Spaces:
Running
on
A100
Running
on
A100
Avijit Ghosh
commited on
Commit
•
85b09dd
1
Parent(s):
ad93a8b
Add SD2
Browse files
app.py
CHANGED
@@ -1,6 +1,13 @@
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import gradio as gr
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import torch
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from diffusers import
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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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@@ -54,7 +61,14 @@ def load_model(model_name):
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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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@@ -77,6 +91,8 @@ def getimgen(prompt, model_name):
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return pipeline_text2image(prompt=prompt, negative_prompt=neg_prompt).images[0]
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elif model_name == "stabilityai/stable-diffusion-3-medium-diffusers":
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return pipeline_text2image(prompt=prompt, negative_prompt="", num_inference_steps=28, guidance_scale=7.0).images[0]
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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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@@ -167,7 +183,8 @@ This demo provides an insightful look into how current text-to-image models hand
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choices=[
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"stabilityai/stable-diffusion-3-medium-diffusers",
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"stabilityai/sdxl-turbo",
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"ByteDance/SDXL-Lightning",
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"runwayml/stable-diffusion-v1-5",
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"segmind/SSD-1B"
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],
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import gradio as gr
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import torch
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from diffusers import (
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DiffusionPipeline,
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StableDiffusionPipeline,
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StableDiffusionXLPipeline,
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EulerDiscreteScheduler,
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UNet2DConditionModel,
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StableDiffusion3Pipeline
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)
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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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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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elif model_name == "stabilityai/stable-diffusion-2":
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scheduler = EulerDiscreteScheduler.from_pretrained(model_name, subfolder="scheduler")
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pipeline = StableDiffusionPipeline.from_pretrained(
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model_name,
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scheduler=scheduler,
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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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return pipeline_text2image(prompt=prompt, negative_prompt=neg_prompt).images[0]
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elif model_name == "stabilityai/stable-diffusion-3-medium-diffusers":
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return pipeline_text2image(prompt=prompt, negative_prompt="", num_inference_steps=28, guidance_scale=7.0).images[0]
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elif model_name == "stabilityai/stable-diffusion-2":
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return pipeline_text2image(prompt=prompt).images[0]
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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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choices=[
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"stabilityai/stable-diffusion-3-medium-diffusers",
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"stabilityai/sdxl-turbo",
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"ByteDance/SDXL-Lightning",
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"stabilityai/stable-diffusion-2",
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"runwayml/stable-diffusion-v1-5",
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"segmind/SSD-1B"
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],
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