Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -5,21 +5,24 @@ from PIL import Image
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from diffusers import DiffusionPipeline
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import random
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import uuid
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import numpy as np
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import time
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import zipfile
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import os
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# Description for the app
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DESCRIPTION = """## Qwen Image
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# Helper functions
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def save_image(img):
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unique_name = str(uuid.uuid4()) + ".png"
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img.save(unique_name)
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return unique_name
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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return seed
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@@ -27,24 +30,32 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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# Load Qwen/Qwen-Image pipeline
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dtype = torch.bfloat16
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# --- Model Loading
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pipe_qwen = DiffusionPipeline.from_pretrained(
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).to(device)
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# Aspect ratios
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aspect_ratios = {
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"1:1": (
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"16:9": (
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"9:16": (
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"4:3": (
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"3:4": (
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}
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# Generation function for Qwen/Qwen-Image
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@@ -281,4 +292,4 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
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)
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if __name__ == "__main__":
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demo.queue(max_size=50).launch(share=False
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from diffusers import DiffusionPipeline
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import random
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import uuid
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from typing import Union, List, Optional
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import numpy as np
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import time
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import zipfile
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import os
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# Description for the app
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DESCRIPTION = """## Qwen Image Hpc/."""
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# Helper functions
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def save_image(img):
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"""Saves a PIL image to a file with a unique name."""
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unique_name = str(uuid.uuid4()) + ".png"
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img.save(unique_name)
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return unique_name
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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"""Generates a random seed if the randomize option is enabled."""
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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return seed
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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# Load Qwen/Qwen-Image pipeline
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dtype = torch.bfloat16
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# --- Model Loading ---
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# Load the pipeline from pretrained weights
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pipe_qwen = DiffusionPipeline.from_pretrained(
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"Qwen/Qwen-Image",
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torch_dtype=dtype
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).to(device)
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# --- Regional Compilation ---
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# Apply regional compilation to speed up cold-starts while retaining benefits.
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# This compiles the repeated transformer blocks for ~2x faster initialization.
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print("Applying regional compilation...")
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pipe_qwen.transformer.compile(fullgraph=True, mode="reduce-overhead")
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print("Compilation complete.")
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# Aspect ratios
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aspect_ratios = {
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"1:1": (1024, 1024),
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"16:9": (1344, 768),
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"9:16": (768, 1344),
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"4:3": (1152, 896),
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"3:4": (896, 1152)
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}
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# Generation function for Qwen/Qwen-Image
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)
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if __name__ == "__main__":
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demo.queue(max_size=50).launch(share=False)
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