File size: 8,713 Bytes
0f8ec45
 
 
 
 
2d30d63
0f8ec45
1fd4564
cf0bce4
2d30d63
 
0f8ec45
 
 
ed2d2b6
0f8ec45
 
 
 
 
 
 
2d30d63
 
 
 
 
 
 
 
 
 
 
 
 
 
ed2d2b6
2d30d63
 
 
 
 
cf0bce4
2d30d63
 
 
 
 
 
 
0f8ec45
ed2d2b6
cf0bce4
0f8ec45
 
ed2d2b6
0f8ec45
 
cf0bce4
 
0f8ec45
ed2d2b6
 
0f8ec45
 
 
 
 
 
ed2d2b6
0f8ec45
9941ce1
0f8ec45
 
 
 
ed2d2b6
0f8ec45
 
 
 
ed2d2b6
0f8ec45
 
 
afeabee
 
 
 
 
 
ed2d2b6
afeabee
 
 
 
 
 
 
0f8ec45
cf0bce4
 
0f8ec45
 
 
 
 
 
 
 
 
 
 
cf0bce4
 
0f8ec45
 
ed2d2b6
0f8ec45
 
 
 
543cd64
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0f8ec45
 
 
 
 
 
 
 
 
8f1a540
cf80990
8f1a540
 
cf80990
1332b31
 
8f1a540
 
 
cf80990
1332b31
 
8f1a540
 
 
cf80990
1332b31
 
8f1a540
 
 
2d30d63
1332b31
 
8f1a540
 
 
1332b31
8f1a540
1332b31
8f1a540
 
 
0f8ec45
 
 
 
 
 
582e489
0f8ec45
582e489
0f8ec45
 
 
 
 
 
 
56b6710
0f8ec45
 
 
 
 
 
 
 
 
8f1a540
cf80990
8f1a540
 
 
8b1b541
8f1a540
 
0f8ec45
 
 
6aef7b0
 
 
2d30d63
 
 
 
0f8ec45
 
 
 
 
 
 
 
 
 
 
 
 
 
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
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
from __future__ import annotations
import math
import random
import spaces
import gradio as gr
import numpy as np
import torch
from PIL import Image
from diffusers import StableDiffusionXLPipeline, EDMEulerScheduler, StableDiffusionXLInstructPix2PixPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
from custom_pipeline import CosStableDiffusionXLInstructPix2PixPipeline
from huggingface_hub import hf_hub_download
from huggingface_hub import InferenceClient

help_text = """
To optimize image results:
- Adjust the **Image CFG weight** if the image isn't changing enough or is changing too much. Lower it to allow bigger changes, or raise it to preserve original details.
- Modify the **Text CFG weight** to influence how closely the edit follows text instructions. Increase it to adhere more to the text, or decrease it for subtler changes.
- Experiment with different **random seeds** and **CFG values** for varied outcomes.
- **Rephrase your instructions** for potentially better results.
- **Increase the number of steps** for enhanced edits.
"""

def set_timesteps_patched(self, num_inference_steps: int, device = None):
    self.num_inference_steps = num_inference_steps
    
    ramp = np.linspace(0, 1, self.num_inference_steps)
    sigmas = torch.linspace(math.log(self.config.sigma_min), math.log(self.config.sigma_max), len(ramp)).exp().flip(0)
    
    sigmas = (sigmas).to(dtype=torch.float32, device=device)
    self.timesteps = self.precondition_noise(sigmas)
    
    self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
    self._step_index = None
    self._begin_index = None
    self.sigmas = self.sigmas.to("cpu") 

# Image Editor
edit_file = hf_hub_download(repo_id="stabilityai/cosxl", filename="cosxl_edit.safetensors")
normal_file = hf_hub_download(repo_id="stabilityai/cosxl", filename="cosxl.safetensors")

EDMEulerScheduler.set_timesteps = set_timesteps_patched

# Use a pre-converted fp16 VAE for faster loading and inference
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

pipe_edit = StableDiffusionXLInstructPix2PixPipeline.from_single_file(
    edit_file, num_in_channels=8, is_cosxl_edit=True, vae=vae, torch_dtype=torch.float16,
)
pipe_edit.scheduler = EDMEulerScheduler(sigma_min=0.002, sigma_max=120.0, sigma_data=1.0, prediction_type="v_prediction")
pipe_edit.to("cuda")

# Image Generator
# Keep the models loaded globally for reuse
if torch.cuda.is_available():
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "fluently/Fluently-XL-v4",
        torch_dtype=torch.float16,
        use_safetensors=True,
    ).to("cuda")
    pipe.enable_xformers_memory_efficient_attention() # Enable memory efficient attention for less VRAM usage
    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
    pipe.set_adapters("dalle")

def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, 999999)
    return seed

