fix dat english part 2

because boy was it tragic

turns out all the geniuses behind these huge spaces are foreign

app.py

@@ -192,21 +192,21 @@ def get_example():
         [
             "./examples/musk_resize.jpeg",
             "./examples/poses/pose2.jpg",
-            "a man flying in the sky in Mars",
+            "a man flying through the sky on mars",
             "Mars",
             "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
         ],
         [
             "./examples/sam_resize.png",
             "./examples/poses/pose4.jpg",
-            "a man doing a silly pose wearing a suite",
+            "a man doing a silly pose wearing a suit",
             "Jungle",
             "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, gree",
         ],
         [
             "./examples/schmidhuber_resize.png",
             "./examples/poses/pose3.jpg",
-            "a man sit on a chair",
+            "a man sitting in a chair",
             "Neon",
             "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
         ],
@@ -433,11 +433,11 @@ title = r"""
 description = r"""
 <b>Official 🤗 Gradio demo</b> for <a href='https://github.com/InstantID/InstantID' target='_blank'><b>InstantID: Zero-shot Identity-Preserving Generation in Seconds</b></a>.<br>
 
-We are organizing a Spring Festival event with HuggingFace from 2.7 to 2.25, and you can now generate pictures of Spring Festival costumes. Happy Dragon Year 🐲 ! Share the joy with your family.<br>
+We are organizing a Spring Festival event with HuggingFace from February 7 to February 25. You can now generate pictures of Spring Festival costumes. Happy Dragon Year 🐲! Share the joy with your family.<br>
 
 How to use:<br>
-1. Upload an image with a face. For images with multiple faces, we will only detect the largest face. Ensure the face is not too small and is clearly visible without significant obstructions or blurring.
-2. (Optional) You can upload another image as a reference for the face pose. If you don't, we will use the first detected face image to extract facial landmarks. If you use a cropped face at step 1, it is recommended to upload it to define a new face pose.
+1. Upload an image with a face. For images with multiple faces, we will only detect the largest face. Ensure the face is clearly visible without significant obstructions or blurring.
+2. (Optional) You can upload another image as a reference for the face pose. If you don't, we will use the first detected face image to extract facial landmarks. If you used a cropped face in step 1, it is recommended to upload it to define a new face pose.
 3. (Optional) You can select multiple ControlNet models to control the generation process. The default is to use the IdentityNet only. The ControlNet models include pose skeleton, canny, and depth. You can adjust the strength of each ControlNet model to control the generation process.
 4. Enter a text prompt, as done in normal text-to-image models.
 5. Click the <b>Submit</b> button to begin customization.
@@ -447,7 +447,7 @@ article = r"""
 ---
 📝 **Citation**
 <br>
-If our work is helpful for your research or applications, please cite us via:
+If our work is helpful to you for your research or applications, please cite us via:
 ```bibtex
 @article{wang2024instantid,
   title={InstantID: Zero-shot Identity-Preserving Generation in Seconds},
@@ -458,15 +458,15 @@ If our work is helpful for your research or applications, please cite us via:
 ```
 📧 **Contact**
 <br>
-If you have any questions, please feel free to open an issue or directly reach us out at <b>haofanwang.ai@gmail.com</b>.
+If you have any questions, please feel free to open an issue or reach out to us directly at <b>haofanwang.ai@gmail.com</b>.
 """
 
 tips = r"""
-### Usage tips of InstantID
-1. If you're not satisfied with the similarity, try increasing the weight of "IdentityNet Strength" and "Adapter Strength."    
+### Usage Tips for InstantID
+1. If you're not satisfied with the similarity, try increasing the weight of "IdentityNet Strength" and "Adapter Strength."
 2. If you feel that the saturation is too high, first decrease the Adapter strength. If it remains too high, then decrease the IdentityNet strength.
-3. If you find that text control is not as expected, decrease Adapter strength.
-4. If you find that realistic style is not good enough, go for our Github repo and use a more realistic base model.
+3. If text control is not as expected, decrease the Adapter strength.
+4. If the realistic style is not good enough, visit our GitHub repo and use a more realistic base model.
 """
 
 css = """
@@ -493,7 +493,7 @@ with gr.Blocks(css=css) as demo:
             # prompt
             prompt = gr.Textbox(
                 label="Prompt",
-                info="Give simple prompt is enough to achieve good face fidelity",
+                info="Give a simple prompt in order to achieve good face fidelity.",
                 placeholder="A photo of a person",
                 value="",
             )
@@ -501,7 +501,7 @@ with gr.Blocks(css=css) as demo:
             submit = gr.Button("Submit", variant="primary")
             enable_LCM = gr.Checkbox(
                 label="Enable Fast Inference with LCM", value=enable_lcm_arg,
-                info="LCM speeds up the inference step, the trade-off is the quality of the generated image. It performs better with portrait face images rather than distant faces",
+                info="LCM speeds up the inference step, but the trade-off is the quality of the generated image. It performs better with portrait face images rather than distant faces.",
             )
             style = gr.Dropdown(
                 label="Style template",
@@ -527,7 +527,7 @@ with gr.Blocks(css=css) as demo:
             with gr.Accordion("Controlnet"):
                 controlnet_selection = gr.CheckboxGroup(
                     ["pose", "canny", "depth"], label="Controlnet", value=["pose"],
-                    info="Use pose for skeleton inference, canny for edge detection, and depth for depth map estimation. You can try all three to control the generation process"
+                    info="Use pose for skeleton inference, canny for edge detection, and depth for depth map estimation. You can try all three to control the generation process."
                 )
                 pose_strength = gr.Slider(
                     label="Pose strength",