v2p1

app.py

@@ -1,5 +1,6 @@
 import os
 import gc
+import random
 import gradio as gr
 import numpy as np
 import torch
@@ -38,7 +39,6 @@ torch.backends.cudnn.benchmark = False
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-
 def load_pipeline(model_name):
     vae = AutoencoderKL.from_pretrained(
         "madebyollin/sdxl-vae-fp16-fix",
@@ -64,8 +64,32 @@ def load_pipeline(model_name):
     pipe.to(device)
     return pipe
 
+def parse_json_parameters(json_str):
+    try:
+        params = json.loads(json_str)
+        return params
+    except json.JSONDecodeError:
+        return None
+
+def apply_json_parameters(json_str):
+    params = parse_json_parameters(json_str)
+    if params:
+        return (
+            params.get("prompt", ""),
+            params.get("negative_prompt", ""),
+            params.get("seed", 0),
+            params.get("width", 1024),
+            params.get("height", 1024),
+            params.get("guidance_scale", 7.0),
+            params.get("num_inference_steps", 30),
+            params.get("sampler", "DPM++ 2M SDE Karras"),
+            params.get("aspect_ratio", "1024 x 1024"),
+            params.get("use_upscaler", False),
+            params.get("upscaler_strength", 0.55),
+            params.get("upscale_by", 1.5),
+        )
+    return [gr.update()] * 12
 
-@spaces.GPU
 def generate(
     prompt: str,
     negative_prompt: str = "",
@@ -159,7 +183,12 @@ def generate(
                 filepath = utils.save_image(image, metadata, OUTPUT_DIR)
                 logger.info(f"Image saved as {filepath} with metadata")
 
-        return images, metadata
+        # Update history after generation
+        history = gr.get_state("history") or []
+        history.insert(0, {"prompt": prompt, "image": images[0], "metadata": metadata})
+        gr.set_state("history", history[:10])  # Keep only the last 10 entries
+
+        return images, metadata, gr.update(choices=[h["prompt"] for h in history])
     except Exception as e:
         logger.exception(f"An error occurred: {e}")
         raise
@@ -169,6 +198,21 @@ def generate(
         pipe.scheduler = backup_scheduler
         utils.free_memory()
 
+def get_random_prompt():
+    anime_characters = [
+        "Naruto Uzumaki", "Monkey D. Luffy", "Goku", "Eren Yeager", "Light Yagami",
+        "Lelouch Lamperouge", "Edward Elric", "Levi Ackerman", "Spike Spiegel",
+        "Sakura Haruno", "Mikasa Ackerman", "Asuka Langley Soryu", "Rem", "Megumin",
+        "Violet Evergarden"
+    ]
+    styles = ["pixel art", "stylized anime", "digital art", "watercolor", "sketch"]
+    scores = ["score_9", "score_8_up", "score_7_up"]
+    
+    character = random.choice(anime_characters)
+    style = random.choice(styles)
+    score = ", ".join(random.sample(scores, k=3))
+    
+    return f"{score}, {character}, {style}, show accurate"
 
 if torch.cuda.is_available():
     pipe = load_pipeline(MODEL)
@@ -285,8 +329,19 @@ with gr.Blocks(css="style.css") as demo:
                     step=1,
                     value=28,
                 )
+    with gr.Accordion(label="JSON Parameters", open=False):
+        json_input = gr.TextArea(label="Input JSON parameters")
+        apply_json_button = gr.Button("Apply JSON Parameters")
+
+    with gr.Row():
+        clear_button = gr.Button("Clear All")
+        random_prompt_button = gr.Button("Random Prompt")
+
+    history_dropdown = gr.Dropdown(label="Generation History", choices=[], interactive=True)
+    
     with gr.Accordion(label="Generation Parameters", open=False):
         gr_metadata = gr.JSON(label="Metadata", show_label=False)
+    
     gr.Examples(
         examples=config.examples,
         inputs=prompt,
@@ -294,6 +349,7 @@ with gr.Blocks(css="style.css") as demo:
         fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs),
         cache_examples=CACHE_EXAMPLES,
     )
+    
     use_upscaler.change(
         fn=lambda x: [gr.update(visible=x), gr.update(visible=x)],
         inputs=use_upscaler,
@@ -333,7 +389,7 @@ with gr.Blocks(css="style.css") as demo:
     ).then(
         fn=generate,
         inputs=inputs,
-        outputs=result,
+        outputs=[result, gr_metadata, history_dropdown],
         api_name="run",
     )
     negative_prompt.submit(
@@ -345,19 +401,6 @@ with gr.Blocks(css="style.css") as demo:
     ).then(
         fn=generate,
         inputs=inputs,
-        outputs=result,
-        api_name=False,
-    )
-    run_button.click(
-        fn=utils.randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=generate,
-        inputs=inputs,
-        outputs=[result, gr_metadata],
+        outputs=[result, gr_metadata, history_dropdown],
         api_name=False,
-    )
-demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)
+    )