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remove logs
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@ -113,10 +113,10 @@ class MD_ImageToMotionPrompt:
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),
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"max_tokens": ("INT", {"min": 1, "max": 2048, "default": 200}),
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},
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# "optional": {
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# "temperature": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.2}),
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# "top_p": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.9}),
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# }
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"optional": {
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"temperature": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.2}),
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"top_p": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.7}),
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}
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}
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@ -127,9 +127,9 @@ class MD_ImageToMotionPrompt:
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def generate_completion(
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self, pre_prompt: str, post_prompt: str, Image: torch.Tensor, clip, prompt: str, negative_prompt: str,
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# temperature: float,
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# top_p: float,
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max_tokens: int
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temperature: float = 0.2,
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top_p: float = 0.7,
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max_tokens: int = 256
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) -> Tuple[str]:
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# start a timer
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start_time = time.time()
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@ -139,8 +139,8 @@ class MD_ImageToMotionPrompt:
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response = requests.post("http://127.0.0.1:5010/inference", json={
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"image_url": f"data:image/jpeg;base64,{b64image}",
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"prompt": prompt,
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"temperature": 0.2,
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"top_p": 0.7,
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"temperature": temperature,
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"top_p": top_p,
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"max_gen_len": max_tokens,
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})
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if response.status_code != 200:
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@ -339,7 +339,6 @@ class MD_CompressAdjustNode:
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image_cv2 = cv2.cvtColor(np.array(tensor2pil(image)), cv2.COLOR_RGB2BGR)
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# calculate the crf based on the image
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analysis_results = self.analyze_compression_artifacts(image_cv2, width=width, height=height)
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logger.info(f"compression analysis_results: {analysis_results}")
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calculated_crf = self.calculate_crf(analysis_results, self.ideal_blockiness, self.ideal_edge_density,
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self.ideal_color_variation, self.blockiness_weight,
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self.edge_density_weight, self.color_variation_weight)
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@ -347,8 +346,6 @@ class MD_CompressAdjustNode:
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if desired_crf is 0:
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desired_crf = calculated_crf
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logger.info(f"calculated_crf: {calculated_crf}")
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logger.info(f"desired_crf: {desired_crf}")
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args = [
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utils.ffmpeg_path,
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"-v", "error",
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