| 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} |
