Spaces:
Running
on
Zero
Running
on
Zero
File size: 9,538 Bytes
b177a48 02ff012 b177a48 02ff012 b177a48 b6dc501 b257e01 b177a48 a4b32da b177a48 b257e01 b177a48 b257e01 b6dc501 b177a48 b6dc501 b177a48 a4b32da b177a48 a4b32da |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 |
import replicate
from PIL import Image
import requests
import io
import os
import base64
Replicate_MODEl_NAME_MAP = {
"SDXL": "stability-ai/sdxl:7762fd07cf82c948538e41f63f77d685e02b063e37e496e96eefd46c929f9bdc",
"SD-v3.0": "stability-ai/stable-diffusion-3",
"SD-v2.1": "stability-ai/stable-diffusion:ac732df83cea7fff18b8472768c88ad041fa750ff7682a21affe81863cbe77e4",
"SD-v1.5": "stability-ai/stable-diffusion:b3d14e1cd1f9470bbb0bb68cac48e5f483e5be309551992cc33dc30654a82bb7",
"SDXL-Lightning": "bytedance/sdxl-lightning-4step:5f24084160c9089501c1b3545d9be3c27883ae2239b6f412990e82d4a6210f8f",
"Kandinsky-v2.0": "ai-forever/kandinsky-2:3c6374e7a9a17e01afe306a5218cc67de55b19ea536466d6ea2602cfecea40a9",
"Kandinsky-v2.2": "ai-forever/kandinsky-2.2:ad9d7879fbffa2874e1d909d1d37d9bc682889cc65b31f7bb00d2362619f194a",
"Proteus-v0.2": "lucataco/proteus-v0.2:06775cd262843edbde5abab958abdbb65a0a6b58ca301c9fd78fa55c775fc019",
"Playground-v2.0": "playgroundai/playground-v2-1024px-aesthetic:42fe626e41cc811eaf02c94b892774839268ce1994ea778eba97103fe1ef51b8",
"Playground-v2.5": "playgroundai/playground-v2.5-1024px-aesthetic:a45f82a1382bed5c7aeb861dac7c7d191b0fdf74d8d57c4a0e6ed7d4d0bf7d24",
"Dreamshaper-xl-turbo": "lucataco/dreamshaper-xl-turbo:0a1710e0187b01a255302738ca0158ff02a22f4638679533e111082f9dd1b615",
"SDXL-Deepcache": "lucataco/sdxl-deepcache:eaf678fb34006669e9a3c6dd5971e2279bf20ee0adeced464d7b6d95de16dc93",
"Openjourney-v4": "prompthero/openjourney:ad59ca21177f9e217b9075e7300cf6e14f7e5b4505b87b9689dbd866e9768969",
"LCM-v1.5": "fofr/latent-consistency-model:683d19dc312f7a9f0428b04429a9ccefd28dbf7785fef083ad5cf991b65f406f",
"Realvisxl-v3.0": "fofr/realvisxl-v3:33279060bbbb8858700eb2146350a98d96ef334fcf817f37eb05915e1534aa1c",
"Realvisxl-v2.0": "lucataco/realvisxl-v2.0:7d6a2f9c4754477b12c14ed2a58f89bb85128edcdd581d24ce58b6926029de08",
"Pixart-Sigma": "cjwbw/pixart-sigma:5a54352c99d9fef467986bc8f3a20205e8712cbd3df1cbae4975d6254c902de1",
"SSD-1b": "lucataco/ssd-1b:b19e3639452c59ce8295b82aba70a231404cb062f2eb580ea894b31e8ce5bbb6",
"Open-Dalle-v1.1": "lucataco/open-dalle-v1.1:1c7d4c8dec39c7306df7794b28419078cb9d18b9213ab1c21fdc46a1deca0144",
"Deepfloyd-IF": "andreasjansson/deepfloyd-if:fb84d659df149f4515c351e394d22222a94144aa1403870c36025c8b28846c8d",
"Zeroscope-v2-xl": "anotherjesse/zeroscope-v2-xl:9f747673945c62801b13b84701c783929c0ee784e4748ec062204894dda1a351",
# "Damo-Text-to-Video": "cjwbw/damo-text-to-video:1e205ea73084bd17a0a3b43396e49ba0d6bc2e754e9283b2df49fad2dcf95755",
"Animate-Diff": "lucataco/animate-diff:beecf59c4aee8d81bf04f0381033dfa10dc16e845b4ae00d281e2fa377e48a9f",
"OpenSora": "camenduru/open-sora:8099e5722ba3d5f408cd3e696e6df058137056268939337a3fbe3912e86e72ad",
"LaVie": "cjwbw/lavie:0bca850c4928b6c30052541fa002f24cbb4b677259c461dd041d271ba9d3c517",
"VideoCrafter2": "lucataco/video-crafter:7757c5775e962c618053e7df4343052a21075676d6234e8ede5fa67c9e43bce0",
"Stable-Video-Diffusion": "sunfjun/stable-video-diffusion:d68b6e09eedbac7a49e3d8644999d93579c386a083768235cabca88796d70d82",
"FLUX.1-schnell": "black-forest-labs/flux-schnell",
"FLUX.1-pro": "black-forest-labs/flux-pro",
"FLUX.1-dev": "black-forest-labs/flux-dev",
}
class ReplicateModel():
