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useful_code / exp_code /1_benchmark /Pyramid-Flow /infer.py

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import os
import json
import torch
import numpy as np
import PIL
from PIL import Image
from IPython.display import HTML
from pyramid_dit import PyramidDiTForVideoGeneration
from IPython.display import Image as ipython_image
from diffusers.utils import load_image, export_to_video, export_to_gif

# variant='diffusion_transformer_384p'       # For low resolution variant
model_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/rain1011/pyramid-flow-miniflux/"   # The downloaded checkpoint dir
model_name = "pyramid_flux"   # select the model "pyramid_flux" or "pyramid_mmdit"
variant='diffusion_transformer_384p'     # For high resolution variant
model_dtype = 'bf16'


prompt = "A movie trailer featuring the adventures of the 30 year old space man wearing a red wool knitted motorcycle helmet, blue sky, salt desert, cinematic style, shot on 35mm film, vivid colors"
# used for 384p model variant
width = 640
height = 384
# used for 768p model variant
# width = 1280
# height = 768
temp = 16   # temp in [1, 31] <=> frame in [1, 241] <=> duration in [0, 10s]
# Noting that, for the 384p version, only supports maximum 5s generation (temp = 16)

device_id = 0
torch.cuda.set_device(device_id)

model = PyramidDiTForVideoGeneration(
    model_path,
    model_dtype,
    model_name=model_name,
    model_variant=variant,
)

model.vae.to("cuda")
model.dit.to("cuda")
model.text_encoder.to("cuda")

model.vae.enable_tiling()

if model_dtype == "bf16":
    torch_dtype = torch.bfloat16 
elif model_dtype == "fp16":
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

with torch.no_grad(), torch.amp.autocast('cuda', enabled=True if model_dtype != 'fp32' else False, dtype=torch_dtype):
    frames = model.generate(
        prompt=prompt,
        num_inference_steps=[20, 20, 20],
        video_num_inference_steps=[10, 10, 10],
        height=height,
        width=width,
        temp=temp,
        guidance_scale=7.0,         # The guidance for the first frame, set it to 7 for 384p variant
        video_guidance_scale=5.0,   # The guidance for the other video latent
        output_type="pil",
        save_memory=True,           # If you have enough GPU memory, set it to `False` to improve vae decoding speed
    )

export_to_video(frames, "./text_to_video_sample.mp4", fps=24)

	import os
	import json
	import torch
	import numpy as np
	import PIL
	from PIL import Image
	from IPython.display import HTML
	from pyramid_dit import PyramidDiTForVideoGeneration
	from IPython.display import Image as ipython_image
	from diffusers.utils import load_image, export_to_video, export_to_gif

	# variant='diffusion_transformer_384p' # For low resolution variant
	model_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/rain1011/pyramid-flow-miniflux/" # The downloaded checkpoint dir
	model_name = "pyramid_flux" # select the model "pyramid_flux" or "pyramid_mmdit"
	variant='diffusion_transformer_384p' # For high resolution variant
	model_dtype = 'bf16'


	prompt = "A movie trailer featuring the adventures of the 30 year old space man wearing a red wool knitted motorcycle helmet, blue sky, salt desert, cinematic style, shot on 35mm film, vivid colors"
	# used for 384p model variant
	width = 640
	height = 384
	# used for 768p model variant
	# width = 1280
	# height = 768
	temp = 16 # temp in [1, 31] <=> frame in [1, 241] <=> duration in [0, 10s]
	# Noting that, for the 384p version, only supports maximum 5s generation (temp = 16)

	device_id = 0
	torch.cuda.set_device(device_id)

	model = PyramidDiTForVideoGeneration(
	model_path,
	model_dtype,
	model_name=model_name,
	model_variant=variant,
	)

	model.vae.to("cuda")
	model.dit.to("cuda")
	model.text_encoder.to("cuda")

	model.vae.enable_tiling()

	if model_dtype == "bf16":
	torch_dtype = torch.bfloat16
	elif model_dtype == "fp16":
	torch_dtype = torch.float16
	else:
	torch_dtype = torch.float32

	with torch.no_grad(), torch.amp.autocast('cuda', enabled=True if model_dtype != 'fp32' else False, dtype=torch_dtype):
	frames = model.generate(
	prompt=prompt,
	num_inference_steps=[20, 20, 20],
	video_num_inference_steps=[10, 10, 10],
	height=height,
	width=width,
	temp=temp,
	guidance_scale=7.0, # The guidance for the first frame, set it to 7 for 384p variant
	video_guidance_scale=5.0, # The guidance for the other video latent
	output_type="pil",
	save_memory=True, # If you have enough GPU memory, set it to `False` to improve vae decoding speed
	)

	export_to_video(frames, "./text_to_video_sample.mp4", fps=24)