ford442 commited on
Commit
775ee85
·
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1 Parent(s): 05b8565

Update app.py

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Files changed (1) hide show
  1. app.py +20 -7
app.py CHANGED
@@ -14,8 +14,8 @@ from PIL import Image
14
  import torch
15
  from diffusers import AutoencoderKL, StableDiffusionXLPipeline, UNet2DConditionModel
16
  from diffusers import EulerAncestralDiscreteScheduler
17
- from diffusers import DPMSolverMultistepScheduler
18
- from diffusers import AsymmetricAutoencoderKL
19
  from typing import Tuple
20
  import paramiko
21
  import gc
@@ -92,7 +92,7 @@ def load_and_prepare_model(model_id):
92
  model_dtypes = {"ford442/RealVisXL_V5.0_BF16": torch.bfloat16,}
93
  dtype = model_dtypes.get(model_id, torch.bfloat16) # Default to float32 if not found
94
  #vaeX = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None)
95
- #vaeX = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False)
96
  #vae = AutoencoderKL.from_pretrained('cross-attention/asymmetric-autoencoder-kl-x-2',use_safetensors=False)
97
  #vae = AutoencoderKL.from_single_file('https://huggingface.co/ford442/sdxl-vae-bf16/mySLR/myslrVAE_v10.safetensors')
98
  #vaeX = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse",use_safetensors=True)
@@ -239,6 +239,7 @@ def generate_30(
239
  randomize_seed: bool = False,
240
  use_resolution_binning: bool = True,
241
  num_images: int = 1,
 
242
  progress=gr.Progress(track_tqdm=True) # Add progress as a keyword argument
243
  ):
244
  torch.backends.cudnn.benchmark = False
@@ -246,6 +247,8 @@ def generate_30(
246
  gc.collect()
247
  global models
248
  pipe = models[model_choice]
 
 
249
  seed = int(randomize_seed_fn(seed, randomize_seed))
250
  generator = torch.Generator(device='cuda').manual_seed(seed)
251
  #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
@@ -295,7 +298,8 @@ def generate_60(
295
  num_inference_steps: int = 250,
296
  randomize_seed: bool = False,
297
  use_resolution_binning: bool = True,
298
- num_images: int = 1,
 
299
  progress=gr.Progress(track_tqdm=True) # Add progress as a keyword argument
300
  ):
301
  torch.backends.cudnn.benchmark = True
@@ -303,6 +307,8 @@ def generate_60(
303
  gc.collect()
304
  global models
305
  pipe = models[model_choice]
 
 
306
  seed = int(randomize_seed_fn(seed, randomize_seed))
307
  generator = torch.Generator(device='cuda').manual_seed(seed)
308
  #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
@@ -353,6 +359,7 @@ def generate_90(
353
  randomize_seed: bool = False,
354
  use_resolution_binning: bool = True,
355
  num_images: int = 1,
 
356
  progress=gr.Progress(track_tqdm=True) # Add progress as a keyword argument
357
  ):
358
  torch.backends.cudnn.benchmark = True
@@ -360,6 +367,8 @@ def generate_90(
360
  gc.collect()
361
  global models
362
  pipe = models[model_choice]
 
 
363
  seed = int(randomize_seed_fn(seed, randomize_seed))
364
  generator = torch.Generator(device='cuda').manual_seed(seed)
365
  #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
@@ -479,6 +488,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
479
  value=0,
480
  )
481
  randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
482
  with gr.Row():
483
  width = gr.Slider(
484
  label="Width",
@@ -541,7 +551,8 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
541
  guidance_scale,
542
  num_inference_steps,
543
  randomize_seed,
544
- num_images,
 
545
  ],
546
  outputs=[result, seed],
547
  )
@@ -564,7 +575,8 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
564
  guidance_scale,
565
  num_inference_steps,
566
  randomize_seed,
567
- num_images,
 
568
  ],
569
  outputs=[result, seed],
570
  )
@@ -587,7 +599,8 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
587
  guidance_scale,
588
  num_inference_steps,
589
  randomize_seed,
590
- num_images,
 
