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Update app.py
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app.py
CHANGED
@@ -51,8 +51,8 @@ pipe_edit.to("cuda")
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def promptifier(prompt):
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client1 = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
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system_instructions1 = "<s>[SYSTEM] Your task is to modify prompt by USER to more better prompt for Image Generation in Stable Diffusion XL,
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formatted_prompt = f"{system_instructions1} {prompt} [
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stream = client1.text_generation(formatted_prompt, max_new_tokens=80, stream=True, details=True, return_full_text=False)
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return "".join([response.token.text for response in stream if response.token.text != "</s>"])
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@@ -95,15 +95,19 @@ def king(type ,
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seed = random.randint(0, 999999)
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generator = torch.Generator().manual_seed(seed)
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if fast:
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steps=int(steps/2.5)
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guidance_scale2=(guidance_scale/
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refine = pipe_fast( prompt = instruction,
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guidance_scale = guidance_scale2,
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num_inference_steps = steps,
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width = width, height = height,
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generator = generator,
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).images
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else:
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if enhance_prompt:
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print(f"BEFORE: {instruction} ")
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@@ -119,12 +123,12 @@ def king(type ,
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generator = generator, output_type="latent",
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).images
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negative_prompt = negative_prompt,
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guidance_scale = guidance_scale,
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num_inference_steps= steps,
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image=image, generator=generator,
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return seed, refine
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client = InferenceClient()
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def promptifier(prompt):
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client1 = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
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system_instructions1 = "<s>[SYSTEM] Your task is to modify prompt by USER to more better prompt for Image Generation in Stable Diffusion XL, you have to optiomize prompt and also add some keywords like, 4k, realistic, featuristic according to prompt and also break prompt into sub-lines using comma, Your task is to reply with final optimized prompt only. Just reply with prompt only.[USER]"
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formatted_prompt = f"{system_instructions1} {prompt} [PROMPT]"
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stream = client1.text_generation(formatted_prompt, max_new_tokens=80, stream=True, details=True, return_full_text=False)
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return "".join([response.token.text for response in stream if response.token.text != "</s>"])
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seed = random.randint(0, 999999)
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generator = torch.Generator().manual_seed(seed)
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if fast:
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if enhance_prompt:
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print(f"BEFORE: {instruction} ")
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instruction = promptifier(instruction)
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print(f"AFTER: {instruction} ")
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steps=int(steps/2.5)
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guidance_scale2=(guidance_scale/2)
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refine = pipe_fast( prompt = instruction,
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guidance_scale = guidance_scale2,
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num_inference_steps = steps,
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width = width, height = height,
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generator = generator, output_type="latent",
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).images
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else:
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if enhance_prompt:
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print(f"BEFORE: {instruction} ")
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generator = generator, output_type="latent",
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).images
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refine = refiner( prompt=instruction,
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negative_prompt = negative_prompt,
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guidance_scale = guidance_scale,
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num_inference_steps= steps,
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image=image, generator=generator,
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).images[0]
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return seed, refine
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client = InferenceClient()
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