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import shutil
import time
from pathlib import Path
import cv2
import gradio as gr
import PIL.Image
import torch
from diffusers import (
DiffusionPipeline, # type: ignore
QwenImageEditPlusPipeline, # type: ignore
)
# from diffusers.utils import load_image
from nunchaku import NunchakuQwenImageTransformer2DModel
from nunchaku.utils import get_gpu_memory, get_precision
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
RANK = 128
TRANSFORMER_ID = f"nunchaku-tech/nunchaku-qwen-image-edit-2509/svdq-{get_precision()}_r{RANK}-qwen-image-edit-2509.safetensors"
PIPELINE_ID = "Qwen/Qwen-Image-Edit-2509"
OUTPUT_DIR = Path(__file__).parent / "output"
IMAGES_DIR = OUTPUT_DIR / "images"
IMAGES_DIR.mkdir(parents=True, exist_ok=True)
VIDEO_PATH = OUTPUT_DIR / "video.mp4"
class Model:
def __init__(self):
self.progress = gr.Progress()
self.num_inference_steps = 50
self.current_inference_step = 0
transformer = NunchakuQwenImageTransformer2DModel.from_pretrained(
TRANSFORMER_ID
)
pipeline = QwenImageEditPlusPipeline.from_pretrained(
PIPELINE_ID,
transformer=transformer,
torch_dtype=torch.bfloat16,
)
if get_gpu_memory() > 18:
pipeline.enable_model_cpu_offload()
else:
transformer.set_offload(
True,
use_pin_memory=False,
num_blocks_on_gpu=1,
)
pipeline._exclude_from_cpu_offload.append("transformer")
pipeline.enable_sequential_cpu_offload()
self.pipeline = pipeline
def compute(
self,
images: list[PIL.Image.Image],
prompt: str,
negative_prompt: str = " ",
true_cfg_scale: float = 4.0,
num_inference_steps: int = 40,
image_width: int = 512,
image_height: int = 512,
) -> tuple[PIL.Image.Image, Path]:
self.num_inference_steps = num_inference_steps
self.current_inference_step = 0
self.progress((self.current_inference_step, self.num_inference_steps))
shutil.rmtree(IMAGES_DIR, ignore_errors=True)
IMAGES_DIR.mkdir(parents=True, exist_ok=True)
# Validate inputs
if not images:
raise gr.Error("No images provided. Please upload at least one image.")
# Ensure all images are valid PIL Images
processed_images = []
for i, img in enumerate(images):
if img is None:
raise gr.Error(f"Image {i + 1} is invalid or could not be loaded.")
processed_images.append(img)
inputs = dict(
image=processed_images,
prompt=prompt,
negative_prompt=negative_prompt,
true_cfg_scale=true_cfg_scale,
num_inference_steps=num_inference_steps,
width=image_width,
height=image_height,
generator=torch.manual_seed(0),
callback_on_step_end=self.callback,
# output_type="latent"
)
output = self.pipeline(**inputs)
output_image = output.images[0]
# Create video from saved images
print(list(IMAGES_DIR.glob("*.png")))
# Get all PNG files and sort them
image_files = sorted(IMAGES_DIR.glob("step_*.png"))
if image_files:
# Read first image to get dimensions
first_img = cv2.imread(str(image_files[0]))
height, width, _ = first_img.shape
# Create video writer
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
fps = 10 # Adjust frame rate as needed
video_writer = cv2.VideoWriter(
str(VIDEO_PATH.absolute()), fourcc, fps, (width, height)
)
# Add each image to video
for img_path in image_files:
img = cv2.imread(str(img_path))
video_writer.write(img)
video_writer.release()
print(f"Video saved to: {VIDEO_PATH}")
time.sleep(3)
return output_image, VIDEO_PATH
def callback(
self,
pipeline: DiffusionPipeline,
step: int,
timestep: int,
callback_kwargs: dict,
):
latents = callback_kwargs.get("latents", None)
height = callback_kwargs.get("height", 800)
width = callback_kwargs.get("width", 512)
if latents is not None:
print(f"Latents shape: {latents.shape}, dtype: {latents.dtype}")
latents = pipeline._unpack_latents(
latents, height, width, pipeline.vae_scale_factor
)
latents = latents.to(pipeline.vae.dtype)
latents_mean = (
torch.tensor(pipeline.vae.config.latents_mean)
.view(1, pipeline.vae.config.z_dim, 1, 1, 1)
.to(latents.device, latents.dtype)
)
latents_std = 1.0 / torch.tensor(pipeline.vae.config.latents_std).view(
1, pipeline.vae.config.z_dim, 1, 1, 1
).to(latents.device, latents.dtype)
latents = latents / latents_std + latents_mean
image = pipeline.vae.decode(latents, return_dict=False)[0][:, :, 0]
image = pipeline.image_processor.postprocess(image, output_type="pil")
image = image[0]
image.save(IMAGES_DIR / f"step_{step:03d}.png")
self.current_inference_step += 1
self.progress((self.current_inference_step, self.num_inference_steps))
return {}
with gr.Blocks() as demo:
gr.Markdown("# Nunchaku Qwen-Image-Edit-2509")
with gr.Row():
with gr.Column():
gr.Markdown("## Input Images")
image_inputs = gr.Gallery(
label="Input Images",
show_label=True,
elem_id="gallery",
columns=3,
rows=2,
object_fit="contain",
height="auto",
type="pil",
allow_preview=True,
interactive=True,
)
with gr.Column():
gr.Markdown("## Output Image")
image_output = gr.Image(
label="Output Image",
format="png",
)
with gr.Column():
gr.Markdown("## Output Video")
video_output = gr.Video(
label="Output Video",
format="mp4",
show_download_button=True,
streaming=True,
autoplay=True,
loop=False,
)
with gr.Row():
with gr.Column():
gr.Markdown("## Prompts")
prompt = gr.Textbox(label="Prompt:", lines=1)
negative_prompt = gr.Textbox(label="Negative Prompt:", lines=1)
with gr.Column():
gr.Markdown("## Settings")
true_cfg_scale = gr.Slider(
0,
20,
value=4.0,
step=0.1,
interactive=True,
label="True CFG scale:",
)
num_inference_steps = gr.Slider(
1,
300,
value=50,
step=1,
interactive=True,
label="Number of denoising steps:",
)
image_width = gr.Slider(
128,
1024,
value=512,
step=16,
interactive=True,
label="Image Width:",
)
image_height = gr.Slider(
128,
1024,
value=800,
step=16,
interactive=True,
label="Image Height:",
)
with gr.Row():
run_button = gr.Button("Run")
model = Model()
def process_images(
images,
prompt,
negative_prompt,
true_cfg_scale,
num_inference_steps,
image_width,
image_height,
):
"""Wrapper function to handle errors gracefully"""
pil_images = []
for contents in images:
for content in contents:
if isinstance(content, PIL.Image.Image):
pil_images.append(content)
break
try:
return model.compute(
pil_images,
prompt,
negative_prompt,
true_cfg_scale,
num_inference_steps,
image_width,
image_height,
)
except Exception as e:
print(f"Error processing images: {e}")
raise gr.Error(f"Failed to process images: {str(e)}")
# Connect the button to the detection function
run_button.click(
fn=process_images,
inputs=[
image_inputs,
prompt,
negative_prompt,
true_cfg_scale,
num_inference_steps,
image_width,
image_height,
],
outputs=[
image_output,
video_output,
],
)
if __name__ == "__main__":
demo.launch(
allowed_paths=["output/video.mp4"],
share=True,
)
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