Spaces:
Running
on
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Running
on
Zero
prithivMLmods
commited on
Commit
•
f674364
1
Parent(s):
752fcb0
Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,724 @@
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1 |
+
import os
|
2 |
+
import json
|
3 |
+
import copy
|
4 |
+
import time
|
5 |
+
import random
|
6 |
+
import logging
|
7 |
+
import numpy as np
|
8 |
+
from typing import Any, Dict, List, Optional, Union
|
9 |
+
|
10 |
+
import torch
|
11 |
+
from PIL import Image
|
12 |
+
import gradio as gr
|
13 |
+
|
14 |
+
from diffusers import (
|
15 |
+
DiffusionPipeline,
|
16 |
+
AutoencoderTiny,
|
17 |
+
AutoencoderKL,
|
18 |
+
AutoPipelineForImage2Image,
|
19 |
+
FluxPipeline,
|
20 |
+
FlowMatchEulerDiscreteScheduler)
|
21 |
+
|
22 |
+
from huggingface_hub import (
|
23 |
+
hf_hub_download,
|
24 |
+
HfFileSystem,
|
25 |
+
ModelCard,
|
26 |
+
snapshot_download)
|
27 |
+
|
28 |
+
import spaces
|
29 |
+
|
30 |
+
def calculate_shift(
|
31 |
+
image_seq_len,
|
32 |
+
base_seq_len: int = 256,
|
33 |
+
max_seq_len: int = 4096,
|
34 |
+
base_shift: float = 0.5,
|
35 |
+
max_shift: float = 1.16,
|
36 |
+
):
|
37 |
+
m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
|
38 |
+
b = base_shift - m * base_seq_len
|
39 |
+
mu = image_seq_len * m + b
|
40 |
+
return mu
|
41 |
+
|
42 |
+
def retrieve_timesteps(
|
43 |
+
scheduler,
|
44 |
+
num_inference_steps: Optional[int] = None,
|
45 |
+
device: Optional[Union[str, torch.device]] = None,
|
46 |
+
timesteps: Optional[List[int]] = None,
|
47 |
+
sigmas: Optional[List[float]] = None,
|
48 |
+
**kwargs,
|
49 |
+
):
|
50 |
+
if timesteps is not None and sigmas is not None:
|
51 |
+
raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
|
52 |
+
if timesteps is not None:
|
53 |
+
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
|
54 |
+
timesteps = scheduler.timesteps
|
55 |
+
num_inference_steps = len(timesteps)
|
56 |
+
elif sigmas is not None:
|
57 |
+
scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
|
58 |
+
timesteps = scheduler.timesteps
|
59 |
+
num_inference_steps = len(timesteps)
|
60 |
+
else:
|
61 |
+
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
|
62 |
+
timesteps = scheduler.timesteps
|
63 |
+
return timesteps, num_inference_steps
|
64 |
+
|
65 |
+
# FLUX pipeline
|
66 |
+
@torch.inference_mode()
|
67 |
+
def flux_pipe_call_that_returns_an_iterable_of_images(
|
68 |
+
self,
|
69 |
+
prompt: Union[str, List[str]] = None,
|
70 |
+
prompt_2: Optional[Union[str, List[str]]] = None,
|
71 |
+
height: Optional[int] = None,
|
72 |
+
width: Optional[int] = None,
|
73 |
+
num_inference_steps: int = 28,
|
74 |
+
timesteps: List[int] = None,
|
75 |
+
guidance_scale: float = 3.5,
|
76 |
+
num_images_per_prompt: Optional[int] = 1,
|
77 |
+
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
78 |
+
latents: Optional[torch.FloatTensor] = None,
|
79 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
80 |
+
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
81 |
+
output_type: Optional[str] = "pil",
|
82 |
+
return_dict: bool = True,
|
83 |
+
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
84 |
+
max_sequence_length: int = 512,
|
85 |
+
good_vae: Optional[Any] = None,
|
86 |
+
):
|
87 |
+
height = height or self.default_sample_size * self.vae_scale_factor
|
88 |
+
width = width or self.default_sample_size * self.vae_scale_factor
|
89 |
+
|
90 |
+
self.check_inputs(
|
91 |
+
prompt,
|
92 |
+
prompt_2,
|
93 |
+
height,
|
94 |
+
width,
|
95 |
+
prompt_embeds=prompt_embeds,
|
96 |
+
pooled_prompt_embeds=pooled_prompt_embeds,
|
97 |
+
max_sequence_length=max_sequence_length,
|
98 |
+
)
|
99 |
+
|
100 |
+
self._guidance_scale = guidance_scale
|
101 |
+
self._joint_attention_kwargs = joint_attention_kwargs
|
102 |
+
self._interrupt = False
|
103 |
+
|
104 |
+
batch_size = 1 if isinstance(prompt, str) else len(prompt)
|
105 |
+
device = self._execution_device
|
106 |
+
|
107 |
+
lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
|
108 |
+
prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
|
109 |
+
prompt=prompt,
|
110 |
+
prompt_2=prompt_2,
|
111 |
+
prompt_embeds=prompt_embeds,
|
