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# ------------------------------------------
# TextDiffuser: Diffusion Models as Text Painters
# Paper Link: https://arxiv.org/abs/2305.10855
# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser
# Copyright (c) Microsoft Corporation.
# This file provides the inference script.
# ------------------------------------------
import os
from PIL import Image
import numpy as np
import torch
from tqdm import tqdm
import argparse
import cv2
import torchvision.transforms as transforms
to_pil_image = transforms.ToPILImage()
def load_stablediffusion():
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
return pipe
def test_stablediffusion(prompt, save_path, num_images_per_prompt=4,
pipe=None, generator=None):
images = pipe(prompt, num_inference_steps=50, generator=generator, num_images_per_prompt=num_images_per_prompt).images
for idx, image in enumerate(images):
image.save(save_path.replace('.jpg', '_' + str(idx) + '.jpg').replace('/images/', '/images_'+ str(idx) +'/'))
def load_deepfloyd_if():
from diffusers import DiffusionPipeline
stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
# stage_1.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0
stage_1.enable_model_cpu_offload()
stage_2 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16",
torch_dtype=torch.float16)
# stage_2.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0
stage_2.enable_model_cpu_offload()
safety_modules = {"feature_extractor": stage_1.feature_extractor, "safety_checker": stage_1.safety_checker,
"watermarker": stage_1.watermarker}
stage_3 = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler", **safety_modules,
torch_dtype=torch.float16)
# stage_3.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0
stage_3.enable_model_cpu_offload()
return stage_1, stage_2, stage_3
def test_deepfloyd_if(stage_1, stage_2, stage_3, prompt, save_path, num_images_per_prompt=4, generator=None):
idx = num_images_per_prompt - 1 # if the last image of a case exists, then return
new_save_path = save_path.replace('.jpg', '_' + str(idx) + '.jpg').replace('/images/', '/images_' + str(idx) + '/')
if os.path.exists(new_save_path):
return
if not stage_1 or not stage_2 or not stage_3:
stage_1, stage_2, stage_3 = load_deepfloyd_if()
if generator is None:
generator = torch.manual_seed(0)
prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
stage_1.set_progress_bar_config(disable=True)
stage_2.set_progress_bar_config(disable=True)
stage_3.set_progress_bar_config(disable=True)
images = stage_1(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator,
output_type="pt", num_images_per_prompt=num_images_per_prompt).images
for idx, image in enumerate(images):
image = stage_2(image=image.unsqueeze(0), prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds,
generator=generator, output_type="pt").images
image = stage_3(prompt=prompt, image=image, generator=generator, noise_level=100).images
# image = to_pil_image(image[0].cpu())
new_save_path = save_path.replace('.jpg', '_' + str(idx) + '.jpg').replace('/images/', '/images_'+ str(idx) +'/')
image[0].save(new_save_path)
def load_controlnet_cannyedge():
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet,
safety_checker=None, torch_dtype=torch.float16)
pipe.set_progress_bar_config(disable=True)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
return pipe
def test_controlnet_cannyedge(prompt, save_path, canny_path, num_images_per_prompt=4,
pipe=None, generator=None, low_threshold=100, high_threshold=200):
'''ref: https://github.com/huggingface/diffusers/blob/131312caba0af97da98fc498dfdca335c9692f8c/docs/source/en/api/pipelines/stable_diffusion/controlnet.mdx'''
from diffusers.utils import load_image
if pipe is None:
pipe = load_controlnet_cannyedge()
if os.path.exists(canny_path):
canny_path = Image.open(canny_path)
image = load_image(canny_path)
image = np.array(image)
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
image = Image.fromarray(image)
images = pipe(prompt, image, num_inference_steps=20, generator=generator, num_images_per_prompt=num_images_per_prompt).images
for idx, image in enumerate(images):
image.save(save_path.replace('.jpg', '_' + str(idx) + '.jpg').replace('/images/', '/images_'+ str(idx) +'/'))
def MARIOEval_generate_results(root, dataset, method='controlnet', num_images_per_prompt=4, split=0, total_split=1):
root_eval = os.path.join(root, "MARIOEval")
render_path = os.path.join(root_eval, dataset, 'render')
root_res = os.path.join(root, "generation", method)
for idx in range(num_images_per_prompt):
os.makedirs(os.path.join(root_res, dataset, 'images_' + str(idx)), exist_ok=True)
generator = torch.Generator(device="cuda").manual_seed(0)
if method == 'controlnet':
pipe = load_controlnet_cannyedge()
elif method == 'stablediffusion':
pipe = load_stablediffusion()
elif method == 'deepfloyd':
stage_1, stage_2, stage_3 = load_deepfloyd_if()
with open(os.path.join(root_eval, dataset, dataset + '.txt'), 'r') as fr:
prompts = fr.readlines()
prompts = [_.strip() for _ in prompts]
for idx, prompt in tqdm(enumerate(prompts)):
if idx < split * len(prompts) / total_split or idx > (split + 1) * len(prompts) / total_split:
continue
if method == 'controlnet':
test_controlnet_cannyedge(prompt=prompt, num_images_per_prompt=num_images_per_prompt,
save_path=os.path.join(root_res, dataset, 'images', str(idx) + '.jpg'),
canny_path=os.path.join(render_path, str(idx) + '.png'),
pipe=pipe, generator=generator)
elif method == 'stablediffusion':
test_stablediffusion(prompt=prompt, num_images_per_prompt=num_images_per_prompt,
save_path=os.path.join(root_res, dataset, 'images', str(idx) + '.jpg'),
pipe=pipe, generator=generator)
elif method == 'deepfloyd':
test_deepfloyd_if(stage_1, stage_2, stage_3, num_images_per_prompt=num_images_per_prompt,
save_path=os.path.join(root_res, dataset, 'images', str(idx) + '.jpg'),
prompt=prompt, generator=generator)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--dataset",
type=str,
default='TMDBEval500',
required=False,
choices=['TMDBEval500', 'OpenLibraryEval500', 'LAIONEval4000',
'ChineseDrawText', 'DrawBenchText', 'DrawTextCreative']
)
parser.add_argument(
"--root",
type=str,
default="/path/to/eval",
required=True,
)
parser.add_argument(
"--method",
type=str,
default='controlnet',
required=False,
choices=['controlnet', 'deepfloyd', 'stablediffusion', 'textdiffuser']
)
parser.add_argument(
"--gpu",
type=int,
default=0,
required=False,
)
parser.add_argument(
"--split",
type=int,
default=0,
required=False,
)
parser.add_argument(
"--total_split",
type=int,
default=1,
required=False,
)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
MARIOEval_generate_results(root=args.root, dataset=args.dataset, method=args.method,
split=args.split, total_split=args.total_split)
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