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.gitattributes CHANGED
@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ example_images/2d0fbcc50e88065a040a537b717620e964fb4453314b71d83f3ed3425addcef6.png filter=lfs diff=lfs merge=lfs -text
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+ example_images/annual_rep_14.png filter=lfs diff=lfs merge=lfs -text
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+ example_images/annual_rep_15.png filter=lfs diff=lfs merge=lfs -text
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+ example_images/gazette_de_france.jpg filter=lfs diff=lfs merge=lfs -text
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+ example_images/paper_3.png filter=lfs diff=lfs merge=lfs -text
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+ example_images/redhat.png filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,12 +1,13 @@
1
  ---
2
- title: SmolDocling 256M Demo
3
- emoji: πŸ–Ό
4
- colorFrom: purple
5
- colorTo: red
6
  sdk: gradio
7
- sdk_version: 5.0.1
8
  app_file: app.py
9
  pinned: false
 
10
  ---
11
 
12
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
  ---
2
+ title: SmolVLM
3
+ emoji: πŸ“Š
4
+ colorFrom: blue
5
+ colorTo: green
6
  sdk: gradio
7
+ sdk_version: 5.12.0
8
  app_file: app.py
9
  pinned: false
10
+ license: apache-2.0
11
  ---
12
 
13
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py CHANGED
@@ -1,154 +1,152 @@
1
  import gradio as gr
2
- import numpy as np
3
- import random
4
-
5
- # import spaces #[uncomment to use ZeroGPU]
6
- from diffusers import DiffusionPipeline
7
  import torch
 
 
 
 
 
8
 
9
- device = "cuda" if torch.cuda.is_available() else "cpu"
10
- model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use
11
-
12
- if torch.cuda.is_available():
13
- torch_dtype = torch.float16
14
- else:
15
- torch_dtype = torch.float32
16
-
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- pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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- pipe = pipe.to(device)
19
-
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- MAX_SEED = np.iinfo(np.int32).max
21
- MAX_IMAGE_SIZE = 1024
22
-
23
-
24
- # @spaces.GPU #[uncomment to use ZeroGPU]
25
- def infer(
26
- prompt,
27
- negative_prompt,
28
- seed,
29
- randomize_seed,
30
- width,
31
- height,
32
- guidance_scale,
33
- num_inference_steps,
34
- progress=gr.Progress(track_tqdm=True),
35
- ):
36
- if randomize_seed:
37
- seed = random.randint(0, MAX_SEED)
38
-
39
- generator = torch.Generator().manual_seed(seed)
40
-
41
- image = pipe(
42
- prompt=prompt,
43
- negative_prompt=negative_prompt,
44
- guidance_scale=guidance_scale,
45
- num_inference_steps=num_inference_steps,
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- width=width,
47
- height=height,
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- generator=generator,
49
- ).images[0]
50
-
51
- return image, seed
52
-
53
-
54
- examples = [
55
- "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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- "An astronaut riding a green horse",
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- "A delicious ceviche cheesecake slice",
58
- ]
59
-
60
- css = """
61
- #col-container {
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- margin: 0 auto;
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- max-width: 640px;
64
- }
65
- """
66
-
67
- with gr.Blocks(css=css) as demo:
68
- with gr.Column(elem_id="col-container"):
69
- gr.Markdown(" # Text-to-Image Gradio Template")
70
-
71
- with gr.Row():
72
- prompt = gr.Text(
73
- label="Prompt",
74
- show_label=False,
75
- max_lines=1,
76
- placeholder="Enter your prompt",
77
- container=False,
78
- )
79
-
80
- run_button = gr.Button("Run", scale=0, variant="primary")
81
-
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- result = gr.Image(label="Result", show_label=False)
83
-
84
- with gr.Accordion("Advanced Settings", open=False):
85
- negative_prompt = gr.Text(
86
- label="Negative prompt",
87
- max_lines=1,
88
- placeholder="Enter a negative prompt",
89
- visible=False,
90
- )
91
 
92
- seed = gr.Slider(
93
- label="Seed",
94
- minimum=0,
95
- maximum=MAX_SEED,
96
- step=1,
97
- value=0,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
98
  )
99
 
