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80 lines
3.6 KiB
Python
80 lines
3.6 KiB
Python
import os
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import time
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import base64
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import re
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from diffusionkit import data_utils
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from diffusionkit.types import GenerateImageRequest
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from sd_internal import TaskData
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filename_regex = re.compile('[^a-zA-Z0-9]')
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# keep in sync with `ui/media/js/dnd.js`
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TASK_TEXT_MAPPING = {
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'prompt': 'Prompt',
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'width': 'Width',
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'height': 'Height',
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'seed': 'Seed',
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'num_inference_steps': 'Steps',
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'guidance_scale': 'Guidance Scale',
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'prompt_strength': 'Prompt Strength',
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'use_face_correction': 'Use Face Correction',
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'use_upscale': 'Use Upscaling',
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'sampler_name': 'Sampler',
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'negative_prompt': 'Negative Prompt',
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'use_stable_diffusion_model': 'Stable Diffusion model',
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'use_hypernetwork_model': 'Hypernetwork model',
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'hypernetwork_strength': 'Hypernetwork Strength'
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}
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def save_to_disk(images: list, filtered_images: list, req: GenerateImageRequest, task_data: TaskData):
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save_folder_path = os.path.join(task_data.save_to_disk_path, filename_regex.sub('_', task_data.session_id))
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metadata_entries = get_metadata_entries(req, task_data)
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if task_data.show_only_filtered_image or filtered_images == images:
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data_utils.save_images(filtered_images, save_folder_path, file_name=make_filename_callback(req), output_format=task_data.output_format, output_quality=task_data.output_quality)
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data_utils.save_metadata(metadata_entries, save_folder_path, file_name=make_filename_callback(req), output_format=task_data.metadata_output_format)
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else:
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data_utils.save_images(images, save_folder_path, file_name=make_filename_callback(req), output_format=task_data.output_format, output_quality=task_data.output_quality)
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data_utils.save_images(filtered_images, save_folder_path, file_name=make_filename_callback(req, suffix='filtered'), output_format=task_data.output_format, output_quality=task_data.output_quality)
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data_utils.save_metadata(metadata_entries, save_folder_path, file_name=make_filename_callback(req, suffix='filtered'), output_format=task_data.metadata_output_format)
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def get_metadata_entries(req: GenerateImageRequest, task_data: TaskData):
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metadata = get_printable_request(req)
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metadata.update({
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'use_stable_diffusion_model': task_data.use_stable_diffusion_model,
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'use_vae_model': task_data.use_vae_model,
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'use_hypernetwork_model': task_data.use_hypernetwork_model,
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'use_face_correction': task_data.use_face_correction,
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'use_upscale': task_data.use_upscale,
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})
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# if text, format it in the text format expected by the UI
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is_txt_format = (task_data.metadata_output_format.lower() == 'txt')
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if is_txt_format:
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metadata = {TASK_TEXT_MAPPING[key]: val for key, val in metadata.items() if key in TASK_TEXT_MAPPING}
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entries = [metadata.copy() for _ in range(req.num_outputs)]
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for i, entry in enumerate(entries):
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entry['Seed' if is_txt_format else 'seed'] = req.seed + i
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return entries
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def get_printable_request(req: GenerateImageRequest):
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metadata = req.dict()
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del metadata['init_image']
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del metadata['init_image_mask']
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return metadata
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def make_filename_callback(req: GenerateImageRequest, suffix=None):
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def make_filename(i):
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img_id = base64.b64encode(int(time.time()+i).to_bytes(8, 'big')).decode() # Generate unique ID based on time.
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img_id = img_id.translate({43:None, 47:None, 61:None})[-8:] # Remove + / = and keep last 8 chars.
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prompt_flattened = filename_regex.sub('_', req.prompt)[:50]
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name = f"{prompt_flattened}_{img_id}"
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name = name if suffix is None else f'{name}_{suffix}'
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return name
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return make_filename |