| import base64
|
| import gc
|
| import hashlib
|
| import io
|
| import os
|
| import tempfile
|
| from io import BytesIO
|
|
|
| import gradio as gr
|
| import requests
|
| import torch
|
| from fastapi import FastAPI
|
| from PIL import Image
|
|
|
|
|
|
|
| def encode_file_to_base64(file_path):
|
| with open(file_path, "rb") as file:
|
|
|
| file_base64 = base64.b64encode(file.read())
|
| return file_base64
|
|
|
| def update_diffusion_transformer_api(_: gr.Blocks, app: FastAPI, controller):
|
| @app.post("/videox_fun/update_diffusion_transformer")
|
| def _update_diffusion_transformer_api(
|
| datas: dict,
|
| ):
|
| diffusion_transformer_path = datas.get('diffusion_transformer_path', 'none')
|
|
|
| try:
|
| controller.update_diffusion_transformer(
|
| diffusion_transformer_path
|
| )
|
| comment = "Success"
|
| except Exception as e:
|
| torch.cuda.empty_cache()
|
| comment = f"Error. error information is {str(e)}"
|
|
|
| return {"message": comment}
|
|
|
| def download_from_url(url, timeout=10):
|
| try:
|
| response = requests.get(url, timeout=timeout)
|
| response.raise_for_status()
|
| return response.content
|
| except requests.exceptions.RequestException as e:
|
| print(f"Error downloading from {url}: {e}")
|
| return None
|
|
|
| def save_base64_video(base64_string):
|
| video_data = base64.b64decode(base64_string)
|
|
|
| md5_hash = hashlib.md5(video_data).hexdigest()
|
| filename = f"{md5_hash}.mp4"
|
|
|
| temp_dir = tempfile.gettempdir()
|
| file_path = os.path.join(temp_dir, filename)
|
|
|
| with open(file_path, 'wb') as video_file:
|
| video_file.write(video_data)
|
|
|
| return file_path
|
|
|
| def save_base64_image(base64_string):
|
| video_data = base64.b64decode(base64_string)
|
|
|
| md5_hash = hashlib.md5(video_data).hexdigest()
|
| filename = f"{md5_hash}.jpg"
|
|
|
| temp_dir = tempfile.gettempdir()
|
| file_path = os.path.join(temp_dir, filename)
|
|
|
| with open(file_path, 'wb') as video_file:
|
| video_file.write(video_data)
|
|
|
| return file_path
|
|
|
| def save_url_video(url):
|
| video_data = download_from_url(url)
|
| if video_data:
|
| return save_base64_video(base64.b64encode(video_data))
|
| return None
|
|
|
| def save_url_image(url):
|
| image_data = download_from_url(url)
|
| if image_data:
|
| return save_base64_image(base64.b64encode(image_data))
|
| return None
|
|
|
| def infer_forward_api(_: gr.Blocks, app: FastAPI, controller):
|
| @app.post("/videox_fun/infer_forward")
|
| def _infer_forward_api(
|
| datas: dict,
|
| ):
|
| base_model_path = datas.get('base_model_path', 'none')
|
| base_model_2_path = datas.get('base_model_2_path', 'none')
|
| lora_model_path = datas.get('lora_model_path', 'none')
|
| lora_model_2_path = datas.get('lora_model_2_path', 'none')
|
| lora_alpha_slider = datas.get('lora_alpha_slider', 0.55)
|
| prompt_textbox = datas.get('prompt_textbox', None)
|
| negative_prompt_textbox = datas.get('negative_prompt_textbox', 'The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. ')
|
| sampler_dropdown = datas.get('sampler_dropdown', 'Euler')
|
| sample_step_slider = datas.get('sample_step_slider', 30)
|
| resize_method = datas.get('resize_method', "Generate by")
|
| width_slider = datas.get('width_slider', 672)
|
| height_slider = datas.get('height_slider', 384)
|
| base_resolution = datas.get('base_resolution', 512)
|
| is_image = datas.get('is_image', False)
|
| generation_method = datas.get('generation_method', False)
|
| length_slider = datas.get('length_slider', 49)
|
| overlap_video_length = datas.get('overlap_video_length', 4)
|
| partial_video_length = datas.get('partial_video_length', 72)
|
| cfg_scale_slider = datas.get('cfg_scale_slider', 6)
|
| start_image = datas.get('start_image', None)
|
| end_image = datas.get('end_image', None)
|
| validation_video = datas.get('validation_video', None)
