Update app.py

app.py

@@ -1,442 +1,18 @@
-import numpy as np
-import gradio as gr
-import requests
-import time
-import json
-import base64
-import os
-from io import BytesIO
-import PIL
-from PIL.ExifTags import TAGS
-import html
-import re
-from threading import Thread
-
-class Prodia:
-    def __init__(self, api_key, base=None):
-        self.base = base or "https://api.prodia.com/v1"
-        self.headers = {
-            "X-Prodia-Key": api_key
-        }
-
-    def generate(self, params):
-        response = self._post(f"{self.base}/sd/generate", params)
-        return response.json()
-
-    def transform(self, params):
-        response = self._post(f"{self.base}/sd/transform", params)
-        return response.json()
-
-    def controlnet(self, params):
-        response = self._post(f"{self.base}/sd/controlnet", params)
-        return response.json()
-
-    def upscale(self, params):
-        response = self._post(f"{self.base}/upscale", params)
-        return response.json()
-
-    def get_job(self, job_id):
-        response = self._get(f"{self.base}/job/{job_id}")
-        return response.json()
-
-    def wait(self, job):
-        job_result = job
-
-        while job_result['status'] not in ['succeeded', 'failed']:
-            time.sleep(0.5)
-            job_result = self.get_job(job['job'])
-
-        return job_result
-
-    def list_models(self):
-        response = self._get(f"{self.base}/sd/models")
-        return response.json()
-
-    def list_loras(self):
-        response = self._get(f"{self.base}/sd/loras")
-        return response.json()
-
-    def _post(self, url, params):
-        headers = {
-            **self.headers,
-            "Content-Type": "application/json"
-        }
-        response = requests.post(url, headers=headers, data=json.dumps(params))
-
-        if response.status_code != 200:
-            raise Exception(f"Bad Prodia Response: {response.status_code}")
-
-        return response
-
-    def _get(self, url):
-        response = requests.get(url, headers=self.headers)
-
-        if response.status_code != 200:
-            raise Exception(f"Bad Prodia Response: {response.status_code}")
-
-        return response
-
-
-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 get_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 send_to_txt2img(image):
-    result = {tabs: gr.Tabs.update(selected="t2i")}
-
-    try:
-        text = image.info['parameters']
-        data = get_data(text)
-        result[prompt] = gr.update(value=data['prompt'])
-        result[negative_prompt] = gr.update(value=data['negative_prompt']) if data[
-                                                                                  'negative_prompt'] is not None else gr.update()
-        result[steps] = gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update()
-        result[seed] = gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update()
-        result[cfg_scale] = gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update()
-        result[width] = gr.update(value=int(data['w'])) if data['w'] is not None else gr.update()
-        result[height] = gr.update(value=int(data['h'])) if data['h'] is not None else gr.update()
-        result[sampler] = gr.update(value=data['sampler']) if data['sampler'] is not None else gr.update()
-        if data['model'] in model_names:
-            result[model] = gr.update(value=model_names[data['model']])
-        else:
-            result[model] = gr.update()
-        return result
-
-    except Exception as e:
-        print(e)
-        result[prompt] = gr.update()
-        result[negative_prompt] = gr.update()
-        result[steps] = gr.update()
-        result[seed] = gr.update()
-        result[cfg_scale] = gr.update()
-        result[width] = gr.update()
-        result[height] = gr.update()
-        result[sampler] = gr.update()
-        result[model] = gr.update()
-
-        return result
-
-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> "
-
-prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
-model_list = prodia_client.list_models()
-lora_list = prodia_client.list_loras()
-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 txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, batch_count, gallery):
-    yield {
-        text_button: gr.update(visible=False),
-        stop_btn: gr.update(visible=True),
