Spaces:
Runtime error
Runtime error
File size: 7,655 Bytes
71a7048 755aa6f 5d23e43 755aa6f bb9d17d 755aa6f bb9d17d 755aa6f bb9d17d 755aa6f bb9d17d 755aa6f 5d23e43 755aa6f 5d23e43 755aa6f 5d23e43 755aa6f 6239abf 755aa6f 5b94f14 755aa6f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 |
import spaces
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()
@spaces.GPU()
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": "change the cat with a dog"}, # example 3
{"role": "assistant", "content": "cat, dog"}, # example 3
{"role": "user", "content": "replace the human with a boat"}, # example 4
{"role": "assistant", "content": "human, boat"}, # example 4
{"role": "user", "content": "in the above example change the background to the alps"}, # example 5
{"role": "assistant", "content": "background, alps"}, # example 5
{"role": "user", "content": "edit the house into a mansion"}, # example 6
{"role": "assistant", "content": "house, a mansion"}, # example 6
{"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)
with torch.no_grad():
generated_ids = language_model.generate(
model_inputs.input_ids,
max_new_tokens=512,
temperature=0.0,
do_sample=False
)
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)
@spaces.GPU()
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
@spaces.GPU()
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
@spaces.GPU()
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],
)
gr.Examples(
examples=[["dog.jpeg", "replace the dog with a tiger"]],
inputs=[input_image_placeholder, edit_prompt_placeholder],
outputs=[output_image_placeholder],
fn=lambda image, edit_prompt: run_open_gen_fill(
image=image,
edit_prompt=edit_prompt,
),
cache_examples=True,
label="Try this example input!",
)
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)
|