Update app.py

app.py

@@ -1,146 +1,107 @@
 import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
-
+import torch, random, time
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, EulerDiscreteScheduler
 device = "cuda" if torch.cuda.is_available() else "cpu"
-
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
+translations = {
+	'en': {
+		'model': 'Model Path',
+		'loading': 'Loading',
+		'input': 'Input Image',
+		'prompt': 'Prompt',
+		'negative_prompt': 'Negative Prompt',
+		'generate': 'Generate',
+		'strength': 'Strength',
+		'guidance_scale': 'Guidance Scale',
+		'num_inference_steps': 'Number of Inference Steps',
+		'width': 'Width',
+		'height': 'Height',
+		'seed': 'Seed',
+	},
+	'zh': {
+		'model': '模型路径',
+		'loading': '载入',
+		'input': '输入图像',
+		'prompt': '提示',
+		'negative_prompt': '负面提示',
+		'generate': '生成',
+		'strength': '强度',
+		'guidance_scale': '指导尺度',
+		'num_inference_steps': '推理步数',
+		'width': '宽度',
+		'height': '高度',
+		'seed': '种子',
+	}
 }
-"""
-
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = 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",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
-    )
+language='zh'
+def generate_new_seed():
+	return random.randint(1, 2147483647)
+def update_language(new_language):
+	return [
+		gr.Textbox.update(placeholder=translations[new_language]['model']),
+		gr.Button.update(value=translations[new_language]['loading']),
+		gr.Image.update(label=translations[new_language]['input']),
+		gr.Textbox.update(placeholder=translations[new_language]['prompt']),
+		gr.Textbox.update(placeholder=translations[new_language]['negative_prompt']),
+		gr.Button.update(value=translations[new_language]['generate']),
+		gr.Slider.update(label=translations[new_language]['strength']),
+		gr.Slider.update(label=translations[new_language]['guidance_scale']),
+		gr.Slider.update(label=translations[new_language]['num_inference_steps']),
+		gr.Slider.update(label=translations[new_language]['width']),
+		gr.Slider.update(label=translations[new_language]['height']),
+		gr.Number.update(label=translations[new_language]['seed'])
+	]
 
-demo.queue().launch()
+text2img = None
+img2img = None
+def Generate(image_input, prompt, negative_prompt, strength, guidance_scale, num_inference_steps, width, height, seed):
+	if seed == -1:
+		seed = generate_new_seed()
+	generator = torch.Generator(device).manual_seed(int(seed))
+	global text2img, img2img
+	start_time = time.time()
+	if image_input is None:
+		image = text2img(prompt=prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, width=width, height=height, num_images_per_prompt=1, generator=generator).images[0]
+	else:
+		image = img2img(image=image_input, strength=0.75, prompt=prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, width=width, height=height, num_images_per_prompt=1, generator=generator).images[0]
+	minutes, seconds = divmod(round(time.time() - start_time), 60)
+	return image, f"{minutes:02d}:{seconds:02d}"
+def Loading(model):
+	global text2img, img2img
+	text2img = StableDiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16).to(device)
+	text2img.safety_checker = None
+	text2img.scheduler = EulerDiscreteScheduler.from_config(text2img.scheduler.config)
+	text2img.enable_xformers_memory_efficient_attention()
+	text2img.vae.enable_xformers_memory_efficient_attention()
+	img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
+	return model
+with gr.Blocks() as demo:
+	with gr.Row():
+		model = gr.Textbox(value="John6666/pony-realism-v21main-sdxl", label=translations[language]['model'])
+		loading = gr.Button(translations[language]['loading'])
+		set_language = gr.Dropdown(list(translations.keys()), label="Language", value=language)
+	with gr.Row():
+		with gr.Column():
+			with gr.Row():
+				image_input = gr.Image(label=translations[language]['input'])
+				with gr.Column():
+					prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder=translations[language]['prompt'], show_label=False, lines=3)
+					negative_prompt = gr.Textbox("deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation,lowres,jpeg artifacts,username,logo,signature,watermark,monochrome,greyscale", placeholder=translations[language]['negative_prompt'], show_label=False, lines=3)
+			generate = gr.Button(translations[language]['generate'])
+			with gr.Row():
+				with gr.Column():
+					strength = gr.Slider(minimum=0, maximum=1, value=0.8, step=0.01, label=translations[language]['strength'])
+					guidance_scale = gr.Slider(minimum=1, maximum=15, value=7.5, step=0.5, label=translations[language]['guidance_scale'])
+					num_inference_steps = gr.Slider(minimum=1, maximum=100, value=50, step=1, label=translations[language]['num_inference_steps'])
+					width = gr.Slider(minimum=512, maximum=2048, value=512, step=8, label=translations[language]['width'])
+					height = gr.Slider(minimum=512, maximum=2048, value=512, step=8, label=translations[language]['height'])
+					with gr.Row():
+						seed = gr.Number(value=-1, label=translations[language]['seed'])
+						set_seed = gr.Button("🎲")
+		with gr.Column():
+			image_output = gr.Image()
+			text_output = gr.Textbox(label="time")
+	set_seed.click(generate_new_seed, None, seed)
+	generate.click(Generate, [image_input, prompt, negative_prompt, strength, guidance_scale, num_inference_steps, width, height, seed], [image_output, text_output])
+	loading.click(Loading, model, model)
+	set_language.change(update_language, set_language, [model, loading, image_input, prompt, negative_prompt, generate, strength, guidance_scale, num_inference_steps, width, height, seed])
+demo.queue(concurrency_count=24, max_size=32).launch(max_threads=128)