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88 lines
2.2 KiB
Markdown
88 lines
2.2 KiB
Markdown
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# 基于Flux的文生图示例
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以下是如何使用FLUX.1模型进行文生图任务的示例。该脚本提供了一个简单的设置，用于从文本描述生成图像。包括下载必要的模型、配置pipeline，以及在启用和禁用 classifier-free guidance 的情况下生成图像。
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其他 DiffSynth 支持的模型详见 [模型.md](模型.md)
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## 准备
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首先，确保已下载并配置了必要的模型：
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```python
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import torch
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from diffsynth import ModelManager, FluxImagePipeline, download_models
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# Download the FLUX.1-dev model files
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download_models(["FLUX.1-dev"])
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```
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下载模型的用法详见 [下载模型.md](下载模型.md)
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## 加载模型
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使用您的设备和数据类型初始化模型管理器
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```python
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model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda")
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model_manager.load_models([
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"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
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"models/FLUX/FLUX.1-dev/text_encoder_2",
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"models/FLUX/FLUX.1-dev/ae.safetensors",
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"models/FLUX/FLUX.1-dev/flux1-dev.safetensors"
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])
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```
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模型加载的用法详见 [ModelManager.md](ModelManager.md)
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## 创建 Pipeline
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从加载的模型管理器中创建FluxImagePipeline实例：
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```python
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pipe = FluxImagePipeline.from_model_manager(model_manager)
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```
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Pipeline 的用法详见 [Pipeline.md](Pipeline.md)
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## 文生图
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使用简短的提示语生成图像。以下是启用和禁用 classifier-free guidance 的图像生成示例。
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### 基础文生图
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```python
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prompt = "A cute little turtle"
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negative_prompt = ""
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torch.manual_seed(6)
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image = pipe(
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prompt=prompt,
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num_inference_steps=30, embedded_guidance=3.5
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)
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image.save("image_1024.jpg")
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```
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### 使用 Classifier-Free Guidance 生成
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```python
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torch.manual_seed(6)
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image = pipe(
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prompt=prompt, negative_prompt=negative_prompt,
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num_inference_steps=30, cfg_scale=2.0, embedded_guidance=3.5
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)
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image.save("image_1024_cfg.jpg")
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```
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### 高分辨率修复
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```python
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torch.manual_seed(7)
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image = pipe(
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prompt=prompt,
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num_inference_steps=30, embedded_guidance=3.5,
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input_image=image.resize((2048, 2048)), height=2048, width=2048, denoising_strength=0.6, tiled=True
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)
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image.save("image_2048_highres.jpg")
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```
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