# ----- Deployment Log ----------------------------------------------------------------- # added beta 4305ed7 # added beta 4307f62 # added presidents beta # added painting concept # added presidents concept # added presidents concept #2 # added philip guston concept (retry) # added Ken Price trainings (retry) # added Andrei Tarkovsky polaroid training # added Andrei Tarkovsky polaroid training (retry) # added HairBot training # redeploy with canny edge tab # try to redeploy # try to redeploy again # add myst training # add coin training # add zodiac coin training # readding artbot tab after dependency crashes fixed # attempt redeploy after crash # attempt redeploy after crash 2 # attempt redeploy after crash 3 # attempt redeploy after crash 4 # attempt redeploy after crash 5 # attempt redeploy after crash 6 # attempt redeploy after crash 7 # attempt redeploy after crash 8 # redeploy after locked up build 1 # added woodblock beta training # attempt redeploy after crash # added new concept # attempting reboot 2 # attempting reboot 1 # restart after configuration error # restart after runtime build error # force test redeploy # ----- General Setup ----------------------------------------------------------------- import requests import os import gradio as gr import wget import torch from torch import autocast from diffusers import StableDiffusionPipeline from huggingface_hub import HfApi from transformers import CLIPTextModel, CLIPTokenizer import html import datetime image_count = 0 community_icon_html = "" loading_icon_html = "" share_js = "" api = HfApi() models_list = api.list_models(author="sd-concepts-library", sort="likes", direction=-1) models = [] my_token = os.environ['api_key'] # pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", revision="fp16", torch_dtype=torch.float16, use_auth_token=my_token).to("cuda") pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", variant="fp16", torch_dtype=torch.float16, use_auth_token=my_token).to("cuda") def check_prompt(prompt): SPAM_WORDS = ['Д', 'oob', 'reast'] # only necessary to limit spam for spam_word in SPAM_WORDS: if spam_word in prompt: return False return True def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None): loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu") _old_token = token # separate token and the embeds trained_token = list(loaded_learned_embeds.keys())[0] embeds = loaded_learned_embeds[trained_token] # cast to dtype of text_encoder dtype = text_encoder.get_input_embeddings().weight.dtype # add the token in tokenizer token = token if token is not None else trained_token num_added_tokens = tokenizer.add_tokens(token) i = 1 while(num_added_tokens == 0): token = f"{token[:-1]}-{i}>" num_added_tokens = tokenizer.add_tokens(token) i+=1 # resize the token embeddings text_encoder.resize_token_embeddings(len(tokenizer)) # get the id for the token and assign the embeds token_id = tokenizer.convert_tokens_to_ids(token) text_encoder.get_input_embeddings().weight.data[token_id] = embeds return token # ----- ControlNet Canny Edges Pipe / Setup ----------------------------------------------------------------- # import gradio as gr # from PIL import Image # import numpy as np # import cv2 # from diffusers import StableDiffusionControlNetPipeline, ControlNetModel # from diffusers import UniPCMultistepScheduler # import torch # controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) # controlnet_pipe = StableDiffusionControlNetPipeline.from_pretrained( # "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 # ) # controlnet_pipe.scheduler = UniPCMultistepScheduler.from_config(controlnet_pipe.scheduler.config) # controlnet_pipe.enable_model_cpu_offload() # controlnet_pipe.enable_xformers_memory_efficient_attention() # ----- Load All models / concepts ----------------------------------------------------------------- ahx_model_list = [model for model in models_list if "ahx" in model.modelId] ahx_dropdown_list = [model for model in models_list if "ahx-model" in model.modelId] for model in ahx_model_list: model_content = {} model_id = model.modelId model_content["id"] = model_id embeds_url = f"https://huggingface.co/{model_id}/resolve/main/learned_embeds.bin" os.makedirs(model_id,exist_ok = True) if not os.path.exists(f"{model_id}/learned_embeds.bin"): try: wget.download(embeds_url, out=model_id) except: continue token_identifier = f"https://huggingface.co/{model_id}/raw/main/token_identifier.txt" response = requests.get(token_identifier) token_name = response.text concept_type = f"https://huggingface.co/{model_id}/raw/main/type_of_concept.txt" response = requests.get(concept_type) concept_name = response.text model_content["concept_type"] = concept_name images = [] for i in range(4): url = f"https://huggingface.co/{model_id}/resolve/main/concept_images/{i}.jpeg" image_download = requests.get(url) url_code = image_download.status_code if(url_code == 200): file = open(f"{model_id}/{i}.jpeg", "wb") ## Creates the file for image file.write(image_download.content) ## Saves file content file.close() images.append(f"{model_id}/{i}.jpeg") model_content["images"] = images #if token cannot be loaded, skip it try: learned_token = load_learned_embed_in_clip(f"{model_id}/learned_embeds.bin", pipe.text_encoder, pipe.tokenizer, token_name) # _learned_token_controlnet = load_learned_embed_in_clip(f"{model_id}/learned_embeds.bin", controlnet_pipe.text_encoder, controlnet_pipe.tokenizer, token_name) except: continue model_content["token"] = learned_token models.append(model_content) models.append(model_content) # ----------------------------------------------------------------------------------------------- model_tags = [model.modelId.split("/")[1] for model in ahx_model_list] model_tags.sort() import random DROPDOWNS = {} for model in model_tags: if model != "ahx-model-1" and model != "ahx-model-2": DROPDOWNS[model] = f" in the style of <{model}>" TOKENS = [] for model in model_tags: if model != "ahx-model-1" and model != "ahx-model-2": TOKENS.append(f"<{model}>") # def image_prompt(prompt, dropdown, guidance, steps, seed, height, width, negative_prompt=""): def image_prompt(prompt, guidance, steps, seed, height, width, negative_prompt=""): # prompt = prompt + DROPDOWNS[dropdown] square_pixels = height * width if square_pixels > 640000: height = 640000 // width generator = torch.Generator(device="cuda").manual_seed(int(seed)) height=int((height // 8) * 8) width=int((width // 8) * 8) # image_count += 1 curr_time = datetime.datetime.now() is_clean = check_prompt(prompt) print("----- advanced tab prompt ------------------------------") print(f"prompt: {prompt}, size: {width}px x {height}px, guidance: {guidance}, steps: {steps}, seed: {int(seed)}") # print(f"image_count: {image_count}, datetime: `{e}`") print(f"datetime: `{curr_time}`") print(f"is_prompt_clean: {is_clean}") print("-------------------------------------------------------") input_prompt = prompt.replace(">", "").replace("<", "") input_prompt = input_prompt.split(" ") tokens = [] prompt_words = [] for word in input_prompt: if "ahx" in word: tokens.append(word.replace("ahx-beta-", "").replace("ahx-model-", "")) else: prompt_words.append(word) joined_prompt_text = f"\"{' '.join(prompt_words)}\"" file_name = f"ahx-{'-'.join(tokens)}-{seed}.png" gallery_label = f"{joined_prompt_text} | {file_name}" if