AP123 commited on
Commit
0277b1d
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1 Parent(s): 6ec4b8d

Update app.py

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Files changed (1) hide show
  1. app.py +7 -12
app.py CHANGED
@@ -13,12 +13,10 @@ from diffusers import (
13
  EulerDiscreteScheduler,
14
  )
15
 
16
- # Initialize ControlNet model
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  controlnet = ControlNetModel.from_pretrained(
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  "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
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  )
20
 
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- # Initialize pipeline
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  pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
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  "XpucT/Deliberate",
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  controlnet=controlnet,
@@ -27,13 +25,11 @@ pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
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  ).to("cuda")
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  pipe.enable_xformers_memory_efficient_attention()
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- # Sampler configurations
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  SAMPLER_MAP = {
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  "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
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  "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
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  }
35
 
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- # Inference function
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  def inference(
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  input_image: Image.Image,
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  prompt: str,
@@ -47,6 +43,8 @@ def inference(
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  if prompt is None or prompt == "":
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  raise gr.Error("Prompt is required")
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  pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
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  generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
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@@ -54,18 +52,15 @@ def inference(
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  prompt=prompt,
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  negative_prompt=negative_prompt,
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  image=input_image,
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- control_image=input_image, # type: ignore
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- width=512, # type: ignore
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- height=512, # type: ignore
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  guidance_scale=float(guidance_scale),
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- controlnet_conditioning_scale=float(controlnet_conditioning_scale), # type: ignore
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  generator=generator,
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  strength=float(strength),
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  num_inference_steps=40,
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  )
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- return out.images[0] # type: ignore
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- # Gradio UI
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  with gr.Blocks() as app:
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  gr.Markdown(
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  '''
@@ -78,7 +73,7 @@ with gr.Blocks() as app:
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  with gr.Row():
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  with gr.Column():
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  input_image = gr.Image(label="Input Illusion", type="pil")
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- prompt = gr.Textbox(label="Prompt", info="Prompt that guides the generation towards")
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  negative_prompt = gr.Textbox(label="Negative Prompt", value="ugly, disfigured, low quality, blurry, nsfw")
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  with gr.Accordion(label="Advanced Options", open=False):
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  controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=1.1, label="Controlnet Conditioning Scale")
@@ -99,4 +94,4 @@ with gr.Blocks() as app:
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  app.queue(concurrency_count=4, max_size=20)
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101
  if __name__ == "__main__":
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- app.launch(debug=True)
 
13
  EulerDiscreteScheduler,
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  )
15
 
 
16
  controlnet = ControlNetModel.from_pretrained(
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  "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
18
  )
19
 
 
20
  pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
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  "XpucT/Deliberate",
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  controlnet=controlnet,
 
25
  ).to("cuda")
26
  pipe.enable_xformers_memory_efficient_attention()
27
 
 
28
  SAMPLER_MAP = {
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  "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
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  "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
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  }
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33
  def inference(
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  input_image: Image.Image,
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  prompt: str,
 
43
  if prompt is None or prompt == "":
44
  raise gr.Error("Prompt is required")
45
 
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+ input_image = input_image.resize((512, 512))
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+
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  pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
49
  generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
50
 
 
52
  prompt=prompt,
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  negative_prompt=negative_prompt,
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  image=input_image,
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+ control_image=input_image,
 
 
56
  guidance_scale=float(guidance_scale),
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+ controlnet_conditioning_scale=float(controlnet_conditioning_scale),
58
  generator=generator,
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  strength=float(strength),
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  num_inference_steps=40,
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  )
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+ return out.images[0]
63
 
 
64
  with gr.Blocks() as app:
65
  gr.Markdown(
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  '''
 
73
  with gr.Row():
74
  with gr.Column():
75
  input_image = gr.Image(label="Input Illusion", type="pil")
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+ prompt = gr.Textbox(label="Prompt")
77
  negative_prompt = gr.Textbox(label="Negative Prompt", value="ugly, disfigured, low quality, blurry, nsfw")
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  with gr.Accordion(label="Advanced Options", open=False):
79
  controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=1.1, label="Controlnet Conditioning Scale")
 
94
  app.queue(concurrency_count=4, max_size=20)
95
 
96
  if __name__ == "__main__":
97
+ app.launch(debug=True)