Spaces:
Running
on
Zero
Running
on
Zero
clementchadebec
commited on
Commit
•
d0b932a
1
Parent(s):
cfed92e
Update app.py
Browse files
app.py
CHANGED
@@ -1,20 +1,36 @@
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import gradio as gr
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import numpy as np
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import random
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from diffusers import
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if torch.cuda.is_available():
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torch.cuda.max_memory_allocated(device=device)
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pipe =
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pipe.enable_xformers_memory_efficient_attention()
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pipe = pipe.to(device)
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else:
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pipe =
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pipe = pipe.to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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@@ -38,9 +54,9 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
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return image
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examples = [
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"
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"
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"A
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]
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css="""
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@@ -79,13 +95,6 @@ with gr.Blocks(css=css) as demo:
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with gr.Accordion("Advanced Settings", open=False):
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=1,
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placeholder="Enter a negative prompt",
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visible=False,
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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@@ -96,34 +105,10 @@ with gr.Blocks(css=css) as demo:
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=512,
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=512,
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance scale",
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minimum=0.0,
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maximum=10.0,
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step=0.1,
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value=0.0,
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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import gradio as gr
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import numpy as np
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import random
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from diffusers import StableDiffusionPipeline, LCMScheduler
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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adapter_id = "jasperai/flash-sd"
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if torch.cuda.is_available():
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torch.cuda.max_memory_allocated(device=device)
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pipe = StableDiffusionPipeline.from_pretrained(
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"runwayml/stable-diffusion-v1-5",
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use_safetensors=True,
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)
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pipe.enable_xformers_memory_efficient_attention()
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pipe = pipe.to(device)
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else:
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pipe = StableDiffusionPipeline.from_pretrained(
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"runwayml/stable-diffusion-v1-5",
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use_safetensors=True,
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)
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pipe = pipe.to(device)
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pipe.scheduler = LCMScheduler.from_pretrained(
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"runwayml/stable-diffusion-v1-5",
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subfolder="scheduler",
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timestep_spacing="trailing",
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)
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pipe.load_lora_weights(adapter_id)
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pipe.fuse_lora()
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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return image
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examples = [
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"The image showcases a freshly baked bread, possibly focaccia, with rosemary sprigs and red pepper flakes sprinkled on top. It's sliced and placed on a wire cooling rack, with a bowl of mixed peppercorns beside it.",
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"A raccoon reading a book in a lush forest.",
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"A serene landscape showcases a winding road alongside a vast, turquoise lake, flanked by majestic snow-capped mountains under a partly cloudy sky.",
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]
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css="""
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with gr.Accordion("Advanced Settings", open=False):
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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