Doc/qwen image

docs/tutorial.md

@@ -88,6 +88,52 @@ We will continuously update DiffSynth-Engine to support more models. (Wan2.2 LoR
 
 After the model is downloaded, load the model with the corresponding pipeline and perform inference.
 
+
+### Image Generation(Qwen-Image)
+
+The following code calls `QwenImagePipeline` to load the [Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) model and generate an image. Recommended resolutions are 928×1664, 1104×1472, 1328×1328, 1472×1104, and 1664×928, with a suggested cfg_scale of 4. If no negative_prompt is provided, it defaults to a single space character (not an empty string). For multi-GPU parallelism, currently only cfg parallelism is supported (parallelism=2), with other optimization efforts underway.
+
+```python
+from diffsynth_engine import fetch_model, QwenImagePipeline, QwenImagePipelineConfig
+
+config = QwenImagePipelineConfig.basic_config(
+    model_path=fetch_model("MusePublic/Qwen-image", revision="v1", path="transformer/*.safetensors"),
+    encoder_path=fetch_model("MusePublic/Qwen-image", revision="v1", path="text_encoder/*.safetensors"),
+    vae_path=fetch_model("MusePublic/Qwen-image", revision="v1", path="vae/*.safetensors"),
+    parallelism=2,
+)
+pipe = QwenImagePipeline.from_pretrained(config)
+
+prompt = """
+    一副典雅庄重的对联悬挂于厅堂之中，房间是个安静古典的中式布置，桌子上放着一些青花瓷，对联上左书“思涌如泉万类灵感皆可触”，右书“智启于问千机代码自天成”，横批“AI脑洞力”，字体飘逸灵动，兼具传统笔意与未来感。中间挂着一幅中国风的画作，内容是岳阳楼，云雾缭绕间似有数据流光隐现，古今交融，意境深远。
+    """
+negative_prompt = " "
+image = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    cfg_scale=4.0,
+    width=1104,
+    height=1472,
+    num_inference_steps=30,
+    seed=42,
+)
+image.save("image.png")
+```
+
+Please note that if some necessary modules, like text encoders, are missing from a model repository, the pipeline will automatically download the required files.
+
+#### Detailed Parameters(Qwen-Image)
+
+In the image generation pipeline `pipe`, we can use the following parameters for fine-grained control:
+
+* `prompt`: The prompt, used to describe the content of the generated image, It supports multiple languages (Chinese, English, Japanese, etc.), e.g., “一只猫” (Chinese), "a cat" (English), or "庭を走る猫" (Japanese).
+* `negative_prompt`: The negative prompt, used to describe content you do not want in the image, it defaults to a single space character (not an empty string), e.g., "ugly".
+* `cfg_scale`: The guidance scale for [Classifier-Free Guidance](https://arxiv.org/abs/2207.12598). A larger value usually results in stronger correlation between the text and the image but reduces the diversity of the generated content.
+* `height`: Image height.
+* `width`: Image width.
+* `num_inference_steps`: The number of inference steps. Generally, more steps lead to longer computation time but higher image quality.
+* `seed`: The random seed. A fixed seed ensures reproducible results.
+
 ### Image Generation
 
 The following code calls `FluxImagePipeline` to load the [MajicFlus](https://www.modelscope.cn/models/MAILAND/majicflus_v1/summary?version=v1.0) model and generate an image. To load other types of models, replace `FluxImagePipeline` and `FluxPipelineConfig` in the code with the corresponding pipeline and config.
@@ -109,16 +155,16 @@ Please note that if some necessary modules, like text encoders, are missing from
 
 In the image generation pipeline `pipe`, we can use the following parameters for fine-grained control:
 
