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swift-main/swift/aigc/diffusers/infer_dreambooth.py 0 → 100644
View file @ 1bfbcff0
# Copyright (c) Alibaba, Inc. and its affiliates.
import argparse
import os
import torch
from diffusers import StableDiffusionPipeline
from modelscope import snapshot_download
def parse_args():
parser = argparse.ArgumentParser(description='Simple example of a dreambooth inference.')
parser.add_argument(
'--model_path',
type=str,
default=None,
required=True,
help='Path to trained model.',
)
parser.add_argument(
'--prompt',
type=str,
default=None,
required=True,
help='The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`',
)
parser.add_argument(
'--image_save_path',
type=str,
default=None,
required=True,
help='The path to save generated image',
)
parser.add_argument(
'--torch_dtype',
type=str,
default=None,
choices=['no', 'fp16', 'bf16'],
help=('Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >='
' 1.10.and an Nvidia Ampere GPU. Default to the value of the'
' mixed_precision passed with the `accelerate.launch` command in training script.'),
)
parser.add_argument(
'--num_inference_steps',
type=int,
default=50,
help=('The number of denoising steps. More denoising steps usually lead to a higher quality image at the \
expense of slower inference.'),
)
parser.add_argument(
'--guidance_scale',
type=float,
default=7.5,
help=('A higher guidance scale value encourages the model to generate images closely linked to the text \
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.'),
)
args = parser.parse_args()
return args
def main():
args = parse_args()
if args.torch_dtype == 'fp16':
torch_dtype = torch.float16
elif args.torch_dtype == 'bf16':
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
pipe = StableDiffusionPipeline.from_pretrained(args.model_path, torch_dtype=torch_dtype).to('cuda')
image = pipe(
args.prompt, num_inference_steps=args.num_inference_steps, guidance_scale=args.guidance_scale).images[0]
image.save(args.image_save_path)
swift-main/swift/aigc/diffusers/infer_dreambooth_lora.py 0 → 100644
View file @ 1bfbcff0
# Copyright (c) Alibaba, Inc. and its affiliates.
import argparse
import os
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from modelscope import snapshot_download
from swift import Swift
def parse_args():
parser = argparse.ArgumentParser(description='Simple example of a text to image inference.')
parser.add_argument(
'--base_model_path',
type=str,
default='AI-ModelScope/stable-diffusion-v1-5',
required=True,
help='Path to pretrained model or model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--revision',
type=str,
default=None,
required=False,
help='Revision of pretrained model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--lora_model_path',
type=str,
default=None,
required=False,
help='The path to trained lora model.',
)
parser.add_argument(
'--prompt',
type=str,
default=None,
required=True,
help='The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`',
)
parser.add_argument(
'--image_save_path',
type=str,
default=None,
required=True,
help='The path to save generated image',
)
parser.add_argument(
'--torch_dtype',
type=str,
default=None,
choices=['no', 'fp16', 'bf16'],
help=('Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >='
' 1.10.and an Nvidia Ampere GPU. Default to the value of the'
' mixed_precision passed with the `accelerate.launch` command in training script.'),
)
parser.add_argument(
'--num_inference_steps',
type=int,
default=50,
help=('The number of denoising steps. More denoising steps usually lead to a higher quality image at the \
expense of slower inference.'),
)
parser.add_argument(
'--guidance_scale',
type=float,
default=7.5,
help=('A higher guidance scale value encourages the model to generate images closely linked to the text \
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.'),
)
args = parser.parse_args()
return args
def main():
args = parse_args()
if os.path.exists(args.base_model_path):
base_model_path = args.base_model_path
else:
base_model_path = snapshot_download(args.base_model_path, revision=args.revision)
if args.torch_dtype == 'fp16':
torch_dtype = torch.float16
elif args.torch_dtype == 'bf16':
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch_dtype)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
if args.lora_model_path is not None:
pipe.unet = Swift.from_pretrained(pipe.unet, args.lora_model_path)
pipe.to('cuda')
image = pipe(args.prompt, num_inference_steps=args.num_inference_steps).images[0]
image.save(args.image_save_path)
swift-main/swift/aigc/diffusers/infer_dreambooth_lora_sdxl.py 0 → 100644
View file @ 1bfbcff0
# Copyright (c) Alibaba, Inc. and its affiliates.
import argparse
import os
import torch
from diffusers import DiffusionPipeline
from modelscope import snapshot_download
from swift import Swift
def parse_args():
parser = argparse.ArgumentParser(description='Simple example of a text to image inference.')
