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refine code

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Artiprocher
2025-01-02 19:54:09 +08:00
parent 2872fdaf48
commit 6f743fc4b6
6 changed files with 263 additions and 247 deletions
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examples/EntityControl/entity_control.py
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@@ -1,57 +1,43 @@
import torch
from diffsynth import ModelManager, FluxImagePipeline, download_customized_models
from modelscope import dataset_snapshot_download
from examples.EntityControl.utils import visualize_masks
from PIL import Image
import requests
from io import BytesIO
import torch
# download and load model
lora_path = download_customized_models(
model_id="DiffSynth-Studio/Eligen",
origin_file_path="model_bf16.safetensors",
local_dir="models/lora/entity_control"
)[0]
model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
model_manager.load_lora(lora_path, lora_alpha=1.)
model_manager.load_lora(
download_customized_models(
model_id="DiffSynth-Studio/Eligen",
origin_file_path="model_bf16.safetensors",
local_dir="models/lora/entity_control"
),
lora_alpha=1
)
pipe = FluxImagePipeline.from_model_manager(model_manager)
# prepare inputs
image_shape = 1024
seed = 4
# set True to apply regional attention in negative prompt prediction for better results with more time
use_seperated_negtive_prompt = False
mask_urls = [
'https://github.com/user-attachments/assets/02905f6e-40c2-4482-9abe-b1ce50ccabbf',
'https://github.com/user-attachments/assets/a4cf4361-abf7-4556-ba94-74683eda4cb7',
'https://github.com/user-attachments/assets/b6595ff4-7269-4d8f-acf0-5df40bd6c59f',
'https://github.com/user-attachments/assets/941d39a7-3aa1-437f-8b2a-4adb15d2fb3e',
'https://github.com/user-attachments/assets/400c4086-5398-4291-b1b5-22d8483c08d9',
'https://github.com/user-attachments/assets/ce324c77-fa1d-4aad-a5cb-698f0d5eca70',
'https://github.com/user-attachments/assets/4e62325f-a60c-44f7-b53b-6da0869bb9db'
]
# prepare entity masks, entity prompts, global prompt and negative prompt
masks = []
for url in mask_urls:
response = requests.get(url)
mask = Image.open(BytesIO(response.content)).resize((image_shape, image_shape), resample=Image.NEAREST)
masks.append(mask)
# download and load mask images
dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern="data/examples/eligen/mask*")
masks = [Image.open(f"./data/examples/eligen/mask{i}.png") for i in range(1, 8)]
entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"]
global_prompt = "A beautiful woman wearing white dress, holding a mirror, with a warm light background;"
negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw"
# generate image
torch.manual_seed(seed)
image = pipe(
prompt=global_prompt,
cfg_scale=3.0,
negative_prompt=negative_prompt,
num_inference_steps=50,
embedded_guidance=3.5,
height=image_shape,
width=image_shape,
entity_prompts=entity_prompts,
entity_masks=masks,
use_seperated_negtive_prompt=use_seperated_negtive_prompt,
seed=4,
height=1024,
width=1024,
eligen_entity_prompts=entity_prompts,
eligen_entity_masks=masks,
enable_eligen_on_negative=False,
)
image.save(f"entity_control.png")
visualize_masks(image, masks, entity_prompts, f"entity_control_with_mask.png")