-*   `prompt`: The prompt, used to describe the content of the generated image, e.g., "a cat".
-*   `negative_prompt`: The negative prompt, used to describe content you do not want in the image, e.g., "ugly".
-*   `cfg_scale`: The guidance scale for [Classifier-Free Guidance](https://arxiv.org/abs/2207.12598). A larger value usually results in stronger correlation between the text and the image but reduces the diversity of the generated content.
-*   `clip_skip`: The number of layers to skip in the [CLIP](https://arxiv.org/abs/2103.00020) text encoder. The more layers skipped, the lower the text-image correlation, but this can lead to interesting variations in the generated content.
-*   `input_image`: Input image, used for image-to-image generation.
-*   `denoising_strength`: The denoising strength. When set to 1, a full generation process is performed. When set to a value between 0 and 1, some information from the input image is preserved.
-*   `height`: Image height.
-*   `width`: Image width.
-*   `num_inference_steps`: The number of inference steps. Generally, more steps lead to longer computation time but higher image quality.
-*   `seed`: The random seed. A fixed seed ensures reproducible results.
+* `prompt`: The prompt, used to describe the content of the generated image, e.g., "a cat".
+* `negative_prompt`: The negative prompt, used to describe content you do not want in the image, e.g., "ugly".
+* `cfg_scale`: The guidance scale for [Classifier-Free Guidance](https://arxiv.org/abs/2207.12598). A larger value usually results in stronger correlation between the text and the image but reduces the diversity of the generated content.
+* `clip_skip`: The number of layers to skip in the [CLIP](https://arxiv.org/abs/2103.00020) text encoder. The more layers skipped, the lower the text-image correlation, but this can lead to interesting variations in the generated content.
+* `input_image`: Input image, used for image-to-image generation.
+* `denoising_strength`: The denoising strength. When set to 1, a full generation process is performed. When set to a value between 0 and 1, some information from the input image is preserved.
+* `height`: Image height.
+* `width`: Image width.
+* `num_inference_steps`: The number of inference steps. Generally, more steps lead to longer computation time but higher image quality.
+* `seed`: The random seed. A fixed seed ensures reproducible results.
 
 #### Loading LoRA
 
@@ -177,17 +223,17 @@ save_video(video, "video.mp4")
 
 In the video generation pipeline `pipe`, we can use the following parameters for fine-grained control:
 
-*   `prompt`: The prompt, used to describe the content of the generated video, e.g., "a cat".
-*   `negative_prompt`: The negative prompt, used to describe content you do not want in the video, e.g., "ugly".
-*   `cfg_scale`: The guidance scale for [Classifier-Free Guidance](https://arxiv.org/abs/2207.12598). A larger value usually results in stronger correlation between the text and the video but reduces the diversity of the generated content.
-*   `input_image`: Input image, only effective in image-to-video models, such as [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P).
-*   `input_video`: Input video, used for video-to-video generation.
-*   `denoising_strength`: The denoising strength. When set to 1, a full generation process is performed. When set to a value between 0 and 1, some information from the input video is preserved.
-*   `height`: Video frame height.
-*   `width`: Video frame width.
-*   `num_frames`: Number of video frames.
-*   `num_inference_steps`: The number of inference steps. Generally, more steps lead to longer computation time but higher video quality.
-*   `seed`: The random seed. A fixed seed ensures reproducible results.
+* `prompt`: The prompt, used to describe the content of the generated video, e.g., "a cat".
+* `negative_prompt`: The negative prompt, used to describe content you do not want in the video, e.g., "ugly".
+* `cfg_scale`: The guidance scale for [Classifier-Free Guidance](https://arxiv.org/abs/2207.12598). A larger value usually results in stronger correlation between the text and the video but reduces the diversity of the generated content.
+* `input_image`: Input image, only effective in image-to-video models, such as [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P).
+* `input_video`: Input video, used for video-to-video generation.
+* `denoising_strength`: The denoising strength. When set to 1, a full generation process is performed. When set to a value between 0 and 1, some information from the input video is preserved.
+* `height`: Video frame height.
+* `width`: Video frame width.
+* `num_frames`: Number of video frames.
+* `num_inference_steps`: The number of inference steps. Generally, more steps lead to longer computation time but higher video quality.
+* `seed`: The random seed. A fixed seed ensures reproducible results.
 