100
- randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
101
-
102
- with gr.Row():
103
- width = gr.Slider(
104
- label="Width",
105
- minimum=256,
106
- maximum=MAX_IMAGE_SIZE,
107
- step=32,
108
- value=1024, # Replace with defaults that work for your model
109
- )
110
-
111
- height = gr.Slider(
112
- label="Height",
113
- minimum=256,
114
- maximum=MAX_IMAGE_SIZE,
115
- step=32,
116
- value=1024, # Replace with defaults that work for your model
117
- )
118
-
119
- with gr.Row():
120
- guidance_scale = gr.Slider(
121
- label="Guidance scale",
122
- minimum=0.0,
123
- maximum=10.0,
124
- step=0.1,
125
- value=0.0, # Replace with defaults that work for your model
126
- )
127
-
128
- num_inference_steps = gr.Slider(
129
- label="Number of inference steps",
130
- minimum=1,
131
- maximum=50,
132
- step=1,
133
- value=2, # Replace with defaults that work for your model
134
- )
135
-
136
- gr.Examples(examples=examples, inputs=[prompt])
137
- gr.on(
138
- triggers=[run_button.click, prompt.submit],
139
- fn=infer,
140
- inputs=[
141
- prompt,
142
- negative_prompt,
143
- seed,
144
- randomize_seed,
145
- width,
146
- height,
147
- guidance_scale,
148
- num_inference_steps,
149
- ],
150
- outputs=[result, seed],
151
- )
152
-
153
- if __name__ == "__main__":
154
- demo.launch()
 
1
  import gradio as gr
2
+ from transformers import AutoProcessor, AutoModelForVision2Seq, TextIteratorStreamer
3
+ from transformers.image_utils import load_image
4
+ from threading import Thread
5
+ import re
6
+ import time
7
  import torch
8
+ import spaces
9
+ import re
10
+ import ast
11
+ import html
12
+ import random
13
 
14
+ from PIL import Image, ImageOps
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
15
 
