Create app.py

app.py

@@ -0,0 +1,273 @@
+import os
+import sys
+
+import gradio as gr
+import numpy as np
+import shutil
+
+import copy
+import json
+import gc
+import random
+from PIL import Image
+
+'''
+models
+images
+custom.css
+sd_cfg.json
+'''
+
+if not os.path.exists("sd-ggml-cpp-dp"):
+    os.system("git clone https://huggingface.co/svjack/sd-ggml-cpp-dp")
+else:
+    shutil.rmtree("sd-ggml-cpp-dp")
+    os.system("git clone https://huggingface.co/svjack/sd-ggml-cpp-dp")
+assert os.path.exists("sd-ggml-cpp-dp")
+os.chdir("sd-ggml-cpp-dp")
+assert os.path.exists("stable-diffusion.cpp")
+os.system("cmake stable-diffusion.cpp")
+os.system("cmake --build . --config Release")
+assert os.path.exists("bin")
+
+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)
+        os.mkdir(output_path)
+    assert os.path.exists(output_path)
+
+    run_format = './bin/sd -m {} --sampling-method "dpm++2mv2" -o "{}/{}.png" -p "{}" --steps {} -H {} -W {} -s {}'
+    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, seed + i)
+        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
+
+model_list = list(map(lambda x: os.path.join("models", x), os.listdir("models")))
+assert model_list
+
+sdxl_loras_raw = []
+with open("sd_cfg.json", "r") as file:
+    data = json.load(file)
+    sdxl_loras_raw = [
+        {
+            "image": item["image"],
+            "title": item["title"],
+            "repo": item["repo"],
+            "trigger_word": item["trigger_word"],
+            "model_path": item["model_path"]
+            #"weights": item["weights"],
+            #"is_compatible": item["is_compatible"],
+            #"is_pivotal": item.get("is_pivotal", False),
+            #"text_embedding_weights": item.get("text_embedding_weights", None),
+            #"likes": item.get("likes", 0),
+            #"downloads": item.get("downloads", 0),
+            #"is_nc": item.get("is_nc", False)
+        }
+        for item in data
+    ]
+
+sdxl_loras_raw = list(filter(lambda d: d["model_path"] in model_list, sdxl_loras_raw))
+assert sdxl_loras_raw
+
+
+def update_selection(selected_state: gr.SelectData, sdxl_loras):
+    lora_repo = sdxl_loras[selected_state.index]["repo"]
+    instance_prompt = sdxl_loras[selected_state.index]["trigger_word"]
+    new_placeholder = "Type a prompt. This applies for all prompts, no need for a trigger word" if instance_prompt == "" else "Type a prompt to use your selected LoRA"
+    #weight_name = sdxl_loras[selected_state.index]["weights"]
+    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨ "
+    is_compatible = True
+    is_pivotal = True
+
+    use_with_diffusers = f'''
+    ## Using [`{lora_repo}`](https://huggingface.co/{lora_repo})
+
+    ## Use it with diffusers:
+    '''
+    use_with_uis = f'''
+    ## Use it with Comfy UI, Invoke AI, SD.Next, AUTO1111:
+
+    ### Download the `*.safetensors` weights of [here](https://huggingface.co/{lora_repo})
+
+    - [ComfyUI guide](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
+    - [Invoke AI guide](https://invoke-ai.github.io/InvokeAI/features/CONCEPTS/?h=lora#using-loras)
+    - [SD.Next guide](https://github.com/vladmandic/automatic)
+    - [AUTOMATIC1111 guide](https://stable-diffusion-art.com/lora/)
+    '''
+    return (
+        updated_text,
+        instance_prompt,
+        gr.update(placeholder=new_placeholder),
+        selected_state,
+        use_with_diffusers,
+        use_with_uis,
+    )
+
+def check_selected(selected_state):
+    if not selected_state:
+        raise gr.Error("You must select a Model")
+
+def shuffle_gallery(sdxl_loras):
+    random.shuffle(sdxl_loras)
+    return [(item["image"], item["title"]) for item in sdxl_loras], sdxl_loras
+
+def swap_gallery(order, sdxl_loras):
+    if(order == "random"):
+        return shuffle_gallery(sdxl_loras)
+    else:
+        #sorted_gallery = sorted(sdxl_loras, key=lambda x: x.get(order, 0), reverse=True)
+        sorted_gallery = sorted(sdxl_loras, key=lambda x: x["title"], reverse=False)
+        return [(item["image"], item["title"]) for item in sorted_gallery], sorted_gallery
+
+'''
+def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras,
+        progress=gr.Progress(track_tqdm=True)):
+'''
+def run_lora(prompt, selected_state, sdxl_loras,
+        image_resolution, sample_steps, seed,
+        progress=gr.Progress(track_tqdm=True)):
+    #global last_lora, last_merged, last_fused, pipe
+
+    '''
+    if negative == "":
+        negative = None
+    '''
+
+    if not selected_state:
+        raise gr.Error("You must select a Model")
+    repo_name = sdxl_loras[selected_state.index]["repo"]
