refactor: change structure

app.py

@@ -7,10 +7,10 @@
 """
 import gradio as gr
 
-from unconditional_diffusion_inference import inference_unconditional
-from class_guidance_inference import inference_class_guidance, GENDER_CHOICES
-from stable_diffusion_inference import inference_sd, MAX_SEED
-from inference_api import inference
+from inference_models.unconditional_diffusion_inference import inference_unconditional
+from inference_models.class_guidance_inference import inference_class_guidance, GENDER_CHOICES
+from inference_models.stable_diffusion_inference import inference_sd, MAX_SEED
+from inference_api import api_unconditional, api_class_guidance, api_sd
 from utils import timer
 
 MAX_IMAGE_SIZE = 1024
@@ -24,11 +24,16 @@ examples = [
 ]
 
 css = """
+body {
+    background: linear-gradient(135deg, #f9e2e6 0%, #e8f3fc 50%, #e2f9f2 100%);
+    min-height: 100vh;
+}
 .gradio-container {
     background-image: url('https://lh3.googleusercontent.com/d/1y7H4WIjnBAcNvvi-3qOV_ORE-jMXP4fr');
     background-repeat: no-repeat;
     background-attachment: fixed;
     background-position: center;
+    background-size: contain;
 }
 #col-container {
     margin: 0 auto;
@@ -209,12 +214,12 @@ with gr.Blocks(theme=theme, css=css) as demo:
                     value=50,
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt], outputs=[result_3], fn=inference_sd,
+        gr.Examples(examples=examples, inputs=[prompt], outputs=[result_3], fn=api_sd,
                     cache_examples=True, cache_mode="lazy")
 
     gr.on(
         triggers=[run_button_1.click],
-        fn=inference_unconditional,
+        fn=api_unconditional,
         inputs=[
             seed_1,
             randomize_seed_1,
@@ -225,7 +230,7 @@ with gr.Blocks(theme=theme, css=css) as demo:
 
     gr.on(
         triggers=[run_button_2.click],
-        fn=inference_class_guidance,
+        fn=api_class_guidance,
         inputs=[
             gender_select_radio,
             seed_2,
@@ -237,7 +242,7 @@ with gr.Blocks(theme=theme, css=css) as demo:
 
     gr.on(
         triggers=[run_button_3.click, prompt.submit],
-        fn=inference_sd,
+        fn=api_sd,
         inputs=[
             prompt,
             negative_prompt,

inference_api.py

@@ -10,12 +10,42 @@ from gradio_client import Client
 inference_url = "https://a6e7d20f9bf8c7fb66.gradio.live/"
 
 
-def inference(prompt,
-              negative_prompt="",
-              seed=0,
-              randomize_seed=False,
-              guidance_scale=7.5,
-              num_inference_steps=20):
+def api_unconditional(seed,
+                      randomize_seed=False,
+                      num_inference_steps=20):
+    client = Client(inference_url)
+
+    result = client.predict(
+        seed=seed,
+        randomize_seed=randomize_seed,
+        num_inference_steps=num_inference_steps,
+        api_name="/inference_unconditional"
+    )
+    return result
+
+
+def api_class_guidance(label_name,
+                       seed=0,
+                       randomize_seed=False,
+                       num_inference_steps=20):
+    client = Client(inference_url)
+
+    result = client.predict(
+        label_name=label_name,
+        seed=seed,
+        randomize_seed=randomize_seed,
+        num_inference_steps=num_inference_steps,
+        api_name="/inference_class_guidance"
+    )
+    return result
+
+
+def api_sd(prompt,
+           negative_prompt="",
+           seed=0,
+           randomize_seed=False,
+           guidance_scale=7.5,
+           num_inference_steps=20):
     client = Client(inference_url)
 
     result = client.predict(
@@ -25,6 +55,6 @@ def inference(prompt,
         randomize_seed=randomize_seed,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
-        api_name="/inference"
+        api_name="/inference_sd"
     )
     return result