16
+ from docling_core.types.doc import DoclingDocument
17
+ from docling_core.types.doc.document import DocTagsDocument
18
+
19
+ def add_random_padding(image, min_percent=0.1, max_percent=0.10):
20
+ image = image.convert("RGB")
21
+
22
+ width, height = image.size
23
+
24
+ pad_w_percent = random.uniform(min_percent, max_percent)
25
+ pad_h_percent = random.uniform(min_percent, max_percent)
26
+
27
+ pad_w = int(width * pad_w_percent)
28
+ pad_h = int(height * pad_h_percent)
29
+
30
+ corner_pixel = image.getpixel((0, 0)) # Top-left corner
31
+ padded_image = ImageOps.expand(image, border=(pad_w, pad_h, pad_w, pad_h), fill=corner_pixel)
32
+
33
+ return padded_image
34
+
35
+ def normalize_values(text, target_max=500):
36
+ def normalize_list(values):
37
+ max_value = max(values) if values else 1
38
+ return [round((v / max_value) * target_max) for v in values]
39
+
40
+ def process_match(match):
41
+ num_list = ast.literal_eval(match.group(0))
42
+ normalized = normalize_list(num_list)
43
+ return "".join([f"<loc_{num}>" for num in normalized])
44
+
45
+ pattern = r"\[([\d\.\s,]+)\]"
46
+ normalized_text = re.sub(pattern, process_match, text)
47
+ return normalized_text
48
+
49
+
50
+ processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview")
51
+ model = AutoModelForVision2Seq.from_pretrained("ds4sd/SmolDocling-256M-preview",
52
+ torch_dtype=torch.bfloat16,
53
+ #_attn_implementation="flash_attention_2"
54
+ ).to("cuda")
55
+
56
+ @spaces.GPU
57
+ def model_inference(
58
+ input_dict, history
59
+ ):
60
+ text = input_dict["text"]
61
+ print(input_dict["files"])
62
+ if len(input_dict["files"]) > 1:
63
+ if "OTSL" in text or "code" in text:
64
+ images = [add_random_padding(load_image(image)) for image in input_dict["files"]]
65
+ else:
66
+ images = [load_image(image) for image in input_dict["files"]]
67
+
68
+ elif len(input_dict["files"]) == 1:
69
+ if "OTSL" in text or "code" in text:
70
+ images = [add_random_padding(load_image(input_dict["files"][0]))]
71
+ else:
72
+ images = [load_image(input_dict["files"][0])]
73
+
74
+ else:
75
+ images = []
76
+
77
+ if text == "" and not images:
78
+ gr.Error("Please input a query and optionally image(s).")
79
+
80
+ if text == "" and images:
81
+ gr.Error("Please input a text query along the image(s).")
82
+
83
+ if "OCR at text at" in text or "Identify element" in text or "formula" in text:
84
+ text = normalize_values(text, target_max=500)
85
+
86
+ resulting_messages = [
87
+ {
88
+ "role": "user",
89
+ "content": [{"type": "image"} for _ in range(len(images))] + [
90
+ {"type": "text", "text": text}
91
+ ]
92
+ }
93
+ ]
94
+ prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True)
95
+ inputs = processor(text=prompt, images=[images], return_tensors="pt").to('cuda')
96
+
97
+ generation_args = {
98
+ "input_ids": inputs.input_ids,
99
+ "pixel_values": inputs.pixel_values,
100
+ "attention_mask": inputs.attention_mask,
101
+ "num_return_sequences": 1,
102
+ "no_repeat_ngram_size": 10,
103
+ "max_new_tokens": 8192,
104
+ }
105
+
106
+ streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=False)
107
+ generation_args = dict(inputs, streamer=streamer, max_new_tokens=8192)
108
+
109
+ thread = Thread(target=model.generate, kwargs=generation_args)
110
+ thread.start()
111
+
112
+ yield "..."
113
+ buffer = ""
114
+ doctag_output = ""
115
+
116
+ for new_text in streamer:
117
+ if new_text != "<end_of_utterance>":
118
+ buffer += html.escape(new_text)
119
+ doctag_output += new_text
120
+ yield buffer
121
+
122
+ if any(tag in doctag_output for tag in ["<doctag>", "<otsl>", "<code>", "<formula>", "<chart>"]):
123
+ # final_output = buffer
124
+ # cleaned_output = final_output[len(inputs.input_ids):] if len(final_output) > prompt_length else final_output
125
+ doc = DoclingDocument(name="Document")
126
+ if "<chart>" in doctag_output:
127
+ doctag_output = doctag_output.replace("<chart>", "<otsl>").replace("</chart>", "</otsl>")
128
+ doctag_output = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', doctag_output)
129
+
130
+ doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([doctag_output], images)
131
+ doc.load_from_doctags(doctags_doc)
132
+ yield f"**MD Output:**\n\n{doc.export_to_markdown()}"
133
+
134
+ examples=[[{"text": "Convert this page to docling.", "files": ["example_images/2d0fbcc50e88065a040a537b717620e964fb4453314b71d83f3ed3425addcef6.png"]}],
135
+ [{"text": "Convert this table to OTSL.", "files": ["example_images/image-2.jpg"]}],
136
+ [{"text": "Convert code to text.", "files": ["example_images/7666.jpg"]}],
137
+ [{"text": "Convert formula to latex.", "files": ["example_images/2433.jpg"]}],
138
+ [{"text": "Convert chart to OTSL.", "files": ["example_images/06236926002285.png"]}],
139
+ [{"text": "OCR the text in location [47, 531, 167, 565]", "files": ["example_images/s2w_example.png"]}],
140
+ [{"text": "Extract all section header elements on the page.", "files": ["example_images/paper_3.png"]}],
141
+ [{"text": "Identify element at location [123, 413, 1059, 1061]", "files": ["example_images/redhat.png"]}],
142
+ [{"text": "Convert this page to docling.", "files": ["example_images/gazette_de_france.jpg"]}],
143
+ ]
144
+
145
+ demo = gr.ChatInterface(fn=model_inference, title="SmolDocling-256M: Ultra-compact VLM for Document Conversion πŸ’«",
146
+ description="Play with [ds4sd/SmolDocling-256M-preview](https://huggingface.co/ds4sd/SmolDocling-256M-preview) in this demo. To get started, upload an image and text or try one of the examples. This demo doesn't use history for the chat, so every chat you start is a new conversation.",
147
+ examples=examples,
148
+ textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True,
149
+ cache_examples=False
150
  )
151
 
152
+ demo.launch(debug=True, share=True)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
example_images/06236926002285.png ADDED
example_images/2433.jpg ADDED
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example_images/s2w_example.png ADDED
requirements.txt CHANGED
@@ -1,6 +1,8 @@
1
- accelerate
2
- diffusers
3
- invisible_watermark
4
  torch
 
 
 
5
  transformers
6
- xformers
 
 
 
 
 
 
1
  torch
2
+ accelerate
3
+ huggingface_hub
4
+ gradio
5
  transformers
6
+ spaces
7
+ docling
8
+ docling-core