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import gradio as gr
import torch
from diffusers import AutoPipelineForInpainting
from PIL import Image
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BlipForConditionalGeneration,
BlipProcessor,
OwlViTForObjectDetection,
OwlViTProcessor,
SamModel,
SamProcessor,
)
def delete_model(model):
model.to("cpu")
del model
torch.cuda.empty_cache()
def run_language_model(edit_prompt, device):
language_model_id = "Qwen/Qwen1.5-0.5B-Chat"
language_model = AutoModelForCausalLM.from_pretrained(
language_model_id, device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(language_model_id)
messages = [
{
"role": "system",
"content": "Follow the examples and return the expected output",
},
{"role": "user", "content": "swap mountain and lion"}, # example 1
{"role": "assistant", "content": "mountain, lion"}, # example 1
{"role": "user", "content": "change the dog with cat"}, # example 2
{"role": "assistant", "content": "dog, cat"}, # example 2
{"role": "user", "content": "replace the human with a boat"}, # example 3
{"role": "assistant", "content": "human, boat"}, # example 3
{"role": "user", "content": edit_prompt},
]
text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = language_model.generate(model_inputs.input_ids, max_new_tokens=512)
generated_ids = [
output_ids[len(input_ids) :]
for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
to_replace, replace_with = response.split(", ")
delete_model(language_model)
return (to_replace, replace_with)
def run_image_captioner(image, device):
caption_model_id = "Salesforce/blip-image-captioning-base"
caption_model = BlipForConditionalGeneration.from_pretrained(caption_model_id).to(
device
)
caption_processor = BlipProcessor.from_pretrained(caption_model_id)
inputs = caption_processor(image, return_tensors="pt").to(device)
with torch.no_grad():
outputs = caption_model.generate(**inputs, max_new_tokens=200)
caption = caption_processor.decode(outputs[0], skip_special_tokens=True)
delete_model(caption_model)
return caption
def run_segmentation(image, object_to_segment, device):
# OWL-ViT for object detection
owl_vit_model_id = "google/owlvit-base-patch32"
processor = OwlViTProcessor.from_pretrained(owl_vit_model_id)
od_model = OwlViTForObjectDetection.from_pretrained(owl_vit_model_id).to(device)
text_queries = [object_to_segment]
inputs = processor(text=text_queries, images=image, return_tensors="pt").to(device)
with torch.no_grad():
outputs = od_model(**inputs)
target_sizes = torch.tensor([image.size]).to(device)
results = processor.post_process_object_detection(
outputs, threshold=0.1, target_sizes=target_sizes
)[0]
boxes = results["boxes"].tolist()
delete_model(od_model)
# SAM for image segmentation
sam_model_id = "facebook/sam-vit-base"
seg_model = SamModel.from_pretrained(sam_model_id).to(device)
processor = SamProcessor.from_pretrained(sam_model_id)
input_boxes = [boxes]
inputs = processor(image, input_boxes=input_boxes, return_tensors="pt").to(device)
with torch.no_grad():
outputs = seg_model(**inputs)
masks = processor.image_processor.post_process_masks(
outputs.pred_masks.cpu(),
inputs["original_sizes"].cpu(),
inputs["reshaped_input_sizes"].cpu(),
)
delete_model(seg_model)
return masks
def run_inpainting(image, replaced_caption, masks, device):
pipeline = AutoPipelineForInpainting.from_pretrained(
"diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
torch_dtype=torch.float16,
variant="fp16",
).to(device)
prompt = replaced_caption
negative_prompt = """lowres, bad anatomy, bad hands,
text, error, missing fingers, extra digit, fewer digits,
cropped, worst quality, low quality"""
output = pipeline(
prompt=prompt,
image=image,
mask_image=Image.fromarray(masks[0][0][0, :, :].numpy()),
negative_prompt=negative_prompt,
guidance_scale=7.5,
strength=0.6,
).images[0]
delete_model(pipeline)
return output
def run_open_gen_fill(image, edit_prompt):
device = "cuda" if torch.cuda.is_available() else "cpu"
# Resize the image to (512, 512)
image = image.resize((512, 512))
# Run the langauge model to extract the objects to be swapped from
# the edit prompt
to_replace, replace_with = run_language_model(
edit_prompt=edit_prompt, device=device
)
# Caption the input image
caption = run_image_captioner(image, device=device)
# Replace the object in the caption with the new object
replaced_caption = caption.replace(to_replace, replace_with)
# Segment the `to_replace` object from the input image
masks = run_segmentation(image, to_replace, device=device)
# Diffusion pipeline for inpainting
return run_inpainting(
image=image, replaced_caption=replaced_caption, masks=masks, device=device
)
def setup_gradio_interface():
block = gr.Blocks()
with block:
gr.Markdown("<h1><center>Open Generative Fill V1<h1><center>")
with gr.Row():
with gr.Column():
input_image_placeholder = gr.Image(type="pil", label="Input Image")
edit_prompt_placeholder = gr.Textbox(label="Enter the editing prompt")
run_button_placeholder = gr.Button(value="Run")
with gr.Column():
output_image_placeholder = gr.Image(type="pil", label="Output Image")
run_button_placeholder.click(
fn=lambda image, edit_prompt: run_open_gen_fill(
image=image,
edit_prompt=edit_prompt,
),
inputs=[input_image_placeholder, edit_prompt_placeholder],
outputs=[output_image_placeholder],
)
return block
if __name__ == "__main__":
gradio_interface = setup_gradio_interface()
gradio_interface.queue(max_size=5)
gradio_interface.launch(share=False, show_api=False, show_error=True)
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