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3c11df8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import torch
from PIL import Image
from modelscope import dataset_snapshot_download
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
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"),
ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"),
ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
dataset_snapshot_download(
dataset_id="DiffSynth-Studio/example_image_dataset",
local_dir="./data/example_image_dataset",
allow_file_pattern="canny/*.jpg"
)
prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。"
controlnet_canny_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
controlnet_inpaint_image = Image.open("./data/example_image_dataset/canny/image_2.jpg").convert("RGB").resize((1328, 1328))
# generate a centered square mask
inpaint_mask = Image.new("L", controlnet_inpaint_image.size, 0)
mask_size = 512
left = (controlnet_inpaint_image.width - mask_size) // 2
top = (controlnet_inpaint_image.height - mask_size) // 2
right = left + mask_size
bottom = top + mask_size
inpaint_mask.paste(255, (left, top, right, bottom))
inpaint_mask = inpaint_mask.resize((1328, 1328)).convert("RGB")
image = pipe(
prompt, seed=0,
input_image=controlnet_inpaint_image, inpaint_mask=inpaint_mask,
blockwise_controlnet_inputs=[
ControlNetInput(image=controlnet_inpaint_image, inpaint_mask=inpaint_mask, controlnet_id=0),
ControlNetInput(image=controlnet_canny_image, controlnet_id=1),
],
num_inference_steps=40,
)
image.save("image.jpg")
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