# Generator
@spaces.GPU(duration=30, queue=False)
def king(type = "Image Generation",
        input_image = None,
        instruction: str = "Eiffel tower",
        steps: int = 8,
        randomize_seed: bool = False,
        seed: int = 25,
        text_cfg_scale: float = 7.3,
        image_cfg_scale: float = 1.7,
        width: int = 1024,
        height: int = 1024,
        guidance_scale: float = 6.2,
        use_resolution_binning: bool = True,
        progress=gr.Progress(track_tqdm=True),
    ):
    if type=="Image Editing" :
        seed = int(randomize_seed_fn(seed, randomize_seed))
        text_cfg_scale = text_cfg_scale
        image_cfg_scale = image_cfg_scale
        input_image = input_image

        steps=steps
        generator = torch.manual_seed(seed)
        output_image = pipe_edit(
            instruction, image=input_image,
            guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale,
            num_inference_steps=steps, generator=generator).images[0]
        return seed, output_image
    else :
        seed = int(randomize_seed_fn(seed, randomize_seed))
        generator = torch.Generator(device="cuda").manual_seed(seed) # Move generator to cuda for speed

        options = {
            "prompt":instruction,
            "width":width,
            "height":height,
            "guidance_scale":guidance_scale,
            "num_inference_steps":steps,
            "generator":generator,
            "use_resolution_binning":use_resolution_binning,
            "output_type":"pil",
        }
        
        with torch.autocast("cuda"): # Use autocast for faster inference
            output_image = pipe(**options).images[0]
        return seed, output_image

# Prompt classifier
def response(instruction, input_image=None):
    if input_image is None:
        output="Image Generation"
    else:
        client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
            
        generate_kwargs = dict(
                max_new_tokens=5,
            )
    
        system="[SYSTEM] You will be provided with text, and your task is to classify task is image generation or image editing answer with only task do not say anything else and stop as soon as possible. [TEXT]"
            
        formatted_prompt = system + instruction + "[TASK]"
        stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
        output = ""
        for response in stream:        
            if not response.token.text == "</s>":
                output += response.token.text
        if "editing" in output:
            output = "Image Editing"
        else:
            output = "Image Generation"
        yield output
    return output

css = '''
.gradio-container{max-width: 600px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''

examples=[
        [
            "Image Generation",
            None,
            "A Super Car",

        ],
        [
            "Image Editing",
            "./supercar.png",
            "make it red",

        ],
        [
            "Image Editing",
            "./red_car.png",
            "add some snow",

        ],
        [
            "Image Generation",
            None,
            "Kids going o school, Anime style",

        ],
        [
            "Image Generation",
            None,
            "Beautiful Eiffel Tower at Night",

        ],
    ]

with gr.Blocks(css=css) as demo:
    gr.Markdown("# Image Generator Pro")
    with gr.Row():
        with gr.Column(scale=4):
            instruction = gr.Textbox(lines=1, label="Instruction", interactive=True)
        with gr.Column(scale=1):
            type = gr.Dropdown(["Image Generation","Image Editing"], label="Task", value="Image Generation",interactive=True, info="AI will select option based on your query, but if it selects wrong, please choose correct one.")
        with gr.Column(scale=1):
            generate_button = gr.Button("Generate")

    with gr.Row():
        input_image = gr.Image(label="Image", type="pil", interactive=True)

    with gr.Row():
        text_cfg_scale = gr.Number(value=7.3, step=0.1, label="Text CFG", interactive=True)
        image_cfg_scale = gr.Number(value=1.7, step=0.1,label="Image CFG", interactive=True)
        steps = gr.Number(value=25, precision=0, label="Steps", interactive=True)
        randomize_seed = gr.Radio(
                ["Fix Seed", "Randomize Seed"],
                value="Randomize Seed",
                type="index",
                show_label=False,
                interactive=True,
            )
        seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)

    gr.Examples(
        examples=examples,
        inputs=[type,input_image, instruction],
        fn=king,
        outputs=[input_image],
        cache_examples=False,
    )

    gr.Markdown(help_text)

    instruction.change(fn=response, inputs=[instruction,input_image], outputs=type, queue=False)

    input_image.upload(fn=response, inputs=[instruction,input_image], outputs=type, queue=False)
    
    gr.on(triggers=[
            generate_button.click,
            instruction.submit
        ],
            fn=king,
            inputs=[type,
                input_image,
                instruction,
                steps,
                randomize_seed,
                seed,
                text_cfg_scale,
                image_cfg_scale,
            ],
            outputs=[seed, input_image],
        )

demo.queue(max_size=99999).launch()