mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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39 lines
1.7 KiB
Python
39 lines
1.7 KiB
Python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
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from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
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from diffsynth import load_state_dict
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import torch
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from PIL import Image
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from diffsynth.controlnets.processors import Annotator
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import os
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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"),
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
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ModelConfig(path="models/DiffSynth-Studio/BlockWiseControlnet/model_init.safetensors"),
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],
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tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
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)
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state_dict = load_state_dict("models/train/Qwen-Image-BlockWiseControlNet_full_lr1e-3_wd1e-6/step-26000.safetensors")
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pipe.blockwise_controlnet.load_state_dict(state_dict)
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prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
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image = Image.open("test_image.jpg").convert("RGB").resize((1024, 1024))
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canny_image = Annotator("canny")(image)
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canny_image.save("canny_image_test.jpg")
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controlnet_input = ControlNetInput(
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image=canny_image,
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scale=1.0,
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processor_id="canny",
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)
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for seed in range(100, 200):
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image = pipe(prompt, seed=seed, height=1024, width=1024, controlnet_inputs=[controlnet_input], num_inference_steps=30, cfg_scale=4.0)
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image.save(f"test_image_controlnet_step2k_1_{seed}.jpg")
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