Upload main.py

main.py

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+import gradio as gr
+from fetch import get_values
+from dotenv import load_dotenv
+load_dotenv()
+import prodia
+import requests
+import random
+from datetime import datetime
+import os
+
+prodia_key = os.getenv('PRODIA_X_KEY', None)
+if prodia_key is None:
+    print("Please set PRODIA_X_KEY in .env, closing...")
+    exit()
+client = prodia.Client(api_key=prodia_key)
+
+def process_input_text2img(prompt, negative_prompt, steps, cfg_scale, number, seed, model, sampler, aspect_ratio, upscale, save):
+    images = []
+    for image in range(number):
+        result = client.txt2img(prompt=prompt, negative_prompt=negative_prompt, model=model, sampler=sampler,
+                                steps=steps, cfg_scale=cfg_scale, seed=seed, aspect_ratio=aspect_ratio, upscale=upscale)
+        images.append(result.url)
+        if save:
+            date = datetime.now()
+            if not os.path.isdir(f'./outputs/{date.year}-{date.month}-{date.day}'):
+                os.mkdir(f'./outputs/{date.year}-{date.month}-{date.day}')
+            img_data = requests.get(result.url).content
+            with open(f"./outputs/{date.year}-{date.month}-{date.day}/{random.randint(1, 10000000000000)}_{result.seed}.png", "wb") as f:
+                f.write(img_data)
+    return images
+
+def process_input_img2img(init, prompt, negative_prompt, steps, cfg_scale, number, seed, model, sampler, ds, upscale, save):
+    images = []
+    for image in range(number):
+        result = client.img2img(imageUrl=init, prompt=prompt, negative_prompt=negative_prompt, model=model, sampler=sampler,
+                                steps=steps, cfg_scale=cfg_scale, seed=seed, denoising_strength=ds, upscale=upscale)
+        images.append(result.url)
+        if save:
+            date = datetime.now()
+            if not os.path.isdir(f'./outputs/{date.year}-{date.month}-{date.day}'):
+                os.mkdir(f'./outputs/{date.year}-{date.month}-{date.day}')
+            img_data = requests.get(result.url).content
+            with open(f"./outputs/{date.year}-{date.month}-{date.day}/{random.randint(1, 10000000000000)}_{result.seed}.png", "wb") as f:
+                f.write(img_data)
+    return images
+
+"""
+def process_input_control(init, prompt, negative_prompt, steps, cfg_scale, number, seed, model, control_model, sampler):
+    images = []
+    for image in range(number):
+        result = client.controlnet(imageUrl=init, prompt=prompt, negative_prompt=negative_prompt, model=model, sampler=sampler,
+                                steps=steps, cfg_scale=cfg_scale, seed=seed, controlnet_model=control_model)
+        images.append(result.url)
+    return images
+"""
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # Prodia API web-ui by @zenafey
+    
+    This is simple web-gui for using Prodia API easily, build on Python, gradio, prodiapy
+    """)
+    with gr.Tab(label="text2img"):
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(label="Prompt", lines=2)
+                negative = gr.Textbox(label="Negative Prompt", lines=3, placeholder="badly drawn")
+
+                with gr.Row():
+                    steps = gr.Slider(label="Steps", value=30, step=1, maximum=50, minimum=1, interactive=True)
+                    cfg = gr.Slider(label="CFG Scale", maximum=20, minimum=1, value=7, interactive=True)
+
+                with gr.Row():
+                    num = gr.Slider(label="Number of images", value=1, step=1, minimum=1, interactive=True)
+                    seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967295, interactive=True)
+
+                with gr.Row():
+                    model = gr.Dropdown(label="Model", choices=get_values()[0], value="v1-5-pruned-emaonly.ckpt [81761151]", interactive=True)
+                    sampler = gr.Dropdown(label="Sampler", choices=get_values()[1], value="DDIM", interactive=True)
+
+                with gr.Row():
+                    ar = gr.Radio(label="Aspect Ratio", choices=["square", "portrait", "landscape"], value="square", interactive=True)
+                    with gr.Column():
+                        upscale = gr.Checkbox(label="upscale", interactive=True)
+                        save = gr.Checkbox(label="auto save", interactive=True)
+
+                with gr.Row():
+                    run_btn = gr.Button("Run", variant="primary")
+            with gr.Column():
+                result_image = gr.Gallery(label="Result Image(s)")
+        run_btn.click(
+            process_input_text2img,
+            inputs=[
+                prompt,
+                negative,
+                steps,
+                cfg,
+                num,
+                seed,
+                model,
+                sampler,
+                ar,
+                upscale,
+                save
+            ],
+            outputs=[result_image],
+        )
+
+    with gr.Tab(label="img2img"):
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(label="Prompt", lines=2)
+
+                with gr.Row():
+                    negative = gr.Textbox(label="Negative Prompt", lines=3, placeholder="badly drawn")
+                    init_image = gr.Textbox(label="Init Image Url", lines=2, placeholder="https://cdn.openai.com/API/images/guides/image_generation_simple.webp")
+
+
+                with gr.Row():
+                    steps = gr.Slider(label="Steps", value=30, step=1, maximum=50, minimum=1, interactive=True)
+                    cfg = gr.Slider(label="CFG Scale", maximum=20, minimum=1, value=7, interactive=True)
+
+                with gr.Row():
+                    num = gr.Slider(label="Number of images", value=1, step=1, minimum=1, interactive=True)
+                    seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967295, interactive=True)
+
+                with gr.Row():
+                    model = gr.Dropdown(label="Model", choices=get_values()[0], value="v1-5-pruned-emaonly.ckpt [81761151]", interactive=True)
+                    sampler = gr.Dropdown(label="Sampler", choices=get_values()[1], value="DDIM", interactive=True)
+
+                with gr.Row():
+                    ds = gr.Slider(label="Denoising strength", maximum=0.9, minimum=0.1, value=0.5, interactive=True)
+                    with gr.Column():
+                        upscale = gr.Checkbox(label="upscale", interactive=True)
+                        save = gr.Checkbox(label="auto save", interactive=True)
+
+                with gr.Row():
+                    run_btn = gr.Button("Run", variant="primary")
+            with gr.Column():
+                result_image = gr.Gallery(label="Result Image(s)")
+        run_btn.click(
+            process_input_img2img,
+            inputs=[
+                init_image,
+                prompt,
+                negative,
+                steps,
+                cfg,
+                num,
+                seed,
+                model,
+                sampler,
+                ds,
+                upscale,
+                save
+            ],
+            outputs=[result_image],
+        )
+
+    with gr.Tab(label="controlnet(coming soon)"):
+        gr.Button(label="lol")
+
+
+if __name__ == "__main__":
+    demo.launch(show_api=True)
+