akhaliq HF staff commited on
Commit
ee2bf71
1 Parent(s): 3d5a1a1

Update app.py

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Files changed (1) hide show
  1. app.py +274 -4
app.py CHANGED
@@ -1,8 +1,278 @@
 
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  import gradio as gr
 
 
 
 
 
 
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- description = """<div>
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- <img src="https://i.imgur.com/FEA7N1p.png">
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  </div>
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- """
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- gr.Interface.load("models/Linaqruf/anything-v3.0", description=description).launch()
 
 
 
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+ from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
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  import gradio as gr
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+ import torch
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+ from PIL import Image
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+ import utils
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+ import datetime
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+ import time
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+ import psutil
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+ start_time = time.time()
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+ is_colab = utils.is_google_colab()
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+
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+ class Model:
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+ def __init__(self, name, path="", prefix=""):
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+ self.name = name
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+ self.path = path
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+ self.prefix = prefix
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+ self.pipe_t2i = None
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+ self.pipe_i2i = None
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+
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+ models = [
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+ Model("anything v3", "Linaqruf/anything-v3.0", "anything v3 style"),
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+ ]
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+ # Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
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+ # Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
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+ # Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
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+ # Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ")
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+ #Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
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+ #Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
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+ #Model("Robo Diffusion", "nousr/robo-diffusion", ""),
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+
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+ scheduler = DPMSolverMultistepScheduler(
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+ beta_start=0.00085,
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+ beta_end=0.012,
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+ beta_schedule="scaled_linear",
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+ num_train_timesteps=1000,
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+ trained_betas=None,
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+ predict_epsilon=True,
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+ thresholding=False,
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+ algorithm_type="dpmsolver++",
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+ solver_type="midpoint",
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+ lower_order_final=True,
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+ )
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+
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+ custom_model = None
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+ if is_colab:
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+ models.insert(0, Model("Custom model"))
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+ custom_model = models[0]
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+
50
+ last_mode = "txt2img"
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+ current_model = models[1] if is_colab else models[0]
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+ current_model_path = current_model.path
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+
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+ if is_colab:
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+ pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
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+
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+ else: # download all models
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+ print(f"{datetime.datetime.now()} Downloading vae...")
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+ vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
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+ for model in models:
61
+ try:
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+ print(f"{datetime.datetime.now()} Downloading {model.name} model...")
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+ unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16)
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+ model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
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+ model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
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+ except Exception as e:
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+ print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e))
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+ models.remove(model)
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+ pipe = models[0].pipe_t2i
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+
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+ if torch.cuda.is_available():
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+ pipe = pipe.to("cuda")
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+
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+ device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
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+
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+ def error_str(error, title="Error"):
77
+ return f"""#### {title}
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+ {error}""" if error else ""
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+
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+ def custom_model_changed(path):
81
+ models[0].path = path
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+ global current_model
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+ current_model = models[0]
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+
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+ def on_model_change(model_name):
86
+
87
+ prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!"
88
+
89
+ return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
90
+
91
+ def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
92
+
93
+ print(psutil.virtual_memory()) # print memory usage
94
+
95
+ global current_model
96
+ for model in models:
97
+ if model.name == model_name:
98
+ current_model = model
99
+ model_path = current_model.path
100
+
101
+ generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
102
+
103
+ try:
104
+ if img is not None:
105
+ return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
106
+ else:
107
+ return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator), None
108
+ except Exception as e:
109
+ return None, error_str(e)
110
+
111
+ def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator):
112
+
113
+ print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
114
+
115
+ global last_mode
116
+ global pipe
117
+ global current_model_path
118
+ if model_path != current_model_path or last_mode != "txt2img":
119
+ current_model_path = model_path
120
+
121
+ if is_colab or current_model == custom_model:
122
+ pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
123
+ else:
124
+ pipe = pipe.to("cpu")
125
+ pipe = current_model.pipe_t2i
126
+
127
+ if torch.cuda.is_available():
128
+ pipe = pipe.to("cuda")
129
+ last_mode = "txt2img"
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+
131
+ prompt = current_model.prefix + prompt
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+ result = pipe(
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+ prompt,
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+ negative_prompt = neg_prompt,
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+ # num_images_per_prompt=n_images,
136
+ num_inference_steps = int(steps),
137
+ guidance_scale = guidance,
138
+ width = width,
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+ height = height,
140
+ generator = generator)
141
+
142
+ return replace_nsfw_images(result)
143
+
144
+ def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
145
+
146
+ print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
147
+
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+ global last_mode
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+ global pipe
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+ global current_model_path
151
+ if model_path != current_model_path or last_mode != "img2img":
152
+ current_model_path = model_path
153
+
154
+ if is_colab or current_model == custom_model:
155
+ pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
156
+ else:
157
+ pipe = pipe.to("cpu")
158
+ pipe = current_model.pipe_i2i
159
+
160
+ if torch.cuda.is_available():
161
+ pipe = pipe.to("cuda")
162
+ last_mode = "img2img"
163
+
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+ prompt = current_model.prefix + prompt
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+ ratio = min(height / img.height, width / img.width)
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+ img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
167
+ result = pipe(
168
+ prompt,
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+ negative_prompt = neg_prompt,
170
+ # num_images_per_prompt=n_images,
171
+ init_image = img,
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+ num_inference_steps = int(steps),
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+ strength = strength,
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+ guidance_scale = guidance,
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+ width = width,
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+ height = height,
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+ generator = generator)
178
+
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+ return replace_nsfw_images(result)
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+
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+ def replace_nsfw_images(results):
182
+
183
+ if is_colab:
184
+ return results.images[0]
185
+
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+ for i in range(len(results.images)):
187
+ if results.nsfw_content_detected[i]:
188
+ results.images[i] = Image.open("nsfw.png")
189
+ return results.images[0]
190
+
191
+ css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
192
+ """
193
+ with gr.Blocks(css=css) as demo:
194
+ gr.HTML(
195
+ f"""
196
+ <div class="finetuned-diffusion-div">
197
+ <div>
198
+ <h1>Anything V3</h1>
199
  </div>
200
+ <img src="https://i.imgur.com/FEA7N1p.png">
201
+ <p>
202
+ Demo for Anything V3 + in colab notebook you can load any other Diffusers 🧨 SD model hosted on HuggingFace 🤗.
203
+ </p>
204
+ <p>You can skip the queue and load custom models in the colab: <a href="https://colab.research.google.com/drive/109CvcHvmfTv3hlS8DSetiky9kH1WVKFf"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
205
+ Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
206
+ </p>
207
+ </div>
208
+ """
209
+ )
210
+ with gr.Row():
211
+
212
+ with gr.Column(scale=55):
213
+ with gr.Group():
214
+ model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
215
+ with gr.Box(visible=False) as custom_model_group:
216
+ custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
217
+ gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
218
+
219
+ with gr.Row():
220
+ prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
221
+ generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
222
+
223
+
224
+ image_out = gr.Image(height=512)
225
+ # gallery = gr.Gallery(
226
+ # label="Generated images", show_label=False, elem_id="gallery"
227
+ # ).style(grid=[1], height="auto")
228
+ error_output = gr.Markdown()
229
+
230
+ with gr.Column(scale=45):
231
+ with gr.Tab("Options"):
232
+ with gr.Group():
233
+ neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
234
+
235
+ # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
236
+
237
+ with gr.Row():
238
+ guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
239
+ steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
240
+
241
+ with gr.Row():
242
+ width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
243
+ height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
244
+
245
+ seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
246
+
247
+ with gr.Tab("Image to image"):
248
+ with gr.Group():
249
+ image = gr.Image(label="Image", height=256, tool="editor", type="pil")
250
+ strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
251
+
252
+ if is_colab:
253
+ model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
254
+ custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
255
+ # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
256
+
257
+ inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
258
+ outputs = [image_out, error_output]
259
+ prompt.submit(inference, inputs=inputs, outputs=outputs)
260
+ generate.click(inference, inputs=inputs, outputs=outputs)
261
+
262
+ ex = gr.Examples([
263
+ [models[0].name, "iron man", 7.5, 50],
264
+
265
+ ], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False)
266
+
267
+ gr.HTML("""
268
+ <div style="border-top: 1px solid #303030;">
269
+ <br>
270
+ <p>Model by TopdeckingLands.</p>
271
+ </div>
272
+ """)
273
+
274
+ print(f"Space built in {time.time() - start_time:.2f} seconds")
275
 
276
+ if not is_colab:
277
+ demo.queue(concurrency_count=1)
278
+ demo.launch(debug=is_colab, share=is_colab)