112 |
+
pooled_prompt_embeds=pooled_prompt_embeds,
|
113 |
+
device=device,
|
114 |
+
num_images_per_prompt=num_images_per_prompt,
|
115 |
+
max_sequence_length=max_sequence_length,
|
116 |
+
lora_scale=lora_scale,
|
117 |
+
)
|
118 |
+
|
119 |
+
num_channels_latents = self.transformer.config.in_channels // 4
|
120 |
+
latents, latent_image_ids = self.prepare_latents(
|
121 |
+
batch_size * num_images_per_prompt,
|
122 |
+
num_channels_latents,
|
123 |
+
height,
|
124 |
+
width,
|
125 |
+
prompt_embeds.dtype,
|
126 |
+
device,
|
127 |
+
generator,
|
128 |
+
latents,
|
129 |
+
)
|
130 |
+
|
131 |
+
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
|
132 |
+
image_seq_len = latents.shape[1]
|
133 |
+
mu = calculate_shift(
|
134 |
+
image_seq_len,
|
135 |
+
self.scheduler.config.base_image_seq_len,
|
136 |
+
self.scheduler.config.max_image_seq_len,
|
137 |
+
self.scheduler.config.base_shift,
|
138 |
+
self.scheduler.config.max_shift,
|
139 |
+
)
|
140 |
+
timesteps, num_inference_steps = retrieve_timesteps(
|
141 |
+
self.scheduler,
|
142 |
+
num_inference_steps,
|
143 |
+
device,
|
144 |
+
timesteps,
|
145 |
+
sigmas,
|
146 |
+
mu=mu,
|
147 |
+
)
|
148 |
+
self._num_timesteps = len(timesteps)
|
149 |
+
|
150 |
+
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
|
151 |
+
|
152 |
+
for i, t in enumerate(timesteps):
|
153 |
+
if self.interrupt:
|
154 |
+
continue
|
155 |
+
|
156 |
+
timestep = t.expand(latents.shape[0]).to(latents.dtype)
|
157 |
+
|
158 |
+
noise_pred = self.transformer(
|
159 |
+
hidden_states=latents,
|
160 |
+
timestep=timestep / 1000,
|
161 |
+
guidance=guidance,
|
162 |
+
pooled_projections=pooled_prompt_embeds,
|
163 |
+
encoder_hidden_states=prompt_embeds,
|
164 |
+
txt_ids=text_ids,
|
165 |
+
img_ids=latent_image_ids,
|
166 |
+
joint_attention_kwargs=self.joint_attention_kwargs,
|
167 |
+
return_dict=False,
|
168 |
+
)[0]
|
169 |
+
|
170 |
+
latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
|
171 |
+
latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
|
172 |
+
image = self.vae.decode(latents_for_image, return_dict=False)[0]
|
173 |
+
yield self.image_processor.postprocess(image, output_type=output_type)[0]
|
174 |
+
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
|
175 |
+
torch.cuda.empty_cache()
|
176 |
+
|
177 |
+
latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
|
178 |
+
latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
|
179 |
+
image = good_vae.decode(latents, return_dict=False)[0]
|
180 |
+
self.maybe_free_model_hooks()
|
181 |
+
torch.cuda.empty_cache()
|
182 |
+
yield self.image_processor.postprocess(image, output_type=output_type)[0]
|
183 |
+
|
184 |
+
#-----------------------------------------------------------------------------------LoRA's--------------------------------------------------------------------------#
|
185 |
+
loras = [
|
186 |
+
#1
|
187 |
+
{
|
188 |
+
"image": "https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-FaceRealism/resolve/main/images/11.png",
|
189 |
+
"title": "Flux Face Realism",
|
190 |
+
"repo": "prithivMLmods/Canopus-LoRA-Flux-FaceRealism",
|
191 |
+
"trigger_word": "Realism"
|
192 |
+
},
|
193 |
+
#2
|
194 |
+
{
|
195 |
+
"image": "https://huggingface.co/alvdansen/softserve_anime/resolve/main/images/ComfyUI_00134_.png",
|
196 |
+
"title": "Softserve Anime",
|
197 |
+
"repo": "alvdansen/softserve_anime",
|
198 |
+
"trigger_word": "sftsrv style illustration"
|
199 |
+
},
|
200 |
+
#3
|
201 |
+
{
|
202 |
+
"image": "https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-Anime/resolve/main/assets/4.png",
|
203 |
+
"title": "Flux Anime",
|
204 |
+
"repo": "prithivMLmods/Canopus-LoRA-Flux-Anime",
|
205 |
+
"trigger_word": "Anime"
|
206 |
+
},
|
207 |
+
#4
|
208 |
+
{
|
209 |
+
"image": "https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-One-Click-Creative-Template/resolve/main/images/f2cc649985648e57b9b9b14ca7a8744ac8e50d75b3a334ed4df0f368.jpg",
|
210 |
+
"title": "Creative Template",
|
211 |
+
"repo": "Shakker-Labs/FLUX.1-dev-LoRA-One-Click-Creative-Template",
|
212 |
+
"trigger_word": "The background is 4 real photos, and in the middle is a cartoon picture summarizing the real photos."
