TastyRice commited on
Commit
032bb54
1 Parent(s): f240016

Delete app.py

Browse files
Files changed (1) hide show
  1. app.py +0 -395
app.py DELETED
@@ -1,395 +0,0 @@
1
- import os
2
- import gc
3
- import gradio as gr
4
- import numpy as np
5
- import torch
6
- import json
7
- import spaces
8
- import config
9
- import utils
10
- import logging
11
- from PIL import Image, PngImagePlugin
12
- from datetime import datetime
13
- from diffusers.models import AutoencoderKL
14
- from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
15
-
16
- logging.basicConfig(level=logging.INFO)
17
- logger = logging.getLogger(__name__)
18
-
19
- DESCRIPTION = "Animagine XL 3.1"
20
- if not torch.cuda.is_available():
21
- DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>"
22
- IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1"
23
- HF_TOKEN = os.getenv("HF_TOKEN")
24
- CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
25
- MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512"))
26
- MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
27
- USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
28
- ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
29
- OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs")
30
-
31
- MODEL = os.getenv(
32
- "MODEL",
33
- "https://huggingface.co/cagliostrolab/animagine-xl-3.1/blob/main/animagine-xl-3.1.safetensors",
34
- )
35
-
36
- torch.backends.cudnn.deterministic = True
37
- torch.backends.cudnn.benchmark = False
38
-
39
- device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
40
-
41
-
42
- def load_pipeline(model_name):
43
- vae = AutoencoderKL.from_pretrained(
44
- "madebyollin/sdxl-vae-fp16-fix",
45
- torch_dtype=torch.float16,
46
- )
47
- pipeline = (
48
- StableDiffusionXLPipeline.from_single_file
49
- if MODEL.endswith(".safetensors")
50
- else StableDiffusionXLPipeline.from_pretrained
51
- )
52
-
53
- pipe = pipeline(
54
- model_name,
55
- vae=vae,
56
- torch_dtype=torch.float16,
57
- custom_pipeline="lpw_stable_diffusion_xl",
58
- use_safetensors=True,
59
- add_watermarker=False,
60
- use_auth_token=HF_TOKEN,
61
- )
62
-
63
- pipe.to(device)
64
- return pipe
65
-
66
-
67
- @spaces.GPU
68
- def generate(
69
- prompt: str,
70
- negative_prompt: str = "",
71
- seed: int = 0,
72
- custom_width: int = 1024,
73
- custom_height: int = 1024,
74
- guidance_scale: float = 7.0,
75
- num_inference_steps: int = 28,
76
- sampler: str = "Euler a",
77
- aspect_ratio_selector: str = "896 x 1152",
78
- style_selector: str = "(None)",
79
- quality_selector: str = "Standard v3.1",
80
- use_upscaler: bool = False,
81
- upscaler_strength: float = 0.55,
82
- upscale_by: float = 1.5,
83
- add_quality_tags: bool = True,
84
- progress=gr.Progress(track_tqdm=True),
85
- ):
86
- generator = utils.seed_everything(seed)
87
-
88
- width, height = utils.aspect_ratio_handler(
89
- aspect_ratio_selector,
90
- custom_width,
91
- custom_height,
92
- )
93
-
94
- prompt = utils.add_wildcard(prompt, wildcard_files)
95
-
96
- prompt, negative_prompt = utils.preprocess_prompt(
97
- quality_prompt, quality_selector, prompt, negative_prompt, add_quality_tags
98
- )
99
- prompt, negative_prompt = utils.preprocess_prompt(
100
- styles, style_selector, prompt, negative_prompt
101
- )
102
-
103
- width, height = utils.preprocess_image_dimensions(width, height)
104
-
105
- backup_scheduler = pipe.scheduler
106
- pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler)
107
-
108
- if use_upscaler:
109
- upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components)
110
- metadata = {
111
- "prompt": prompt,
112
- "negative_prompt": negative_prompt,
113
- "resolution": f"{width} x {height}",
114
- "guidance_scale": guidance_scale,
115
- "num_inference_steps": num_inference_steps,
116