591
  ],
592
  outputs=[result, seed],
593
  )
 
14
  import torch
15
  from diffusers import AutoencoderKL, StableDiffusionXLPipeline, UNet2DConditionModel
16
  from diffusers import EulerAncestralDiscreteScheduler
17
+ #from diffusers import DPMSolverMultistepScheduler
18
+ #from diffusers import AsymmetricAutoencoderKL
19
  from typing import Tuple
20
  import paramiko
21
  import gc
 
92
  model_dtypes = {"ford442/RealVisXL_V5.0_BF16": torch.bfloat16,}
93
  dtype = model_dtypes.get(model_id, torch.bfloat16) # Default to float32 if not found
94
  #vaeX = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None)
95
+ vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False).to(device=device, dtype=torch.bfloat16)
96
  #vae = AutoencoderKL.from_pretrained('cross-attention/asymmetric-autoencoder-kl-x-2',use_safetensors=False)
97
  #vae = AutoencoderKL.from_single_file('https://huggingface.co/ford442/sdxl-vae-bf16/mySLR/myslrVAE_v10.safetensors')
98
  #vaeX = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse",use_safetensors=True)
 
239
  randomize_seed: bool = False,
240
  use_resolution_binning: bool = True,
241
  num_images: int = 1,
242
+ juggernaut: bool = True,
243
  progress=gr.Progress(track_tqdm=True) # Add progress as a keyword argument
244
  ):
245
  torch.backends.cudnn.benchmark = False
 
247
  gc.collect()
248
  global models
249
  pipe = models[model_choice]
250
+ if juggernaut == False:
251
+ pipe.vae=vaeXL
252
  seed = int(randomize_seed_fn(seed, randomize_seed))
253
  generator = torch.Generator(device='cuda').manual_seed(seed)
254
  #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
 
298
  num_inference_steps: int = 250,
299
  randomize_seed: bool = False,
300
  use_resolution_binning: bool = True,
301
+ num_images: int = 1,
302
+ juggernaut: bool = True,
303
  progress=gr.Progress(track_tqdm=True) # Add progress as a keyword argument
304
  ):
305
  torch.backends.cudnn.benchmark = True
 
307
  gc.collect()
308
  global models
309
  pipe = models[model_choice]
310
+ if juggernaut == False:
311
+ pipe.vae=vaeXL
312
  seed = int(randomize_seed_fn(seed, randomize_seed))
313
  generator = torch.Generator(device='cuda').manual_seed(seed)
314
  #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
 
359
  randomize_seed: bool = False,
360
  use_resolution_binning: bool = True,
361
  num_images: int = 1,
362
+ juggernaut: bool = True,
363
  progress=gr.Progress(track_tqdm=True) # Add progress as a keyword argument
364
  ):
365
  torch.backends.cudnn.benchmark = True
 
367
  gc.collect()
368
  global models
369
  pipe = models[model_choice]
370
+ if juggernaut == False:
371
+ pipe.vae=vaeXL
372
  seed = int(randomize_seed_fn(seed, randomize_seed))
373
  generator = torch.Generator(device='cuda').manual_seed(seed)
374
  #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
 
488
  value=0,
489
  )
490
  randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
491
+ juggernaut = gr.Checkbox(label="Use Juggernaut VAE", value=True)
492
  with gr.Row():
493
  width = gr.Slider(
494
  label="Width",
 
551
  guidance_scale,
552
  num_inference_steps,
553
  randomize_seed,
554
+ num_images,
555
+ juggernaut,
556
  ],
557
  outputs=[result, seed],
558
  )
 
575
  guidance_scale,
576
  num_inference_steps,
577
  randomize_seed,
578
+ num_images,
579
+ juggernaut,
580
  ],
581
  outputs=[result, seed],
582
  )
 
599
  guidance_scale,
600
  num_inference_steps,
601
  randomize_seed,
602
+ num_images,
603
+ juggernaut,
604
  ],
605
  outputs=[result, seed],
606
  )