is_clean: return ( pipe(prompt=prompt, guidance_scale=guidance, num_inference_steps=steps, generator=generator, height=height, width=width, negative_prompt=negative_prompt).images[0], f"{gallery_label}\n\nprompt: '{prompt}', seed = {int(seed)},\nheight: {height}px, width: {width}px,\nguidance: {guidance}, steps: {steps}, negative prompt: {negative_prompt}" ) else: return ( pipe(prompt="", guidance_scale=0, num_inference_steps=1, generator=generator, height=32, width=32).images[0], f"Prompt violates Hugging Face's Terms of Service" ) # New ArtBot image function ------------------------------------------------- # def image_prompt(prompt, dropdown, guidance, steps, seed, height, width, negative_prompt=""): # def artbot_image(prompt, guidance, steps, seed, height, width, negative_prompt=""): def artbot_image(): guidance = 7.5 steps = 30 height = 768 width = 768 negative_prompt = "" all_models = [token for token in TOKENS if 'ahx-' in token] model_1 = random.choice(all_models) model_2 = random.choice(all_models) prompt = f"{model_1} {model_2}" seed = random_seed() square_pixels = height * width if square_pixels > 640000: height = 640000 // width generator = torch.Generator(device="cuda").manual_seed(int(seed)) height=int((height // 8) * 8) width=int((width // 8) * 8) # image_count += 1 curr_time = datetime.datetime.now() is_clean = check_prompt(prompt) print("----- advanced tab prompt ------------------------------") print(f"prompt: {prompt}, size: {width}px x {height}px, guidance: {guidance}, steps: {steps}, seed: {int(seed)}") # print(f"image_count: {image_count}, datetime: `{e}`") print(f"datetime: `{curr_time}`") print(f"is_prompt_clean: {is_clean}") print("-------------------------------------------------------") if is_clean: return ( pipe(prompt=prompt, guidance_scale=guidance, num_inference_steps=steps, generator=generator, height=height, width=width, negative_prompt=negative_prompt).images[0], f"prompt: '{prompt}', seed = {int(seed)},\nheight: {height}px, width: {width}px,\nguidance: {guidance}, steps: {steps}, negative prompt: {negative_prompt}" ) else: return ( pipe(prompt="", guidance_scale=0, num_inference_steps=1, generator=generator, height=32, width=32).images[0], f"Prompt violates Hugging Face's Terms of Service" ) def default_guidance(): return 7.5 def default_steps(): return 30 def default_pixel(): return 768 def random_seed(): return random.randint(0, 99999999999999) # <-- this is a random gradio limit, the seed range seems to actually be 0-18446744073709551615 def get_models_text(): # make markdown text for available models... markdown_model_tags = [f"<{model}>" for model in model_tags if model != "ahx-model-1" and model != "ahx-model-2"] markdown_model_text = "\n".join(markdown_model_tags) # make markdown text for available betas... markdown_betas_tags = [f"<{model}>" for model in model_tags if "beta" in model] markdown_betas_text = "\n".join(markdown_model_tags) return f"## Available Artist Models / Concepts:\n" + markdown_model_text + "\n\n## Available Beta Models / Concepts:\n" + markdown_betas_text # ----- Advanced Tab ----------------------------------------------------------------- with gr.Blocks(css=".gradio-container {max-width: 650px}") as advanced_tab: gr.Markdown(''' # Advanced Prompting Freely prompt artist models / concepts with open controls for size, inference steps, seed number etc. Text prompts need to manually include artist concept / model tokens which can be found in the welcome tab and beta tab (ie "an alien in the style of "). You can also mix and match models (ie "a landscape in the style of and >"). To see example images or for more information see the links below.

