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superlabs-jinwoo commited on
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Init project

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  1. .gitignore +1 -0
  2. app.py +109 -0
  3. hidiffusion/__init__.py +3 -0
  4. hidiffusion/hidiffusion.py +0 -0
  5. hidiffusion/sd_module_key/sd15_module_key.txt +685 -0
  6. hidiffusion/sd_module_key/sdxl_module_key.txt +0 -0
  7. hidiffusion/utils.py +31 -0
  8. requirements.txt +7 -0
.gitignore ADDED
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+ __pycache__
app.py ADDED
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+ """HiDiffusion demo for sd1.5 and sdxl."""
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+ from functools import lru_cache
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+
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+ import gradio as gr
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+ import PIL
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+ import torch
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+ from diffusers import DDIMScheduler, DiffusionPipeline, StableDiffusionPipeline, StableDiffusionXLPipeline
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+
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+ from hidiffusion import apply_hidiffusion
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+
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+ pretrained_models = {
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+ "sd1.5": "runwayml/stable-diffusion-v1-5",
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+ "sdxl": "stabilityai/stable-diffusion-xl-base-1.0",
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+ }
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+
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+ pipeline_types = {
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+ "sd1.5": StableDiffusionPipeline,
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+ "sdxl": StableDiffusionXLPipeline,
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+ }
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+
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
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+
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+ @lru_cache
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+ def load_pipeline(model_type: str) -> DiffusionPipeline:
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+ """Load a pretrained model"""
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+ pretrained_model, pipeline_cls = pretrained_models[model_type], pipeline_types[model_type]
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+ scheduler = DDIMScheduler.from_pretrained(pretrained_model, subfolder="scheduler")
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+ pipe = pipeline_cls.from_pretrained(
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+ pretrained_model, scheduler=scheduler, torch_dtype=torch.float16, variant="fp16"
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+ ).to(device)
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+ pipe.enable_xformers_memory_efficient_attention()
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+ pipe.enable_vae_tiling()
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+ return pipe
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+
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+
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+ def generate(
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+ model_type: str, use_hidiffusion: bool, positive: str, negative: str, width: int, height: int, guidance_scale: float
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+ ) -> PIL.Image.Image:
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+ pipe = load_pipeline(model_type)
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+ print(f"{model_type} pipeline is loaded")
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+ if use_hidiffusion:
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+ apply_hidiffusion(pipe)
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+ print("hidiffusion is applied")
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+ image = pipe(
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+ positive, negative_prompt=negative, guidance_scale=guidance_scale, height=height, width=width, eta=1.0
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+ ).images[0]
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+ print("generation is done")
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+ return image
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+
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+
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+ demo = gr.Interface(
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+ fn=generate,
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+ inputs=[
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+ gr.Radio(choices=["sd1.5", "sdxl"], label="Model Type", value="sd1.5"),
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+ gr.Checkbox(value=True, label="Use HiDiffusion"),
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+ gr.Textbox(label="Positive Prompt"),
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+ gr.Textbox(label="Negative Prompt"),
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+ gr.Slider(512, 4096, value=1024, step=1, label="width"),
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+ gr.Slider(512, 4096, value=1024, step=1, label="height"),
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+ gr.Slider(0.0, 20.0, value=7.5, step=0.1, label="Guidance Scale"),
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+ ],
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+ outputs=gr.Image(),
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+ allow_flagging="never",
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+ title="HiDiffusion Demo",
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+ description="""
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+ HiDiffusion is a training-free method that increases the resolution and speed of pretrained diffusion models.\n
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+ It is designed as a plug-and-play implementation. It can be integrated into diffusion pipelines by only adding a single line of code!\n
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+ More information: https://github.com/megvii-research/HiDiffusion
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+ """,
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+ examples=[
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+ [
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+ "sd1.5",
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+ True,
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+ # positive
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+ "thick strokes, bright colors, an exotic fox, cute, chibi kawaii,"
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+ "detailed fur, hyperdetailed , big reflective eyes, fairytale, artstation,"
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+ "centered composition, perfect composition, centered, vibrant colors, muted colors, high detailed, 8k.",
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+ # negative
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+ "ugly, tiling, poorly drawn face, out of frame, disfigured, deformed, blurry, bad anatomy, blurred",
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+ # width
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+ 1024,
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+ # height
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+ 1024,
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+ # guidance scale
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+ 7.5,
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+ ],
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+ [
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+ "sdxl",
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+ True,
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+ # positive
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+ "thick strokes, bright colors, an exotic fox, cute, chibi kawaii,"
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+ "detailed fur, hyperdetailed , big reflective eyes, fairytale, artstation,"
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+ "centered composition, perfect composition, centered, vibrant colors, muted colors, high detailed, 8k.",
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+ # negative
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+ "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
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+ # width
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+ 2048,
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+ # height
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+ 2048,
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+ # guidance scale
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+ 7.5,
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+ ],
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+ ],
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+ )
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+
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+
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+ if __name__ == "__main__":
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+ demo.launch(server_name="0.0.0.0")
hidiffusion/__init__.py ADDED
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+ from .hidiffusion import apply_hidiffusion, remove_hidiffusion
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+
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+ __all__ = ["apply_hidiffusion", "remove_hidiffusion"]
hidiffusion/hidiffusion.py ADDED
The diff for this file is too large to render. See raw diff
 
hidiffusion/sd_module_key/sd15_module_key.txt ADDED
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+ conv_in
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+ time_proj
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+ time_embedding
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+ time_embedding.linear_1
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+ time_embedding.act
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+ time_embedding.linear_2
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+ down_blocks
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hidiffusion/sd_module_key/sdxl_module_key.txt ADDED
The diff for this file is too large to render. See raw diff
 
hidiffusion/utils.py ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+
3
+
4
+ def isinstance_str(x: object, cls_name: str):
5
+ """
6
+ Checks whether x has any class *named* cls_name in its ancestry.
7
+ Doesn't require access to the class's implementation.
8
+
9
+ Useful for patching!
10
+ """
11
+
12
+ for _cls in x.__class__.__mro__:
13
+ if _cls.__name__ == cls_name:
14
+ return True
15
+
16
+ return False
17
+
18
+
19
+ def init_generator(device: torch.device, fallback: torch.Generator = None):
20
+ """
21
+ Forks the current default random generator given device.
22
+ """
23
+ if device.type == "cpu":
24
+ return torch.Generator(device="cpu").set_state(torch.get_rng_state())
25
+ elif device.type == "cuda":
26
+ return torch.Generator(device=device).set_state(torch.cuda.get_rng_state())
27
+ else:
28
+ if fallback is None:
29
+ return init_generator(torch.device("cpu"))
30
+ else:
31
+ return fallback
requirements.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ torch==1.13.1
2
+ diffusers==0.27.0
3
+ transformers==4.27.4
4
+ accelerate==0.18.0
5
+ xformers==0.0.16rc425
6
+ triton==2.3.0
7
+ gradio==4.27.0