File size: 19,446 Bytes
5ddef29
 
 
 
 
 
 
e042973
bab1e75
90c9c61
5005e7e
928dc00
d49e1e5
5ddef29
f467a89
 
 
 
 
 
 
 
f1d8f65
7e726ee
f1d8f65
dda9682
f1d8f65
 
341f459
7e726ee
 
 
 
 
 
 
 
 
 
341f459
7e726ee
dda9682
341f459
dda9682
90c9c61
7e726ee
f467a89
 
 
 
 
b33f45e
5ddef29
 
 
 
 
 
f467a89
5ddef29
4fff7a9
5ddef29
f467a89
5ddef29
4fff7a9
5ddef29
f467a89
5ddef29
4fff7a9
5ddef29
f467a89
5ddef29
 
 
 
 
 
 
 
 
 
 
 
 
 
2f14be5
5ddef29
 
f429ce6
 
 
 
5ddef29
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8637ff9
 
 
 
f467a89
8637ff9
 
5ddef29
 
 
b33f45e
b62f01b
 
 
 
 
 
 
 
 
b33f45e
b62f01b
 
 
 
 
 
 
f467a89
b62f01b
 
 
f467a89
b62f01b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f467a89
7494c56
 
b33f45e
 
f467a89
b33f45e
b62f01b
 
 
 
 
f467a89
 
b62f01b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5ddef29
 
b62f01b
 
 
 
 
 
5ddef29
b33f45e
ef3889f
341f459
 
 
5ddef29
 
 
 
 
 
3bebd7a
 
79e5823
 
5ddef29
341f459
5ddef29
341f459
ae7d1dc
5ddef29
b33f45e
ef3889f
341f459
 
 
 
 
8637ff9
 
 
 
 
 
 
 
 
 
 
 
 
341f459
8637ff9
 
341f459
ae7d1dc
8637ff9
d49e1e5
5ddef29
 
 
 
 
 
8637ff9
98511b0
 
f467a89
 
98511b0
b62f01b
 
 
 
dc887cd
f467a89
dc887cd
b62f01b
 
f467a89
b62f01b
 
 
 
 
f467a89
 
 
b62f01b
2e7bb9d
f467a89
b62f01b
 
 
 
f467a89
b62f01b
 
 
f467a89
 
b62f01b
333a149
b62f01b
f467a89
dc887cd
 
6f838ab
f71d017
b33f45e
 
f467a89
8637ff9
 
 
dc887cd
f467a89
dc887cd
8637ff9
 
f467a89
8637ff9
 
 
 
 
 
 
f467a89
 
 
8637ff9
0370bc2
b33f45e
8637ff9
 
 
 
f467a89
8637ff9
 
 
f467a89
8637ff9
 
 
b33f45e
8637ff9
f467a89
dc887cd
f467a89
b33f45e
 
 
29d2d55
f467a89
b62f01b
 
 
f467a89
b62f01b
f467a89
 
b62f01b
 
f467a89
b62f01b
 
 
 
 
 
 
f467a89
 
b62f01b
 
 
f467a89
b62f01b
f467a89
 
b62f01b
 
 
dc887cd
fedcafb
b62f01b
 
dc887cd
 
f467a89
b62f01b
d31b23b
b33f45e
 
 
 
6f838ab
d31b23b
6f838ab
f467a89
 
dda9682
 
f467a89
 
dc887cd
a61266c
dc887cd
f467a89
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dda9682
f467a89
 
f660159
f467a89
7494c56
 
 
 
 
 
 
4a4c05f
 
7172358
 
f467a89
b33f45e
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
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
import gradio as gr
import requests
import time
import json
import base64
import os
from io import BytesIO
import io
import html
import PIL
from PIL import Image
import re


def query(payload, model):
    HF_TOKEN = os.getenv("HF_TOKEN")
    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
    url = "https://api-inference.huggingface.co/models/"
    API_URL = f"{url}{model}"
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.content

def hf_inference(prompt, negative, model, steps, sampler, guidance, width, height, seed, progress=gr.Progress()):
    try:
        progress(0, desc="Starting")
        images=[]
        time.sleep(1)
        progress(0.05)
        progress(025, desc="Generaing")
        image_bytes = query(payload={
            "inputs": f"{prompt}",
            "parameters": {
                "negative_prompt": f"{negative}",
                "num_inference_steps": steps,
                "guidance_scale": guidance,
                "width": width, "height": height,
                "seed": seed,
            },
        }, model=model)
        progress(75, desc="Opening image")
        image = Image.open(io.BytesIO(image_bytes))
        images.append(image)
        progress(99, desc="Sending image")
        return images
    except PIL.UnidentifiedImageError:
        gr.Warning("This model is not loaded now. Try others models.")






