Spaces:
Sleeping
Sleeping
testing model precision type option
Browse files
app.py
CHANGED
@@ -4,7 +4,7 @@ import random
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
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-
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if torch.cuda.is_available():
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device = "cuda"
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@@ -24,8 +24,11 @@ def generate(
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top_p,
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top_k,
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seed,
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):
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input_text = f"{system_prompt}, {prompt}"
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
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@@ -49,6 +52,9 @@ def generate(
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better_prompt = better_prompt.replace("<pad»", "").replace("</s>", "")
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return better_prompt
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prompt = gr.Textbox(label="Prompt", interactive=True)
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system_prompt = gr.Textbox(label="System Prompt", interactive=True)
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@@ -65,7 +71,6 @@ top_k = gr.Slider(value=1, minimum=1, maximum=100, step=1, interactive=True, lab
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seed = gr.Number(value=42, interactive=True, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
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-
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examples = [
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[
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"A storefront with 'Text to Image' written on it.",
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
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+
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if torch.cuda.is_available():
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device = "cuda"
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top_p,
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top_k,
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seed,
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+
precision_model
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):
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model = T5ForConditionalGeneration.from_pretrained("roborovski/superprompt-v1", torch_dtype=precision_model)
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input_text = f"{system_prompt}, {prompt}"
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
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better_prompt = better_prompt.replace("<pad»", "").replace("</s>", "")
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return better_prompt
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precision_model = gr.Radio([('fp32', torch.float32), ('fp16', toch.float16)], label="Model Precision Type", info="fp32 is more precised but slower, fp16 is faster and less resource consuming but less pricse")
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prompt = gr.Textbox(label="Prompt", interactive=True)
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system_prompt = gr.Textbox(label="System Prompt", interactive=True)
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seed = gr.Number(value=42, interactive=True, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
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examples = [
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[
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"A storefront with 'Text to Image' written on it.",
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