|
213 |
+
},
|
214 |
+
#5
|
215 |
+
{
|
216 |
+
"image": "https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0/resolve/main/images/3.png",
|
217 |
+
"title": "Ultra Realism",
|
218 |
+
"repo": "prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0",
|
219 |
+
"trigger_word": "Ultra realistic"
|
220 |
+
},
|
221 |
+
#6
|
222 |
+
{
|
223 |
+
"image": "https://huggingface.co/gokaygokay/Flux-Game-Assets-LoRA-v2/resolve/main/images/example_y2bqpuphc.png",
|
224 |
+
"title": "Game Assets",
|
225 |
+
"repo": "gokaygokay/Flux-Game-Assets-LoRA-v2",
|
226 |
+
"trigger_word": "wbgmsst, white background"
|
227 |
+
},
|
228 |
+
#7
|
229 |
+
{
|
230 |
+
"image": "https://huggingface.co/alvdansen/softpasty-flux-dev/resolve/main/images/ComfyUI_00814_%20(2).png",
|
231 |
+
"title": "Softpasty",
|
232 |
+
"repo": "alvdansen/softpasty-flux-dev",
|
233 |
+
"trigger_word": "araminta_illus illustration style"
|
234 |
+
},
|
235 |
+
#8
|
236 |
+
{
|
237 |
+
"image": "https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-add-details/resolve/main/images/0.png",
|
238 |
+
"title": "Details Add",
|
239 |
+
"repo": "Shakker-Labs/FLUX.1-dev-LoRA-add-details",
|
240 |
+
"trigger_word": ""
|
241 |
+
},
|
242 |
+
#9
|
243 |
+
{
|
244 |
+
"image": "https://huggingface.co/alvdansen/frosting_lane_flux/resolve/main/images/content%20-%202024-08-11T010011.238.jpeg",
|
245 |
+
"title": "Frosting Lane",
|
246 |
+
"repo": "alvdansen/frosting_lane_flux",
|
247 |
+
"trigger_word": "frstingln illustration"
|
248 |
+
},
|
249 |
+
#10
|
250 |
+
{
|
251 |
+
"image": "https://huggingface.co/aleksa-codes/flux-ghibsky-illustration/resolve/main/images/example5.jpg",
|
252 |
+
"title": "Ghibsky Illustration",
|
253 |
+
"repo": "aleksa-codes/flux-ghibsky-illustration",
|
254 |
+
"trigger_word": "GHIBSKY style painting"
|
255 |
+
},
|
256 |
+
#11
|
257 |
+
{
|
258 |
+
"image": "https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-Dark-Fantasy/resolve/main/images/c2215bd73da9f14fcd63cc93350e66e2901bdafa6fb8abaaa2c32a1b.jpg",
|
259 |
+
"title": "Dark Fantasy",
|
260 |
+
"repo": "Shakker-Labs/FLUX.1-dev-LoRA-Dark-Fantasy",
|
261 |
+
"trigger_word": ""
|
262 |
+
},
|
263 |
+
#12
|
264 |
+
{
|
265 |
+
"image": "https://huggingface.co/Norod78/Flux_1_Dev_LoRA_Paper-Cutout-Style/resolve/main/d13591878d5043f3989dd6eb1c25b710_233c18effb4b491cb467ca31c97e90b5.png",
|
266 |
+
"title": "Paper Cutout",
|
267 |
+
"repo": "Norod78/Flux_1_Dev_LoRA_Paper-Cutout-Style",
|
268 |
+
"trigger_word": "Paper Cutout Style"
|
269 |
+
},
|
270 |
+
#13
|
271 |
+
{
|
272 |
+
"image": "https://huggingface.co/alvdansen/mooniverse/resolve/main/images/out-0%20(17).webp",
|
273 |
+
"title": "Mooniverse",
|
274 |
+
"repo": "alvdansen/mooniverse",
|
275 |
+
"trigger_word": "surreal style"
|
276 |
+
},
|
277 |
+
#14
|
278 |
+
{
|
279 |
+
"image": "https://huggingface.co/alvdansen/pola-photo-flux/resolve/main/images/out-0%20-%202024-09-22T130819.351.webp",
|
280 |
+
"title": "Pola Photo",
|
281 |
+
