Create app.py

app.py

@@ -0,0 +1,87 @@
+import os
+import sys
+
+import gradio as gr
+import numpy as np
+import torch
+import random
+import shutil
+
+if not os.path.exists("sd-ggml"):
+    os.system("git clone https://huggingface.co/svjack/sd-ggml")
+else:
+    shutil.rmtree("sd-ggml")
+assert os.path.exists("sd-ggml")
+os.chdir("sd-ggml")
+assert os.path.exists("stable-diffusion.cpp")
+os.system("cmake stable-diffusion.cpp")
+os.system("cmake --build . --config Release")
+assert os.path.exists("bin")
+
+'''
+./bin/sd -m ../../../Downloads1/deliberate-ggml-model-q4_0.bin --sampling-method "euler_a" -o "fire-fighter-euler_a-7.png" -p "Anthropomorphic cat dressed as a fire fighter" --steps 7
+./bin/sd -m ../../../Downloads/anime-ggml-model-q4_0.bin --sampling-method "dpm++2mv2" -o "couple-dpm++2mv2-7-anime.png" -p "In this scene, there's a couple (represented by 👨 and 👩) who share an intense passion or attraction towards each other (symbolized by 🔥). The setting takes place in cold weather conditions represented by snowflakes ❄️" --steps 7
+'''
+
+def process(model_path ,prompt, num_samples, image_resolution, sample_steps, seed,):
+    from PIL import Image
+    from uuid import uuid1
+    output_path = "output_image_dir"
+    if not os.path.exists(output_path):
+        os.mkdir(output_path)
+    else:
+        shutil.rmtree(output_path)
+    assert os.path.exists(output_path)
+
+    run_format = './bin/sd -m {} --sampling-method "dpm++2mv2" -o "{}/{}.png" -p "{}" --steps {} -H {} -W {}'
+    images = []
+    for i in range(num_samples):
+        uid = str(uuid1())
+        run_cmd = run_format.format(model_path, output_path,
+        uid, prompt, sample_steps, image_resolution,
+        image_resolution)
+        print("run cmd: {}".format(run_cmd))
+        os.system(run_cmd)
+        assert os.path.exists(os.path.join(output_path, "{}.png".format(uid)))
+        image = Image.open(os.path.join(output_path, "{}.png".format(uid)))
+        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")
+            model_list = list(map(lambda x: os.path.join("models", x), os.listdir("models")))
+            assert model_list
+            model_path = gr.Dropdown(
+            model_list, value = model_list[0],
+             label="GGML Models"
+            )
+            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=8, 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 = [model_path ,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')