parser.add_argument(
'--base_model_path',
type=str,
default='AI-ModelScope/stable-diffusion-xl-base-1.0',
required=True,
help='Path to pretrained model or model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--revision',
type=str,
default=None,
required=False,
help='Revision of pretrained model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--lora_model_path',
type=str,
default=None,
required=False,
help='The path to trained lora model.',
)
parser.add_argument(
'--prompt',
type=str,
default=None,
required=True,
help='The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`',
)
parser.add_argument(
'--image_save_path',
type=str,
default=None,
required=True,
help='The path to save generated image',
)
parser.add_argument(
'--torch_dtype',
type=str,
default=None,
choices=['no', 'fp16', 'bf16'],
help=('Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >='
' 1.10.and an Nvidia Ampere GPU. Default to the value of the'
' mixed_precision passed with the `accelerate.launch` command in training script.'),
)
parser.add_argument('--seed', type=int, default=None, help='A seed for inference.')
parser.add_argument(
'--num_inference_steps',
type=int,
default=50,
help=('The number of denoising steps. More denoising steps usually lead to a higher quality image at the \
expense of slower inference.'),
)
parser.add_argument(
'--guidance_scale',
type=float,
default=7.5,
help=('A higher guidance scale value encourages the model to generate images closely linked to the text \
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.'),
)
args = parser.parse_args()
return args
def main():
args = parse_args()
if os.path.exists(args.base_model_path):
base_model_path = args.base_model_path
else:
base_model_path = snapshot_download(args.base_model_path, revision=args.revision)
if args.torch_dtype == 'fp16':
torch_dtype = torch.float16
elif args.torch_dtype == 'bf16':
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch_dtype)
if args.lora_model_path is not None:
pipe.unet = Swift.from_pretrained(pipe.unet, args.lora_model_path)
pipe = pipe.to('cuda')
image = pipe(args.prompt, num_inference_steps=args.num_inference_steps).images[0]
image.save(args.image_save_path)
swift-main/swift/aigc/diffusers/infer_text_to_image.py 0 → 100644
View file @ 1bfbcff0
# Copyright (c) Alibaba, Inc. and its affiliates.
import argparse
import os
import torch
from diffusers import StableDiffusionPipeline, UNet2DConditionModel
from modelscope import snapshot_download
def parse_args():
parser = argparse.ArgumentParser(description='Simple example of a text to image inference.')
parser.add_argument(
'--pretrained_model_name_or_path',
type=str,
default='AI-ModelScope/stable-diffusion-v1-5',
required=True,
help='Path to pretrained model or model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--revision',
type=str,
default=None,
required=False,
help='Revision of pretrained model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--unet_model_path',
type=str,
default=None,
required=False,
help='The path to trained unet model.',
)
parser.add_argument(
'--prompt',
type=str,
default=None,
required=True,
help='The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`',
)
parser.add_argument(
'--image_save_path',
type=str,
default=None,
required=True,
help='The path to save generated image',
)
parser.add_argument(
'--torch_dtype',
type=str,
default=None,
choices=['no', 'fp16', 'bf16'],
help=('Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >='
' 1.10.and an Nvidia Ampere GPU. Default to the value of the'
' mixed_precision passed with the `accelerate.launch` command in training script.'),
)
parser.add_argument(
'--num_inference_steps',
type=int,
default=50,
help=('The number of denoising steps. More denoising steps usually lead to a higher quality image at the \
expense of slower inference.'),
)
parser.add_argument(
'--guidance_scale',
type=float,
default=7.5,
help=('A higher guidance scale value encourages the model to generate images closely linked to the text \
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.'),
)
args = parser.parse_args()
return args
def main():
args = parse_args()
if os.path.exists(args.pretrained_model_name_or_path):
model_path = args.pretrained_model_name_or_path
else:
model_path = snapshot_download(args.pretrained_model_name_or_path, revision=args.revision)
if args.torch_dtype == 'fp16':
torch_dtype = torch.float16
elif args.torch_dtype == 'bf16':
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch_dtype)
if args.unet_model_path is not None:
pipe.unet = UNet2DConditionModel.from_pretrained(args.unet_model_path, torch_dtype=torch_dtype)
pipe.to('cuda')
image = pipe(
prompt=args.prompt, num_inference_steps=args.num_inference_steps, guidance_scale=args.guidance_scale).images[0]
image.save(args.image_save_path)