"repo": "alvdansen/pola-photo-flux",
|
282 |
+
"trigger_word": "polaroid style"
|
283 |
+
},
|
284 |
+
#15
|
285 |
+
{
|
286 |
+
"image": "https://huggingface.co/multimodalart/flux-tarot-v1/resolve/main/images/7e180627edd846e899b6cd307339140d_5b2a09f0842c476b83b6bd2cb9143a52.png",
|
287 |
+
"title": "Flux Tarot",
|
288 |
+
"repo": "multimodalart/flux-tarot-v1",
|
289 |
+
"trigger_word": "in the style of TOK a trtcrd tarot style"
|
290 |
+
},
|
291 |
+
#16
|
292 |
+
{
|
293 |
+
"image": "https://huggingface.co/prithivMLmods/Flux-Dev-Real-Anime-LoRA/resolve/main/images/111.png",
|
294 |
+
"title": "Real Anime",
|
295 |
+
"repo": "prithivMLmods/Flux-Dev-Real-Anime-LoRA",
|
296 |
+
"trigger_word": "Real Anime"
|
297 |
+
},
|
298 |
+
#17
|
299 |
+
{
|
300 |
+
"image": "https://huggingface.co/diabolic6045/Flux_Sticker_Lora/resolve/main/images/example_s3pxsewcb.png",
|
301 |
+
"title": "Stickers",
|
302 |
+
"repo": "diabolic6045/Flux_Sticker_Lora",
|
303 |
+
"trigger_word": "5t1cker 5ty1e"
|
304 |
+
},
|
305 |
+
#18
|
306 |
+
{
|
307 |
+
"image": "https://huggingface.co/VideoAditor/Flux-Lora-Realism/resolve/main/images/feel-the-difference-between-using-flux-with-lora-from-xlab-v0-j0ehybmvxehd1.png",
|
308 |
+
"title": "Realism",
|
309 |
+
"repo": "XLabs-AI/flux-RealismLora",
|
310 |
+
"trigger_word": ""
|
311 |
+
},
|
312 |
+
#19
|
313 |
+
{
|
314 |
+
"image": "https://huggingface.co/alvdansen/flux-koda/resolve/main/images/ComfyUI_00583_%20(1).png",
|
315 |
+
"title": "Koda",
|
316 |
+
"repo": "alvdansen/flux-koda",
|
317 |
+
"trigger_word": "flmft style"
|
318 |
+
},
|
319 |
+
#20
|
320 |
+
{
|
321 |
+
"image": "https://huggingface.co/mgwr/Cine-Aesthetic/resolve/main/images/00019-1333633802.png",
|
322 |
+
"title": "Cine Aesthetic",
|
323 |
+
"repo": "mgwr/Cine-Aesthetic",
|
324 |
+
"trigger_word": "mgwr/cine"
|
325 |
+
},
|
326 |
+
#21
|
327 |
+
{
|
328 |
+
"image": "https://huggingface.co/SebastianBodza/flux_cute3D/resolve/main/images/astronaut.webp",
|
329 |
+
"title": "Cute 3D",
|
330 |
+
"repo": "SebastianBodza/flux_cute3D",
|
331 |
+
"trigger_word": "NEOCUTE3D"
|
332 |
+
},
|
333 |
+
#22
|
334 |
+
{
|
335 |
+
"image": "https://huggingface.co/bingbangboom/flux_dreamscape/resolve/main/images/3.jpg",
|
336 |
+
"title": "Dreamscape",
|
337 |
+
"repo": "bingbangboom/flux_dreamscape",
|
338 |
+
"trigger_word": "in the style of BSstyle004"
|
339 |
+
},
|
340 |
+
#23
|
341 |
+
{
|
342 |
+
"image": "https://huggingface.co/prithivMLmods/Canopus-Cute-Kawaii-Flux-LoRA/resolve/main/images/11.png",
|
343 |
+
"title": "Cute Kawaii",
|
344 |
+
"repo": "prithivMLmods/Canopus-Cute-Kawaii-Flux-LoRA",
|
345 |
+
"trigger_word": "cute-kawaii"
|
346 |
+
},
|
347 |
+
#24
|
348 |
+
{
|
349 |
+
"image": "https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/_jyra-jKP_prXhzxYkg1O.png",
|
350 |
+
"title": "Pastel Anime",
|
351 |
+
"repo": "Raelina/Flux-Pastel-Anime",
|
352 |
+
"trigger_word": "Anime"
|
353 |
+
},
|
354 |
+
#25
|
355 |
+
{
|
356 |
+
