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import gradio as gr | |
from config import howManyModelsToUse,num_models,max_images,inference_timeout,MAX_SEED,thePrompt,preSetPrompt,negPreSetPrompt | |
from all_models import models | |
import asyncio | |
from externalmod import gr_Interface_load, save_image, randomize_seed | |
import os | |
from threading import RLock | |
lock = RLock() | |
HF_TOKEN = os.getenv("ohgoddamn") | |
default_models = models[:num_models] | |
def get_current_time(): | |
from datetime import datetime | |
now = datetime.now() | |
current_time = now.strftime("%y-%m-%d %H:%M:%S") | |
return current_time | |
def load_fn(models, HF_TOKEN): | |
global models_load | |
models_load = {} | |
for model in models: | |
if model not in models_load: | |
try: | |
m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN) | |
models_load[model] = m.fn # Store the callable | |
except Exception as error: | |
print(error) | |
models_load[model] = lambda **kwargs: None | |
async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=120, hf_token=None): | |
print(f"{prompt}\n{model_str}\n{timeout}\n") | |
kwargs = {} | |
if height > 0: kwargs["height"] = height | |
if width > 0: kwargs["width"] = width | |
if steps > 0: kwargs["num_inference_steps"] = steps | |
if cfg > 0: kwargs["guidance_scale"] = cfg | |
kwargs["negative_prompt"] = nprompt | |
theSeed = randomize_seed() if seed == -1 else seed | |
kwargs["seed"] = theSeed | |
if hf_token: | |
kwargs["token"] = hf_token | |
try: | |
task = asyncio.create_task(asyncio.to_thread(models_load[model_str], prompt=prompt, **kwargs)) | |
result = await asyncio.wait_for(task, timeout=timeout) | |
except asyncio.TimeoutError as e: | |
print(f"Timeout: {model_str}") | |
if not task.done(): task.cancel() | |
raise Exception(f"Timeout: {model_str}") from e | |
except Exception as e: | |
print(f"Exception: {model_str} -> {e}") | |
if not task.done(): task.cancel() | |
raise Exception(f"Inference failed: {model_str}") from e | |
if result is not None and not isinstance(result, tuple): | |
with lock: | |
png_path = model_str.replace("/", "_") + " - " + get_current_time() + "_" + str(theSeed) + ".png" | |
image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, theSeed) | |
return image | |
return None | |
def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, inference_timeout2=120): | |
try: | |
loop = asyncio.new_event_loop() | |
result = loop.run_until_complete(infer(model_str, prompt, nprompt, | |
height, width, steps, cfg, seed, inference_timeout2, HF_TOKEN)) | |
except Exception as e: | |
print(f"gen_fn: Task aborted: {model_str} -> {e}") | |
raise gr.Error(f"Task aborted: {model_str}, Error: {e}") | |
finally: | |
loop.close() | |
return result | |
''' | |
def load_fn(models,HF_TOKEN): | |
global models_load | |
models_load = {} | |
for model in models: | |
if model not in models_load.keys(): | |
try: | |
m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN) | |
except Exception as error: | |
print(error) | |
m = gr.Interface(lambda: None, ['text'], ['image']) | |
models_load.update({model: m}) | |
async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout): | |
print(f"{prompt}\n") | |
print(f"{model_str}\n") | |
print(f"{timeout}\n") | |
kwargs = {} | |
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: | |
theSeed = randomize_seed() | |
else: | |
theSeed = seed | |
kwargs["seed"] = theSeed | |
task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, prompt=prompt, negative_prompt=nprompt, **kwargs, token=HF_TOKEN)) | |
print(f"await") | |
await asyncio.sleep(20) | |
try: | |
result = await asyncio.wait_for(task, timeout=timeout) | |
except asyncio.TimeoutError as e: | |
print(e) | |
print(f"infer: Task timed out: {model_str}") | |
if not task.done(): task.cancel() | |
result = None | |
raise Exception(f"Task timed out: {model_str}") from e | |
except Exception as e: | |
print(e) | |
print(f"infer: exception: {model_str}") | |
if not task.done(): task.cancel() | |
result = None | |
raise Exception() from e | |
if task.done() and result is not None and not isinstance(result, tuple): | |
print(f"{result}") | |
with lock: | |
png_path = model_str.replace("/", "_") + " - " + get_current_time() + "_" + str(theSeed) + ".png" | |
image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, theSeed) | |
return image | |
return None | |
def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, inference_timeout2=120): | |
try: | |
loop = asyncio.new_event_loop() | |
result = loop.run_until_complete(infer(model_str, prompt, nprompt, | |
height, width, steps, cfg, seed, inference_timeout2)) | |
except (Exception, asyncio.CancelledError) as e: | |
print(e) | |
print(f"gen_fn: Task aborted: {model_str}") | |
result = None | |
raise gr.Error(f"Task aborted: {model_str}, Error: {e}") | |
finally: | |
loop.close() | |
return result | |
''' |