| | |
| | import base64 |
| | import gc |
| | import hashlib |
| | import io |
| | import os |
| | import tempfile |
| | from io import BytesIO |
| |
|
| | import gradio as gr |
| | import requests |
| | import torch |
| | import torch.distributed as dist |
| | from fastapi import FastAPI, HTTPException |
| | from PIL import Image |
| |
|
| | from .api import download_from_url, encode_file_to_base64 |
| |
|
| | try: |
| | import ray |
| | except: |
| | print("Ray is not installed. If you want to use multi gpus api. Please install it by running 'pip install ray'.") |
| | ray = None |
| |
|
| | def save_base64_video_dist(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) |
| |
|
| | if dist.is_initialized(): |
| | if dist.get_rank() == 0: |
| | with open(file_path, 'wb') as video_file: |
| | video_file.write(video_data) |
| | dist.barrier() |
| | else: |
| | with open(file_path, 'wb') as video_file: |
| | video_file.write(video_data) |
| | return file_path |
| |
|
| | def save_base64_image_dist(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) |
| |
|
| | if dist.is_initialized(): |
| | if dist.get_rank() == 0: |
| | with open(file_path, 'wb') as video_file: |
| | video_file.write(video_data) |
| | dist.barrier() |
| | else: |
| | with open(file_path, 'wb') as video_file: |
| | video_file.write(video_data) |
| | return file_path |
| |
|
| | def save_url_video_dist(url): |
| | video_data = download_from_url(url) |
| | if video_data: |
| | return save_base64_video_dist(base64.b64encode(video_data)) |
| | return None |
| |
|
| | def save_url_image_dist(url): |
| | image_data = download_from_url(url) |
| | if image_data: |
| | return save_base64_image_dist(base64.b64encode(image_data)) |
| | return None |
| |
|
| | if ray is not None: |
| | @ray.remote(num_gpus=1) |
| | class MultiNodesGenerator: |
| | def __init__( |
| | self, rank: int, world_size: int, Controller, |
| | GPU_memory_mode, scheduler_dict, model_name=None, model_type="Inpaint", |
| | config_path=None, ulysses_degree=1, ring_degree=1, |
| | fsdp_dit=False, fsdp_text_encoder=False, compile_dit=False, |
| | weight_dtype=None, savedir_sample=None, |
| | ): |
| | |
| | os.environ["RANK"] = str(rank) |
| | os.environ["WORLD_SIZE"] = str(world_size) |
| | os.environ["MASTER_ADDR"] = "127.0.0.1" |
| | os.environ["MASTER_PORT"] = "29500" |
| | |
| | self.rank = rank |
| | self.controller = Controller( |
| | GPU_memory_mode, scheduler_dict, model_name=model_name, model_type=model_type, config_path=config_path, |
| | ulysses_degree=ulysses_degree, ring_degree=ring_degree, |
| | fsdp_dit=fsdp_dit, fsdp_text_encoder=fsdp_text_encoder, compile_dit=compile_dit, |
| | weight_dtype=weight_dtype, savedir_sample=savedir_sample, |
| | ) |
| |
|
| | def generate(self, datas): |
| | try: |
| | 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_dist(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_dist(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_dist(validation_video) |
| | else: |
| | validation_video = save_base64_video_dist(validation_video) |
| |
|
| | if validation_video_mask is not None: |
| | if validation_video_mask.startswith('http'): |
| | validation_video_mask = save_url_image_dist(validation_video_mask) |
| | else: |
| | validation_video_mask = save_base64_image_dist(validation_video_mask) |
| |
|
| | if control_video is not None: |
| | if control_video.startswith('http'): |
| | control_video = save_url_video_dist(control_video) |
| | else: |
| | control_video = save_base64_video_dist(control_video) |
| | |
| | if ref_image is not None: |
| | if ref_image.startswith('http'): |
| | ref_image = save_url_image_dist(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 = self.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)}" |
| | if dist.is_initialized(): |
| | if dist.get_rank() == 0: |
| | return {"message": comment, "save_sample_path": None, "base64_encoding": None} |
| | else: |
| | return None |
| | else: |
| | return {"message": comment, "save_sample_path": None, "base64_encoding": None} |
| |
|
| |
|
| | if dist.is_initialized(): |
| | if dist.get_rank() == 0: |
| | 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": None, "base64_encoding": None} |
| | else: |
| | return None |
| | else: |
| | 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": None, "base64_encoding": None} |
| |
|
| | except Exception as e: |
| | print(f"Error generating: {str(e)}") |
| | comment = f"Error generating: {str(e)}" |
| | if dist.is_initialized(): |
| | if dist.get_rank() == 0: |
| | return {"message": comment, "save_sample_path": None, "base64_encoding": None} |
| | else: |
| | return None |
| | else: |
| | return {"message": comment, "save_sample_path": None, "base64_encoding": None} |
| |
|
| | class MultiNodesEngine: |
| | def __init__( |
| | self, |
| | world_size, |
| | Controller, |
| | GPU_memory_mode, |
| | scheduler_dict, |
| | model_name, |
| | model_type, |
| | config_path, |
| | ulysses_degree=1, |
| | ring_degree=1, |
| | fsdp_dit=False, |
| | fsdp_text_encoder=False, |
| | compile_dit=False, |
| | weight_dtype=torch.bfloat16, |
| | savedir_sample="samples" |
| | ): |
| | |
| | if not ray.is_initialized(): |
| | ray.init() |
| | |
| | num_workers = world_size |
| | self.workers = [ |
| | MultiNodesGenerator.remote( |
| | rank, world_size, Controller, |
| | GPU_memory_mode, scheduler_dict, model_name=model_name, model_type=model_type, config_path=config_path, |
| | ulysses_degree=ulysses_degree, ring_degree=ring_degree, |
| | fsdp_dit=fsdp_dit, fsdp_text_encoder=fsdp_text_encoder, compile_dit=compile_dit, |
| | weight_dtype=weight_dtype, savedir_sample=savedir_sample, |
| | ) |
| | for rank in range(num_workers) |
| | ] |
| | print("Update workers done") |
| | |
| | async def generate(self, data): |
| | results = ray.get([ |
| | worker.generate.remote(data) |
| | for worker in self.workers |
| | ]) |
| |
|
| | return next(path for path in results if path is not None) |
| |
|
| | def multi_nodes_infer_forward_api(_: gr.Blocks, app: FastAPI, engine): |
| |
|
| | @app.post("/videox_fun/infer_forward") |
| | async def _multi_nodes_infer_forward_api( |
| | datas: dict, |
| | ): |
| | try: |
| | result = await engine.generate(datas) |
| | return result |
| | except Exception as e: |
| | if isinstance(e, HTTPException): |
| | raise e |
| | raise HTTPException(status_code=500, detail=str(e)) |
| | else: |
| | MultiNodesEngine = None |
| | MultiNodesGenerator = None |
| | multi_nodes_infer_forward_api = None |
