Spaces:
Running
on
Zero
Running
on
Zero
Add pause length
Browse files
app.py
CHANGED
@@ -144,7 +144,7 @@ def generate_diffusion_text(input_ids):
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return sampled, conf
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# --- Inference Wrapper ---
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-
def diffusion_chat(question, eot_weight, max_it, sharpness,
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placeholder = "What do you know about the city of New York?"
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if question.strip() == "":
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question = placeholder
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@@ -193,7 +193,7 @@ def diffusion_chat(question, eot_weight, max_it, sharpness, noise_clipping, use_
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prev_decoded_tokens = decoded_tokens
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yield f"<b>Iteration {i+1}/{max_it} (after generation):</b><br>" + "".join(highlighted).replace('\n', '<br>')
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time.sleep(
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# --- Early stopping ---
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last_tokens.append(current_tokens)
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@@ -235,7 +235,7 @@ def diffusion_chat(question, eot_weight, max_it, sharpness, noise_clipping, use_
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highlighted.append(token_str)
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yield f"<b>Iteration {i+1}/{max_it} (before noising):</b><br>" + "".join(highlighted).replace('\n', '<br>')
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time.sleep(
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final_tokens = tokenizer.convert_ids_to_tokens(current_tokens[answer_start:])
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@@ -255,10 +255,12 @@ demo = gr.Interface(
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gr.Textbox(label="User Question", lines=2, placeholder="What do you know about the city of New York?"),
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gr.Slider(0, 1, value=0.4, step=0.05, label="↓ = longer answers (EOT weight)"),
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gr.Slider(1, 512, value=64, step=1, label="↑ = more iterations"),
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gr.Slider(1.0, 20.0, value=5.0, step=0.5, label="↓ = more noising (sharpness)"),
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gr.Slider(0.01, 1.0, value=0.05, step=0.01, label="↓ = more confidence guidance (noise clipping)"),
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gr.Checkbox(value=False, label="Use confidence-guided noising"),
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gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="↑ = more clustered noising (fewer, larger edits)")
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],
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outputs=[gr.HTML(label="Diffusion Output")],
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title="Diffusion Language Model Chat",
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return sampled, conf
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# --- Inference Wrapper ---
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+
def diffusion_chat(question, eot_weight, max_it, pause_length, sharpness, clustering, use_confidence_noising, noise_clipping):
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placeholder = "What do you know about the city of New York?"
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if question.strip() == "":
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question = placeholder
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prev_decoded_tokens = decoded_tokens
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yield f"<b>Iteration {i+1}/{max_it} (after generation):</b><br>" + "".join(highlighted).replace('\n', '<br>')
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time.sleep(pause_length)
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# --- Early stopping ---
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last_tokens.append(current_tokens)
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highlighted.append(token_str)
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yield f"<b>Iteration {i+1}/{max_it} (before noising):</b><br>" + "".join(highlighted).replace('\n', '<br>')
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time.sleep(pause_length)
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final_tokens = tokenizer.convert_ids_to_tokens(current_tokens[answer_start:])
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gr.Textbox(label="User Question", lines=2, placeholder="What do you know about the city of New York?"),
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gr.Slider(0, 1, value=0.4, step=0.05, label="↓ = longer answers (EOT weight)"),
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gr.Slider(1, 512, value=64, step=1, label="↑ = more iterations"),
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gr.Slider(0.01, 5, value=0.01, step=0.01, label="↑ = longer pause (for visualization)"),
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gr.Slider(1.0, 20.0, value=5.0, step=0.5, label="↓ = more noising (sharpness)"),
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gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="↑ = more clustered noising (fewer, larger edits)")
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gr.Checkbox(value=False, label="Use confidence-guided noising"),
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gr.Slider(0.01, 1.0, value=0.05, step=0.01, label="↓ = more confidence guidance (noise clipping)"),
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],
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outputs=[gr.HTML(label="Diffusion Output")],
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title="Diffusion Language Model Chat",
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