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import os
import requests
import torch
import gradio as gr
import spaces
from huggingface_hub import login
# from diffusers.utils import load_image
#
# from models.transformer_sd3 import SD3Transformer2DModel
# from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
import torch
from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel
from diffusers.utils import load_image
# ----------------------------
# Step 1: Download IP Adapter if not exists
# ----------------------------
url = "https://huggingface.co/InstantX/SD3.5-Large-IP-Adapter/resolve/main/ip-adapter.bin"
file_path = "ip-adapter.bin"
if not os.path.exists(file_path):
print("File not found, downloading...")
response = requests.get(url, stream=True)
with open(file_path, "wb") as file:
for chunk in response.iter_content(chunk_size=1024):
if chunk:
file.write(chunk)
print("Download completed!")
# ----------------------------
# Step 2: Hugging Face Login
# ----------------------------
token = os.getenv("HF_TOKEN")
if not token:
raise ValueError("Hugging Face token not found. Set the 'HF_TOKEN' environment variable.")
login(token=token)
# model_path = 'stabilityai/stable-diffusion-3.5-large'
ip_adapter_path = './ip-adapter.bin'
image_encoder_path = "google/siglip-so400m-patch14-384"
#
# transformer = SD3Transformer2DModel.from_pretrained(
# model_path, subfolder="transformer", torch_dtype=torch.bfloat16
# )
#
# pipe = StableDiffusion3Pipeline.from_pretrained(
# model_path, transformer=transformer, torch_dtype=torch.bfloat16
# ).to("cuda")
controlnet = SD3ControlNetModel.from_pretrained("stabilityai/stable-diffusion-3.5-large-controlnet-depth", torch_dtype=torch.float16)
pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-large",
controlnet=controlnet,
torch_dtype=torch.float16,
).to("cuda")
pipe.init_ipadapter(
ip_adapter_path=ip_adapter_path,
image_encoder_path=image_encoder_path,
nb_token=64,
)
# ----------------------------
# Step 6: Gradio Function
# ----------------------------
@spaces.GPU
def gui_generation(prompt,negative_prompt, ref_img, guidance_scale, ipadapter_scale):
ref_img = load_image(ref_img.name).convert('RGB')
control_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_lcm_depth.png")
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipe(
width=1024,
height=1024,
prompt=prompt,
negative_prompt=negative_prompt,
control_image=control_image,
guidance_scale=guidance_scale,
clip_image=ref_img,
num_inference_steps=40,
generator=generator,
max_sequence_length=77,
ipadapter_scale=ipadapter_scale,
).images[0]
return image
# ----------------------------
# Step 7: Gradio Interface
# ----------------------------
prompt_box = gr.Textbox(label="Prompt", placeholder="Enter your image generation prompt")
negative_prompt_box = gr.Textbox(label="Negative Prompt", placeholder="Enter your image generation prompt",value="lowres, low quality, worst quality")
ref_img = gr.File(label="Upload Reference Image")
guidance_slider = gr.Slider(
label="Guidance Scale",
minimum=2,
maximum=16,
value=7,
step=0.5,
info="Controls adherence to the text prompt"
)
ipadapter_slider = gr.Slider(
label="IP-Adapter Scale",
minimum=0,
maximum=1,
value=0.5,
step=0.1,
info="Controls influence of the image prompt"
)
interface = gr.Interface(
fn=gui_generation,
inputs=[prompt_box,negative_prompt_box, ref_img, guidance_slider, ipadapter_slider],
outputs="image",
title="Image Generation with Stable Diffusion 3.5 Large and IP-Adapter",
description="Generates an image based on a text prompt and a reference image using Stable Diffusion 3.5 Large with IP-Adapter."
)
# ----------------------------
# Step 8: Launch Gradio App
# ----------------------------
interface.launch()
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