Spaces:
Paused
Paused
File size: 3,423 Bytes
4f91ffe 51f8f41 4ec6616 6dcb6b3 51f8f41 4f91ffe 6dcb6b3 4f91ffe 1ae6c5e 6dcb6b3 4fbc46c 6dcb6b3 c1497a6 3aadc38 d5f11d4 6dcb6b3 b2d0aef 51f8f41 d5f11d4 6dcb6b3 e129330 51f8f41 e129330 51f8f41 e129330 6dcb6b3 d09f5de |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
import os
import requests
import torch
import gradio as gr
import spaces
from PIL import Image
from huggingface_hub import login
import torchvision.transforms as T
from diffusers.utils import load_image
from models.transformer_sd3 import SD3Transformer2DModel
from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
# ----------------------------
# 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)
# ----------------------------
# Step 6: Gradio Function
# ----------------------------
@spaces.GPU
def gui_generation(prompt, ref_img, guidance_scale, ipadapter_scale):
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")
pipe.init_ipadapter(
ip_adapter_path=ip_adapter_path,
image_encoder_path=image_encoder_path,
nb_token=64,
)
ref_img = load_image(ref_img.name).convert('RGB')
# please note that SD3.5 Large is sensitive to highres generation like 1536x1536
image = pipe(
width=1024,
height=1024,
prompt=prompt,
negative_prompt="lowres, low quality, worst quality",
num_inference_steps=24,
guidance_scale=guidance_scale,
generator=torch.Generator("cuda").manual_seed(42),
clip_image=ref_img,
ipadapter_scale=ipadapter_scale,
).images[0]
return image
# ----------------------------
# Step 7: Gradio Interface
# ----------------------------
prompt_box = gr.Textbox(label="Prompt", placeholder="Enter your image generation prompt")
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, 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()
|