"image": "https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-Vector-Journey/resolve/main/images/f7a66b51c89896854f31bef743dc30f33c6ea3c0ed8f9ff04d24b702.jpg",
|
357 |
+
"title": "Vector",
|
358 |
+
"repo": "Shakker-Labs/FLUX.1-dev-LoRA-Vector-Journey",
|
359 |
+
"trigger_word": "artistic style blends reality and illustration elements"
|
360 |
+
},
|
361 |
+
#26
|
362 |
+
{
|
363 |
+
"image": "https://huggingface.co/bingbangboom/flux-miniature-worlds/resolve/main/images/2.jpg",
|
364 |
+
"title": "Miniature",
|
365 |
+
"repo": "bingbangboom/flux-miniature-worlds",
|
366 |
+
"trigger_word": "Image in the style of MNTRWRLDS"
|
367 |
+
},
|
368 |
+
#27
|
369 |
+
{
|
370 |
+
"image": "https://huggingface.co/glif-loradex-trainer/bingbangboom_flux_surf/resolve/main/samples/1729012111574__000002000_0.jpg",
|
371 |
+
"title": "Surf Bingbangboom",
|
372 |
+
"repo": "glif-loradex-trainer/bingbangboom_flux_surf",
|
373 |
+
"trigger_word": "SRFNGV01"
|
374 |
+
},
|
375 |
+
#28
|
376 |
+
{
|
377 |
+
"image": "https://huggingface.co/prithivMLmods/Canopus-Snoopy-Charlie-Brown-Flux-LoRA/resolve/main/000.png",
|
378 |
+
"title": "Snoopy Charlie",
|
379 |
+
"repo": "prithivMLmods/Canopus-Snoopy-Charlie-Brown-Flux-LoRA",
|
380 |
+
"trigger_word": "Snoopy Charlie Brown"
|
381 |
+
},
|
382 |
+
#29
|
383 |
+
{
|
384 |
+
"image": "https://huggingface.co/alvdansen/sonny-anime-fixed/resolve/main/images/uqAuIMqA6Z7mvPkHg4qJE_f4c3cbe64e0349e7b946d02adeacdca3.png",
|
385 |
+
"title": "Fixed Sonny",
|
386 |
+
"repo": "alvdansen/sonny-anime-fixed",
|
387 |
+
"trigger_word": "nm22 style"
|
388 |
+
},
|
389 |
+
#30
|
390 |
+
{
|
391 |
+
"image": "https://huggingface.co/davisbro/flux-multi-angle/resolve/main/multi-angle-examples/3.png",
|
392 |
+
"title": "Multi Angle",
|
393 |
+
"repo": "davisbro/flux-multi-angle",
|
394 |
+
"trigger_word": "in the style of TOK"
|
395 |
+
}
|
396 |
+
#add--new LoRA Below ↓ - Before that Use(,)
|
397 |
+
]
|
398 |
+
|
399 |
+
#--------------------------------------------------Model Initialization-----------------------------------------------------------------------------------------#
|
400 |
+
|
401 |
+
dtype = torch.bfloat16
|
402 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
403 |
+
base_model = "black-forest-labs/FLUX.1-dev"
|
404 |
+
|
405 |
+
#TAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. FLUX.1 is useful for real-time previewing of the FLUX.1 generation process.#
|
406 |
+
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
|
407 |
+
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
|
408 |
+
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
|
409 |
+
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model,
|
410 |
+
vae=good_vae,
|
411 |
+
transformer=pipe.transformer,
|
412 |
+
text_encoder=pipe.text_encoder,
|
413 |
+
tokenizer=pipe.tokenizer,
|
414 |
+
text_encoder_2=pipe.text_encoder_2,
|
415 |
+
tokenizer_2=pipe.tokenizer_2,
|
416 |
+
torch_dtype=dtype
|
417 |
+
)
|
418 |
+
|
419 |
+
MAX_SEED = 2**32-1
|
