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import sys
import os
from utils.wrapper import StreamV2VWrapper
import torch
from config import Args
from pydantic import BaseModel, Field
from PIL import Image
import math
base_model = "runwayml/stable-diffusion-v1-5"
default_prompt = "A man is talking"
page_content = """<h1 class="text-3xl font-bold">StreamV2V by <a
href="https://jeff-liangf.github.io/projects/streamv2v/"
target="_blank"
class="text-blue-500 underline hover:no-underline">Jeff-LiangF
</a></h1>
<h2>Duplicate this space for fast and private usage - thank you!</h2>
<p class="text-sm">
This demo showcases
<a
href="https://jeff-liangf.github.io/projects/streamv2v/"
target="_blank"
class="text-blue-500 underline hover:no-underline">StreamV2V
</a>
video-to-video pipeline using
<a
href="https://huggingface.co/latent-consistency/lcm-lora-sdv1-5"
target="_blank"
class="text-blue-500 underline hover:no-underline">4-step LCM LORA</a
> with a MJPEG stream server.
</p>
<p class="text-sm">
The base model is <a
href="https://huggingface.co/runwayml/stable-diffusion-v1-5"
target="_blank"
class="text-blue-500 underline hover:no-underline">SD 1.5</a
>. We also build in <a
href="https://github.com/Jeff-LiangF/streamv2v/tree/main/demo_w_camera#download-lora-weights-for-better-stylization"
target="_blank"
class="text-blue-500 underline hover:no-underline">some LORAs
</a> for better stylization.
</p>
"""
class Pipeline:
class Info(BaseModel):
name: str = "StreamV2V"
input_mode: str = "image"
page_content: str = page_content
class InputParams(BaseModel):
prompt: str = Field(
default_prompt,
title="Prompt",
field="textarea",
id="prompt",
)
# negative_prompt: str = Field(
# default_negative_prompt,
# title="Negative Prompt",
# field="textarea",
# id="negative_prompt",
# )
width: int = Field(
512, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
)
height: int = Field(
512, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
)
def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
params = self.InputParams()
self.stream = StreamV2VWrapper(
model_id_or_path=base_model,
t_index_list=[30, 35, 40, 45],
frame_buffer_size=1,
width=params.width,
height=params.height,
warmup=10,
acceleration=args.acceleration,
do_add_noise=True,
output_type="pil",
use_denoising_batch=True,
use_cached_attn=True,
use_feature_injection=True,
feature_injection_strength=0.8,
feature_similarity_threshold=0.98,
cache_interval=4,
cache_maxframes=1,
use_tome_cache=True,
seed=1,
)
self._init_lora()
self.last_prompt = default_prompt
self.stream.prepare(
prompt=default_prompt,
num_inference_steps=50,
guidance_scale=1.0,
)
self.lora_active = False
self.lora_trigger_words = ['pixelart', 'pixel art', 'Pixel art', 'PixArFK'
'lowpoly', 'low poly', 'Low poly',
'Claymation', 'claymation',
'crayons', 'Crayons', 'crayons doodle', 'Crayons doodle',
'sketch', 'Sketch', 'pencil drawing', 'Pencil drawing',
'oil painting', 'Oil painting']
def _init_lora(self):
self.stream.stream.load_lora("./lora_weights/PixelArtRedmond15V-PixelArt-PIXARFK.safetensors", adapter_name='pixelart')
self.stream.stream.load_lora("./lora_weights/low_poly.safetensors", adapter_name='lowpoly')
self.stream.stream.load_lora("./lora_weights/Claymation.safetensors", adapter_name='claymation')
self.stream.stream.load_lora("./lora_weights/doodle.safetensors", adapter_name='crayons')
self.stream.stream.load_lora("./lora_weights/Sketch_offcolor.safetensors", adapter_name='sketch')
self.stream.stream.load_lora("./lora_weights/bichu-v0612.safetensors", adapter_name='oilpainting')
def _activate_lora(self, prompt: str):
if any(word in prompt for word in ['pixelart', 'pixel art', 'Pixel art', 'PixArFK']):
self.stream.stream.pipe.set_adapters(["lcm", "pixelart"], adapter_weights=[1.0, 1.0])
print("Use LORA: pixelart in ./lora_weights/PixelArtRedmond15V-PixelArt-PIXARFK.safetensors")
elif any(word in prompt for word in ['lowpoly', 'low poly', 'Low poly']):
self.stream.stream.pipe.set_adapters(["lcm", "lowpoly"], adapter_weights=[1.0, 1.0])
print("Use LORA: lowpoly in ./lora_weights/low_poly.safetensors")
elif any(word in prompt for word in ['Claymation', 'claymation']):
self.stream.stream.pipe.set_adapters(["lcm", "claymation"], adapter_weights=[1.0, 1.0])
print("Use LORA: claymation in ./lora_weights/Claymation.safetensors")
elif any(word in prompt for word in ['crayons', 'Crayons', 'crayons doodle', 'Crayons doodle']):
self.stream.stream.pipe.set_adapters(["lcm", "crayons"], adapter_weights=[1.0, 1.0])
print("Use LORA: crayons in ./lora_weights/doodle.safetensors")
elif any(word in prompt for word in ['sketch', 'Sketch', 'pencil drawing', 'Pencil drawing']):
self.stream.stream.pipe.set_adapters(["lcm", "sketch"], adapter_weights=[1.0, 1.0])
print("Use LORA: sketch in ./lora_weights/Sketch_offcolor.safetensors")
elif any(word in prompt for word in ['oil painting', 'Oil painting']):
self.stream.stream.pipe.set_adapters(["lcm", "oilpainting"], adapter_weights=[1.0, 1.0])
print("Use LORA: oilpainting in ./lora_weights/bichu-v0612.safetensors")
def _deactivate_lora(self):
self.stream.stream.pipe.set_adapters("lcm")
print("Deactivate LORA, back to SD1.5")
def _check_trigger_words(self, prompt: str):
return any(word in prompt for word in self.lora_trigger_words)
def predict(self, params: "Pipeline.InputParams") -> Image.Image:
if self._check_trigger_words(params.prompt):
if not self.lora_active:
self._activate_lora(params.prompt)
self.lora_active = True
else:
if self.lora_active:
self._deactivate_lora()
self.lora_active = False
image_tensor = self.stream.preprocess_image(params.image)
output_image = self.stream(image=image_tensor, prompt=params.prompt)
return output_image
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