forked from extern/easydiffusion
131 lines
5.3 KiB
Python
131 lines
5.3 KiB
Python
import queue
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import time
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import json
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import pprint
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from easydiffusion import device_manager
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from easydiffusion.types import TaskData, Response, Image as ResponseImage, UserInitiatedStop, GenerateImageRequest
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from easydiffusion.utils import get_printable_request, save_images_to_disk, log
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from sdkit import Context
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from sdkit.generate import generate_images
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from sdkit.filter import apply_filters
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from sdkit.utils import img_to_buffer, img_to_base64_str, latent_samples_to_images, gc
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context = Context() # thread-local
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'''
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runtime data (bound locally to this thread), for e.g. device, references to loaded models, optimization flags etc
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'''
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def init(device):
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'''
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Initializes the fields that will be bound to this runtime's context, and sets the current torch device
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'''
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context.stop_processing = False
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context.temp_images = {}
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context.partial_x_samples = None
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device_manager.device_init(context, device)
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def make_images(req: GenerateImageRequest, task_data: TaskData, data_queue: queue.Queue, task_temp_images: list, step_callback):
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context.stop_processing = False
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print_task_info(req, task_data)
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images = make_images_internal(req, task_data, data_queue, task_temp_images, step_callback)
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res = Response(req, task_data, images=construct_response(images, task_data, base_seed=req.seed))
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res = res.json()
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data_queue.put(json.dumps(res))
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log.info('Task completed')
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return res
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def print_task_info(req: GenerateImageRequest, task_data: TaskData):
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req_str = pprint.pformat(get_printable_request(req)).replace("[","\[")
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task_str = pprint.pformat(task_data.dict()).replace("[","\[")
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log.info(f'request: {req_str}')
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log.info(f'task data: {task_str}')
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def make_images_internal(req: GenerateImageRequest, task_data: TaskData, data_queue: queue.Queue, task_temp_images: list, step_callback):
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images, user_stopped = generate_images_internal(req, task_data, data_queue, task_temp_images, step_callback, task_data.stream_image_progress)
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filtered_images = filter_images(task_data, images, user_stopped)
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if task_data.save_to_disk_path is not None:
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save_images_to_disk(images, filtered_images, req, task_data)
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return filtered_images if task_data.show_only_filtered_image or (task_data.use_face_correction is None and task_data.use_upscale is None) else images + filtered_images
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def generate_images_internal(req: GenerateImageRequest, task_data: TaskData, data_queue: queue.Queue, task_temp_images: list, step_callback, stream_image_progress: bool):
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context.temp_images.clear()
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callback = make_step_callback(req, task_data, data_queue, task_temp_images, step_callback, stream_image_progress)
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try:
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images = generate_images(context, callback=callback, **req.dict())
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user_stopped = False
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except UserInitiatedStop:
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images = []
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user_stopped = True
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if context.partial_x_samples is not None:
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images = latent_samples_to_images(context, context.partial_x_samples)
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context.partial_x_samples = None
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finally:
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gc(context)
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return images, user_stopped
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def filter_images(task_data: TaskData, images: list, user_stopped):
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if user_stopped or (task_data.use_face_correction is None and task_data.use_upscale is None):
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return images
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filters_to_apply = []
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if task_data.use_face_correction and 'gfpgan' in task_data.use_face_correction.lower(): filters_to_apply.append('gfpgan')
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if task_data.use_upscale and 'realesrgan' in task_data.use_upscale.lower(): filters_to_apply.append('realesrgan')
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return apply_filters(context, filters_to_apply, images, scale=task_data.upscale_amount)
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def construct_response(images: list, task_data: TaskData, base_seed: int):
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return [
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ResponseImage(
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data=img_to_base64_str(img, task_data.output_format, task_data.output_quality),
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seed=base_seed + i
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) for i, img in enumerate(images)
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]
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def make_step_callback(req: GenerateImageRequest, task_data: TaskData, data_queue: queue.Queue, task_temp_images: list, step_callback, stream_image_progress: bool):
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n_steps = req.num_inference_steps if req.init_image is None else int(req.num_inference_steps * req.prompt_strength)
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last_callback_time = -1
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def update_temp_img(x_samples, task_temp_images: list):
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partial_images = []
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images = latent_samples_to_images(context, x_samples)
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for i, img in enumerate(images):
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buf = img_to_buffer(img, output_format='JPEG')
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context.temp_images[f"{task_data.request_id}/{i}"] = buf
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task_temp_images[i] = buf
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partial_images.append({'path': f"/image/tmp/{task_data.request_id}/{i}"})
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del images
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return partial_images
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def on_image_step(x_samples, i):
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nonlocal last_callback_time
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context.partial_x_samples = x_samples
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step_time = time.time() - last_callback_time if last_callback_time != -1 else -1
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last_callback_time = time.time()
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progress = {"step": i, "step_time": step_time, "total_steps": n_steps}
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if stream_image_progress and i % 5 == 0:
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progress['output'] = update_temp_img(x_samples, task_temp_images)
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data_queue.put(json.dumps(progress))
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step_callback()
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if context.stop_processing:
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raise UserInitiatedStop("User requested that we stop processing")
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return on_image_step
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