420 |
+
|
421 |
+
pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
|
422 |
+
|
423 |
+
class calculateDuration:
|
424 |
+
def __init__(self, activity_name=""):
|
425 |
+
self.activity_name = activity_name
|
426 |
+
|
427 |
+
def __enter__(self):
|
428 |
+
self.start_time = time.time()
|
429 |
+
return self
|
430 |
+
|
431 |
+
def __exit__(self, exc_type, exc_value, traceback):
|
432 |
+
self.end_time = time.time()
|
433 |
+
self.elapsed_time = self.end_time - self.start_time
|
434 |
+
if self.activity_name:
|
435 |
+
print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
|
436 |
+
else:
|
437 |
+
print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
|
438 |
+
|
439 |
+
def update_selection(evt: gr.SelectData, width, height):
|
440 |
+
selected_lora = loras[evt.index]
|
441 |
+
new_placeholder = f"Type a prompt for {selected_lora['title']}"
|
442 |
+
lora_repo = selected_lora["repo"]
|
443 |
+
updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✅"
|
444 |
+
if "aspect" in selected_lora:
|
445 |
+
if selected_lora["aspect"] == "portrait":
|
446 |
+
width = 768
|
447 |
+
height = 1024
|
448 |
+
elif selected_lora["aspect"] == "landscape":
|
449 |
+
width = 1024
|
450 |
+
height = 768
|
451 |
+
else:
|
452 |
+
width = 1024
|
453 |
+
height = 1024
|
454 |
+
return (
|
455 |
+
gr.update(placeholder=new_placeholder),
|
456 |
+
updated_text,
|
457 |
+
evt.index,
|
458 |
+
width,
|
459 |
+
height,
|
460 |
+
)
|
461 |
+
|
462 |
+
@spaces.GPU(duration=70)
|
463 |
+
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
|
464 |
+
pipe.to("cuda")
|
465 |
+
generator = torch.Generator(device="cuda").manual_seed(seed)
|
466 |
+
with calculateDuration("Generating image"):
|
467 |
+
# Generate image
|
468 |
+
for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
|
469 |
+
prompt=prompt_mash,
|
470 |
+
num_inference_steps=steps,
|
471 |
+
guidance_scale=cfg_scale,
|
472 |
+
width=width,
|
473 |
+
height=height,
|
474 |
+
generator=generator,
|
475 |
+
joint_attention_kwargs={"scale": lora_scale},
|
476 |
+
output_type="pil",
|
477 |
+
good_vae=good_vae,
|
478 |
+
):
|
479 |
+
yield img
|
480 |
+
|
481 |
+
def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
|
482 |
+
generator = torch.Generator(device="cuda").manual_seed(seed)
|
483 |
+
pipe_i2i.to("cuda")
|
484 |
+
image_input = load_image(image_input_path)
|
485 |
+
final_image = pipe_i2i(
|
486 |
+
prompt=prompt_mash,
|
487 |
+
image=image_input,
|
488 |
+
strength=image_strength,
|
489 |
+
num_inference_steps=steps,
|
490 |
+
guidance_scale=cfg_scale,
|
491 |
+
width=width,
|
492 |
+
height=height,
|
493 |
+
generator=generator,
|
494 |
+
joint_attention_kwargs={"scale": lora_scale},
|
495 |
+
output_type="pil",
|
496 |
+
).images[0]
|
497 |
+
return final_image
|
498 |
+
|
499 |
+
@spaces.GPU(duration=70)
|
500 |
+
def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
|
501 |
+
if selected_index is None:
|
502 |
+
raise gr.Error("You must select a LoRA before proceeding.")
