import spaces
import gradio as gr
import torch
from PIL import Image
from diffusers.utils import load_image
from pipeline import FluxConditionalPipeline
from transformer import FluxTransformer2DConditionalModel
import os
pipe = None
CHECKPOINT = "primecai/dsd_model"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
transformer = FluxTransformer2DConditionalModel.from_pretrained(
CHECKPOINT,
subfolder="transformer",
torch_dtype=dtype,
low_cpu_mem_usage=False,
ignore_mismatched_sizes=True,
use_auth_token=os.getenv("HF_TOKEN"),
)
pipe = FluxConditionalPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
transformer=transformer,
torch_dtype=dtype,
use_auth_token=os.getenv("HF_TOKEN"),
)
pipe.load_lora_weights(
CHECKPOINT,
weight_name="pytorch_lora_weights.safetensors",
use_auth_token=os.getenv("HF_TOKEN"),
)
pipe.to(device, dtype=dtype)
@spaces.GPU
def generate_image(
image: Image.Image,
text: str,
gemini_prompt: bool = True,
guidance: float = 3.5,
i_guidance: float = 1.0,
t_guidance: float = 1.0
):
w, h, min_size = image.size[0], image.size[1], min(image.size)
image = image.crop(
((w - min_size) // 2, (h - min_size) // 2, (w + min_size) // 2, (h + min_size) // 2)
).resize((512, 512))
control_image = load_image(image)
result_image = pipe(
prompt=text.strip(),
negative_prompt="",
num_inference_steps=28,
height=512,
width=1024,
guidance_scale=guidance,
image=control_image,
guidance_scale_real_i=i_guidance,
guidance_scale_real_t=t_guidance,
gemini_prompt=gemini_prompt,
).images[0]
return result_image
def get_samples():
sample_list = [
{
"image": "assets/hf-logo.png",
"text": "This item, holding a sign that reads 'DSD!', is placed on a shiny glass table.",
},
{
"image": "assets/seededit_example.png",
"text": "an adorable small creature with big round orange eyes, fluffy brown fur, wearing a blue scarf with a golden charm, sitting atop a towering stack of colorful books in the middle of a vibrant futuristic city street with towering buildings and glowing neon signs, soft daylight illuminating the scene, detailed and whimsical 3D style.",
},
{
"image": "assets/wanrong_character.png",
"text": "A chibi-style girl with pink hair, green eyes, wearing a black and gold ornate dress, dancing gracefully in a flower garden, anime art style with clean and detailed lines.",
},
{
"image": "assets/ben_character_squared.png",
"text": "A confident green-eye young woman with platinum blonde hair in a high ponytail, wearing an oversized orange jacket and black pants, is striking a dynamic pose, anime-style with sharp details and vibrant colors.",
},
{
"image": "assets/action_hero_figure.jpeg",
"text": "A cartoonish muscular action hero figure with long blue hair and red headband sits on a crowded sidewalk on a Christmas evening, covered in snow and wearing a Christmas hat, holding a sign that reads 'DSD!', dramatic cinematic lighting, close-up view, 3D-rendered in a stylized, vibrant art style.",
},
{
"image": "assets/anime_soldier.jpeg",
"text": "An adorable cartoon goat soldier sits under a beach umbrella with 'DSD!' written on it, bright teal background with soft lighting, 3D-rendered in a playful and vibrant art style.",
},
{
"image": "assets/goat_logo.jpeg",
"text": "A shirt with this logo on it.",
},
{
"image": "assets/cartoon_cat.png",
"text": "A cheerful cartoon orange cat sits under a beach umbrella with 'DSD!' written on it under a sunny sky, simplistic and humorous comic art style.",
},
]
return [
[
Image.open(sample["image"]),
sample["text"],
]
for sample in sample_list
]
demo = gr.Blocks()
with demo:
gr.HTML(
"""
Diffusion Self-Distillation (beta)
"""
)
iface = gr.Interface(
fn=generate_image,
inputs=[
gr.Image(type="pil", width=512),
gr.Textbox(lines=2, label="text", info="Could be something as simple as 'this character playing soccer'."),
gr.Checkbox(label="Gemini prompt", value=True, info="Use Gemini to enhance the prompt. This is recommended for most cases, unless you have a specific prompt similar to the examples in mind."),
gr.Slider(minimum=1.0, maximum=6.0, step=0.5, value=3.5, label="guidance scale", info="Tip: start with 3.5, then gradually increase if the consistency is consistently off"),
gr.Slider(minimum=1.0, maximum=2.0, step=0.05, value=1.5, label="real guidance scale for image", info="Tip: increase if the image is not consistent"),
gr.Slider(minimum=1.0, maximum=2.0, step=0.05, value=1.0, label="real guidance scale for prompt", info="Tip: increase if the prompt is not consistent"),
],
outputs=gr.Image(type="pil"),
# examples=get_samples(),
live=False,
)
gr.Examples(
examples=get_samples(),
inputs=iface.input_components,
outputs=iface.output_components,
run_on_click=False # Prevents auto-submission
)
gr.HTML(
"""
"""
)
if __name__ == "__main__":
demo.launch(debug=False, share=True)