python sdk
1.0.0
該SDK基於官方API文檔。
加入我們的Discord服務器尋求幫助：
pip install novita-client import os
from novita_client import NovitaClient
from novita_client . utils import base64_to_image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . cleanup (
image = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" ,
mask = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
)
base64_to_image ( res . image_file ). save ( "./cleanup.png" ) #!/usr/bin/env python
# -*- coding: UTF-8 -*-
import os
from novita_client import NovitaClient , Img2ImgV3Request , Img2ImgV3ControlNetUnit , ControlnetUnit , Samplers , Img2ImgV3Embedding
from novita_client . utils import base64_to_image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . img2img_v3 (
input_image = "https://img.freepik.com/premium-photo/close-up-dogs-face-with-big-smile-generative-ai_900101-62851.jpg" ,
model_name = "dreamshaper_8_93211.safetensors" ,
prompt = "a cute dog" ,
sampler_name = Samplers . DPMPP_M_KARRAS ,
width = 512 ,
height = 512 ,
steps = 30 ,
controlnet_units = [
Img2ImgV3ControlNetUnit (
image_base64 = "https://img.freepik.com/premium-photo/close-up-dogs-face-with-big-smile-generative-ai_900101-62851.jpg" ,
model_name = "control_v11f1p_sd15_depth" ,
strength = 1.0
)
],
embeddings = [ Img2ImgV3Embedding ( model_name = _ ) for _ in [
"BadDream_53202" ,
]],
seed = - 1 ,
)
base64_to_image ( res . images_encoded [ 0 ]). save ( "./img2img-controlnet.png" ) import pdb
import os
from novita_client import NovitaClient , Img2ImgV3ControlNetUnit , ControlNetPreprocessor , Img2ImgV3Embedding
from novita_client . utils import base64_to_image , input_image_to_pil
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . img2img_v3 (
model_name = "MeinaHentai_V5.safetensors" ,
steps = 30 ,
height = 512 ,
width = 512 ,
input_image = "https://img.freepik.com/premium-photo/close-up-dogs-face-with-big-smile-generative-ai_900101-62851.jpg" ,
prompt = "1 cute dog" ,
strength = 0.5 ,
guidance_scale = 7 ,
embeddings = [ Img2ImgV3Embedding ( model_name = _ ) for _ in [
"bad-image-v2-39000" ,
"verybadimagenegative_v1.3_21434" ,
"BadDream_53202" ,
"badhandv4_16755" ,
"easynegative_8955.safetensors" ]],
seed = - 1 ,
sampler_name = "DPM++ 2M Karras" ,
clip_skip = 2 ,
# controlnet_units=[Img2ImgV3ControlNetUnit(
# model_name="control_v11f1p_sd15_depth",
# preprocessor="depth",
# image_base64="./20240309-003206.jpeg",
# strength=1.0
# )]
)
base64_to_image ( res . images_encoded [ 0 ]). save ( "./img2img.png" ) import os
from novita_client import NovitaClient
from novita_client . utils import base64_to_image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URNOVITA_API_URII' , None ))
res = client . img2video (
model_name = "SVD-XT" ,
steps = 30 ,
frames_num = 25 ,
image = "https://replicate.delivery/pbxt/JvLi9smWKKDfQpylBYosqQRfPKZPntuAziesp0VuPjidq61n/rocket.png" ,
enable_frame_interpolation = True
)
with open ( "test.mp4" , "wb" ) as f :
f . write ( res . video_bytes [ 0 ]) import os
import base64
from novita_client import NovitaClient
from novita_client . utils import base64_to_image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . inpainting (
model_name = "realisticVisionV40_v40VAE-inpainting_81543.safetensors" ,
image = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" ,
mask = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" ,
seed = 1 ,
guidance_scale = 15 ,
steps = 20 ,
image_num = 4 ,
prompt = "black rabbit" ,
negative_prompt = "white rabbit" ,
sampler_name = "Euler a" ,
inpainting_full_res = 1 ,
inpainting_full_res_padding = 32 ,
inpainting_mask_invert = 0 ,
initial_noise_multiplier = 1 ,
mask_blur = 1 ,
clip_skip = 1 ,
strength = 0.85 ,
)
with open ( "result/result_image/inpaintingsdk.jpeg" , "wb" ) as image_file :
image_file . write ( base64 . b64decode ( res . images_encoded [ 0 ])) `` `
### instantid
` `` python
import os
from novita_client import NovitaClient , InstantIDControlnetUnit
import base64
if __name__ == '__main__' :
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . instant_id (
model_name = "sdxlUnstableDiffusers_v8HEAVENSWRATH_133813.safetensors" ,
face_images = [
"https://raw.githubusercontent.com/InstantID/InstantID/main/examples/yann-lecun_resize.jpg" ,
],
prompt = "Flat illustration, a Chinese a man, ancient style, wearing a red cloth, smile face, white skin, clean background, fireworks blooming, red lanterns" ,
negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green" ,
id_strength = 0.8 ,
adapter_strength = 0.8 ,
