_device=onload_device,
    offload_device=offload_device,
    offload_type="leaf_level",
    use_stream=True
)

pipeline = WanPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-14B-Diffusers",
    vae=vae,
    transformer=transformer,
    text_encoder=text_encoder,
    torch_dtype=torch.bfloat16
)
pipeline.to("cuda")

prompt = """
The camera rushes from far to near in a low-angle shot, 
revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in 
for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. 
Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic 
shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
"""
negative_prompt = """
Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, 
low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, 
misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
"""

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_frames=81,
    guidance_scale=5.0,
).frames[0]
export_to_video(output, "output.mp4", fps=16)

# pip install ftfy

import torch
from diffusers import WanPipeline
from diffusers import AutoModel, WanPipeline
from diffusers.quantizers import PipelineQuantizationConfig
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
from transformers import UMT5EncoderModel
from diffusers.utils import export_to_video

# quantize transformer and text encoder weights with bitsandbytes
pipeline_quant_config = PipelineQuantizationConfig(
  quant_backend="bitsandbytes_4bit",
  quant_kwargs={"load_in_4bit": True},
  components_to_quantize=["transformer", "text_encoder"]
)

vae = AutoModel.from_pretrained(
  "Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="vae", torch_dtype=torch.float32
)
pipeline = WanPipeline.from_pretrained(
  "Wan-AI/Wan2.1-T2V-14B-Diffusers", vae=vae, quantization_config=pipeline_quant_config, torch_dtype=torch.bfloat16
)
pipeline.scheduler = UniPCMultistepScheduler.from_config(
  pipeline.scheduler.config, flow_shift=5.0
)
pipeline.to("cuda")

pipeline.load_lora_weights("benjamin-paine/steamboat-willie-14b", adapter_name="steamboat-willie")
pipeline.set_adapters("steamboat-willie")

pipeline.enable_model_cpu_offload()

# use "steamboat willie style" to trigger the LoRA
prompt = """
steamboat willie style, golden era animation, The camera rushes from far to near in a low-angle shot, 
revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in 
for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. 
Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts 
dynamic shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
"""

output = pipeline(
  prompt=prompt,
  num_frames=81,
  guidance_scale=5.0,
).frames[0]
export_to_video(output, "output.mp4", fps=16)

import torch
from diffusers import CogVideoXPipeline, CogVideoXTransformer3DModel
from diffusers.utils import export_to_video

pipeline = CogVideoXPipeline.from_pretrained(
  "THUDM/CogVideoX-2b",
  torch_dtype=torch.float16
).to("cuda")

# torch.compile
pipeline.transformer.to(memory_format=torch.channels_last)
pipeline.transformer = torch.compile(
    pipeline.transformer, mode="max-autotune", fullgraph=True
)

prompt = """
A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. 
The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. 
Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, 
with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.
"""

video = pipeline(
  prompt=prompt,
  guidance_scale=6,
  num_inference_steps=50
).frames[0]
export_to_video(video, "output.mp4", fps=8)