http://www.astronaut.horse
https://discord.com

''') with gr.Row(): prompt = gr.Textbox(label="image prompt...", elem_id="input-text") with gr.Row(): seed = gr.Slider(0, 99999999999999, label="seed", dtype=int, value=random_seed, interactive=True, step=1) negative_prompt = gr.Textbox(label="negative prompt (optional)", elem_id="input-text") with gr.Row(): with gr.Column(): guidance = gr.Slider(0, 10, label="guidance", dtype=float, value=default_guidance, step=0.1, interactive=True) with gr.Column(): steps = gr.Slider(1, 100, label="inference steps", dtype=int, value=default_steps, step=1, interactive=True) with gr.Row(): with gr.Column(): width = gr.Slider(144, 4200, label="width", dtype=int, value=default_pixel, step=8, interactive=True) with gr.Column(): height = gr.Slider(144, 4200, label="height", dtype=int, value=default_pixel, step=8, interactive=True) gr.Markdown("heads-up: Height multiplied by width should not exceed about 645,000 or an error may occur. If an error occours refresh your browser tab or errors will continue. If you exceed this range the app will attempt to avoid an error by lowering your input height. We are actively seeking out ways to handle higher resolutions!") go_button = gr.Button("generate image", elem_id="go-button") output = gr.Image(elem_id="output-image") output_text = gr.Text(elem_id="output-text") go_button.click(fn=image_prompt, inputs=[prompt, guidance, steps, seed, height, width, negative_prompt], outputs=[output, output_text]) gr.Markdown("For a complete list of usable models and beta concepts check out the dropdown selectors in the welcome and beta concepts tabs or the project's main website or our discord.\n\nhttp://www.astronaut.horse/concepts") # ----------------------------------------------------------------------------------------------- model_tags = [model.modelId.split("/")[1] for model in ahx_model_list] model_tags.sort() import random DROPDOWNS = {} # set a default for empty entries... DROPDOWNS[''] = '' # populate the dropdowns with full appendable style strings... for model in model_tags: if model != "ahx-model-1" and model != "ahx-model-2": DROPDOWNS[model] = f" in the style of <{model}>" # set pipe param defaults... def default_guidance(): return 7.5 def default_steps(): return 30 def default_pixel(): return 768 def random_seed(): return random.randint(0, 99999999999999) # <-- this is a random gradio limit, the seed range seems to actually be 0-18446744073709551615 def simple_image_prompt(prompt, dropdown, size_dropdown): seed = random_seed() guidance = 7.5 if size_dropdown == 'landscape': height = 624 width = 1024 elif size_dropdown == 'portrait': height = 1024 width = 624 elif size_dropdown == 'square': height = 768 width = 768 else: height = 1024 width = 624 steps = 30 height=int((height // 8) * 8) width=int((width // 8) * 8) prompt = prompt + DROPDOWNS[dropdown] generator = torch.Generator(device="cuda").manual_seed(int(seed)) curr_time = datetime.datetime.now() is_clean = check_prompt(prompt) print("----- welcome / beta tab prompt ------------------------------") print(f"prompt: {prompt}, size: {width}px x {height}px, guidance: {guidance}, steps: {steps}, seed: {int(seed)}") print(f"datetime: `{curr_time}`") print(f"is_prompt_clean: {is_clean}") print("-------------------------------------------------------") if is_clean: return ( pipe(prompt=prompt, guidance_scale=guidance, num_inference_steps=steps, generator=generator, height=height, width=width).images[0], f"prompt: '{prompt}', seed = {int(seed)},\nheight: {height}px, width: {width}px,\nguidance: {guidance}, steps: {steps}" ) else: return ( pipe(prompt="", guidance_scale=0, num_inference_steps=1, generator=generator, height=32, width=32).images[0], f"Prompt violates Hugging Face's Terms of Service" ) # ----- Welcome Tab ----------------------------------------------------------------- rand_model_int = 2 with gr.Blocks(css=".gradio-container {max-width: 650px}") as new_welcome: gr.Markdown(''' # Stable Diffusion Artist Collaborations Use the dropdown below to select models / concepts trained on images chosen by collaborating visual artists. Prompt concepts with any text. To see example images or for more information on the project see the main project page or the discord community linked below. The images you generate here are not recorded unless you save them, they belong to everyone and no one.

http://www.astronaut.horse
https://discord.com
''') with gr.Row(): dropdown = gr.Dropdown([dropdown for dropdown in list(DROPDOWNS) if 'ahx-model' in dropdown], label="choose style...") size_dropdown = gr.Dropdown(['square', 'portrait', 'landscape'], label="choose size...") prompt = gr.Textbox(label="image prompt...", elem_id="input-text") go_button = gr.Button("generate image", elem_id="go-button") output = gr.Image(elem_id="output-image") output_text = gr.Text(elem_id="output-text") go_button.click(fn=simple_image_prompt, inputs=[prompt, dropdown, size_dropdown], outputs=[output, output_text]) # Old Text --> This tool allows you to run your own text prompts into fine-tuned artist concepts from an ongoing series of Stable Diffusion collaborations with visual artists linked below. Select an artist's fine-tuned concept / model from the dropdown and enter any desired text prompt. You can check out example output images and project details on the project's webpage. Additionally you can play around with more controls in the Advanced Prompting tab.
