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Update app.py

311c4d3 verified 4 months ago

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import gradio as gr
import numpy as np
import random

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    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, seed


examples = [
    "一只猫在吃猫粮,细节丰富,8k",
    "一个宇航员在天上",
    "一个猫咪在玩耍",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # 文本转图片生成")

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="输入提示词",
                container=False,
            )

            run_button = gr.Button("生成", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("高级设置", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="输入提示词",
                visible=False,
            )

            seed = gr.Slider(
                label="种子",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="随机种子", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="宽度",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="高度",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="精细程度",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="步骤推理数量",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=2,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()

	import gradio as gr
	import numpy as np
	import random

	# import spaces #[uncomment to use ZeroGPU]
	from diffusers import DiffusionPipeline
	import torch

	device = "cuda" if torch.cuda.is_available() else "cpu"
	model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use

	if torch.cuda.is_available():
	torch_dtype = torch.float16
	else:
	torch_dtype = torch.float32

	pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
	pipe = pipe.to(device)

	MAX_SEED = np.iinfo(np.int32).max
	MAX_IMAGE_SIZE = 1024


	# @spaces.GPU #[uncomment to use ZeroGPU]
	def infer(
	prompt,
	negative_prompt,
	seed,
	randomize_seed,
	width,
	height,
	guidance_scale,
	num_inference_steps,
	progress=gr.Progress(track_tqdm=True),
	):
	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, seed


	examples = [
	"一只猫在吃猫粮,细节丰富,8k",
	"一个宇航员在天上",
	"一个猫咪在玩耍",
	]

	css = """
	#col-container {
	margin: 0 auto;
	max-width: 640px;
	}
	"""

	with gr.Blocks(css=css) as demo:
	with gr.Column(elem_id="col-container"):
	gr.Markdown(" # 文本转图片生成")

	with gr.Row():
	prompt = gr.Text(
	label="Prompt",
	show_label=False,
	max_lines=1,
	placeholder="输入提示词",
	container=False,
	)

	run_button = gr.Button("生成", scale=0, variant="primary")

	result = gr.Image(label="Result", show_label=False)

	with gr.Accordion("高级设置", open=False):
	negative_prompt = gr.Text(
	label="Negative prompt",
	max_lines=1,
	placeholder="输入提示词",
	visible=False,
	)

	seed = gr.Slider(
	label="种子",
	minimum=0,
	maximum=MAX_SEED,
	step=1,
	value=0,
	)

	randomize_seed = gr.Checkbox(label="随机种子", value=True)

	with gr.Row():
	width = gr.Slider(
	label="宽度",
	minimum=256,
	maximum=MAX_IMAGE_SIZE,
	step=32,
	value=1024, # Replace with defaults that work for your model
	)

	height = gr.Slider(
	label="高度",
	minimum=256,
	maximum=MAX_IMAGE_SIZE,
	step=32,
	value=1024, # Replace with defaults that work for your model
	)

	with gr.Row():
	guidance_scale = gr.Slider(
	label="精细程度",
	minimum=0.0,
	maximum=10.0,
	step=0.1,
	value=0.0, # Replace with defaults that work for your model
	)

	num_inference_steps = gr.Slider(
	label="步骤推理数量",
	minimum=1,
	maximum=50,
	step=1,
	value=2, # Replace with defaults that work for your model
	)

	gr.Examples(examples=examples, inputs=[prompt])
	gr.on(
	triggers=[run_button.click, prompt.submit],
	fn=infer,
	inputs=[
	prompt,
	negative_prompt,
	seed,
	randomize_seed,
	width,
	height,
	guidance_scale,
	num_inference_steps,
	],
	outputs=[result, seed],
	)

	if __name__ == "__main__":
	demo.launch()