|
| validation_video_mask = datas.get('validation_video_mask', None)
|
| control_video = datas.get('control_video', None)
|
| denoise_strength = datas.get('denoise_strength', 0.70)
|
| seed_textbox = datas.get("seed_textbox", 43)
|
|
|
| ref_image = datas.get('ref_image', None)
|
| enable_teacache = datas.get('enable_teacache', True)
|
| teacache_threshold = datas.get('teacache_threshold', 0.10)
|
| num_skip_start_steps = datas.get('num_skip_start_steps', 1)
|
| teacache_offload = datas.get('teacache_offload', False)
|
| cfg_skip_ratio = datas.get('cfg_skip_ratio', 0)
|
| enable_riflex = datas.get('enable_riflex', False)
|
| riflex_k = datas.get('riflex_k', 6)
|
| fps = datas.get('fps', None)
|
|
|
| generation_method = "Image Generation" if is_image else generation_method
|
|
|
| if start_image is not None:
|
| if start_image.startswith('http'):
|
| start_image = save_url_image(start_image)
|
| start_image = [Image.open(start_image).convert("RGB")]
|
| else:
|
| start_image = base64.b64decode(start_image)
|
| start_image = [Image.open(BytesIO(start_image)).convert("RGB")]
|
|
|
| if end_image is not None:
|
| if end_image.startswith('http'):
|
| end_image = save_url_image(end_image)
|
| end_image = [Image.open(end_image).convert("RGB")]
|
| else:
|
| end_image = base64.b64decode(end_image)
|
| end_image = [Image.open(BytesIO(end_image)).convert("RGB")]
|
|
|
| if validation_video is not None:
|
| if validation_video.startswith('http'):
|
| validation_video = save_url_video(validation_video)
|
| else:
|
| validation_video = save_base64_video(validation_video)
|
|
|
| if validation_video_mask is not None:
|
| if validation_video_mask.startswith('http'):
|
| validation_video_mask = save_url_image(validation_video_mask)
|
| else:
|
| validation_video_mask = save_base64_image(validation_video_mask)
|
|
|
| if control_video is not None:
|
| if control_video.startswith('http'):
|
| control_video = save_url_video(control_video)
|
| else:
|
| control_video = save_base64_video(control_video)
|
|
|
| if ref_image is not None:
|
| if ref_image.startswith('http'):
|
| ref_image = save_url_image(ref_image)
|
| ref_image = [Image.open(ref_image).convert("RGB")]
|
| else:
|
| ref_image = base64.b64decode(ref_image)
|
| ref_image = [Image.open(BytesIO(ref_image)).convert("RGB")]
|
|
|
| try:
|
| save_sample_path, comment = controller.generate(
|
| "",
|
| base_model_path,
|
| lora_model_path,
|
| lora_alpha_slider,
|
| prompt_textbox,
|
| negative_prompt_textbox,
|
| sampler_dropdown,
|
| sample_step_slider,
|
| resize_method,
|
| width_slider,
|
| height_slider,
|
| base_resolution,
|
| generation_method,
|
| length_slider,
|
| overlap_video_length,
|
| partial_video_length,
|
| cfg_scale_slider,
|
| start_image,
|
| end_image,
|
| validation_video,
|
| validation_video_mask,
|
| control_video,
|
| denoise_strength,
|
| seed_textbox,
|
| ref_image = ref_image,
|
| enable_teacache = enable_teacache,
|
| teacache_threshold = teacache_threshold,
|
| num_skip_start_steps = num_skip_start_steps,
|
| teacache_offload = teacache_offload,
|
| cfg_skip_ratio = cfg_skip_ratio,
|
| enable_riflex = enable_riflex,
|
| riflex_k = riflex_k,
|
| base_model_2_dropdown = base_model_2_path,
|
| lora_model_2_dropdown = lora_model_2_path,
|
| fps = fps,
|
| is_api = True,
|
| )
|
| except Exception as e:
|
| gc.collect()
|
| torch.cuda.empty_cache()
|
| torch.cuda.ipc_collect()
|
| save_sample_path = ""
|
| comment = f"Error. error information is {str(e)}"
|
| return {"message": comment, "save_sample_path": None, "base64_encoding": None}
|
|
|
| if save_sample_path != "":
|
| return {"message": comment, "save_sample_path": save_sample_path, "base64_encoding": encode_file_to_base64(save_sample_path)}
|
| else:
|
| return {"message": comment, "save_sample_path": save_sample_path, "base64_encoding": None} |
