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Updated for V2.0
Browse files- model_inferencing.py +4 -6
model_inferencing.py
CHANGED
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@@ -1,8 +1,6 @@
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TargetModel = None
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def generate_test_image(T2IModel, testPrompt):
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#prompt = "The quick brown fox jumps over the lazy dog"
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testImage = TargetModel(testPrompt, num_inference_steps=50).images[0]
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#testImage.save("./image.png")
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return testImage
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@@ -22,13 +20,13 @@ def construct_general_bias_evaluation_prompts(subjects, actions):
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prompts.append('a picture of a ' + subject)
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return prompts
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def generate_test_images(progressBar, barText, prompts, NSamples, NSteps,
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guidance = 7.5
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testImages = []
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imageCaptions = [[], []]
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for prompt, ii in zip(prompts, range(len(prompts))):
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testImages+=TargetModel(prompt, num_images_per_prompt=NSamples, num_inference_steps=NSteps,
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guidance_scale=guidance, width=
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for nn in range(NSamples):
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imageCaptions[0].append(prompt) # actual prompt used
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imageCaptions[1].append("Prompt: "+str(ii+1)+" Sample: "+ str(nn+1)) # caption for the image output
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@@ -38,13 +36,13 @@ def generate_test_images(progressBar, barText, prompts, NSamples, NSteps, imageS
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progressBar.empty()
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return (testImages, imageCaptions)
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def generate_task_oriented_images(progressBar, barText, prompts, ids, NSamples, NSteps,
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guidance = 7.5
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testImages = []
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imageCaptions = [[], []]
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for prompt, jj in zip(prompts, range(len(prompts))):
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testImages+=TargetModel(prompt, num_images_per_prompt=NSamples, num_inference_steps=NSteps,
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guidance_scale=guidance, width=
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for nn in range(NSamples):
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imageCaptions[0].append(prompt) # actual prompt used
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imageCaptions[1].append("COCO ID: "+ids[jj]+" Sample: "+ str(nn+1)) # caption for the image output
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TargetModel = None
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def generate_test_image(T2IModel, testPrompt):
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testImage = TargetModel(testPrompt, num_inference_steps=50).images[0]
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return testImage
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prompts.append('a picture of a ' + subject)
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return prompts
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def generate_test_images(progressBar, barText, prompts, NSamples, NSteps, imageWidth, imageHeight):
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guidance = 7.5
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testImages = []
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imageCaptions = [[], []]
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for prompt, ii in zip(prompts, range(len(prompts))):
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testImages+=TargetModel(prompt, num_images_per_prompt=NSamples, num_inference_steps=NSteps,
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guidance_scale=guidance, width=imageWidth, height=imageHeight).images
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for nn in range(NSamples):
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imageCaptions[0].append(prompt) # actual prompt used
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imageCaptions[1].append("Prompt: "+str(ii+1)+" Sample: "+ str(nn+1)) # caption for the image output
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progressBar.empty()
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return (testImages, imageCaptions)
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def generate_task_oriented_images(progressBar, barText, prompts, ids, NSamples, NSteps, imageWidth, imageHeight):
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guidance = 7.5
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testImages = []
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imageCaptions = [[], []]
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for prompt, jj in zip(prompts, range(len(prompts))):
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testImages+=TargetModel(prompt, num_images_per_prompt=NSamples, num_inference_steps=NSteps,
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+
guidance_scale=guidance, width=imageWidth, height=imageHeight).images
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for nn in range(NSamples):
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imageCaptions[0].append(prompt) # actual prompt used
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imageCaptions[1].append("COCO ID: "+ids[jj]+" Sample: "+ str(nn+1)) # caption for the image output
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