inference_models/__init__.py

@@ -0,0 +1,7 @@
+# -*- coding: UTF-8 -*-
+"""
+@Time : 30/05/2025 23:56
+@Author : xiaoguangliang
+@File : __init__.py.py
+@Project : Faice_text2face
+"""

class_guidance_inference.py → inference_models/class_guidance_inference.py

@@ -8,7 +8,7 @@
 import torch
 import random
 import numpy as np
-from ccddpm_pipeline import CCDDPMPipeline
+from inference_models.ccddpm_pipeline import CCDDPMPipeline
 from accelerate import Accelerator
 import gradio as gr
 import spaces

local_app.py

@@ -0,0 +1,254 @@
+# -*- coding: UTF-8 -*-
+"""
+@Time : 28/05/2025 16:29
+@Author : xiaoguangliang
+@File : app.py
+@Project : Faice_text2face
+"""
+import gradio as gr
+
+from inference_models.unconditional_diffusion_inference import inference_unconditional
+from inference_models.class_guidance_inference import inference_class_guidance, GENDER_CHOICES
+from inference_models.stable_diffusion_inference import inference_sd, MAX_SEED
+from utils import timer
+
+MAX_IMAGE_SIZE = 1024
+
+examples = [
+    "Portrait of a young woman with long wavy hair, soft studio lighting, high contrast, 4k resolution, professional headshot",
+    "Close-up of a smiling man with sharp jawline, cinematic lighting, shallow depth of field, bokeh background",
+    "Candid portrait, natural light, slight smile, outdoor background, wind-blown hair",
+    "Retro 80s style portrait, neon colors, grainy texture, bold shadows, high contrast",
+    "Black and white portrait of an elderly woman with wrinkles, deep shadows, textured background"
+]
+
+css = """
+.gradio-container {
+    background-image: url('https://lh3.googleusercontent.com/d/1y7H4WIjnBAcNvvi-3qOV_ORE-jMXP4fr');
+    background-repeat: no-repeat;
+    background-attachment: fixed;
+    background-position: center;
+}
+#col-container {
+    margin: 0 auto;
+    max-width: 960px;
+    background-color: rgba(255, 255, 255, 0.85);
+    border-radius: 20px;
+    box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
+    padding: 24px;
+    backdrop-filter: blur(20px);
+}
+.gr-button-primary {
+    background: linear-gradient(90deg, #6b9dfc, #8c6bfc) !important;
+    border: none !important;
+    transition: all 0.3s ease;
+}
+.gr-button-primary:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 5px 15px rgba(108, 99, 255, 0.3);
+}
+.gr-form {
+    border-radius: 12px;
+    background-color: rgba(255, 255, 255, 0.7);
+}
+.gr-accordion {
+    border-radius: 12px;
+    overflow: hidden;
+}
+h1 {
+    background: linear-gradient(90deg, #6b9dfc, #8c6bfc);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    font-weight: 800;
+}
+"""
+
+theme = gr.themes.Ocean(
+    primary_hue="fuchsia",
+)
+
+with gr.Blocks(theme=theme, css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML("""
+                <div align="center" style="margin-bottom: 20px;">
+                    <img src='https://lh3.googleusercontent.com/d/1dmZMaOhZxcC_93Rc0ReYTB9oQEUuFJFu' width="160">
+                    <p style="font-size: 16px; max-width: 960px; margin: 5px auto;">
+                        Human Faces Generation with Diffusion Models.
+                    </p>
+                    <p style="font-size: 15px;">
+                        📜 <a href="https://github.com/frankcholula/faice/blob/main/paper/human_faces_generation_with_diffusion_models.pdf" target="_blank">Report</a>
+                        &nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;
+                        💻 <a href="https://github.com/frankcholula/faice" target="_blank">Code</a>
+                    </p>
+                </div>
+                """)
+
+        gr.Markdown("---")
+        with gr.Row():
+            gr.Markdown("## Part 1. Unconditional Face Generation")
+            run_button_1 = gr.Button("Run", scale=0, variant="primary", elem_classes="gr-button-primary")
+
+        result_1 = gr.Image(label="Result", show_label=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+            seed_1 = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+