|
503 |
+
selected_lora = loras[selected_index]
|
504 |
+
lora_path = selected_lora["repo"]
|
505 |
+
trigger_word = selected_lora["trigger_word"]
|
506 |
+
if(trigger_word):
|
507 |
+
if "trigger_position" in selected_lora:
|
508 |
+
if selected_lora["trigger_position"] == "prepend":
|
509 |
+
prompt_mash = f"{trigger_word} {prompt}"
|
510 |
+
else:
|
511 |
+
prompt_mash = f"{prompt} {trigger_word}"
|
512 |
+
else:
|
513 |
+
prompt_mash = f"{trigger_word} {prompt}"
|
514 |
+
else:
|
515 |
+
prompt_mash = prompt
|
516 |
+
|
517 |
+
with calculateDuration("Unloading LoRA"):
|
518 |
+
pipe.unload_lora_weights()
|
519 |
+
pipe_i2i.unload_lora_weights()
|
520 |
+
|
521 |
+
#LoRA weights flow
|
522 |
+
with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
|
523 |
+
pipe_to_use = pipe_i2i if image_input is not None else pipe
|
524 |
+
weight_name = selected_lora.get("weights", None)
|
525 |
+
|
526 |
+
pipe_to_use.load_lora_weights(
|
527 |
+
lora_path,
|
528 |
+
weight_name=weight_name,
|
529 |
+
low_cpu_mem_usage=True
|
530 |
+
)
|
531 |
+
|
532 |
+
with calculateDuration("Randomizing seed"):
|
533 |
+
if randomize_seed:
|
534 |
+
seed = random.randint(0, MAX_SEED)
|
535 |
+
|
536 |
+
if(image_input is not None):
|
537 |
+
|
538 |
+
final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed)
|
539 |
+
yield final_image, seed, gr.update(visible=False)
|
540 |
+
else:
|
541 |
+
image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
|
542 |
+
|
543 |
+
final_image = None
|
544 |
+
step_counter = 0
|
545 |
+
for image in image_generator:
|
546 |
+
step_counter+=1
|
547 |
+
final_image = image
|
548 |
+
progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
|
549 |
+
yield image, seed, gr.update(value=progress_bar, visible=True)
|
550 |
+
|
551 |
+
yield final_image, seed, gr.update(value=progress_bar, visible=False)
|
552 |
+
|
553 |
+
def get_huggingface_safetensors(link):
|
554 |
+
split_link = link.split("/")
|
555 |
+
if(len(split_link) == 2):
|
556 |
+
model_card = ModelCard.load(link)
|
557 |
+
base_model = model_card.data.get("base_model")
|
558 |
+
print(base_model)
|
559 |
+
|
560 |
+
if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
|
561 |
+
raise Exception("Flux LoRA Not Found!")
|
562 |
+
# Only allow "black-forest-labs/FLUX.1-dev"
|
563 |
+
#if base_model != "black-forest-labs/FLUX.1-dev":
|
564 |
+
#raise Exception("Only FLUX.1-dev is supported, other LoRA models are not allowed!")
|
565 |
+
|
566 |
+
image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
|
567 |
+
trigger_word = model_card.data.get("instance_prompt", "")
|
568 |
+
image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
|
569 |
+
fs = HfFileSystem()
|
570 |
+
try:
|
571 |
+
list_of_files = fs.ls(link, detail=False)
|
572 |
+
for file in list_of_files:
|
573 |
+
if(file.endswith(".safetensors")):
|
574 |
+
safetensors_name = file.split("/")[-1]
|
575 |
+
if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
|
576 |
+
image_elements = file.split("/")
|
577 |
+
image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
|
578 |
+
except Exception as e:
|
579 |
+
print(e)
|
580 |
+
gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
|
581 |
+
raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
|
582 |
+
return split_link[1], link, safetensors_name, trigger_word, image_url
|
583 |
+
|
584 |
+
def check_custom_model(link):
|
585 |
+
if(link.startswith("https://")):
|
586 |
+
if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
|
587 |
+
link_split = link.split("huggingface.co/")
|
588 |
+
return get_huggingface_safetensors(link_split[1])
|
589 |
+
else:
|
590 |
+
return get_huggingface_safetensors(link)
|
591 |
+
|
592 |
+
def add_custom_lora(custom_lora):
|
593 |
+
global loras
|
594 |
+
if(custom_lora):
|
595 |
+
try:
|
596 |
+
title, repo, path, trigger_word, image = check_custom_model(custom_lora)
|
597 |
+
print(f"Loaded custom LoRA: {repo}")
|
598 |
+
card = f'''
|
599 |
+
<div class="custom_lora_card">
|
600 |
+
<span>Loaded custom LoRA:</span>
|
601 |
+
<div class="card_internal">
|
602 |
+
<img src="{image}" />
|
603 |
+
<div>
|
604 |
+
<h3>{title}</h3>
|
605 |
+
<small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
|
606 |
+
</div>
|
607 |
+
</div>
|
608 |
+
</div>
|
609 |
+
'''
|
610 |
+
existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
|
611 |
+
if(not existing_item_index):
|
612 |
+
new_item = {
|
613 |
+
"image": image,
|
614 |
+
"title": title,
|
615 |
+
"repo": repo,
|
616 |
+
"weights": path,
|