class Prodia:
    def __init__(self, api_key, base=None):
        self.base = base or "https://api.prodia.com/v1"
        self.headers = {
            "X-Prodia-Key": api_key
        }

    def generate(self, params):
        response = self._post(f"{self.base}/sd/generate", params)
        return response.json()

    def transform(self, params):
        response = self._post(f"{self.base}/sd/transform", params)
        return response.json()

    def controlnet(self, params):
        response = self._post(f"{self.base}/sd/controlnet", params)
        return response.json()

    def get_job(self, job_id):
        response = self._get(f"{self.base}/job/{job_id}")
        return response.json()

    def wait(self, job):
        job_result = job

        while job_result['status'] not in ['succeeded', 'failed']:
            time.sleep(0.25)
            job_result = self.get_job(job['job'])

        return job_result

    def list_models(self):
        response = self._get(f"{self.base}/sd/models")
        return response.json()

    def list_samplers(self):
        response = self._get(f"{self.base}/sd/samplers")
        return response.json()

    def _post(self, url, params):
        headers = {
            **self.headers,
            "Content-Type": "application/json"
        }
        response = requests.post(url, headers=headers, data=json.dumps(params))

        if response.status_code != 200:
            raise Exception(f"Bad Prodia Response: {response.status_code}")

        return response

    def _get(self, url):
        response = requests.get(url, headers=self.headers)

        if response.status_code != 200:
            raise Exception(f"Bad Prodia Response: {response.status_code}")

        return response


def image_to_base64(image):
    # Convert the image to bytes
    buffered = BytesIO()
    image.save(buffered, format="PNG")  # You can change format to PNG if needed

    # Encode the bytes to base64
    img_str = base64.b64encode(buffered.getvalue())

    return img_str.decode('utf-8')  # Convert bytes to string


def remove_id_and_ext(text):
    text = re.sub(r'\[.*\]$', '', text)
    extension = text[-12:].strip()
    if extension == "safetensors":
        text = text[:-13]
    elif extension == "ckpt":
        text = text[:-4]
    return text


def get_data(text):
    results = {}
    patterns = {
        'prompt': r'(.*)',
        'negative_prompt': r'Negative prompt: (.*)',
        'steps': r'Steps: (\d+),',
        'seed': r'Seed: (\d+),',
        'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)',
        'model': r'Model:\s*([^\s,]+)',
        'cfg_scale': r'CFG scale:\s*([\d\.]+)',
        'size': r'Size:\s*([0-9]+x[0-9]+)'
    }
    for key in ['prompt', 'negative_prompt', 'steps', 'seed', 'sampler', 'model', 'cfg_scale', 'size']:
        match = re.search(patterns[key], text)
        if match:
            results[key] = match.group(1)
        else:
            results[key] = None
    if results['size'] is not None:
        w, h = results['size'].split("x")
        results['w'] = w
        results['h'] = h
    else:
        results['w'] = None
        results['h'] = None
    return results


def send_to_img2img_def(images):
    return images


def send_to_txt2img(image):
    result = {tabs: gr.update(selected="t2i")}

    try:
        text = image.info['parameters']
        data = get_data(text)
        result[prompt] = gr.update(value=data['prompt'])
        result[negative_prompt] = gr.update(value=data['negative_prompt']) if data[
                                                                                  'negative_prompt'] is not None else gr.update()
        result[steps] = gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update()
        result[seed] = gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update()
        result[cfg_scale] = gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update()
        result[width] = gr.update(value=int(data['w'])) if data['w'] is not None else gr.update()
        result[height] = gr.update(value=int(data['h'])) if data['h'] is not None else gr.update()
        result[sampler] = gr.update(value=data['sampler']) if data['sampler'] is not None else gr.update()
        if model in model_names:
            result[model] = gr.update(value=model_names[model])
        else:
            result[model] = gr.update()
        return result

    except Exception as e:
        print(e)

        return result


prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
model_list = prodia_client.list_models()
model_names = {}

