Upload 4 files

css.py

@@ -0,0 +1,87 @@
+css = """
+:root, .dark{
+    --checkbox-label-gap: 0.25em 0.1em;
+    --section-header-text-size: 12pt;
+    --block-background-fill: transparent;
+}
+.block.padded:not(.gradio-accordion) {
+    padding: 0 !important;
+}
+div.gradio-container{
+    max-width: unset !important;
+}
+.compact{
+    background: transparent !important;
+    padding: 0 !important;
+}
+div.form{
+    border-width: 0;
+    box-shadow: none;
+    background: transparent;
+    overflow: visible;
+    gap: 0.5em;
+}
+.block.gradio-dropdown,
+.block.gradio-slider,
+.block.gradio-checkbox,
+.block.gradio-textbox,
+.block.gradio-radio,
+.block.gradio-checkboxgroup,
+.block.gradio-number,
+.block.gradio-colorpicker {
+    border-width: 0 !important;
+    box-shadow: none !important;
+}
+.gradio-dropdown label span:not(.has-info),
+.gradio-textbox label span:not(.has-info),
+.gradio-number label span:not(.has-info)
+{
+    margin-bottom: 0;
+}
+.gradio-dropdown ul.options{
+    z-index: 3000;
+    min-width: fit-content;
+    max-width: inherit;
+    white-space: nowrap;
+}
+.gradio-dropdown ul.options li.item {
+    padding: 0.05em 0;
+}
+.gradio-dropdown ul.options li.item.selected {
+    background-color: var(--neutral-100);
+}
+.dark .gradio-dropdown ul.options li.item.selected {
+    background-color: var(--neutral-900);
+}
+.gradio-dropdown div.wrap.wrap.wrap.wrap{
+    box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
+}
+.gradio-dropdown:not(.multiselect) .wrap-inner.wrap-inner.wrap-inner{
+    flex-wrap: unset;
+}
+.gradio-dropdown .single-select{
+    white-space: nowrap;
+    overflow: hidden;
+}
+.gradio-dropdown .token-remove.remove-all.remove-all{
+    display: none;
+}
+.gradio-dropdown.multiselect .token-remove.remove-all.remove-all{
+    display: flex;
+}
+.gradio-slider input[type="number"]{
+    width: 6em;
+}
+.block.gradio-checkbox {
+    margin: 0.75em 1.5em 0 0;
+}
+.gradio-html div.wrap{
+    height: 100%;
+}
+div.gradio-html.min{
+    min-height: 0;
+}
+#model_dd {
+    width: 16%;
+}
+"""

grutils.py

@@ -0,0 +1,60 @@
+import gradio as gr
+from utils import extract_data, remove_id_and_ext
+from prodiapy import Custom
+import os
+
+pipe = Custom(os.getenv('PRODIA_API_KEY'))
+
+
+model_list = pipe.constant("/sd/models")
+model_names = {}
+
+for model_name in model_list:
+    name_without_ext = remove_id_and_ext(model_name)
+    model_names[name_without_ext] = model_name
+
+
+def update_btn_start():
+    return [
+        gr.update(visible=False),
+        gr.update(visible=True)
+    ]
+
+
+def update_btn_end():
+    return [
+        gr.update(visible=True),
+        gr.update(visible=False)
+    ]
+
+
+def switch_to_t2i():
+    return gr.Tabs.update(selected="t2i")
+
+
+def send_to_txt2img(image):
+    try:
+        text = image.info['parameters']
+        data = extract_data(text)
+
+        if data['model'] in model_names:
+            model = gr.update(value=model_names[data['model']])
+        else:
+            model = gr.update()
+
+        result = [
+            gr.update(value=data['prompt']),
+            gr.update(value=data['negative_prompt']) if data['negative_prompt'] is not None else gr.update(),
+            gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update(),
+            gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update(),
+            gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update(),
+            gr.update(value=int(data['w'])) if data['w'] is not None else gr.update(),
+            gr.update(value=int(data['h'])) if data['h'] is not None else gr.update(),
+            gr.update(value=data['sampler']) if data['sampler'] is not None else gr.update(),
+            model
+        ]
+        return result
+
+    except Exception as e:
+        print(e)
+        return