+            randomize_seed_1 = gr.Checkbox(label="Randomize seed", value=True)
+
+            with gr.Row():
+                num_inference_steps_1 = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=20,
+                    maximum=1000,
+                    step=1,
+                    value=100,
+                )
+
+        gr.Markdown("---")
+        with gr.Row():
+            gr.Markdown("## Part 2. Class Guidance Face Generation")
+            run_button_2 = gr.Button("Run", scale=0, variant="primary")
+
+        gender_select_radio = gr.Radio(
+            label="Select Gender",
+            choices=GENDER_CHOICES,
+            value=GENDER_CHOICES[0],
+        )
+        result_2 = gr.Image(label="Result", show_label=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+            seed_2 = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+
+            randomize_seed_2 = gr.Checkbox(label="Randomize seed", value=True)
+
+            with gr.Row():
+                num_inference_steps_2 = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=20,
+                    maximum=1000,
+                    step=1,
+                    value=100,
+                )
+
+        gr.Markdown("---")
+        gr.Markdown("## Part 3. Text-to-Face Generation")
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+
+            run_button_3 = gr.Button("Run", scale=0, variant="primary")
+
+        result_3 = gr.Image(label="Result", show_label=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+            )
+
+            seed_3 = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+
+            randomize_seed_3 = gr.Checkbox(label="Randomize seed", value=True)
+
+            # with gr.Row():
+            #     width = gr.Slider(
+            #         label="Width",
+            #         minimum=512,
+            #         maximum=MAX_IMAGE_SIZE,
+            #         step=32,
+            #         value=1024,
+            #     )
+            #
+            #     height = gr.Slider(
+            #         label="Height",
+            #         minimum=512,
+            #         maximum=MAX_IMAGE_SIZE,
+            #         step=32,
+            #         value=1024,
+            #     )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=7.5,
+                    step=0.1,
+                    value=7.5,
+                )
+
+                num_inference_steps_3 = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=100,
+                    step=1,
+                    value=50,
+                )
+
+        gr.Examples(examples=examples, inputs=[prompt], outputs=[result_3], fn=inference_sd,
+                    cache_examples=True, cache_mode="lazy")
+
+    gr.on(
+        triggers=[run_button_1.click],
+        fn=inference_unconditional,
+        inputs=[
+            seed_1,
+            randomize_seed_1,
+            num_inference_steps_1,
+        ],
+        outputs=[result_1],
+    )
+
+    gr.on(
+        triggers=[run_button_2.click],
+        fn=inference_class_guidance,
+        inputs=[
+            gender_select_radio,
+            seed_2,
+            randomize_seed_2,
+            num_inference_steps_2,
+        ],
+        outputs=[result_2],
+    )
+
+    gr.on(
+        triggers=[run_button_3.click, prompt.submit],
+        fn=inference_sd,
+        inputs=[
+            prompt,
+            negative_prompt,
+            seed_3,
+            randomize_seed_3,
+            guidance_scale,
+            num_inference_steps_3,
+        ],
+        outputs=[result_3],
+    )
+
+if __name__ == "__main__":
+    with timer("All tasks"):
+        # demo.launch(mcp_server=True)
+        demo.launch(share=True, allowed_paths=["./"])

test/test_inference.py

@@ -5,7 +5,7 @@
 @File : test_inference.py
 @Project : Faice_text2face
 """
-from stable_diffusion_inference import inference_sd
+from inference_models.stable_diffusion_inference import inference_sd
 from utils import timer
 
 prompt = "Portrait of a young woman with long wavy hair, soft studio lighting, high contrast, 4k resolution, professional headshot"