[Docs] update inference demo markdown (#1921)

* updata inference demo markdown

* fix lint

* Empty-Commit

---------

Co-authored-by: Yanhong Zeng <zengyh1900@gmail.com>

docs/en/user_guides/inference.md

@@ -2,6 +2,387 @@
 
 MMagic provides Hign-level APIs for you to easily play with state-of-the-art models on your own images or videos.
 
-Please follow our [tutorials](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_tutorial.ipynb) to learn to use inference API `MMagicInferencer`. You can also use one line of command to run our demos [here](https://github.com/open-mmlab/mmagic/blob/main/demo/README.md).
+In the new API, only two lines of code are needed to implement inference:
+
+```python
+from mmagic.apis import MMagicInferencer
+
+# Create a MMagicInferencer instance
+editor = MMagicInferencer('pix2pix')
+# Infer a image. Input image path and output image path is needed.
+results = editor.infer(img='../resources/input/translation/gt_mask_0.png', result_out_dir='../resources/output/translation/tutorial_translation_pix2pix_res.jpg')
+```
+
+MMagic supports various fundamental generative models, including:
+
+unconditional Generative Adversarial Networks (GANs), conditional GANs, diffusion models, etc.
+
+MMagic also supports various applications, including:
+
+text-to-image, image-to-image translation, 3D-aware generation, image super-resolution, video super-resolution, video frame interpolation, image inpainting, image matting, image restoration, image colorization, image generation, etc.
+
+In this section, we will specify how to play with our pre-trained models.
+
+- [Tutorial 3: Inference with Pre-trained Models](#tutorial-3-inference-with-pre-trained-models)
+  - [Prepare some images or videos for inference](#Prepare-some-images-or-videos-for-inference)
+  - [Generative Models](#Generative-Models)
+    - [Unconditional Generative Adversarial Networks (GANs)](<#Unconditional-Generative-Adversarial-Networks-(GANs)>)
+    - [Conditional Generative Adversarial Networks (GANs)](<#Conditional-Generative-Adversarial-Networks-(GANs)>)
+    - [Diffusion Models](#Diffusion-Models)
+  - [Applications](#Applications)
+    - [Text-to-Image](#Text-to-Image)
+    - [Image-to-image translation](#Image-to-image-translation)
+    - [3D-aware generation](#3D-aware-generation)
+    - [Image super-resolution](#Image-super-resolution)
+    - [Video super-resolution](#Video-super-resolution)
+    - [Video frame interpolation](Video-frame-interpolation)
+    - [Image inpainting](#Image-inpainting)
+    - [Image matting](#Image-matting)
+    - [Image restoration](#Image-restoration)
+    - [Image colorization](#Image-colorization)
+- [Previous Versions](#Previous-Versions)
+
+## Prepare some images or videos for inference
+
+Please refer to our [tutorials](https://github.com/open-mmlab/mmagic/blob/main/demo/mmagic_inference_tutorial.ipynb) for details.
+
+## Generative Models
+
+### Unconditional Generative Adversarial Networks (GANs)
+
+MMagic provides high-level APIs for sampling images with unconditional GANs. Unconditional GAN models do not need input, and output a image. We take 'styleganv1' as an example.
+
+```python
+import mmcv
+import matplotlib.pyplot as plt
+from mmagic.apis import MMagicInferencer
+
+# Create a MMagicInferencer instance and infer
+result_out_dir = './resources/output/unconditional/tutorial_unconditional_styleganv1_res.png'
+editor = MMagicInferencer('styleganv1')
+results = editor.infer(result_out_dir=result_out_dir)
+```
+
+Indeed, we have already provided a more friendly demo script to users. You can use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name styleganv1 \
+        --result-out-dir demo_unconditional_styleganv1_res.jpg
+```
+
+### Conditional Generative Adversarial Networks (GANs)
+
+MMagic provides high-level APIs for sampling images with conditional GANs. Conditional GAN models take a label as input and output a image. We take 'biggan' as an example..
+
+```python
+import mmcv
+import matplotlib.pyplot as plt
+from mmagic.apis import MMagicInferencer
+
+# Create a MMagicInferencer instance and infer
+result_out_dir = './resources/output/conditional/tutorial_conditinal_biggan_res.jpg'
+editor = MMagicInferencer('biggan', model_setting=1)
+results = editor.infer(label=1, result_out_dir=result_out_dir)
+```
+
+Indeed, we have already provided a more friendly demo script to users. You can use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name biggan \
+        --model-setting 1 \
+        --label 1 \