-    }
-    data = {
-        "prompt": prompt,
-        "negative_prompt": negative_prompt,
-        "model": model,
-        "steps": steps,
-        "sampler": sampler,
-        "cfg_scale": cfg_scale,
-        "width": width,
-        "height": height,
-        "seed": seed
-    }
-
-    total_images = []
-    threads = []
-
-    def generate_one_image():
-        result = prodia_client.generate(data)
-        job = prodia_client.wait(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()
-
-    new_images_list = [img['name'] for img in gallery]
-
-    for image in total_images:
-        new_images_list.insert(0, image)
-
-    if batch_count > 1:
-        results = gr.update(value=total_images, preview=False)
-    else:
-        results = gr.update(value=total_images, preview=True)
-
-    yield {
-        text_button: gr.update(visible=True),
-        stop_btn: gr.update(visible=False),
-        image_output: results,
-        gallery_obj: gr.update(value=new_images_list),
-    }
-
-
-def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed,
-            batch_count, gallery):
-    if input_image is None:
-        return
-    yield {
-        i2i_text_button: gr.update(visible=False),
-        i2i_stop_btn: gr.update(visible=True),
-    }
-    data = {
-        "imageData": image_to_base64(input_image),
-        "denoising_strength": denoising,
-        "prompt": prompt,
-        "negative_prompt": negative_prompt,
-        "model": model,
-        "steps": steps,
-        "sampler": sampler,
-        "cfg_scale": cfg_scale,
-        "width": width,
-        "height": height,
-        "seed": seed
-    }
-
-    total_images = []
-    threads = []
-
-    def generate_one_image():
-        result = prodia_client.transform(data)
-        job = prodia_client.wait(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()
-
-    new_images_list = [img['name'] for img in gallery]
-
-    for image in total_images:
-        new_images_list.insert(0, image)
-
-    if batch_count > 1:
-        results = gr.update(value=total_images, preview=False)
-    else:
-        results = gr.update(value=total_images, preview=True)
-
-    yield {
-        i2i_text_button: gr.update(visible=True),
-        i2i_stop_btn: gr.update(visible=False),
-        i2i_image_output: results,
-        gallery_obj: gr.update(value=new_images_list),
-    }
-
-def upscale_fn(image, scale):
-    if image is None:
-        return
-    yield {
-        upscale_btn: gr.update(visible=False),
-        upscale_stop: gr.update(visible=True),
-    }
-    job = prodia_client.upscale({
-        'imageData': image_to_base64(image),
-        'resize': scale
-    })
-
-    result = prodia_client.wait(job)
-    yield {
-        upscale_output: result['imageUrl'],
-        upscale_btn: gr.update(visible=True),
-        upscale_stop: gr.update(visible=False)
-    }
-
-def stop_upscale():
-    return {
-        upscale_btn: gr.update(visible=True),
-        upscale_stop: gr.update(visible=False)
-    }
-
-def stop_t2i():
-    return {
-        text_button: gr.update(visible=True),
-        stop_btn: gr.update(visible=False)
-    }
-
-def stop_i2i():
-    return {
-        i2i_text_button: gr.update(visible=True),
-        i2i_stop_btn: gr.update(visible=False)
-    }
-
-
-
-samplers = [
-    "Euler",
-    "Euler a",
-    "LMS",
-    "Heun",
-    "DPM2",
-    "DPM2 a",
-    "DPM++ 2S a",
-    "DPM++ 2M",
-    "DPM++ SDE",
-    "DPM fast",
-    "DPM adaptive",
-    "LMS Karras",
-    "DPM2 Karras",
-    "DPM2 a Karras",
-    "DPM++ 2S a Karras",
-    "DPM++ 2M Karras",
-    "DPM++ SDE Karras",
-    "DDIM",
-    "PLMS",
-]
-
-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%;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True,
-                                label="Stable Diffusion Checkpoint", choices=prodia_client.list_models(), elem_id="model_dd")
+# original code by zenafey
+
+from utils import place_lora, get_exif_data
+from css import css
+from grutils import *
+import inference
+
+
+lora_list = pipe.constant("/sd/loras")
+samplers = pipe.constant("/sd/samplers")
+
+
+with gr.Blocks(css=css, theme="zenafey/prodia-web") as demo:
+    model = gr.Dropdown(interactive=True, value=model_list[0], show_label=True, label="Stable Diffusion Checkpoint",
+                        choices=model_list, elem_id="model_dd")
 