swift-main/swift/aigc/diffusers/infer_text_to_image_lora.py 0 → 100644
View file @ 1bfbcff0
# Copyright (c) Alibaba, Inc. and its affiliates.
import argparse
import os
import torch
from diffusers import StableDiffusionPipeline
from modelscope import snapshot_download
from swift import Swift
def parse_args():
parser = argparse.ArgumentParser(description='Simple example of a text to image inference.')
parser.add_argument(
'--pretrained_model_name_or_path',
type=str,
default='AI-ModelScope/stable-diffusion-v1-5',
required=True,
help='Path to pretrained model or model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--revision',
type=str,
default=None,
required=False,
help='Revision of pretrained model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--lora_model_path',
type=str,
default=None,
required=False,
help='The path to trained lora model.',
)
parser.add_argument(
'--prompt',
type=str,
default=None,
required=True,
help='The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`',
)
parser.add_argument(
'--image_save_path',
type=str,
default=None,
required=True,
help='The path to save generated image',
)
parser.add_argument(
'--torch_dtype',
type=str,
default=None,
choices=['no', 'fp16', 'bf16'],
help=('Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >='
' 1.10.and an Nvidia Ampere GPU. Default to the value of the'
' mixed_precision passed with the `accelerate.launch` command in training script.'),
)
parser.add_argument(
'--num_inference_steps',
type=int,
default=30,
help=('The number of denoising steps. More denoising steps usually lead to a higher quality image at the \
expense of slower inference.'),
)
parser.add_argument(
'--guidance_scale',
type=float,
default=7.5,
help=('A higher guidance scale value encourages the model to generate images closely linked to the text \
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.'),
)
args = parser.parse_args()
return args
def main():
args = parse_args()
if os.path.exists(args.pretrained_model_name_or_path):
model_path = args.pretrained_model_name_or_path
else:
model_path = snapshot_download(args.pretrained_model_name_or_path, revision=args.revision)
if args.torch_dtype == 'fp16':
torch_dtype = torch.float16
elif args.torch_dtype == 'bf16':
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch_dtype)
if args.lora_model_path is not None:
pipe.unet = Swift.from_pretrained(pipe.unet, args.lora_model_path)
pipe.to('cuda')
image = pipe(
prompt=args.prompt, num_inference_steps=args.num_inference_steps, guidance_scale=args.guidance_scale).images[0]
image.save(args.image_save_path)
swift-main/swift/aigc/diffusers/infer_text_to_image_lora_sdxl.py 0 → 100644
View file @ 1bfbcff0
# Copyright (c) Alibaba, Inc. and its affiliates.
import argparse
import os
import torch
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel
from modelscope import snapshot_download
from swift import Swift
def parse_args():
parser = argparse.ArgumentParser(description='Simple example of a text to image lora sdxl inference.')
