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Commit
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b64eca0
1
Parent(s):
575f433
change guessing to use buttons
Browse files
app.py
CHANGED
@@ -1,9 +1,6 @@
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import torch
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import gradio as gr
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from diffusers import FluxPipeline, FluxTransformer2DModel
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from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
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from transformers import T5EncoderModel
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from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
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import gc
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import random
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from PIL import Image
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@@ -131,6 +128,9 @@ def generate_images(prompt, quantization_choice, progress=gr.Progress(track_tqdm
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current_pipe = None # Keep track of the current pipe for cleanup
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for i, (label, load_func) in enumerate(model_configs):
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progress(i / len(model_configs), desc=f"Loading {label} model...")
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print(f"\n--- Loading {label} Model ---")
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@@ -143,8 +143,9 @@ def generate_images(prompt, quantization_choice, progress=gr.Progress(track_tqdm
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progress((i + 0.5) / len(model_configs), desc=f"Generating with {label} model...")
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print(f"--- Generating with {label} Model ---")
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gen_start_time = time.time()
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image_list = current_pipe(**pipe_kwargs, generator=torch.manual_seed(
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image = image_list[0]
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gen_end_time = time.time()
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results.append({"label": label, "image": image})
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print(f"--- Finished Generation with {label} Model in {gen_end_time - gen_start_time:.2f} seconds ---")
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@@ -219,11 +220,11 @@ with gr.Blocks(title="FLUX Quantization Challenge", theme=gr.themes.Soft()) as d
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gr.Markdown(
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"Compare the original FLUX.1-dev (BF16) model against a quantized version (4-bit or 8-bit). "
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"Enter a prompt, choose the quantization method, and generate two images. "
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"The images will be shuffled
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)
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with gr.Row():
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prompt_input = gr.Textbox(label="Enter Prompt",
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quantization_choice_radio = gr.Radio(
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choices=["8-bit", "4-bit"],
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label="Select Quantization",
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@@ -243,20 +244,8 @@ with gr.Blocks(title="FLUX Quantization Challenge", theme=gr.themes.Soft()) as d
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gr.Markdown("### Which image used the selected quantization method?")
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with gr.Row():
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pass
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with gr.Column(scale=2): # Column for the radio button
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guess_radio = gr.Radio(
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choices=[],
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label="Your Guess",
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info="Select the image you believe was generated with the quantized model.",
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interactive=False # Disabled until images are generated
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)
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with gr.Column(scale=1): # Column for the button
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submit_guess_button = gr.Button("Submit Guess")
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with gr.Column(scale=1): # Dummy column for spacing
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pass
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feedback_box = gr.Textbox(label="Feedback", interactive=False, lines=1)
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@@ -267,20 +256,18 @@ with gr.Blocks(title="FLUX Quantization Challenge", theme=gr.themes.Soft()) as d
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generate_button.click(
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fn=generate_images,
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inputs=[prompt_input, quantization_choice_radio],
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outputs=[output_gallery, correct_mapping_state,
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).then(
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lambda: "", # Clear feedback box on new generation
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outputs=[feedback_box]
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)
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inputs=[guess_radio, correct_mapping_state], # Pass the selected guess and the state
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outputs=[feedback_box]
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)
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if __name__ == "__main__":
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demo.launch(share=True)
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import torch
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import gradio as gr
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from diffusers import FluxPipeline, FluxTransformer2DModel
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import gc
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import random
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from PIL import Image
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current_pipe = None # Keep track of the current pipe for cleanup
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seed = random.getrandbits(64)
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print(f"Using seed: {seed}")
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for i, (label, load_func) in enumerate(model_configs):
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progress(i / len(model_configs), desc=f"Loading {label} model...")
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print(f"\n--- Loading {label} Model ---")
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progress((i + 0.5) / len(model_configs), desc=f"Generating with {label} model...")
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print(f"--- Generating with {label} Model ---")
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gen_start_time = time.time()
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image_list = current_pipe(**pipe_kwargs, generator=torch.manual_seed(seed)).images
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image = image_list[0]
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# image.save(f"{load_start_time}.png")
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gen_end_time = time.time()
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results.append({"label": label, "image": image})
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print(f"--- Finished Generation with {label} Model in {gen_end_time - gen_start_time:.2f} seconds ---")
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gr.Markdown(
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"Compare the original FLUX.1-dev (BF16) model against a quantized version (4-bit or 8-bit). "
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"Enter a prompt, choose the quantization method, and generate two images. "
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"The images will be shuffled, can you spot which one was quantized?"
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)
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with gr.Row():
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prompt_input = gr.Textbox(label="Enter Prompt", scale=3)
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quantization_choice_radio = gr.Radio(
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choices=["8-bit", "4-bit"],
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label="Select Quantization",
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gr.Markdown("### Which image used the selected quantization method?")
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with gr.Row():
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image1_btn = gr.Button("Image 1")
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image2_btn = gr.Button("Image 2")
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feedback_box = gr.Textbox(label="Feedback", interactive=False, lines=1)
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generate_button.click(
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fn=generate_images,
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inputs=[prompt_input, quantization_choice_radio],
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outputs=[output_gallery, correct_mapping_state],
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).then(lambda: "", outputs=[feedback_box]) # clear feedback on new run
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# helper wrappers so we can supply the fixed choice string
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def choose_img1(mapping):
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return check_guess("Image 1", mapping)
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def choose_img2(mapping):
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return check_guess("Image 2", mapping)
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image1_btn.click(choose_img1, inputs=[correct_mapping_state], outputs=[feedback_box])
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image2_btn.click(choose_img2, inputs=[correct_mapping_state], outputs=[feedback_box])
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if __name__ == "__main__":
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demo.launch(share=True)
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demo.launch()
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