steps = 20 ,
seed = 42 ,
width = 1024 ,
height = 1024 ,
controlnets = [
InstantIDControlnetUnit (
model_name = 'controlnet-openpose-sdxl-1.0' ,
strength = 0.4 ,
preprocessor = 'openpose' ,
),
InstantIDControlnetUnit (
model_name = 'controlnet-canny-sdxl-1.0' ,
strength = 0.3 ,
preprocessor = 'canny' ,
),
],
response_image_type = 'jpeg' ,
enterprise_plan = False ,
)
print ( 'res:' , res )
if hasattr ( res , 'images_encoded' ):
with open ( f"instantid.png" , "wb" ) as f :
f . write ( base64 . b64decode ( res . images_encoded [ 0 ])) import os
from novita_client import NovitaClient
from novita_client . utils import base64_to_image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . merge_face (
image = "https://toppng.com/uploads/preview/cut-out-people-png-personas-en-formato-11563277290kozkuzsos5.png" ,
face_image = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQDy7sXtuvCNUQoQZvTbLRbX6qK9_kP3PlQfg&s" ,
enterprise_plan = False ,
)
base64_to_image ( res . image_file ). save ( "./merge_face.png" ) #!/usr/bin/env python
# -*- coding: UTF-8 -*-
from novita_client import NovitaClient , ModelType
# get your api key refer to https://docs.novita.ai/get-started/
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
# filter by model type
print ( "lora count" , len ( client . models (). filter_by_type ( ModelType . LORA )))
print ( "checkpoint count" , len ( client . models (). filter_by_type ( ModelType . CHECKPOINT )))
print ( "textinversion count" , len (
client . models (). filter_by_type ( ModelType . TEXT_INVERSION )))
print ( "vae count" , len ( client . models (). filter_by_type ( ModelType . VAE )))
print ( "controlnet count" , len ( client . models (). filter_by_type ( ModelType . CONTROLNET )))
# filter by civitai tags
client . models (). filter_by_civi_tags ( 'anime' )
# filter by nsfw
client . models (). filter_by_nsfw ( False ) # or True
# sort by civitai download
client . models (). sort_by_civitai_download ()
# chain filters
client . models ().
filter_by_type ( ModelType . CHECKPOINT ).
filter_by_nsfw ( False ).
filter_by_civitai_tags ( 'anime' ) import os
from novita_client import NovitaClient
from novita_client . utils import base64_to_image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . reimagine (
image = "/home/anyisalin/develop/novita-client-python/examples/doodle-generated.png"
)
base64_to_image ( res . image_file ). save ( "./reimagine.png" ) import os
from novita_client import NovitaClient
from novita_client . utils import base64_to_image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . remove_background (
image = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" ,
)
base64_to_image ( res . image_file ). save ( "./remove_background.png" ) import os
from novita_client import NovitaClient
from novita_client . utils import base64_to_image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . remove_text (
image = "https://images.uiiiuiii.com/wp-content/uploads/2023/07/i-banner-20230714-1.jpg"
)
base64_to_image ( res . image_file ). save ( "./remove_text.png" ) import os
from novita_client import NovitaClient
from novita_client . utils import base64_to_image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . replace_background (
image = "./telegram-cloud-photo-size-2-5408823814353177899-y.jpg" ,
prompt = "in living room, Christmas tree" ,
)
base64_to_image ( res . image_file ). save ( "./replace_background.png" ) import os
from novita_client import NovitaClient , Samplers , Txt2ImgV3HiresFix
from novita_client . utils import base64_to_image
from PIL import Image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . txt2img_v3 (
model_name = 'dreamshaper_8_93211.safetensors' ,
prompt = "a cute girl" ,
width = 384 ,
height = 512 ,
image_num = 1 ,
guidance_scale = 7.5 ,
seed = 12345 ,
sampler_name = Samplers . EULER_A ,
hires_fix = Txt2ImgV3HiresFix (
# upscaler='Latent'
target_width = 768 ,
target_height = 1024 ,
strength = 0.5
)
)
base64_to_image ( res . images_encoded [ 0 ]). save ( "./txt2img_with_hiresfix.png" ) #!/usr/bin/env python
# -*- coding: UTF-8 -*-
import os
from novita_client import NovitaClient , Txt2ImgV3LoRA , Samplers , ProgressResponseStatusCode , ModelType , add_lora_to_prompt , save_image
from novita_client . utils import base64_to_image , input_image_to_pil
from PIL import Image
def make_image_grid ( images , rows : int , cols : int , resize : int = None ):
"""
Prepares a single grid of images. Useful for visualization purposes.