+        --result-out-dir demo_conditional_biggan_res.jpg
+```
+
+### Diffusion Models
+
+MMagic provides high-level APIs for sampling images with diffusion models. f
+
+```python
+import mmcv
+import matplotlib.pyplot as plt
+from mmagic.apis import MMagicInferencer
+
+# Create a MMagicInferencer instance and infer
+editor = MMagicInferencer(model_name='stable_diffusion')
+text_prompts = 'A panda is having dinner at KFC'
+result_out_dir = './resources/output/text2image/tutorial_text2image_sd_res.png'
+editor.infer(text=text_prompts, result_out_dir=result_out_dir)
+```
+
+Use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name stable_diffusion \
+        --text "A panda is having dinner at KFC" \
+        --result-out-dir demo_text2image_stable_diffusion_res.png
+```
+
+## Applications
+
+### Text-to-Image
+
+Text-to-image models take text as input, and output a image. We take 'controlnet-canny' as an example.
+
+```python
+import cv2
+import numpy as np
+import mmcv
+from mmengine import Config
+from PIL import Image
+
+from mmagic.registry import MODELS
+from mmagic.utils import register_all_modules
+
+register_all_modules()
+
+cfg = Config.fromfile('configs/controlnet/controlnet-canny.py')
+controlnet = MODELS.build(cfg.model).cuda()
+
+control_url = 'https://user-images.githubusercontent.com/28132635/230288866-99603172-04cb-47b3-8adb-d1aa532d1d2c.jpg'
+control_img = mmcv.imread(control_url)
+control = cv2.Canny(control_img, 100, 200)
+control = control[:, :, None]
+control = np.concatenate([control] * 3, axis=2)
+control = Image.fromarray(control)
+
+prompt = 'Room with blue walls and a yellow ceiling.'
+
+output_dict = controlnet.infer(prompt, control=control)
+samples = output_dict['samples']
+```
+
+Use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name controlnet \
+        --model-setting 1 \
+        --text "Room with blue walls and a yellow ceiling." \
+        --control 'https://user-images.githubusercontent.com/28132635/230297033-4f5c32df-365c-4cf4-8e4f-1b76a4cbb0b7.png' \
+        --result-out-dir demo_text2image_controlnet_canny_res.png
+```
+
+### Image-to-image translation
+
+MMagic provides high-level APIs for translating images by using image translation models. Here is an example of building Pix2Pix and obtaining the translated images.
+
+```python
+import mmcv
+import matplotlib.pyplot as plt
+from mmagic.apis import MMagicInferencer
+
+# Create a MMagicInferencer instance and infer
+editor = MMagicInferencer('pix2pix')
+results = editor.infer(img=img_path, result_out_dir=result_out_dir)
+```
+
+Use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name pix2pix \
+        --img ${IMAGE_PATH} \
+        --result-out-dir ${SAVE_PATH}
+```
+
+### 3D-aware generation
+
+```python
+import mmcv
+import matplotlib.pyplot as plt
+from mmagic.apis import MMagicInferencer
+
+# Create a MMagicInferencer instance and infer
+result_out_dir = './resources/output/eg3d-output'
+editor = MMagicInferencer('eg3d')
+results = editor.infer(result_out_dir=result_out_dir)
+```
+
+Use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+    --model-name eg3d \
+    --result-out-dir ./resources/output/eg3d-output
+```
+
+### Image super-resolution
+
+Image super resolution models take a image as input, and output a high resolution image. We take 'esrgan' as an example.
+
+```python
+import mmcv
+import matplotlib.pyplot as plt
+from mmagic.apis import MMagicInferencer
+
+# Create a MMagicInferencer instance and infer
+img = './resources/input/restoration/0901x2.png'
+result_out_dir = './resources/output/restoration/tutorial_restoration_esrgan_res.png'
+editor = MMagicInferencer('esrgan')
+results = editor.infer(img=img, result_out_dir=result_out_dir)
+```
+
+Use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name esrgan \
+        --img ${IMAGE_PATH} \
+        --result-out-dir ${SAVE_PATH}
+```
+
+### Video super-resolution
+
+```python
+import os
+from mmagic.apis import MMagicInferencer
+from mmengine import mkdir_or_exist
+
+# Create a MMagicInferencer instance and infer
+video = './resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4'
+result_out_dir = './resources/output/video_super_resolution/tutorial_video_super_resolution_basicvsr_res.mp4'
+mkdir_or_exist(os.path.dirname(result_out_dir))
+editor = MMagicInferencer('basicvsr')
+results = editor.infer(video=video, result_out_dir=result_out_dir)
+```
+
+Use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name basicvsr \
