Upload 2 files

app.py

@@ -1,11 +1,8 @@
 import gradio as gr
-import os 
-import sys
-from pathlib import Path
+import os
 from all_models import models
-from externalmod import gr_Interface_load
+from externalmod import gr_Interface_load, save_image, randomize_seed
 from prompt_extend import extend_prompt
-from random import randint
 import asyncio
 from threading import RLock
 lock = RLock()
@@ -32,53 +29,47 @@ def send_it1(inputs, model_choice, neg_input, height, width, steps, cfg, seed):
 
 # https://huggingface.co/docs/api-inference/detailed_parameters
 # https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
-async def infer(model_index, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1, timeout=inference_timeout):
-    from pathlib import Path
+async def infer(model_index, 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(models2[model_index].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)
     except asyncio.TimeoutError as e:
         print(e)
-        print(f"Task timed out: {models2[model_index]}")
+        print(f"Task timed out: {models[model_index]}")
         if not task.done(): task.cancel()
         result = None
-        raise Exception(f"Task timed out: {models2[model_index]}")
+        raise Exception(f"Task timed out: {models[model_index]}") 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, models[model_index], prompt, nprompt, height, width, steps, cfg, seed)
         return image
     return None
 
-def gen_fn(model_index, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1):
+def gen_fn(model_index, 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_index, prompt, nprompt,
                                          height, width, steps, cfg, seed, inference_timeout))
     except (Exception, asyncio.CancelledError) as e:
         print(e)
-        print(f"Task aborted: {models2[model_index]}")
+        print(f"Task aborted: {models[model_index]}")
         result = None
-        raise gr.Error(f"Task aborted: {models2[model_index]}, Error: {e}")
+        raise gr.Error(f"Task aborted: {models[model_index]}, Error: {e}")
     finally:
         loop.close()
     return result
@@ -127,8 +118,9 @@ with gr.Blocks(theme='John6666/YntecDark', fill_width=True, css=css) as myface:
                         height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0, elem_classes=["gr-box", "gr-input"])
                     with gr.Row():
                         steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0, elem_classes=["gr-box", "gr-input"])
-                        cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0, elem_classes=["gr-box", "gr-input"])
+                        cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=-1, elem_classes=["gr-box", "gr-input"])
                         seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1, elem_classes=["gr-box", "gr-input"])
+                        seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
             run = gr.Button("Generate Image", variant="primary", elem_classes="gr-button")
 
     with gr.Row():
@@ -153,8 +145,9 @@ with gr.Blocks(theme='John6666/YntecDark', fill_width=True, css=css) as myface:
         concurrency_limit=None,
         queue=False,
     )
-    use_short.click(short_prompt, inputs=[input_text], outputs=magic1, queue=False)
-    see_prompts.click(text_it1, inputs=[input_text], outputs=magic1, queue=False)
+    use_short.click(short_prompt, inputs=[input_text], outputs=magic1)
+    see_prompts.click(text_it1, inputs=[input_text], outputs=magic1)
+    seed_rand.click(randomize_seed, None, [seed], queue=False)
     
 myface.queue(default_concurrency_limit=200, max_size=200)
 myface.launch(show_api=False, max_threads=400)

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