parser.add_argument(
'--pretrained_model_name_or_path',
type=str,
default='AI-ModelScope/stable-diffusion-v1-5',
required=True,
help='Path to pretrained model or model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--revision',
type=str,
default=None,
required=False,
help='Revision of pretrained model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--lora_model_path',
type=str,
default=None,
required=False,
help='The path to trained lora model.',
)
parser.add_argument(
'--prompt',
type=str,
default=None,
required=True,
help='The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`',
)
parser.add_argument(
'--image_save_path',
type=str,
default=None,
required=True,
help='The path to save generated image',
)
parser.add_argument(
'--torch_dtype',
type=str,
default=None,
choices=['no', 'fp16', 'bf16'],
help=('Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >='
' 1.10.and an Nvidia Ampere GPU. Default to the value of the'
' mixed_precision passed with the `accelerate.launch` command in training script.'),
)
parser.add_argument(
'--num_inference_steps',
type=int,
default=30,
help=('The number of denoising steps. More denoising steps usually lead to a higher quality image at the \
expense of slower inference.'),
)
parser.add_argument(
'--guidance_scale',
type=float,
default=7.5,
help=('A higher guidance scale value encourages the model to generate images closely linked to the text \
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.'),
)
args = parser.parse_args()
return args
def main():
args = parse_args()
if os.path.exists(args.pretrained_model_name_or_path):
model_path = args.pretrained_model_name_or_path
else:
model_path = snapshot_download(args.pretrained_model_name_or_path, revision=args.revision)
if args.torch_dtype == 'fp16':
torch_dtype = torch.float16
elif args.torch_dtype == 'bf16':
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
pipe = StableDiffusionXLPipeline.from_pretrained(model_path, torch_dtype=torch_dtype)
if args.lora_model_path is not None:
pipe.unet = Swift.from_pretrained(pipe.unet, args.lora_model_path)
pipe.to('cuda')
image = pipe(
prompt=args.prompt, num_inference_steps=args.num_inference_steps, guidance_scale=args.guidance_scale).images[0]
image.save(args.image_save_path)
swift-main/swift/aigc/diffusers/infer_text_to_image_sdxl.py 0 → 100644
View file @ 1bfbcff0
# Copyright (c) Alibaba, Inc. and its affiliates.
import argparse
import os
import torch
from diffusers import DiffusionPipeline, UNet2DConditionModel
from modelscope import snapshot_download
def parse_args():
parser = argparse.ArgumentParser(description='Simple example of a text to image inference.')
parser.add_argument(
'--pretrained_model_name_or_path',
type=str,
default='AI-ModelScope/stable-diffusion-v1-5',
required=True,
help='Path to pretrained model or model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--revision',
type=str,
default=None,
required=False,
help='Revision of pretrained model identifier from modelscope.cn/models.',
)
parser.add_argument(
'--unet_model_path',
type=str,
default=None,
required=False,
help='The path to trained unet model.',
)
parser.add_argument(
'--prompt',
type=str,
default=None,
required=True,
help='The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`',
)
parser.add_argument(
'--image_save_path',
type=str,
default=None,
required=True,
help='The path to save generated image',
)
parser.add_argument(
'--torch_dtype',
type=str,
default=None,
choices=['no', 'fp16', 'bf16'],
help=('Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >='
' 1.10.and an Nvidia Ampere GPU. Default to the value of the'
' mixed_precision passed with the `accelerate.launch` command in training script.'),
)
parser.add_argument(
'--num_inference_steps',
type=int,
default=30,
help=('The number of denoising steps. More denoising steps usually lead to a higher quality image at the \
expense of slower inference.'),
)
parser.add_argument(
'--guidance_scale',
type=float,
default=7.5,
help=('A higher guidance scale value encourages the model to generate images closely linked to the text \
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.'),
)
args = parser.parse_args()
return args
def main():
args = parse_args()
if os.path.exists(args.pretrained_model_name_or_path):
model_path = args.pretrained_model_name_or_path
else:
model_path = snapshot_download(args.pretrained_model_name_or_path, revision=args.revision)
if args.torch_dtype == 'fp16':
torch_dtype = torch.float16
elif args.torch_dtype == 'bf16':
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch_dtype)
if args.unet_model_path is not None:
pipe.unet = UNet2DConditionModel.from_pretrained(args.unet_model_path, torch_dtype=torch_dtype)
pipe.to('cuda')
image = pipe(
prompt=args.prompt, num_inference_steps=args.num_inference_steps, guidance_scale=args.guidance_scale).images[0]
image.save(args.image_save_path)
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