Spaces:
Paused
Paused
File size: 2,814 Bytes
4f91ffe 683afc3 88d1237 3aadc38 0737dc8 68e88ea 3aadc38 4fbc46c c1497a6 3aadc38 68e88ea d8f1f69 3aadc38 d8f1f69 68e88ea 3aadc38 68e88ea 3aadc38 68e88ea d8f1f69 3aadc38 68e88ea 3aadc38 d8f1f69 68e88ea 91a655a 68e88ea d8f1f69 3aadc38 d8f1f69 3aadc38 68e88ea d8f1f69 68e88ea a4cc7b2 68e88ea d8f1f69 68e88ea |
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 |
import os
import requests
url = "https://huggingface.co/InstantX/SD3.5-Large-IP-Adapter/resolve/main/ip-adapter.bin"
file_path = "ip-adapter.bin"
# Check if the file already exists
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!")
else:
print("File already exists.")
from models.transformer_sd3 import SD3Transformer2DModel
import gradio as gr
import torch
from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
import os
import spaces
from huggingface_hub import login
token = os.getenv("HF_TOKEN")
login(token=token)
# Model and Pipeline Setup
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,
)
# Load transformer and pipeline
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")
# Initialize IP Adapter
pipe.init_ipadapter(
ip_adapter_path=ip_adapter_path,
image_encoder_path=image_encoder_path,
nb_token=64,
)
@spaces.GPU
def gui_generation(prompt, ref_img):
"""
Generate images using Stable Diffusion 3.5
"""
image = pipe(
width=1024,
height=1024,
prompt=prompt,
negative_prompt="lowres, low quality, worst quality",
num_inference_steps=24,
guidance_scale=5.0,
generator=torch.Generator("cuda").manual_seed(42),
clip_image=ref_img,
ipadapter_scale=0.5,
).images[0]
return image
# Create Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Stable Diffusion 3.5 Image Generation")
with gr.Row():
prompt_box = gr.Textbox(label="Prompt", placeholder="Enter your image generation prompt")
with gr.Row():
ref_img = gr.Image(type="pil", label="Upload Reference Image")
gallery = gr.Image(type="pil", label="Generated Image")
generate_btn = gr.Button("Generate")
generate_btn.click(
fn=gui_generation,
inputs=[prompt_box, ref_img],
outputs=gallery
)
demo.launch()
|