chore: Update Dockerfile and requirements.txt

Former-commit-id: 0caea23b864f7ac1e3663f3f59551de462daf546
Former-commit-id: 69e7898d5bb389e4e83b2115cd78a7088774cb9c

Dockerfile

@@ -1,14 +1,14 @@
 # Use the official Python base image
-FROM python:3.11-slim
+FROM python:3.10-slim
 
 # Set the initial working directory
-WORKDIR /api
+WORKDIR /app
 
 # Copy the requirements.txt file from the api directory
-COPY api/requirements.txt ./
+COPY ../api/requirements.txt ./
 
 # Install dependencies specified in requirements.txt
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+RUN pip install  -r requirements.txt
 
 # Create a non-root user and set up the environment
 RUN useradd -m -u 1000 user

api/requirements.txt

@@ -7,7 +7,7 @@ lightning==2.2.3
 logfire==0.42.0
 Pillow==10.3.0
 pydantic==2.7.4
-torch==2.2.0
+torch
 utils==1.0.2
 uvicorn==0.30.1
 boto3

api/routers/sdxl_text_to_image.py

@@ -10,7 +10,6 @@ import uuid
 from diffusers import DiffusionPipeline
 import torch
 from functools import lru_cache
-from s3_manager import S3ManagerService
 from PIL import Image
 import io
 from utils import accelerator

gradio-ui/ui.py

@@ -1,59 +0,0 @@
-import gradio as gr
-import requests
-from pydantic import BaseModel
-from diffusers.utils import load_image
-
-
-SDXL_LORA_API_URL = 'http://127.0.0.1:8000/api/v1/product-diffusion/sdxl_v0_lora_inference'
-
-# Define the InpaintingRequest model
-class InpaintingRequest(BaseModel):
-    prompt: str
-    num_inference_steps: int
-    guidance_scale: float
-    negative_prompt: str
-    num_images: int
-    mode: str
-
-def generate_sdxl_lora_image(prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode):
-    # Prepare the payload for SDXL LORA API
-    payload = InpaintingRequest(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale,
-        num_images=num_images,
-        mode=mode
-    ).model_dump()
-    
-    response = requests.post(SDXL_LORA_API_URL, json=payload)
-    response_json = response.json()
-    url = response_json['url']
-    
-    image = load_image(url)
-    return image
-
-with gr.Blocks(theme='gradio/soft') as demo:
-    with gr.Tab("SDXL LORA TEXT-TO-IMAGE"):
-        with gr.Row():
-            with gr.Column(scale=1):
-              
-                    prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
-                    negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
-                   
-
-               
-                    with gr.Column(scale=1):
-                        num_inference_steps = gr.Slider(minimum=1, maximum=1000, step=1, value=20, label="Inference Steps")
-                        guidance_scale = gr.Slider(minimum=1.0, maximum=10.0, step=0.1, value=7.5, label="Guidance Scale")
-                        num_images = gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Number of Images")
-                        mode = gr.Dropdown(choices=["s3_json", "b64_json"], value="s3_json", label="Mode")
-                        generate_button = gr.Button("Generate Image")
-            
-            
-            image_preview = gr.Image(label="Generated Image", height=512, width=512,scale=1,show_download_button=True,show_share_button=True,container=True)
-        
-        generate_button.click(generate_sdxl_lora_image, inputs=[prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode], outputs=[image_preview])
-    
-
-demo.launch()

requirements.txt

@@ -1,25 +0,0 @@
-diffusers
-datasets
-fastapi
-wandb
-lightning
-torchvision
-pandas
-numpy
-rich
-tqdm
-transformers
-fastapi
-uvicorn
-matplotlib
-accelerate
-torchvision
-ftfy
-tensorboard
-Jinja2
-datasets
-peft
-async-batcher
-ultralytics
-opencv-python-headless
-boto3

scripts/inpainting_pipeline.py

@@ -5,7 +5,6 @@ from utils import accelerator, ImageAugmentation
 import hydra
 from omegaconf import DictConfig
 from PIL import Image
-from functools import lru_cache
 
 
 def load_pipeline(model_name: str, device, enable_compile: bool = True):