     with gr.Tabs() as tabs:
         with gr.Tab("txt2img", id='t2i'):
@@ -447,8 +23,9 @@ with gr.Blocks(css=css) as demo:
                     negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
                                                  value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
                 with gr.Row():
-                    text_button = gr.Button("Generate", variant='primary', elem_id="generate")
-                    stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False)
+                    t2i_generate_btn = gr.Button("Generate", variant='primary', elem_id="generate")
+
+                    t2i_stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False)
 
             with gr.Row():
                 with gr.Column():
@@ -491,7 +68,7 @@ with gr.Blocks(css=css) as demo:
                     i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
                                                      value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
                 with gr.Row():
-                    i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate")
+                    i2i_generate_btn = gr.Button("Generate", variant='primary', elem_id="generate")
                     i2i_stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False)
 
             with gr.Row():
@@ -536,38 +113,13 @@ with gr.Blocks(css=css) as demo:
                     with gr.Column():
                         upscale_image_input = gr.Image(type="pil")
                         upscale_btn = gr.Button("Generate", variant="primary")
-                        upscale_stop = gr.Button("Stop", variant="stop", visible=False)
+                        upscale_stop_btn = gr.Button("Stop", variant="stop", visible=False)
                         with gr.Tab("Scale by"):
-                            scale_by = gr.Radio([2, 4], value=2, label="Resize")
+                            upscale_scale = gr.Radio([2, 4], value=2, label="Resize")
 
                 upscale_output = gr.Image()
 
         with gr.Tab("PNG Info"):
-            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
-
-
             with gr.Row():
                 with gr.Column():
                     image_input = gr.Image(type="pil")
@@ -576,27 +128,65 @@ with gr.Blocks(css=css) as demo:
                     exif_output = gr.HTML(label="EXIF Data")
                     send_to_txt2img_btn = gr.Button("Send to txt2img")
 
-        with gr.Tab("Gallery"):
-            gallery_obj = gr.Gallery(height=1000, columns=6)
-
-        t2i_event = text_button.click(txt2img,
-                                      inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height,
-                                              seed, batch_count, gallery_obj], outputs=[image_output, gallery_obj, text_button, stop_btn])
-        stop_btn.click(fn=stop_t2i, outputs=[text_button, stop_btn], cancels=[t2i_event])
+        with gr.Tab("Past generations"):
+            inference.gr_user_history.render()
+
+        t2i_event_start = t2i_generate_btn.click(
+            update_btn_start,
+            outputs=[t2i_generate_btn, t2i_stop_btn]
+        )
+        t2i_event = t2i_event_start.then(
+            inference.txt2img,
+            inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, batch_count],
+            outputs=[image_output]
+        )
+        t2i_event_end = t2i_event.then(
+            update_btn_end,
+            outputs=[t2i_generate_btn, t2i_stop_btn]
+        )
+
+        t2i_stop_btn.click(fn=update_btn_end, outputs=[t2i_generate_btn, t2i_stop_btn], cancels=[t2i_event])
 
         image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output)
-        send_to_txt2img_btn.click(send_to_txt2img, inputs=[image_input],
-                                  outputs=[tabs, prompt, negative_prompt, steps, seed, model, sampler, width, height,
-                                           cfg_scale])
-
-        i2i_event = i2i_text_button.click(img2img,
-                                          inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt,
-                                                  model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height,
-                                                  i2i_seed, i2i_batch_count, gallery_obj],
-                                          outputs=[i2i_image_output, gallery_obj, i2i_text_button, i2i_stop_btn])
-        i2i_stop_btn.click(fn=stop_i2i, outputs=[i2i_text_button, i2i_stop_btn], cancels=[i2i_event])
-
-        upscale_event = upscale_btn.click(fn=upscale_fn, inputs=[upscale_image_input, scale_by], outputs=[upscale_output, upscale_btn, upscale_stop])
-        upscale_stop.click(fn=stop_upscale, outputs=[upscale_btn, upscale_stop], cancels=[upscale_event])
 
-demo.queue().launch(max_threads=256)
+        send_to_txt2img_btn.click(
+            fn=switch_to_t2i,
+            outputs=[tabs]
+        ).then(
+            fn=send_to_txt2img,
+            inputs=[image_input],
+            outputs=[prompt, negative_prompt, steps, seed, model, sampler, width, height, cfg_scale]
+        )
+
+        i2i_event_start = i2i_generate_btn.click(
+            update_btn_start,
+            outputs=[i2i_generate_btn, i2i_stop_btn]
+        )
+        i2i_event = i2i_event_start.then(inference.img2img,
+                                         inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt,
+                                                 model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height,
+                                                 i2i_seed, i2i_batch_count],
+                                         outputs=[i2i_image_output])
+        i2i_event_end = i2i_event.then(
+            update_btn_end,
+            outputs=[i2i_generate_btn, i2i_stop_btn]
+        )
+        i2i_stop_btn.click(fn=update_btn_end, outputs=[i2i_generate_btn, i2i_stop_btn], cancels=[i2i_event])
+
+        upscale_event_start = upscale_btn.click(
+            fn=update_btn_start,
+            outputs=[upscale_btn, upscale_stop_btn]
+        )
+        upscale_event = upscale_event_start.then(
+            fn=inference.upscale,
+            inputs=[upscale_image_input, upscale_scale],
+            outputs=[upscale_output]
+        )
+        upscale_event_end = upscale_event.then(
+            fn=update_btn_end,
+            outputs=[upscale_btn, upscale_stop_btn]
+        )
+
+        upscale_stop_btn.click(fn=update_btn_end, outputs=[upscale_btn, upscale_stop_btn], cancels=[upscale_event])
+
+demo.queue().launch(max_threads=256)