Upload 2 files

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
-from random import randint
 from all_models import models
-from externalmod import gr_Interface_load
+from externalmod import gr_Interface_load, save_image, randomize_seed
 import asyncio
 import os
 from threading import RLock
@@ -49,21 +48,16 @@ def random_choices():
 
 # https://huggingface.co/docs/api-inference/detailed_parameters
 # https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
-async def infer(model_str, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1, timeout=inference_timeout):
-    from pathlib import Path
+async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout):
     kwargs = {}
-    if height is not None and height >= 256: kwargs["height"] = height
-    if width is not None and width >= 256: kwargs["width"] = width
-    if steps is not None and steps >= 1: kwargs["num_inference_steps"] = steps
-    if cfg is not None and cfg > 0: cfg = kwargs["guidance_scale"] = cfg
-    noise = ""
-    if seed >= 0: kwargs["seed"] = seed
-    else:
-        rand = randint(1, 500)
-        for i in range(rand):
-            noise += " "
+    if height > 0: kwargs["height"] = height
+    if width > 0: kwargs["width"] = width
+    if steps > 0: kwargs["num_inference_steps"] = steps
+    if cfg > 0: cfg = kwargs["guidance_scale"] = cfg
+    if seed == -1: kwargs["seed"] = randomize_seed()
+    else: kwargs["seed"] = seed
     task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
-                               prompt=f'{prompt} {noise}', negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
+                               prompt=prompt, negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
     await asyncio.sleep(0)
     try:
         result = await asyncio.wait_for(task, timeout=timeout)
@@ -72,22 +66,21 @@ async def infer(model_str, prompt, nprompt="", height=None, width=None, steps=No
         print(f"Task timed out: {model_str}")
         if not task.done(): task.cancel()
         result = None
-        raise Exception(f"Task timed out: {model_str}")
+        raise Exception(f"Task timed out: {model_str}") from e
     except Exception as e:
         print(e)
         if not task.done(): task.cancel()
         result = None
-        raise Exception(e)
+        raise Exception() from e
     if task.done() and result is not None and not isinstance(result, tuple):
         with lock:
             png_path = "image.png"
-            result.save(png_path)
-            image = str(Path(png_path).resolve())
+            image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, seed)
         return image
     return None
 
 
-def gen_fn(model_str, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1):
+def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1):
     try:
         loop = asyncio.new_event_loop()
         result = loop.run_until_complete(infer(model_str, prompt, nprompt,
@@ -131,6 +124,8 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
                         steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
                         cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
                         seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
+                        seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
+                        seed_rand.click(randomize_seed, None, [seed], queue=False)
             with gr.Row():
                 gen_button = gr.Button(f'Generate up to {int(num_models)} images in up to 3 minutes total', variant='primary', scale=3)
                 random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1)
@@ -179,6 +174,8 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
                         steps2 = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
                         cfg2 = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
                         seed2 = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
+                        seed_rand2 = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
+                        seed_rand2.click(randomize_seed, None, [seed2], queue=False)
             num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')
             with gr.Row():
                 gen_button2 = gr.Button('Generate', variant='primary', scale=2)

externalmod.py

@@ -583,3 +583,30 @@ def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="l
            models.append(model.id)
            if len(models) == limit: break
     return models
+
+
+def save_image(image, savefile, modelname, prompt, nprompt, height=0, width=0, steps=0, cfg=0, seed=-1):
+    from PIL import Image, PngImagePlugin
+    import json
+    try:
+        metadata = {"prompt": prompt, "negative_prompt": nprompt, "Model": {"Model": modelname.split("/")[-1]}}
+        if steps > 0: metadata["num_inference_steps"] = steps
+        if cfg > 0: metadata["guidance_scale"] = cfg
+        if seed != -1: metadata["seed"] = seed
+        if width > 0 and height > 0: metadata["resolution"] = f"{width} x {height}"
+        metadata_str = json.dumps(metadata)
+        info = PngImagePlugin.PngInfo()
+        info.add_text("metadata", metadata_str)
+        image.save(savefile, "PNG", pnginfo=info)
+        return str(Path(savefile).resolve())
+    except Exception as e:
+        print(f"Failed to save image file: {e}")
+        raise Exception(f"Failed to save image file:") from e
+
+
+def randomize_seed():
+    from random import seed, randint
+    MAX_SEED = 2**32-1
+    seed()
+    rseed = randint(0, MAX_SEED)
+    return rseed