使用Stable-Diffusion生成视频的完整教程

#install the diffuser package
#pip install –upgrade pip
!pip install –upgrade diffusers transformers scipy

#load the model from stable-diffusion model card
import torch
from diffusers import StableDiffusionPipeline

from huggingface_hub import notebook_login

if not (Path.home()/.huggingface/token).exists(): notebook_login()

model_id = “CompVis/stable-diffusion-v1-4”
device = “cuda”
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to(device)

%%time
#Provide the Keywords
prompts = [
“a couple holding hands with plants growing out of their heads, growth of a couple, rainy day, atmospheric, bokeh matte masterpiece by artgerm by wlop by alphonse muhca “,
“detailed portrait beautiful Neon Operator Girl, cyberpunk futuristic neon, reflective puffy coat, decorated with traditional Japanese ornaments by Ismail inceoglu dragan bibin hans thoma greg rutkowski Alexandros Pyromallis Nekro Rene Maritte Illustrated, Perfect face, fine details, realistic shaded, fine-face, pretty face”,
“symmetry!! portrait of minotaur, sci – fi, glowing lights!! intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8 k “,
“Human, Simon Stalenhag in forest clearing style, trends on artstation, artstation HD, artstation, unreal engine, 4k, 8k”,
“portrait of a young ruggedly handsome but joyful pirate, male, masculine, upper body, red hair, long hair, d & d, fantasy, roguish smirk, intricate, elegant, highly detailed, digital painting, artstation, concept art, matte, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha “,
“Symmetry!! portrait of a sith lord, warrior in sci-fi armour, tech wear, muscular!! sci-fi, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha”,
“highly detailed portrait of a cat knight wearing heavy armor, stephen bliss, unreal engine, greg rutkowski, loish, rhads, beeple, makoto shinkai and lois van baarle, ilya kuvshinov, rossdraws, tom bagshaw, tom whalen, alphonse mucha, global illumination, god rays, detailed and intricate environment “,
“black and white portrait photo, the most beautiful girl in the world, earth, year 2447, cdx”
]

%%time
#show the results
images = pipe(prompts).images
images

#show a single result
images[0]

#show the results in grid
from PIL import Image
def image_grid(imgs, rows, cols):
w,h = imgs[0].size
grid = Image.new(RGB, size=(cols*w, rows*h))
for i, img in enumerate(imgs): grid.paste(img, box=(i%cols*w, i//cols*h))
return grid

grid = image_grid(images, rows=2, cols=4)
grid

#Save the results
grid.save(“result_images.png”)

%%time
import torch
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to(device)
pipe.enable_attention_slicing()

images2 = pipe(prompts)
images2[0]

grid2 = image_grid(images, rows=2, cols=4)
grid2

%%time
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

model_id = “CompVis/stable-diffusion-v1-4″
# Use the Euler scheduler here instead
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder=”scheduler”)
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
pipe = pipe.to(“cuda”)
images3 = pipe(prompts)
images3[0][0]

#save the final output
grid3.save(“results_stable_diffusionv1.4.png”)

#results are saved in tuple
images3[0][0]

grid3 = image_grid(images3[0], rows=2, cols=4)
grid3

#save the final output
grid3.save(“results_stable_diffusionv1.4.png”)

pip install -U stable_diffusion_videos

from huggingface_hub import notebook_login
notebook_login()
#Making Videos
from stable_diffusion_videos import StableDiffusionWalkPipeline
import torch
#”CompVis/stable-diffusion-v1-4″ for 1.4

pipeline = StableDiffusionWalkPipeline.from_pretrained(
“runwayml/stable-diffusion-v1-5″,
torch_dtype=torch.float16,
revision=”fp16”,
).to(“cuda”)
#Generate the video Prompts 1
video_path = pipeline.walk(
prompts=[environment living room interior, mid century modern, indoor garden with fountain, retro,m vintage, designer furniture made of wood and plastic, concrete table, wood walls, indoor potted tree, large window, outdoor forest landscape, beautiful sunset, cinematic, concept art, sunstainable architecture, octane render, utopia, ethereal, cinematic light, –ar 16:9 –stylize 45000,
environment living room interior, mid century modern, indoor garden with fountain, retro,m vintage, designer furniture made of wood and plastic, concrete table, wood walls, indoor potted tree, large window, outdoor forest landscape, beautiful sunset, cinematic, concept art, sunstainable architecture, octane render, utopia, ethereal, cinematic light, –ar 16:9 –stylize 45000,
environment living room interior, mid century modern, indoor garden with fountain, retro,m vintage, designer furniture made of wood and plastic, concrete table, wood walls, indoor potted tree, large window, outdoor forest landscape, beautiful sunset, cinematic, concept art, sunstainable architecture, octane render, utopia, ethereal, cinematic light, –ar 16:9 –stylize 45000,
environment living room interior, mid century modern, indoor garden with fountain, retro,m vintage, designer furniture made of wood and plastic, concrete table, wood walls, indoor potted tree, large window, outdoor forest landscape, beautiful sunset, cinematic, concept art, sunstainable architecture, octane render, utopia, ethereal, cinematic light, –ar 16:9 –stylize 45000,
environment living room interior, mid century modern, indoor garden with fountain, retro,m vintage, designer furniture made of wood and plastic, concrete table, wood walls, indoor potted tree, large window, outdoor forest landscape, beautiful sunset, cinematic, concept art, sunstainable architecture, octane render, utopia, ethereal, cinematic light, –ar 16:9 –stylize 45000],
seeds=[42,333,444,555],
num_interpolation_steps=50,
#height=1280, # use multiples of 64 if > 512. Multiples of 8 if < 512.
#width=720, # use multiples of 64 if > 512. Multiples of 8 if < 512.
output_dir=dreams, # Where images/videos will be saved
name=imagine, # Subdirectory of output_dir where images/videos will be saved
guidance_scale=8.5, # Higher adheres to prompt more, lower lets model take the wheel
num_inference_steps=50, # Number of diffusion steps per image generated. 50 is good default

)

from stable_diffusion_videos import RealESRGANModel
model = RealESRGANModel.from_pretrained(nateraw/real-esrgan)
model.upsample_imagefolder(/kaggle/working/dreams/imagine/imagine_000000/, /kaggle/working/dreams/imagine4K_00)

%%capture
! pip install youtube-dl
! youtube-dl -f bestaudio –extract-audio –audio-format mp3 –audio-quality 0 -o “music/thoughts.%(ext)s” https://soundcloud.com/nateraw/thoughts

from IPython.display import Audio

Audio(filename=music/thoughts.mp3)

# Seconds in the song.
audio_offsets = [7, 9]
fps = 8

# Convert seconds to frames
num_interpolation_steps = [(b-a) * fps for a, b in zip(audio_offsets, audio_offsets[1:])]

video_path = pipeline.walk(
prompts=[blueberry spaghetti, strawberry spaghetti],
seeds=[42, 1337],
num_interpolation_steps=num_interpolation_steps,
height=512, # use multiples of 64
width=512, # use multiples of 64
audio_filepath=music/thoughts.mp3, # Use your own file
audio_start_sec=audio_offsets[0], # Start second of the provided audio
fps=fps, # important to set yourself based on the num_interpolation_steps you defined
batch_size=4, # increase until you go out of memory.
output_dir=dreams, # Where images will be saved
name=None, # Subdir of output dir. will be timestamp by default
)