- "seed": seed,
117
- "sampler": sampler,
118
- "sdxl_style": style_selector,
119
- "add_quality_tags": add_quality_tags,
120
- "quality_tags": quality_selector,
121
- }
122
-
123
- if use_upscaler:
124
- new_width = int(width * upscale_by)
125
- new_height = int(height * upscale_by)
126
- metadata["use_upscaler"] = {
127
- "upscale_method": "nearest-exact",
128
- "upscaler_strength": upscaler_strength,
129
- "upscale_by": upscale_by,
130
- "new_resolution": f"{new_width} x {new_height}",
131
- }
132
- else:
133
- metadata["use_upscaler"] = None
134
- metadata["Model"] = {
135
- "Model": DESCRIPTION,
136
- "Model hash": "e3c47aedb0",
137
- }
138
-
139
- logger.info(json.dumps(metadata, indent=4))
140
-
141
- try:
142
- if use_upscaler:
143
- latents = pipe(
144
- prompt=prompt,
145
- negative_prompt=negative_prompt,
146
- width=width,
147
- height=height,
148
- guidance_scale=guidance_scale,
149
- num_inference_steps=num_inference_steps,
150
- generator=generator,
151
- output_type="latent",
152
- ).images
153
- upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by)
154
- images = upscaler_pipe(
155
- prompt=prompt,
156
- negative_prompt=negative_prompt,
157
- image=upscaled_latents,
158
- guidance_scale=guidance_scale,
159
- num_inference_steps=num_inference_steps,
160
- strength=upscaler_strength,
161
- generator=generator,
162
- output_type="pil",
163
- ).images
164
- else:
165
- images = pipe(
166
- prompt=prompt,
167
- negative_prompt=negative_prompt,
168
- width=width,
169
- height=height,
170
- guidance_scale=guidance_scale,
171
- num_inference_steps=num_inference_steps,
172
- generator=generator,
173
- output_type="pil",
174
- ).images
175
-
176
- if images:
177
- image_paths = [
178
- utils.save_image(image, metadata, OUTPUT_DIR, IS_COLAB)
179
- for image in images
180
- ]
181
-
182
- for image_path in image_paths:
183
- logger.info(f"Image saved as {image_path} with metadata")
184
-
185
- return image_paths, metadata
186
- except Exception as e:
187
- logger.exception(f"An error occurred: {e}")
188
- raise
189
- finally:
190
- if use_upscaler:
191
- del upscaler_pipe
192
- pipe.scheduler = backup_scheduler
193
- utils.free_memory()
194
-
195
-
196
- if torch.cuda.is_available():
197
- pipe = load_pipeline(MODEL)
198
- logger.info("Loaded on Device!")
199
- else:
200
- pipe = None
201
-
202
- styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.style_list}
203
- quality_prompt = {
204
- k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.quality_prompt_list
205
- }
206
-
207
- wildcard_files = utils.load_wildcard_files("wildcard")
208
-
209
- with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") as demo:
210
- title = gr.HTML(
211
- f"""<h1><span>{DESCRIPTION}</span></h1>""",
212
- elem_id="title",
213
- )
214
- gr.Markdown(
215
- f"""Gradio demo for [cagliostrolab/animagine-xl-3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1)""",
216
- elem_id="subtitle",
217
- )
218
- gr.DuplicateButton(
219
- value="Duplicate Space for private use",
220
- elem_id="duplicate-button",
221
- visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
222
- )
223
- with gr.Row():
224
- with gr.Column(scale=2):
225
- with gr.Tab("Txt2img"):
226
- with gr.Group():
227
- prompt = gr.Text(
228
- label="Prompt",
229
- max_lines=5,
230
- placeholder="Enter your prompt",
231
- )
232
- negative_prompt = gr.Text(
233
- label="Negative Prompt",
234
- max_lines=5,
235
- placeholder="Enter a negative prompt",
236
- )
237
- with gr.Accordion(label="Quality Tags", open=True):
238
- add_quality_tags = gr.Checkbox(
239
- label="Add Quality Tags", value=True
240
- )
241
- quality_selector = gr.Dropdown(
242
- label="Quality Tags Presets",
243
- interactive=True,
244
- choices=list(quality_prompt.keys()),
245
