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import io
import gc
import base64
import torch
import gradio as gr
import tempfile
import hashlib
import os
from fastapi import FastAPI
from io import BytesIO
from PIL import Image
# Function to encode a file to Base64
def encode_file_to_base64(file_path):
with open(file_path, "rb") as file:
# Encode the data to Base64
file_base64 = base64.b64encode(file.read())
return file_base64
def update_edition_api(_: gr.Blocks, app: FastAPI, controller):
@app.post("/cogvideox_fun/update_edition")
def _update_edition_api(
datas: dict,
):
edition = datas.get('edition', 'v2')
try:
controller.update_edition(
edition
)
comment = "Success"
except Exception as e:
torch.cuda.empty_cache()
comment = f"Error. error information is {str(e)}"
return {"message": comment}
def update_diffusion_transformer_api(_: gr.Blocks, app: FastAPI, controller):
@app.post("/cogvideox_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 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 infer_forward_api(_: gr.Blocks, app: FastAPI, controller):
@app.post("/cogvideox_fun/infer_forward")
def _infer_forward_api(
datas: dict,
):
base_model_path = datas.get('base_model_path', 'none')
lora_model_path = datas.get('lora_model_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)
generation_method = "Image Generation" if is_image else generation_method
if start_image is not None:
start_image = base64.b64decode(start_image)
start_image = [Image.open(BytesIO(start_image))]
if end_image is not None:
end_image = base64.b64decode(end_image)
end_image = [Image.open(BytesIO(end_image))]
if validation_video is not None:
validation_video = save_base64_video(validation_video)
if validation_video_mask is not None:
validation_video_mask = save_base64_image(validation_video_mask)
if control_video is not None:
control_video = save_base64_video(control_video)
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,
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}
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} |