mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
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from diffsynth.models.qwen_image_dit import RMSNorm
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from diffsynth.vram_management.layers import enable_vram_management, AutoWrappedLinear, AutoWrappedModule
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import torch
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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", offload_dtype=torch.float8_e4m3fn),
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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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],
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tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
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)
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enable_vram_management(
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pipe.dit,
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module_map = {
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RMSNorm: AutoWrappedModule,
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},
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module_config = dict(
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offload_dtype=torch.bfloat16,
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offload_device="cuda",
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onload_dtype=torch.bfloat16,
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onload_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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vram_limit=None,
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)
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enable_vram_management(
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pipe.dit,
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module_map = {
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torch.nn.Linear: AutoWrappedLinear,
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},
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module_config = dict(
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offload_dtype=torch.float8_e4m3fn,
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offload_device="cuda",
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onload_dtype=torch.float8_e4m3fn,
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onload_device="cuda",
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computation_dtype=torch.float8_e4m3fn,
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computation_device="cuda",
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),
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vram_limit=None,
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)
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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 = pipe(prompt, seed=0, num_inference_steps=40, enable_fp8_attention=True)
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image.save("image.jpg")
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