for model_name in model_list:
    name_without_ext = remove_id_and_ext(model_name)
    model_names[name_without_ext] = model_name


def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, progress=gr.Progress()):
    progress(0, desc="Starting")
    time.sleep(2.5)
    progress(25, desc="Generating")
    result = prodia_client.generate({
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "model": model,
        "steps": steps,
        "sampler": sampler,
        "cfg_scale": cfg_scale,
        "width": width,
        "height": height,
        "seed": seed
    })
    progress(75, desc="Opening image")
    job = prodia_client.wait(result)
    progress(99, desc="Sending image")
    return job["imageUrl"]


def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, progress=gr.Progress()):
    progress(0, desc="Starting")
    time.sleep(1.5)
    progress(10, desc="Uploading input image")
    time.sleep(1.5)
    progress(25, desc="Generating")
    result = prodia_client.transform({
        "imageData": image_to_base64(input_image),
        "denoising_strength": denoising,
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "model": model,
        "steps": steps,
        "sampler": sampler,
        "cfg_scale": cfg_scale,
        "width": width,
        "height": height,
        "seed": seed
    })
    progress(75, desc="Opening image")

    job = prodia_client.wait(result)
    progress(99, desc="Sending image")
    return job["imageUrl"]


css = """
#generate {
    height: 100%;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column(scale=6):
            model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True,
                                label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())

    with gr.Tabs() as tabs:
        with gr.Tab("txt2img", id='t2i'):
            with gr.Row():
                with gr.Column(scale=6, min_width=600):
                    prompt = gr.Textbox(placeholder="Prompt", show_label=False, lines=3)
                    negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
                                                 value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation")
                with gr.Column():
                    text_button = gr.Button("Generate", variant='primary', elem_id="generate")

            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Tab("Generation"):
                        with gr.Row():
                            with gr.Column(scale=1):
                                sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method",
                                                      choices=prodia_client.list_samplers())

                            with gr.Column(scale=1):
                                steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)

                        with gr.Row():
                            with gr.Column(scale=1):
                                width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
                                height = gr.Slider(label="Height", maximum=1024, value=512, step=8)

                            with gr.Column(scale=1):
                                batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
                                batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)

                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=8, step=1)
                        seed = gr.Number(label="Seed", value=-1)

                with gr.Column(scale=2):
                    image_output = gr.Image(show_label=False, type="filepath", interactive=False)
                    send_to_img2img = gr.Button(value="Send OUTPUT IMAGE to img2img")
                    send_to_png = gr.Button(value="Send OUTPUT IMAGE to PNG Info")
                    

            text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height,
                                               seed], outputs=image_output, concurrency_limit=64)

        with gr.Tab("img2img", id='i2i'):
            with gr.Row():
                with gr.Column(scale=6, min_width=600):
                    i2i_prompt = gr.Textbox(placeholder="Prompt", show_label=False, lines=3)
                    i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
                                                     value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation")
                with gr.Column():
                    i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate")

            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Tab("Generation"):
                        i2i_image_input = gr.Image(type="pil")

                        with gr.Row():
                            with gr.Column(scale=1):
                                i2i_sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method",
                                                          choices=prodia_client.list_samplers())

                            with gr.Column(scale=1):
                                i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)

                        with gr.Row():
                            with gr.Column(scale=1):
                                i2i_width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
                                i2i_height = gr.Slider(label="Height", maximum=1024, value=512, step=8)

                            with gr.Column(scale=1):
                                i2i_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
                                i2i_batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)

                        i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
                        i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1)
                        i2i_seed = gr.Number(label="Seed", value=-1)

                with gr.Column(scale=2):
                    i2i_image_output = gr.Image(show_label=False, type="filepath", interactive=False)
                    send_to_png_i2i = gr.Button(value="Send INPUT IMAGE to PNG Info")

            i2i_text_button.click(img2img, inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt,
                                                   model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height,
                                                   i2i_seed], outputs=i2i_image_output, concurrency_limit=64)
        send_to_img2img.click(send_to_img2img_def, inputs=image_output, outputs=i2i_image_input)

        with gr.Tab("PNG Info"):
            def plaintext_to_html(text, classname=None):
                content = "<br>\n".join(html.escape(x) for x in text.split('\n'))

                return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"


