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https://github.com/modelscope/DiffSynth-Studio.git
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60 lines
2.7 KiB
Python
60 lines
2.7 KiB
Python
import torch
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from PIL import Image
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from modelscope import dataset_snapshot_download
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from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
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vram_config = {
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"offload_dtype": "disk",
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"offload_device": "disk",
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"onload_dtype": torch.float8_e4m3fn,
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"onload_device": "cpu",
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"preparing_dtype": torch.float8_e4m3fn,
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"preparing_device": "cuda",
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"computation_dtype": torch.bfloat16,
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"computation_device": "cuda",
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}
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pipe = QwenImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
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ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors", **vram_config),
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ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors", **vram_config),
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],
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tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
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vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
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)
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dataset_snapshot_download(
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dataset_id="DiffSynth-Studio/example_image_dataset",
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local_dir="./data/example_image_dataset",
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allow_file_pattern="canny/*.jpg"
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)
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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。"
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controlnet_canny_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
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controlnet_inpaint_image = Image.open("./data/example_image_dataset/canny/image_2.jpg").convert("RGB").resize((1328, 1328))
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# generate a centered square mask
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inpaint_mask = Image.new("L", controlnet_inpaint_image.size, 0)
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mask_size = 512
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left = (controlnet_inpaint_image.width - mask_size) // 2
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top = (controlnet_inpaint_image.height - mask_size) // 2
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right = left + mask_size
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bottom = top + mask_size
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inpaint_mask.paste(255, (left, top, right, bottom))
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inpaint_mask = inpaint_mask.resize((1328, 1328)).convert("RGB")
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image = pipe(
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prompt, seed=0,
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input_image=controlnet_inpaint_image, inpaint_mask=inpaint_mask,
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blockwise_controlnet_inputs=[
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ControlNetInput(image=controlnet_inpaint_image, inpaint_mask=inpaint_mask, controlnet_id=0),
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ControlNetInput(image=controlnet_canny_image, controlnet_id=1),
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],
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num_inference_steps=40,
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)
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image.save("image.jpg")
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