 #### Loading LoRA
 

docs/tutorial_zh.md

@@ -88,6 +88,51 @@ Diffusion 模型包含多种多样的模型结构，每种模型由对应的流
 
 模型下载完毕后，我们可以根据对应的模型类型选择流水线加载模型并进行推理。
 
+### 图像生成(Qwen-Image)
+
+以下代码可以调用 `QwenImagePipeline` 加载[Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)模型生成一张图。推荐分辨率为928×1664, 1104×1472, 1328×1328, 1472×1104, 1664×928，cfg_scale为4，如果没有negative_prompt默认为一个空格而不是空字符串。多卡并行目前支持cfg并行(parallelism=2)，其他优化工作正在进行中。
+
+```python
+from diffsynth_engine import fetch_model, QwenImagePipeline, QwenImagePipelineConfig
+
+config = QwenImagePipelineConfig.basic_config(
+    model_path=fetch_model("MusePublic/Qwen-image", revision="v1", path="transformer/*.safetensors"),
+    encoder_path=fetch_model("MusePublic/Qwen-image", revision="v1", path="text_encoder/*.safetensors"),
+    vae_path=fetch_model("MusePublic/Qwen-image", revision="v1", path="vae/*.safetensors"),
+    parallelism=2,
+)
+pipe = QwenImagePipeline.from_pretrained(config)
+
+prompt = """
+    一副典雅庄重的对联悬挂于厅堂之中，房间是个安静古典的中式布置，桌子上放着一些青花瓷，对联上左书“思涌如泉万类灵感皆可触”，右书“智启于问千机代码自天成”，横批“AI脑洞力”，字体飘逸灵动，兼具传统笔意与未来感。中间挂着一幅中国风的画作，内容是岳阳楼，云雾缭绕间似有数据流光隐现，古今交融，意境深远。
+    """
+negative_prompt = " "
+image = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    cfg_scale=4.0,
+    width=1104,
+    height=1472,
+    num_inference_steps=30,
+    seed=42,
+)
+image.save("image.png")
+```
+
+请注意，某些模型库中缺乏必要的文本编码器等模块，我们的代码会自动补充下载所需的模型文件。
+
+#### 详细参数(Qwen-Image)
+
+在图像生成流水线 `pipe` 中，我们可以通过以下参数进行精细的控制：
+
+* `prompt`: 提示词，用于描述生成图像的内容，支持多种语言(中文/英文/日文等)，例如“一只猫”/"a cat"/"庭を走る猫"。
+* `negative_prompt`: 负面提示词，用于描述不希望图像中出现的内容，例如“ugly”，默认为一个空格而不是空字符串， " "。
+* `cfg_scale`: [Classifier-free guidance](https://arxiv.org/abs/2207.12598) 的引导系数，通常更大的引导系数可以达到更强的文图相关性，但会降低生成内容的多样性，推荐值为4。
+* `height`: 图像高度。
+* `width`: 图像宽度。
+* `num_inference_steps`: 推理步数，通常推理步数越多，计算时间越长，图像质量越高。
+* `seed`: 随机种子，固定的随机种子可以使生成的内容固定。
+
 ### 图像生成
 
 以下代码可以调用 `FluxImagePipeline` 加载[麦橘超然](https://www.modelscope.cn/models/MAILAND/majicflus_v1/summary?version=v1.0)模型生成一张图。如果要加载其他结构的模型，请将代码中的 `FluxImagePipeline` 和 `FluxPipelineConfig` 替换成对应的流水线模块及配置。
@@ -110,15 +155,15 @@ image.save("image.png")
 在图像生成流水线 `pipe` 中，我们可以通过以下参数进行精细的控制：
 