"""
assert len ( images ) == rows * cols
if resize is not None :
images = [ img . resize (( resize , resize )) for img in images ]
w , h = images [ 0 ]. size
grid = Image . new ( "RGB" , size = ( cols * w , rows * h ))
for i , img in enumerate ( images ):
grid . paste ( img , box = ( i % cols * w , i // cols * h ))
return grid
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res1 = client . txt2img_v3 (
prompt = "a photo of handsome man, close up" ,
image_num = 1 ,
guidance_scale = 7.0 ,
sampler_name = Samplers . DPMPP_M_KARRAS ,
model_name = "dreamshaper_8_93211.safetensors" ,
height = 512 ,
width = 512 ,
seed = 1024 ,
)
res2 = client . txt2img_v3 (
prompt = "a photo of handsome man, close up" ,
image_num = 1 ,
guidance_scale = 7.0 ,
sampler_name = Samplers . DPMPP_M_KARRAS ,
model_name = "dreamshaper_8_93211.safetensors" ,
height = 512 ,
width = 512 ,
seed = 1024 ,
loras = [
Txt2ImgV3LoRA (
model_name = "add_detail_44319" ,
strength = 0.9 ,
)
]
)
make_image_grid ([ base64_to_image ( res1 . images_encoded [ 0 ]), base64_to_image ( res2 . images_encoded [ 0 ])], 1 , 2 , 512 ). save ( "./txt2img-lora-compare.png" ) import os
from novita_client import NovitaClient , Txt2ImgV3Refiner , Samplers
from novita_client . utils import base64_to_image
from PIL import Image
def make_image_grid ( images , rows : int , cols : int , resize : int = None ):
"""
Prepares a single grid of images. Useful for visualization purposes.
"""
assert len ( images ) == rows * cols
if resize is not None :
images = [ img . resize (( resize , resize )) for img in images ]
w , h = images [ 0 ]. size
grid = Image . new ( "RGB" , size = ( cols * w , rows * h ))
for i , img in enumerate ( images ):
grid . paste ( img , box = ( i % cols * w , i // cols * h ))
return grid
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
r1 = client . txt2img_v3 (
model_name = 'sd_xl_base_1.0.safetensors' ,
prompt = 'a astronaut riding a bike on the moon' ,
width = 1024 ,
height = 1024 ,
image_num = 1 ,
guidance_scale = 7.5 ,
sampler_name = Samplers . EULER_A ,
)
r2 = client . txt2img_v3 (
model_name = 'sd_xl_base_1.0.safetensors' ,
prompt = 'a astronaut riding a bike on the moon' ,
width = 1024 ,
height = 1024 ,
image_num = 1 ,
guidance_scale = 7.5 ,
sampler_name = Samplers . EULER_A ,
refiner = Txt2ImgV3Refiner (
switch_at = 0.7
)
)
r3 = client . txt2img_v3 (
model_name = 'sd_xl_base_1.0.safetensors' ,
prompt = 'a astronaut riding a bike on the moon' ,
width = 1024 ,
height = 1024 ,
image_num = 1 ,
guidance_scale = 7.5 ,
sampler_name = Samplers . EULER_A ,
refiner = Txt2ImgV3Refiner (
switch_at = 0.5
)
)
make_image_grid ([ base64_to_image ( r1 . images_encoded [ 0 ]), base64_to_image ( r2 . images_encoded [ 0 ]), base64_to_image ( r3 . images_encoded [ 0 ])], 1 , 3 , 1024 ). save ( "./txt2img-refiner-compare.png" ) import os
from novita_client import NovitaClient
from novita_client . utils import save_image
client = NovitaClient ( os . getenv ( 'NOVITA_API_KEY' ), os . getenv ( 'NOVITA_API_URI' , None ))
res = client . txt2video (
model_name = "dreamshaper_8_93211.safetensors" ,
prompts = [{
"prompt" : "A girl, baby, portrait, 5 years old" ,
"frames" : 16 ,},
{
"prompt" : "A girl, child, portrait, 10 years old" ,
"frames" : 16 ,
}
],
steps = 20 ,
guidance_scale = 10 ,
height = 512 ,
width = 768 ,
clip_skip = 4 ,
negative_prompt = "a rainy day" ,
response_video_type = "mp4" ,
)
save_image ( res . video_bytes [ 0 ], 'output.mp4' )
