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Runtime error
Avijit Ghosh
commited on
Commit
•
17497eb
1
Parent(s):
e36c39f
add login
Browse files
app.py
CHANGED
@@ -4,19 +4,15 @@ from diffusers import DiffusionPipeline, StableDiffusionPipeline, StableDiffusio
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from transformers import BlipProcessor, BlipForConditionalGeneration
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from pathlib import Path
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from safetensors.torch import load_file
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from huggingface_hub import hf_hub_download
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from PIL import Image
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import matplotlib.pyplot as plt
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from matplotlib.colors import hex2color
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import stone
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import os
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import spaces
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from huggingface_hub import login
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login()
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# Define model initialization functions
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def load_model(model_name):
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if model_name == "stabilityai/sdxl-turbo":
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pipeline = DiffusionPipeline.from_pretrained(
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model_name,
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@@ -48,9 +44,12 @@ def load_model(model_name):
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variant="fp16"
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).to("cuda")
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elif model_name == "stabilityai/stable-diffusion-3-medium-diffusers":
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pipeline = StableDiffusion3Pipeline.from_pretrained(
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model_name,
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torch_dtype=torch.float16
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).to("cuda")
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else:
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raise ValueError("Unknown model name")
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@@ -60,10 +59,10 @@ def load_model(model_name):
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default_model = "stabilityai/sdxl-turbo"
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pipeline_text2image = load_model(default_model)
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@
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def getimgen(prompt, model_name):
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global pipeline_text2image
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pipeline_text2image = load_model(model_name)
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if model_name == "stabilityai/sdxl-turbo":
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return pipeline_text2image(prompt=prompt, guidance_scale=0.0, num_inference_steps=2).images[0]
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elif model_name == "runwayml/stable-diffusion-v1-5":
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@@ -79,7 +78,7 @@ def getimgen(prompt, model_name):
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blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
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blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda")
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@
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def blip_caption_image(image, prefix):
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inputs = blip_processor(image, prefix, return_tensors="pt").to("cuda", torch.float16)
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out = blip_model.generate(**inputs)
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@@ -118,13 +117,11 @@ def skintoneplot(hex_codes):
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ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=sorted_hex_codes[i]))
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return fig
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@
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def generate_images_plots(prompt, model_name):
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global pipeline_text2image
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pipeline_text2image = load_model(model_name)
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foldername = "temp"
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Path(foldername).mkdir(parents=True, exist_ok=True)
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images = [getimgen(prompt, model_name) for _ in range(10)]
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genders = []
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skintones = []
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for image, i in zip(images, range(10)):
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@@ -142,6 +139,9 @@ def generate_images_plots(prompt, model_name):
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with gr.Blocks(title="Skin Tone and Gender bias in Text to Image Models") as demo:
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gr.Markdown("# Skin Tone and Gender bias in Text to Image Models")
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model_dropdown = gr.Dropdown(
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label="Choose a model",
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choices=[
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@@ -167,6 +167,6 @@ with gr.Blocks(title="Skin Tone and Gender bias in Text to Image Models") as dem
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with gr.Row(equal_height=True):
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skinplot = gr.Plot(label="Skin Tone")
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genplot = gr.Plot(label="Gender")
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btn.click(generate_images_plots, inputs=[prompt, model_dropdown], outputs=[gallery, skinplot, genplot])
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demo.launch(
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from transformers import BlipProcessor, BlipForConditionalGeneration
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from pathlib import Path
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from safetensors.torch import load_file
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from huggingface_hub import hf_hub_download, list_models
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from PIL import Image
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import matplotlib.pyplot as plt
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from matplotlib.colors import hex2color
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import stone
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import os
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# Define model initialization functions
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def load_model(model_name, token=None):
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if model_name == "stabilityai/sdxl-turbo":
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pipeline = DiffusionPipeline.from_pretrained(
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model_name,
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variant="fp16"
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).to("cuda")
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elif model_name == "stabilityai/stable-diffusion-3-medium-diffusers":
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if token is None:
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raise ValueError("Hugging Face token is required to access this model")
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pipeline = StableDiffusion3Pipeline.from_pretrained(
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model_name,
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torch_dtype=torch.float16,
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use_auth_token=token
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).to("cuda")
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else:
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raise ValueError("Unknown model name")
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default_model = "stabilityai/sdxl-turbo"
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pipeline_text2image = load_model(default_model)
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@gr.outputs.Image(type="pil")
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def getimgen(prompt, model_name, token):
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global pipeline_text2image
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pipeline_text2image = load_model(model_name, token)
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if model_name == "stabilityai/sdxl-turbo":
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return pipeline_text2image(prompt=prompt, guidance_scale=0.0, num_inference_steps=2).images[0]
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elif model_name == "runwayml/stable-diffusion-v1-5":
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blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
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blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda")
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@gr.outputs.Textbox(type="str")
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def blip_caption_image(image, prefix):
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inputs = blip_processor(image, prefix, return_tensors="pt").to("cuda", torch.float16)
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out = blip_model.generate(**inputs)
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ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=sorted_hex_codes[i]))
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return fig
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@gr.outputs.Image(type="pil")
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def generate_images_plots(prompt, model_name, token):
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foldername = "temp"
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Path(foldername).mkdir(parents=True, exist_ok=True)
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images = [getimgen(prompt, model_name, token) for _ in range(10)]
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genders = []
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skintones = []
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for image, i in zip(images, range(10)):
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with gr.Blocks(title="Skin Tone and Gender bias in Text to Image Models") as demo:
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gr.Markdown("# Skin Tone and Gender bias in Text to Image Models")
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gr.LoginButton() # Add a login button for Hugging Face
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profile = gr.State()
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token = gr.State()
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model_dropdown = gr.Dropdown(
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label="Choose a model",
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choices=[
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with gr.Row(equal_height=True):
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skinplot = gr.Plot(label="Skin Tone")
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genplot = gr.Plot(label="Gender")
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btn.click(generate_images_plots, inputs=[prompt, model_dropdown, token], outputs=[gallery, skinplot, genplot])
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demo.launch()
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