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
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  from PIL import Image
5
  import random
@@ -31,11 +30,13 @@ main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
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  safety_checker=None,
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  torch_dtype=torch.float16,
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  ).to("cuda")
 
34
  #main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
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  #main_pipe.unet.to(memory_format=torch.channels_last)
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  #main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
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  #model_id = "stabilityai/sd-x2-latent-upscaler"
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- 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)
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  #upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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  #upscaler.to("cuda")
@@ -110,9 +111,11 @@ def inference(
110
 
111
  # Rest of your existing code
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  control_image_small = center_crop_resize(control_image)
 
 
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  main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
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  my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
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- generator = torch.manual_seed(my_seed)
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  out = main_pipe(
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  prompt=prompt,
@@ -126,7 +129,6 @@ def inference(
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  num_inference_steps=15,
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  output_type="latent"
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  )
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- control_image_large = center_crop_resize(control_image, (1024, 1024))
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  upscaled_latents = upscale(out, "nearest-exact", 2)
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  out_image = image_pipe(
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  prompt=prompt,
@@ -201,4 +203,4 @@ with gr.Blocks(css=css) as app:
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  app.queue(max_size=20)
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  if __name__ == "__main__":
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- app.launch()
 
1
  import torch
 
2
  import gradio as gr
3
  from PIL import Image
4
  import random
 
30
  safety_checker=None,
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  torch_dtype=torch.float16,
32
  ).to("cuda")
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+
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  #main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
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  #main_pipe.unet.to(memory_format=torch.channels_last)
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  #main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
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  #model_id = "stabilityai/sd-x2-latent-upscaler"
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+ image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)
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+
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  #image_pipe.unet = torch.compile(image_pipe.unet, mode="reduce-overhead", fullgraph=True)
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  #upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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  #upscaler.to("cuda")
 
111
 
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  # Rest of your existing code
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  control_image_small = center_crop_resize(control_image)
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+ control_image_large = center_crop_resize(control_image, (1024, 1024))
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+
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  main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
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  my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
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+ generator = torch.Generator(device="cuda").manual_seed(my_seed)
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  out = main_pipe(
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  prompt=prompt,
 
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  num_inference_steps=15,
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  output_type="latent"
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  )
 
132
  upscaled_latents = upscale(out, "nearest-exact", 2)
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  out_image = image_pipe(
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  prompt=prompt,
 
203
  app.queue(max_size=20)
204
 
205
  if __name__ == "__main__":
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+ app.launch()
requirements.txt CHANGED
@@ -1,9 +1,11 @@
1
  diffusers
2
  transformers
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  accelerate
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- torch
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  xformers
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  gradio
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  Pillow
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  qrcode
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- filelock
 
 
 
 
1
  diffusers
2
  transformers
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  accelerate
 
4
  xformers
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  gradio
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  Pillow
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  qrcode
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+ filelock
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+
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+ --extra-index-url https://download.pytorch.org/whl/cu118
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+ torch