inference.py

@@ -0,0 +1,96 @@
+from prodiapy import Custom
+from prodiapy.util import load
+from PIL import Image
+from threading import Thread
+from utils import image_to_base64
+import gradio as gr
+import gradio_user_history as gr_user_history
+
+pipe = Custom(os.getenv("PRODIA_API_KEY"))
+
+
+def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, batch_count, profile: gr.OAuthProfile | None):
+    total_images = []
+    threads = []
+
+    def generate_one_image():
+        result = pipe.create(
+            "/sd/generate",
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            model=model,
+            steps=steps,
+            cfg_scale=cfg_scale,
+            sampler=sampler,
+            width=width,
+            height=height,
+            seed=seed
+        )
+        job = pipe.wait_for(result)
+        total_images.append(job['imageUrl'])
+
+    for x in range(batch_count):
+        t = Thread(target=generate_one_image)
+        threads.append(t)
+        t.start()
+
+    for t in threads:
+        t.join()
+
+    for image in total_images:
+        gr_user_history.save_image(label=prompt, image=Image.open(load(image)), profile=profile)
+
+    return gr.update(value=total_images, preview=False)
+
+
+def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed,
+            batch_count):
+    if input_image is None:
+        return
+
+    total_images = []
+    threads = []
+
+    def generate_one_image():
+        result = pipe.create(
+            "/sd/transform",
+            imageData=image_to_base64(input_image),
+            denoising_strength=denoising,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            model=model,
+            steps=steps,
+            cfg_scale=cfg_scale,
+            sampler=sampler,
+            width=width,
+            height=height,
+            seed=seed
+
+        )
+        job = pipe.wait_for(result)
+        total_images.append(job['imageUrl'])
+
+    for x in range(batch_count):
+        t = Thread(target=generate_one_image)
+        threads.append(t)
+        t.start()
+
+    for t in threads:
+        t.join()
+
+    return gr.update(value=total_images, preview=False)
+
+
+def upscale(image, scale, profile: gr.OAuthProfile | None):
+    if image is None:
+        return
+
+    job = pipe.create(
+        '/upscale',
+        imageData=image_to_base64(image),
+        resize=scale
+    )
+    image = pipe.wait_for(job)['imageUrl']
+    gr_user_history.save_image(label=f'upscale by {scale}', image=Image.open(load(image)), profile=profile)
+
+    return image

utils.py

@@ -0,0 +1,87 @@
+import re
+import base64
+import html
+from io import BytesIO
+
+
+def image_to_base64(image):
+    # Convert the image to bytes
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")  # You can change format to PNG if needed
+
+    # Encode the bytes to base64
+    img_str = base64.b64encode(buffered.getvalue())
+
+    return img_str.decode('utf-8')  # Convert bytes to string
+
+
+def remove_id_and_ext(text):
+    text = re.sub(r'\[.*\]$', '', text)
+    extension = text[-12:].strip()
+    if extension == "safetensors":
+        text = text[:-13]
+    elif extension == "ckpt":
+        text = text[:-4]
+    return text
+
+
+def extract_data(text):
+    results = {}
+    patterns = {
+        'prompt': r'(.*)',
+        'negative_prompt': r'Negative prompt: (.*)',
+        'steps': r'Steps: (\d+),',
+        'seed': r'Seed: (\d+),',
+        'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)',
+        'model': r'Model:\s*([^\s,]+)',
+        'cfg_scale': r'CFG scale:\s*([\d\.]+)',
+        'size': r'Size:\s*([0-9]+x[0-9]+)'
+    }
+    for key in ['prompt', 'negative_prompt', 'steps', 'seed', 'sampler', 'model', 'cfg_scale', 'size']:
+        match = re.search(patterns[key], text)
+        if match:
+            results[key] = match.group(1)
+        else:
+            results[key] = None
+    if results['size'] is not None:
+        w, h = results['size'].split("x")
+        results['w'] = w
+        results['h'] = h
+    else:
+        results['w'] = None
+        results['h'] = None
+    return results
+
+
+def place_lora(current_prompt, lora_name):
+    pattern = r"<lora:" + lora_name + r":.*?>"
+
+    if re.search(pattern, current_prompt):
+        yield re.sub(pattern, "", current_prompt)
+    else:
+        yield current_prompt + " <lora:" + lora_name + ":1> "
+
+
+def plaintext_to_html(text, classname=None):
+    content = "<br>\n".join(html.escape(x) for x in text.split('\n'))
+
+    return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"
+
+
+def get_exif_data(image):
+    items = image.info
+
+    info = ''
+    for key, text in items.items():
+        info += f"""
+        <div>
+        <p><b>{plaintext_to_html(str(key))}</b></p>
+        <p>{plaintext_to_html(str(text))}</p>
+        </div>
+        """.strip() + "\n"
+
+    if len(info) == 0:
+        message = "Nothing found in the image."
+        info = f"<div><p>{message}<p></div>"
+
+    return info