app.py

import os
import sys

import gradio as gr
import numpy as np
import torch
import random

from diffusers import AutoPipelineForText2Image
from diffusers.pipelines.wuerstchen.pipeline_wuerstchen_prior import DEFAULT_STAGE_C_TIMESTEPS

device = 'cuda' if torch.cuda.is_available() else 'cpu'
pipe = AutoPipelineForText2Image.from_pretrained("warp-ai/wuerstchen",
    torch_dtype=torch.float32)
pipe.to(device)

pipe.safety_checker = None

'''
#### 9min a sample (2 cores)
caption = "Anthropomorphic cat dressed as a fire fighter"
images = pipe(
    caption,
    width=512,
    height=512,
    prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS, #### length of 30
    prior_guidance_scale=4.0,
    num_images_per_prompt=1,
    num_inference_steps = 6, #### default num of 12, 6 favour
).images
'''

def process(prompt, num_samples, image_resolution, sample_steps, seed,):
    from PIL import Image
    with torch.no_grad():
        if seed == -1:
            seed = random.randint(0, 65535)
        #control_image = Image.fromarray(detected_map)

        # run inference
        #generator = torch.Generator(device=device).manual_seed(seed)
        H = image_resolution
        W = image_resolution
        images = []
        for i in range(num_samples):
            image = pipe(
                prompt,
                prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
                prior_guidance_scale=4.0,
                num_inference_steps = sample_steps,
                num_images_per_prompt=1,
                height=H, width=W).images[0]
            images.append(np.asarray(image))

        results = images
    return results
    #return [255 - detected_map] + results

block = gr.Blocks().queue()
with block:
    with gr.Row():
        gr.Markdown("## Rapid Diffusion model from warp-ai/wuerstchen")
        #gr.Markdown("This _example_ was **drive** from <br/><b><h4>[https://github.com/svjack/ControlLoRA-Chinese](https://github.com/svjack/ControlLoRA-Chinese)</h4></b>\n")
    with gr.Row():
        with gr.Column():
            #input_image = gr.Image(source='upload', type="numpy", value = "hate_dog.png")
            prompt = gr.Textbox(label="Prompt", value = "Anthropomorphic cat dressed as a fire fighter")
            run_button = gr.Button(label="Run")
            with gr.Accordion("Advanced options", open=False):
                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=256)
                #low_threshold = gr.Slider(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1)
                #high_threshold = gr.Slider(label="Canny high threshold", minimum=1, maximum=255, value=200, step=1)
                sample_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=6, step=1)
                #scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
                #eta = gr.Number(label="eta", value=0.0)
                #a_prompt = gr.Textbox(label="Added Prompt", value='')
                #n_prompt = gr.Textbox(label="Negative Prompt",
                #                      value='低质量，模糊，混乱')
        with gr.Column():
            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
    #ips = [None, prompt, None, None, num_samples, image_resolution, sample_steps, None, seed, None, None, None]
    ips = [prompt, num_samples, image_resolution, sample_steps, seed]
    run_button.click(fn=process, inputs=ips, outputs=[result_gallery], show_progress = True)


block.launch(server_name='0.0.0.0')