Initial commit

swift-main/swift/aigc/diffusers/infer_dreambooth.py

+# 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

+# 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

+# 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

+# 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

+# 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

+# 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

+# 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)