+        --video ./resources/input/video_restoration/QUuC4vJs_000084_000094_400x320.mp4 \
+        --result-out-dir ./resources/output/video_restoration/demo_video_restoration_basicvsr_res.mp4
+```
+
+### Video frame interpolation
+
+Video interpolation models take a video as input, and output a interpolated video. We take 'flavr' as an example.
+
+```python
+import os
+from mmagic.apis import MMagicInferencer
+from mmengine import mkdir_or_exist
+
+# Create a MMagicInferencer instance and infer
+video = './resources/input/video_interpolation/b-3LLDhc4EU_000000_000010.mp4'
+result_out_dir = './resources/output/video_interpolation/tutorial_video_interpolation_flavr_res.mp4'
+mkdir_or_exist(os.path.dirname(result_out_dir))
+editor = MMagicInferencer('flavr')
+results = editor.infer(video=video, result_out_dir=result_out_dir)
+```
+
+Use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name flavr \
+        --video ${VIDEO_PATH} \
+        --result-out-dir ${SAVE_PATH}
+```
+
+### Image inpainting
+
+Inpaiting models take a masked image and mask pair as input, and output a inpainted image. We take 'global_local' as an example.
+
+```python
+import mmcv
+import matplotlib.pyplot as plt
+from mmagic.apis import MMagicInferencer
+
+img = './resources/input/inpainting/celeba_test.png'
+mask = './resources/input/inpainting/bbox_mask.png'
+
+# Create a MMagicInferencer instance and infer
+result_out_dir = './resources/output/inpainting/tutorial_inpainting_global_local_res.jpg'
+editor = MMagicInferencer('global_local', model_setting=1)
+results = editor.infer(img=img, mask=mask, result_out_dir=result_out_dir)
+```
+
+Use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name global_local  \
+        --img ./resources/input/inpainting/celeba_test.png \
+        --mask ./resources/input/inpainting/bbox_mask.png \
+        --result-out-dir ./resources/output/inpainting/demo_inpainting_global_local_res.jpg
+```
+
+### Image matting
+
+Inpaiting models take a image and trimap pair as input, and output a alpha image. We take 'gca' as an example.
+
+```python
+import mmcv
+import matplotlib.pyplot as plt
+from mmagic.apis import MMagicInferencer
+
+img = './resources/input/matting/GT05.jpg'
+trimap = './resources/input/matting/GT05_trimap.jpg'
+
+# Create a MMagicInferencer instance and infer
+result_out_dir = './resources/output/matting/tutorial_matting_gca_res.png'
+editor = MMagicInferencer('gca')
+results = editor.infer(img=img, trimap=trimap, result_out_dir=result_out_dir)
+```
+
+Use [demo/mmagic_inference_demo.py](../../../demo/mmagic_inference_demo.py) with the following commands:
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name gca  \
+        --img ./resources/input/matting/GT05.jpg \
+        --trimap ./resources/input/matting/GT05_trimap.jpg \
+        --result-out-dir ./resources/output/matting/demo_matting_gca_res.png
+```
+
+### Image restoration
+
+```python
+import mmcv
+import matplotlib.pyplot as plt
+from mmagic.apis import MMagicInferencer
+
+# Create a MMagicInferencer instance and infer
+img = './resources/input/restoration/0901x2.png'
+result_out_dir = './resources/output/restoration/tutorial_restoration_nafnet_res.png'
+editor = MMagicInferencer('nafnet')
+results = editor.infer(img=img, result_out_dir=result_out_dir)
+```
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name nafnet \
+        --img ./resources/input/restoration/0901x2.png \
+        --result-out-dir ./resources/output/restoration/demo_restoration_nafnet_res.png
+```
+
+### Image colorization
+
+```python
+import mmcv
+import matplotlib.pyplot as plt
+from mmagic.apis import MMagicInferencer
+
+# Create a MMagicInferencer instance and infer
+img = 'https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/245713512-de973677-2be8-4915-911f-fab90bb17c40.jpg'
+result_out_dir = './resources/output/colorization/tutorial_colorization_res.png'
+editor = MMagicInferencer('inst_colorization')
+results = editor.infer(img=img, result_out_dir=result_out_dir)
+```
+
+```shell
+python demo/mmagic_inference_demo.py \
+        --model-name inst_colorization \
+        --img https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/245713512-de973677-2be8-4915-911f-fab90bb17c40.jpg \
+        --result-out-dir demo_colorization_res.png
+```
+
+# Previous Versions
 
 If you want to use deprecated demos, please use [MMagic v1.0.0rc7](https://github.com/open-mmlab/mmagic/tree/v1.0.0rc7) and reference the [old tutorial](https://github.com/open-mmlab/mmagic/blob/v1.0.0rc7/docs/en/user_guides/inference.md).