            def get_exif_data(image):
                items = image.info

                info = ''
                for key, text in items.items():
                    info += f"""
                    <div>
                    <p><b>{plaintext_to_html(str(key))}</b></p>
                    <p>{plaintext_to_html(str(text))}</p>
                    </div>
                    """.strip() + "\n"

                if len(info) == 0:
                    message = "Nothing found in the image."
                    info = f"<div><p>{message}<p></div>"

                return info


            with gr.Row():
                with gr.Column():
                    image_input = gr.Image(type="pil")
            png_button = gr.Button("Get Info")
            with gr.Row():
                with gr.Column():
                    exif_output = gr.HTML(label="EXIF Data")
                    send_to_txt2img_btn = gr.Button("Send PARAMETRS to txt2img")
                    send_to_img2img_png = gr.Button("Send IMAGE to img2img")

            image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output)
            png_button.click(get_exif_data, inputs=[image_input], outputs=exif_output)
            send_to_txt2img_btn.click(send_to_txt2img, inputs=[image_input], outputs=[tabs, prompt, negative_prompt,
                                                                                      steps, seed, model, sampler,
                                                                                      width, height, cfg_scale],
                                      concurrency_limit=64)
        send_to_png.click(send_to_img2img_def, inputs=image_output, outputs=image_input)
        send_to_img2img_png.click(send_to_img2img_def, inputs=image_input, outputs=i2i_image_input)
        send_to_png_i2i.click(send_to_img2img_def, inputs=i2i_image_output, outputs=image_input)
        with gr.Tab("HuggingFace Inference"):
            with gr.Row():
                gr.Markdown("Add your model from HF.co, enter model ID.")
                hf_model = gr.Dropdown(label="HuggingFace checkpoint", choices=["runwayml/stable-diffusion-v1-5", "stabilityai/stable-diffusion-2-1", "dataautogpt3/OpenDalleV1.1", "CompVis/stable-diffusion-v1-4", "playgroundai/playground-v2-1024px-aesthetic", "prompthero/openjourney", "openskyml/dreamdrop-v1", "SG161222/Realistic_Vision_V1.4", "digiplay/AbsoluteReality_v1.8.1", "openskyml/dalle-3-xl", "Lykon/dreamshaper-7", "Pclanglais/Mickey-1928"], value="runwayml/stable-diffusion-v1-5", allow_custom_value=True, interactive=True)
            with gr.Row():
                with gr.Column(scale=6, min_width=600):
                    hf_prompt = gr.Textbox(placeholder="Prompt", show_label=False, lines=3)
                    hf_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
                                                 value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation")
                with gr.Column():
                    hf_text_button = gr.Button("Generate with HF", variant='primary', elem_id="generate")

            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Tab("Generation"):
                        with gr.Row():

                            with gr.Column(scale=1):
                                hf_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)

                        with gr.Row():
                            with gr.Column(scale=1):
                                hf_width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
                                hf_height = gr.Slider(label="Height", maximum=1024, value=512, step=8)

                            with gr.Column(scale=1):
                                hf_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
                                hf_batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)

                        hf_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=8, step=1)
                        hf_seed = gr.Number(label="Seed", value=-1)

                with gr.Column(scale=2):
                    hf_image_output = gr.Gallery(show_label=False, preview=True, columns=4, allow_preview=True)
                    #hf_send_to_img2img = gr.Button(value="Send to img2img")

            hf_text_button.click(hf_inference, inputs=[hf_prompt, hf_negative_prompt, hf_model, hf_steps, sampler, hf_cfg_scale, hf_width, hf_height,
                                               hf_seed], outputs=hf_image_output, concurrency_limit=64)
        with gr.Tab("BLIP"):
            with gr.Tab("Base"):
                gr.load("models/Salesforce/blip-image-captioning-base", title="BLIP-base")
            with gr.Tab("Large"):
                gr.load("models/Salesforce/blip-image-captioning-large", title="BLIP-large")
        with gr.Tab("Classification"):
            gr.load("models/google/vit-base-patch16-224", title="ViT Classification")
        #with gr.Tab("Segmentation"):
        #    gr.load("models/mattmdjaga/segformer_b2_clothes", title="SegFormer Segmentation")
        with gr.Tab("Visual Question Answering"):
            gr.load("models/dandelin/vilt-b32-finetuned-vqa", title="ViLT VQA")
            
demo.queue(max_size=80, api_open=False).launch(max_threads=256, show_api=False)