def __init__(self, model_name, model_type):
self.model_name = model_name
self.model_type = model_type
def __call__(self, *args, **kwargs):
if self.model_type == "text2image":
assert "prompt" in kwargs, "prompt is required for text2image model"
output = replicate.run(
f"{Replicate_MODEl_NAME_MAP[self.model_name]}",
input={
"width": 512,
"height": 512,
"prompt": kwargs["prompt"]
},
)
if 'Openjourney' in self.model_name:
for item in output:
result_url = item
break
elif isinstance(output, list):
result_url = output[0]
else:
result_url = output
print(self.model_name, result_url)
response = requests.get(result_url)
result = Image.open(io.BytesIO(response.content))
return result
elif self.model_type == "text2video":
assert "prompt" in kwargs, "prompt is required for text2image model"
if self.model_name == "Zeroscope-v2-xl":
input = {
"fps": 24,
"width": 512,
"height": 512,
"prompt": kwargs["prompt"],
"guidance_scale": 17.5,
# "negative_prompt": "very blue, dust, noisy, washed out, ugly, distorted, broken",
"num_frames": 48,
}
elif self.model_name == "Damo-Text-to-Video":
input={
"fps": 8,
"prompt": kwargs["prompt"],
"num_frames": 16,
"num_inference_steps": 50
}
elif self.model_name == "Animate-Diff":
input={
"path": "toonyou_beta3.safetensors",
"seed": 255224557,
"steps": 25,
"prompt": kwargs["prompt"],
"n_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth",
"motion_module": "mm_sd_v14",
"guidance_scale": 7.5
}
elif self.model_name == "OpenSora":
input={
"seed": 1234,
"prompt": kwargs["prompt"],
}
elif self.model_name == "LaVie":
input={
"width": 512,
"height": 512,
"prompt": kwargs["prompt"],
"quality": 9,
"video_fps": 8,
"interpolation": False,
"sample_method": "ddpm",
"guidance_scale": 7,
"super_resolution": False,
"num_inference_steps": 50
}
elif self.model_name == "VideoCrafter2":
input={
"fps": 24,
"seed": 64045,
"steps": 40,
"width": 512,
"height": 512,
"prompt": kwargs["prompt"],
}
elif self.model_name == "Stable-Video-Diffusion":
text2image_name = "SD-v2.1"
output = replicate.run(
f"{Replicate_MODEl_NAME_MAP[text2image_name]}",
input={
"width": 512,
"height": 512,
"prompt": kwargs["prompt"]
},
)
if isinstance(output, list):
image_url = output[0]
else:
image_url = output
print(image_url)
input={
"cond_aug": 0.02,
"decoding_t": 14,
"input_image": "{}".format(image_url),
"video_length": "14_frames_with_svd",
"sizing_strategy": "maintain_aspect_ratio",
"motion_bucket_id": 127,
"frames_per_second": 6
}
output = replicate.run(
f"{Replicate_MODEl_NAME_MAP[self.model_name]}",
input=input,
)
if isinstance(output, list):
result_url = output[0]
else:
result_url = output
print(self.model_name)
print(result_url)
# response = requests.get(result_url)
# result = Image.open(io.BytesIO(response.content))
# for event in handler.iter_events(with_logs=True):
# if isinstance(event, fal_client.InProgress):
# print('Request in progress')
# print(event.logs)
# result = handler.get()
# print("result video: ====")
# print(result)
# result_url = result['video']['url']
# return result_url
return result_url
else:
raise ValueError("model_type must be text2image or image2image")
def load_replicate_model(model_name, model_type):
return ReplicateModel(model_name, model_type)
if __name__ == "__main__":
model_name = 'replicate_zeroscope-v2-xl_text2video'
model_source, model_name, model_type = model_name.split("_")
pipe = load_replicate_model(model_name, model_type)
prompt = "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic"
result = pipe(prompt=prompt) |