- value="Standard v3.1",
246
- )
247
- with gr.Tab("Advanced Settings"):
248
- with gr.Group():
249
- style_selector = gr.Radio(
250
- label="Style Preset",
251
- container=True,
252
- interactive=True,
253
- choices=list(styles.keys()),
254
- value="(None)",
255
- )
256
- with gr.Group():
257
- aspect_ratio_selector = gr.Radio(
258
- label="Aspect Ratio",
259
- choices=config.aspect_ratios,
260
- value="896 x 1152",
261
- container=True,
262
- )
263
- with gr.Group(visible=False) as custom_resolution:
264
- with gr.Row():
265
- custom_width = gr.Slider(
266
- label="Width",
267
- minimum=MIN_IMAGE_SIZE,
268
- maximum=MAX_IMAGE_SIZE,
269
- step=8,
270
- value=1024,
271
- )
272
- custom_height = gr.Slider(
273
- label="Height",
274
- minimum=MIN_IMAGE_SIZE,
275
- maximum=MAX_IMAGE_SIZE,
276
- step=8,
277
- value=1024,
278
- )
279
- with gr.Group():
280
- use_upscaler = gr.Checkbox(label="Use Upscaler", value=False)
281
- with gr.Row() as upscaler_row:
282
- upscaler_strength = gr.Slider(
283
- label="Strength",
284
- minimum=0,
285
- maximum=1,
286
- step=0.05,
287
- value=0.55,
288
- visible=False,
289
- )
290
- upscale_by = gr.Slider(
291
- label="Upscale by",
292
- minimum=1,
293
- maximum=1.5,
294
- step=0.1,
295
- value=1.5,
296
- visible=False,
297
- )
298
- with gr.Group():
299
- sampler = gr.Dropdown(
300
- label="Sampler",
301
- choices=config.sampler_list,
302
- interactive=True,
303
- value="Euler a",
304
- )
305
- with gr.Group():
306
- seed = gr.Slider(
307
- label="Seed", minimum=0, maximum=utils.MAX_SEED, step=1, value=0
308
- )
309
- randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
310
- with gr.Group():
311
- with gr.Row():
312
- guidance_scale = gr.Slider(
313
- label="Guidance scale",
314
- minimum=1,
315
- maximum=12,
316
- step=0.1,
317
- value=7.0,
318
- )
319
- num_inference_steps = gr.Slider(
320
- label="Number of inference steps",
321
- minimum=1,
322
- maximum=50,
323
- step=1,
324
- value=28,
325
- )
326
- with gr.Column(scale=3):
327
- with gr.Blocks():
328
- run_button = gr.Button("Generate", variant="primary")
329
- result = gr.Gallery(
330
- label="Result",
331
- columns=1,
332
- height='100%',
333
- preview=True,
334
- show_label=False
335
- )
336
- with gr.Accordion(label="Generation Parameters", open=False):
337
- gr_metadata = gr.JSON(label="metadata", show_label=False)
338
- gr.Examples(
339
- examples=config.examples,
340
- inputs=prompt,
341
- outputs=[result, gr_metadata],
342
- fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs),
343
- cache_examples=CACHE_EXAMPLES,
344
- )
345
- use_upscaler.change(
346
- fn=lambda x: [gr.update(visible=x), gr.update(visible=x)],
347
- inputs=use_upscaler,
348
- outputs=[upscaler_strength, upscale_by],
349
- queue=False,
350
- api_name=False,
351
- )
352
- aspect_ratio_selector.change(
353
- fn=lambda x: gr.update(visible=x == "Custom"),
354
- inputs=aspect_ratio_selector,
355
- outputs=custom_resolution,
356
- queue=False,
357
- api_name=False,
358
- )
359
-
360
- gr.on(
361
- triggers=[
362
- prompt.submit,
363
- negative_prompt.submit,
364
- run_button.click,
365
- ],
366
- fn=utils.randomize_seed_fn,
367
- inputs=[seed, randomize_seed],
368
- outputs=seed,
369
- queue=False,
370
- api_name=False,
371
- ).then(
372
- fn=generate,
373
- inputs=[
374
- prompt,
375
- negative_prompt,
376
- seed,
377
- custom_width,
378
- custom_height,
379
- guidance_scale,
380
- num_inference_steps,
381
- sampler,
382
- aspect_ratio_selector,
383
- style_selector,
384
- quality_selector,
385
- use_upscaler,
386
- upscaler_strength,
387
- upscale_by,
388
- add_quality_tags,
389
- ],
390
- outputs=[result, gr_metadata],
391
- api_name="run",
392
- )
393
-
394
- if __name__ == "__main__":
395
- demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)