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import torch
import random
from PIL import Image, ImageDraw, ImageFont
from modelscope import dataset_snapshot_download, snapshot_download
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False):
# Create a blank image for overlays
overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
colors = [
(165, 238, 173, 80),
(76, 102, 221, 80),
(221, 160, 77, 80),
(204, 93, 71, 80),
(145, 187, 149, 80),
(134, 141, 172, 80),
(157, 137, 109, 80),
(153, 104, 95, 80),
(165, 238, 173, 80),
(76, 102, 221, 80),
(221, 160, 77, 80),
(204, 93, 71, 80),
(145, 187, 149, 80),
(134, 141, 172, 80),
(157, 137, 109, 80),
(153, 104, 95, 80),
]
# Generate random colors for each mask
if use_random_colors:
colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
# Font settings
try:
font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed
except IOError:
font = ImageFont.load_default(font_size)
# Overlay each mask onto the overlay image
for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
# Convert mask to RGBA mode
mask_rgba = mask.convert('RGBA')
mask_data = mask_rgba.getdata()
new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
mask_rgba.putdata(new_data)
# Draw the mask prompt text on the mask
draw = ImageDraw.Draw(mask_rgba)
mask_bbox = mask.getbbox() # Get the bounding box of the mask
text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position
draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
# Alpha composite the overlay with this mask
overlay = Image.alpha_composite(overlay, mask_rgba)
# Composite the overlay onto the original image
result = Image.alpha_composite(image.convert('RGBA'), overlay)
# Save or display the resulting image
result.save(output_path)
return result
def example(pipe, seeds, example_id, global_prompt, entity_prompts):
dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png")
masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB').resize((1024, 1024)) for i in range(len(entity_prompts))]
negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴"
for seed in seeds:
# generate image
image = pipe(
prompt=global_prompt,
cfg_scale=4.0,
negative_prompt=negative_prompt,
num_inference_steps=40,
seed=seed,
height=1024,
width=1024,
eligen_entity_prompts=entity_prompts,
eligen_entity_masks=masks,
)
image.save(f"eligen_example_{example_id}_{seed}.png")
visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png")
pipe = QwenImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen-V2", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-V2", allow_file_pattern="model.safetensors")
pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-V2/model.safetensors")
seeds = [0]
global_prompt = "写实摄影风格. A beautiful asia woman wearing white dress, she is holding a mirror with her right arm, with a beach background."
entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"]
example(pipe, seeds, 7, global_prompt, entity_prompts)
global_prompt = "写实摄影风格, 细节丰富。街头一位漂亮的女孩，穿着衬衫和短裤，手持写有“实体控制”的标牌，背景是繁忙的城市街道，阳光明媚，行人匆匆。"
entity_prompts = ["一个漂亮的女孩", "标牌 '实体控制'", "短裤", "衬衫"]
example(pipe, seeds, 4, global_prompt, entity_prompts)
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