 * `prompt`: 提示词，用于描述生成图像的内容，例如“a cat”。
-* `negative_prompt`：负面提示词，用于描述不希望图像中出现的内容，例如“ugly”。
-* `cfg_scale`：[Classifier-free guidance](https://arxiv.org/abs/2207.12598) 的引导系数，通常更大的引导系数可以达到更强的文图相关性，但会降低生成内容的多样性。
-* `clip_skip`：跳过 [CLIP](https://arxiv.org/abs/2103.00020) 文本编码器的层数，跳过的层数越多，生成的图像与文本的相关性越低，但生成的图像内容可能会出现奇妙的变化。
-* `input_image`：输入图像，用于图生图。
-* `denoising_strength`：去噪力度，当设置为 1 时，执行完整的生成过程，当设置为 0 到 1 之间的值时，会保留输入图像中的部分信息。
-* `height`：图像高度。
-* `width`：图像宽度。
-* `num_inference_steps`：推理步数，通常推理步数越多，计算时间越长，图像质量越高。
-* `seed`：随机种子，固定的随机种子可以使生成的内容固定。
+* `negative_prompt`: 负面提示词，用于描述不希望图像中出现的内容，例如“ugly”。
+* `cfg_scale`: [Classifier-free guidance](https://arxiv.org/abs/2207.12598) 的引导系数，通常更大的引导系数可以达到更强的文图相关性，但会降低生成内容的多样性。
+* `clip_skip`: 跳过 [CLIP](https://arxiv.org/abs/2103.00020) 文本编码器的层数，跳过的层数越多，生成的图像与文本的相关性越低，但生成的图像内容可能会出现奇妙的变化。
+* `input_image`: 输入图像，用于图生图。
+* `denoising_strength`: 去噪力度，当设置为 1 时，执行完整的生成过程，当设置为 0 到 1 之间的值时，会保留输入图像中的部分信息。
+* `height`: 图像高度。
+* `width`: 图像宽度。
+* `num_inference_steps`: 推理步数，通常推理步数越多，计算时间越长，图像质量越高。
+* `seed`: 随机种子，固定的随机种子可以使生成的内容固定。
 
 #### LoRA 加载
 
@@ -175,16 +220,16 @@ save_video(video, "video.mp4")
 在视频生成流水线 `pipe` 中，我们可以通过以下参数进行精细的控制：
 
 * `prompt`: 提示词，用于描述生成图像的内容，例如“a cat”。
-* `negative_prompt`：负面提示词，用于描述不希望图像中出现的内容，例如“ugly”。
-* `cfg_scale`：[Classifier-free guidance](https://arxiv.org/abs/2207.12598) 的引导系数，通常更大的引导系数可以达到更强的文图相关性，但会降低生成内容的多样性。
-* `input_image`：输入图像，只在图生视频模型中有效，例如 [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)。
-* `input_video`：输入视频，用于视频生视频。
-* `denoising_strength`：去噪力度，当设置为 1 时，执行完整的生成过程，当设置为 0 到 1 之间的值时，会保留输入视频中的部分信息。
-* `height`：视频帧高度。
-* `width`：视频帧宽度。
-* `num_frames`：视频帧数。
-* `num_inference_steps`：推理步数，通常推理步数越多，计算时间越长，图像质量越高。
-* `seed`：随机种子，固定的随机种子可以使生成的内容固定。
+* `negative_prompt`: 负面提示词，用于描述不希望图像中出现的内容，例如“ugly”。
+* `cfg_scale`: [Classifier-free guidance](https://arxiv.org/abs/2207.12598) 的引导系数，通常更大的引导系数可以达到更强的文图相关性，但会降低生成内容的多样性。
+* `input_image`: 输入图像，只在图生视频模型中有效，例如 [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)。
+* `input_video`: 输入视频，用于视频生视频。
+* `denoising_strength`: 去噪力度，当设置为 1 时，执行完整的生成过程，当设置为 0 到 1 之间的值时，会保留输入视频中的部分信息。
+* `height`: 视频帧高度。
+* `width`: 视频帧宽度。
+* `num_frames`: 视频帧数。
+* `num_inference_steps`: 推理步数，通常推理步数越多，计算时间越长，图像质量越高。
+* `seed`: 随机种子，固定的随机种子可以使生成的内容固定。
 
 #### LoRA 加载