MohamedRashad commited on
Commit
55002dc
·
1 Parent(s): eecb045

Enhance image generation function by initializing random number generator for device-specific operations; update markdown instructions for clarity and improve HTML header formatting

Browse files
Files changed (1) hide show
  1. app.py +7 -7
app.py CHANGED
@@ -507,6 +507,7 @@ def generate_image(prompt, cfg, tau, h_div_w, seed):
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  torch.cuda.empty_cache()
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  with torch.amp.autocast(device_type=device, dtype=autocast_dtype), torch.no_grad():
 
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  _, _, img_list = infinity.autoregressive_infer_cfg(
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  vae=vae,
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  scale_schedule=scale_schedule,
@@ -520,6 +521,7 @@ def generate_image(prompt, cfg, tau, h_div_w, seed):
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  gt_leak=0, gt_ls_Bl=None, inference_mode=True,
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  sampling_per_bits=args.sampling_per_bits,
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  )
 
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  img = img_list[0]
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  image = img.cpu().numpy()
@@ -531,18 +533,16 @@ def generate_image(prompt, cfg, tau, h_div_w, seed):
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  markdown_description = """### Instructions:
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- 1. Enter a prompt in the text box below.
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- 2. Adjust the CFG (Classifier-Free Guidance) slider to control the strength of the prompt.
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- 3. Adjust the Tau (Temperature) slider to control the randomness of the output.
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- 4. Adjust the Aspect Ratio (Height/Width) slider to set the aspect ratio of the generated image.
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- 5. Click the Generate Image button to generate an image based on the prompt.
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  Arxiv Paper:
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  [Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis](https://arxiv.org/abs/2412.04431).
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  """
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  html_header = """<div style="text-align: center; margin-bottom: 20px;">
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- <h1>Infinity Image Generator</h1>
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- <h2>by <a href="https://github.com/FoundationVision/Infinity" target="_blank" rel="noopener noreferrer">FoundationVision</a></h2>
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  <p style="font-size: 14px; color: #888;">This is not the official implementation from the main developers!</p>
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  </div>"""
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  with gr.Blocks() as demo:
 
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  torch.cuda.empty_cache()
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  with torch.amp.autocast(device_type=device, dtype=autocast_dtype), torch.no_grad():
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+ infinity.rng = torch.Generator(device=device)
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  _, _, img_list = infinity.autoregressive_infer_cfg(
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  vae=vae,
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  scale_schedule=scale_schedule,
 
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  gt_leak=0, gt_ls_Bl=None, inference_mode=True,
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  sampling_per_bits=args.sampling_per_bits,
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  )
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+ infinity.rng = torch.Generator(device="cpu")
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  img = img_list[0]
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  image = img.cpu().numpy()
 
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  markdown_description = """### Instructions:
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+ 1. Enter a detailed prompt with rich visual features or use the "Enhance Prompt" button to generate a more detailed description.
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+ 2. Adjust the "CFG" and "Tau" sliders to control the strength and randomness of the output.
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+ 3. Use the "Aspect Ratio" slider to set the aspect ratio of the generated image.
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+ 4. Click the "Generate Image" button to create the image based on your prompt.
 
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  Arxiv Paper:
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  [Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis](https://arxiv.org/abs/2412.04431).
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  """
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  html_header = """<div style="text-align: center; margin-bottom: 20px;">
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+ <h1>Infinity Image Generator by <a href="https://github.com/FoundationVision/Infinity" target="_blank" rel="noopener noreferrer">FoundationVision</a></h1>
 
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  <p style="font-size: 14px; color: #888;">This is not the official implementation from the main developers!</p>
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  </div>"""
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  with gr.Blocks() as demo: