AP123 patrickvonplaten commited on
Commit
7391723
1 Parent(s): c1f6c62

Improve mem usage (#70)

Browse files

- Improve mem usage (2b0be71ae4a84219bf76feddb29e3a339d2ad2f9)
- improve (dcab47337e68e8397244156ea1f8216685eef465)
- improve (f7bc5db379dbda268146e36d32388273bdd603ca)
- improve (cf27adf590f95fe663b87c0e7df23f535a3d9b68)
- improve (fb9b203ded8b0e9018fc1566ea3dca01e4273b31)
- improve (69cc446845536f34f18ba354dd8515b3ce28b8a9)
- improve (70b9430b16cb8d20a76feb590f8920dcdf2ff938)
- make style (679bb8131dff16a0d5d957af456a886ea1ec1e18)


Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>

Files changed (2) hide show
  1. app.py +7 -5
  2. requirements.txt +4 -2
app.py CHANGED
@@ -1,5 +1,4 @@
1
  import torch
2
- import os
3
  import gradio as gr
4
  from PIL import Image
5
  import random
@@ -31,11 +30,13 @@ main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
31
  safety_checker=None,
32
  torch_dtype=torch.float16,
33
  ).to("cuda")
 
34
  #main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
35
  #main_pipe.unet.to(memory_format=torch.channels_last)
36
  #main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
37
  #model_id = "stabilityai/sd-x2-latent-upscaler"
38
- image_pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(BASE_MODEL, unet=main_pipe.unet, vae=vae, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16).to("cuda")
 
39
  #image_pipe.unet = torch.compile(image_pipe.unet, mode="reduce-overhead", fullgraph=True)
40
  #upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
41
  #upscaler.to("cuda")
@@ -110,9 +111,11 @@ def inference(
110
 
111
  # Rest of your existing code
112
  control_image_small = center_crop_resize(control_image)
 
 
113
  main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
114
  my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
115
- generator = torch.manual_seed(my_seed)
116
 
117
  out = main_pipe(
118
  prompt=prompt,
@@ -126,7 +129,6 @@ def inference(
126
  num_inference_steps=15,
127
  output_type="latent"
128
  )
129
- control_image_large = center_crop_resize(control_image, (1024, 1024))
130
  upscaled_latents = upscale(out, "nearest-exact", 2)
131
  out_image = image_pipe(
132
  prompt=prompt,
@@ -201,4 +203,4 @@ with gr.Blocks(css=css) as app:
201
  app.queue(max_size=20)
202
 
203
  if __name__ == "__main__":
204
- app.launch()
 
1
  import torch
 
2
  import gradio as gr
3
  from PIL import Image
4
  import random
 
30
  safety_checker=None,
31
  torch_dtype=torch.float16,
32
  ).to("cuda")
33
+
34
  #main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
35
  #main_pipe.unet.to(memory_format=torch.channels_last)
36
  #main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
37
  #model_id = "stabilityai/sd-x2-latent-upscaler"
38
+ image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)
39
+
40
  #image_pipe.unet = torch.compile(image_pipe.unet, mode="reduce-overhead", fullgraph=True)
41
  #upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
42
  #upscaler.to("cuda")
 
111
 
112
  # Rest of your existing code
113
  control_image_small = center_crop_resize(control_image)
114
+ control_image_large = center_crop_resize(control_image, (1024, 1024))
115
+
116
  main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
117
  my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
118
+ generator = torch.Generator(device="cuda").manual_seed(my_seed)
119
 
120
  out = main_pipe(
121
  prompt=prompt,
 
129
  num_inference_steps=15,
130
  output_type="latent"
131
  )
 
132
  upscaled_latents = upscale(out, "nearest-exact", 2)
133
  out_image = image_pipe(
134
  prompt=prompt,
 
203
  app.queue(max_size=20)
204
 
205
  if __name__ == "__main__":
206
+ app.launch()
requirements.txt CHANGED
@@ -1,9 +1,11 @@
1
  diffusers
2
  transformers
3
  accelerate
4
- torch
5
  xformers
6
  gradio
7
  Pillow
8
  qrcode
9
- filelock
 
 
 
 
1
  diffusers
2
  transformers
3
  accelerate
 
4
  xformers
5
  gradio
6
  Pillow
7
  qrcode
8
+ filelock
9
+
10
+ --extra-index-url https://download.pytorch.org/whl/cu118
11
+ torch