617 |
+
"trigger_word": trigger_word
|
618 |
+
}
|
619 |
+
print(new_item)
|
620 |
+
existing_item_index = len(loras)
|
621 |
+
loras.append(new_item)
|
622 |
+
|
623 |
+
return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
|
624 |
+
except Exception as e:
|
625 |
+
gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
|
626 |
+
return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=False), gr.update(), "", None, ""
|
627 |
+
else:
|
628 |
+
return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
|
629 |
+
|
630 |
+
def remove_custom_lora():
|
631 |
+
return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
|
632 |
+
|
633 |
+
run_lora.zerogpu = True
|
634 |
+
|
635 |
+
css = '''
|
636 |
+
#gen_btn{height: 100%}
|
637 |
+
#gen_column{align-self: stretch}
|
638 |
+
#title{text-align: center}
|
639 |
+
#title h1{font-size: 3em; display:inline-flex; align-items:center}
|
640 |
+
#title img{width: 100px; margin-right: 0.5em}
|
641 |
+
#gallery .grid-wrap{height: 10vh}
|
642 |
+
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
|
643 |
+
.card_internal{display: flex;height: 100px;margin-top: .5em}
|
644 |
+
.card_internal img{margin-right: 1em}
|
645 |
+
.styler{--form-gap-width: 0px !important}
|
646 |
+
#progress{height:30px}
|
647 |
+
#progress .generating{display:none}
|
648 |
+
.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
|
649 |
+
.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
|
650 |
+
'''
|
651 |
+
|
652 |
+
with gr.Blocks(theme="prithivMLmods/Minecraft-Theme", css=css, delete_cache=(60, 3600)) as app:
|
653 |
+
title = gr.HTML(
|
654 |
+
"""<h1>FLUX LoRA DLC🥳</h1>""",
|
655 |
+
elem_id="title",
|
656 |
+
)
|
657 |
+
selected_index = gr.State(None)
|
658 |
+
with gr.Row():
|
659 |
+
with gr.Column(scale=3):
|
660 |
+
prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Choose the LoRA and type the prompt")
|
661 |
+
with gr.Column(scale=1, elem_id="gen_column"):
|
662 |
+
generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
|
663 |
+
with gr.Row():
|
664 |
+
with gr.Column():
|
665 |
+
selected_info = gr.Markdown("")
|
666 |
+
gallery = gr.Gallery(
|
667 |
+
[(item["image"], item["title"]) for item in loras],
|
668 |
+
label="LoRA DLC's",
|
669 |
+
allow_preview=False,
|
670 |
+
columns=3,
|
671 |
+
elem_id="gallery",
|
672 |
+
show_share_button=False
|
673 |
+
)
|
674 |
+
with gr.Group():
|
675 |
+
custom_lora = gr.Textbox(label="Enter Custom LoRA", placeholder="prithivMLmods/Canopus-LoRA-Flux-Anime")
|
676 |
+
gr.Markdown("[Check the list of FLUX LoRA's](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
|
677 |
+
custom_lora_info = gr.HTML(visible=False)
|
678 |
+
custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
|
679 |
+
with gr.Column():
|
680 |
+
progress_bar = gr.Markdown(elem_id="progress",visible=False)
|
681 |
+
result = gr.Image(label="Generated Image")
|
682 |
+
|
683 |
+
with gr.Row():
|
684 |
+
with gr.Accordion("Advanced Settings", open=False):
|
685 |
+
with gr.Row():
|
686 |
+
input_image = gr.Image(label="Input image", type="filepath")
|
687 |
+
image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
|
688 |
+
with gr.Column():
|
689 |
+
with gr.Row():
|
690 |
+
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
|
691 |
+
steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
|
692 |
+
|
693 |
+
with gr.Row():
|
694 |
+
width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
|
695 |
+
height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
|
696 |
+
|
697 |
+
with gr.Row():
|
698 |
+
randomize_seed = gr.Checkbox(True, label="Randomize seed")
|
699 |
+
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
|
700 |
+
lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=0.95)
|
701 |
+
|
702 |
+
gallery.select(
|
703 |
+
update_selection,
|
704 |
+
inputs=[width, height],
|
705 |
+
outputs=[prompt, selected_info, selected_index, width, height]
|
706 |
+
)
|
707 |
+
custom_lora.input(
|
708 |
+
add_custom_lora,
|
709 |
+
inputs=[custom_lora],
|
710 |
+
outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
|
711 |
+
)
|
712 |
+
custom_lora_button.click(
|
713 |
+
remove_custom_lora,
|
714 |
+
outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
|
715 |
+
)
|
716 |
+
gr.on(
|
717 |
+
triggers=[generate_button.click, prompt.submit],
|
718 |
+
fn=run_lora,
|
719 |
+
inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
|
720 |
+
outputs=[result, seed, progress_bar]
|
721 |
+
)
|
722 |
+
|
723 |
+
app.queue()
|
724 |
+
app.launch()
|