+    model_path = sdxl_loras[selected_state.index]["model_path"]
+    #weight_name = sdxl_loras[selected_state.index]["weights"]
+
+    '''
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative,
+        width=1024,
+        height=1024,
+        num_inference_steps=20,
+        guidance_scale=7.5,
+    ).images[0]
+    last_lora = repo_name
+    gc.collect()
+    '''
+    num_samples = 1
+    #### image_resolution : 512
+    #### sample_steps : 8
+    #### seed : 20
+    image = process(model_path ,prompt, num_samples, image_resolution, sample_steps, seed,)[0]
+    image = Image.fromarray(image.astype(np.uint8))
+    #return image, gr.update(visible=True)
+    return image
+
+with gr.Blocks(css="custom.css") as demo:
+    #with gr.Blocks() as demo:
+    gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
+    title = gr.HTML(
+        """<h1><img src="https://i.imgur.com/vT48NAO.png" alt="SD"> StableDiffusion GGML Explorer</h1>""",
+        elem_id="title",
+    )
+
+    selected_state = gr.State()
+    with gr.Row(elem_id="main_app"):
+        with gr.Box(elem_id="gallery_box"):
+            order_gallery = gr.Radio(choices=["random", "alphabetical"],
+            value="random", label="Order by", elem_id="order_radio")
+            gallery = gr.Gallery(
+                #value=[(item["image"], item["title"]) for item in sdxl_loras_raw],
+                label="SD Model Gallery",
+                allow_preview=True,
+                #rows = 1,
+                columns=2,
+                #scale = 3,
+                min_width = 256,
+                #object_fit = "scale-down",
+                elem_id="gallery",
+                show_share_button=False,
+                height=512
+            )
+        with gr.Column():
+            prompt_title = gr.Markdown(
+                value="### Click on a Model in the gallery to select it",
+                visible=True,
+                elem_id="selected_model",
+            )
+            with gr.Row():
+                prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1,
+                placeholder="Type a prompt after selecting a Model", elem_id="prompt")
+                button = gr.Button("Run", elem_id="run_button")
+            '''
+            with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
+                community_icon = gr.HTML(community_icon_html)
+                loading_icon = gr.HTML(loading_icon_html)
+                share_button = gr.Button("Share to community", elem_id="share-btn")
+            '''
+            result = gr.Image(
+                interactive=False, label="Generated Image", elem_id="result-image"
+            )
+            with gr.Accordion("Advanced options", open=False):
+                #negative = gr.Textbox(label="Negative Prompt")
+                #weight = gr.Slider(0, 10, value=0.8, step=0.1, label="LoRA weight")
+                #negative = ""
+                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=256)
+                sample_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=8, step=1)
+                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
+
+    order_gallery.change(
+        fn=swap_gallery,
+        inputs=[order_gallery, gr_sdxl_loras],
+        outputs=[gallery, gr_sdxl_loras],
+        queue=False
+    )
+    gallery.select(
+        fn=update_selection,
+        inputs=[gr_sdxl_loras],
+        #outputs=[prompt_title, prompt, prompt, selected_state, use_diffusers, use_uis],
+        outputs=[prompt_title, prompt, prompt, selected_state,],
+        queue=False,
+        show_progress=False
+    )
+    prompt.submit(
+        fn=check_selected,
+        inputs=[selected_state],
+        queue=False,
+        show_progress=False
+    ).success(
+        fn=run_lora,
+        #inputs=[prompt, negative, weight, selected_state, gr_sdxl_loras],
+        inputs=[prompt, selected_state, gr_sdxl_loras, image_resolution, sample_steps, seed],
+        #outputs=[result, share_group],
+        #outputs=[result,],
+        outputs = result
+    )
+    button.click(
+        fn=check_selected,
+        inputs=[selected_state],
+        queue=False,
+        show_progress=False
+    ).success(
+        fn=run_lora,
+        #inputs=[prompt, negative, weight, selected_state, gr_sdxl_loras],
+        inputs=[prompt, selected_state, gr_sdxl_loras, image_resolution, sample_steps, seed],
+        #outputs=[result, share_group],
+        #outputs=[result,],
+        outputs = result
+    )
+    #share_button.click(None, [], [], _js=share_js)
+    demo.load(fn=shuffle_gallery, inputs=[gr_sdxl_loras], outputs=[gallery, gr_sdxl_loras], queue=False)
+demo.queue(max_size=20)
+demo.launch()