The images you generate here are not recorded unless you choose to share them. Please share any cool images / prompts on the community tab here or our discord server! # ----- Beta Concepts ----------------------------------------------------------------- with gr.Blocks() as beta: gr.Markdown(''' # Beta Models / Concepts This tool allows you to test out newly trained beta concepts trained by artists. To add your own beta concept see the link below. This uses free access to Google's GPUs but will require a password / key that you can get from the discord server. After a new concept / model is trained it will be automatically added to this tab when the app is redeployed.

train your own beta model / concept
http://www.astronaut.horse
https://discord.com

''') with gr.Row(): dropdown = gr.Dropdown([dropdown for dropdown in list(DROPDOWNS) if 'ahx-beta' in dropdown], label="choose style...") size_dropdown = gr.Dropdown(['square', 'portrait', 'landscape'], label="choose size...") prompt = gr.Textbox(label="image prompt...", elem_id="input-text") go_button = gr.Button("generate image", elem_id="go-button") output = gr.Image(elem_id="output-image") output_text = gr.Text(elem_id="output-text") go_button.click(fn=simple_image_prompt, inputs=[prompt, dropdown, size_dropdown], outputs=[output, output_text]) # ----- Artbot Tab ----------------------------------------------------------------- import random with gr.Blocks(css=".gradio-container {max-width: 650px}") as artbot_1: gr.Markdown(''' # Astronaut Horse ''') with gr.Accordion(label='project information...', open=False): gr.Markdown(''' These images are collaborations between visual artists and Stable Diffusion, a free and open-source generative AI model fine-tuned on input artworks chosen by the artists. The images are generated in real time and cannot be reproduced unless you choose to save them.

The hardware resources to run this process have been generously provided at no cost by Hugging Face via a Community GPU Grant. For full control over all input parameters see the other tabs on this application. For more images and information on the project see the links below.

The images you generate here are not recorded unless you save them, they belong to everyone and no one.

http://www.astronaut.horse
https://discord.com
''') # with gr.Row(): # dropdown = gr.Dropdown([dropdown for dropdown in list(DROPDOWNS) if 'ahx-model' in dropdown], label="choose style...") # size_dropdown = gr.Dropdown(['square', 'portrait', 'landscape'], label="choose size...") # prompt = gr.Textbox(label="image prompt...", elem_id="input-text") go_button = gr.Button("generate image", elem_id="go-button") output = gr.Image(elem_id="output-image") with gr.Accordion(label='image information...', open=False): output_text = gr.Text(elem_id="output-text") # go_button.click(fn=simple_image_prompt, inputs=[prompt, dropdown, size_dropdown], outputs=[output, output_text]) go_button.click(fn=artbot_image, inputs=[], outputs=[output, output_text]) # ----- Canny Edge Tab ----------------------------------------------------------------- from PIL import Image import gradio as gr import numpy as np import cv2 # Define a function to process the uploaded image def canny_process_image(input_image, input_low_threshold, input_high_threshold, input_invert): # Convert the input image to a NumPy array np_image = np.array(input_image) output_image = input_image # For example, just return the input image numpy_image = np.array(output_image) # Return the processed image # low_threshold = 100 # high_threshold = 200 canny_1 = cv2.Canny(numpy_image, input_low_threshold, input_high_threshold) canny_1 = canny_1[:, :, None] canny_1 = np.concatenate([canny_1, canny_1, canny_1], axis=2) if input_invert: canny_1 = 255 - canny_1 canny_2 = Image.fromarray(canny_1) return np.array(canny_2) # Define the input and output interfaces canny_input_image = gr.inputs.Image() canny_input_low_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Lower Threshold:", default=100) canny_input_high_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Upper Threshold:", default=200) canny_input_invert = gr.inputs.Checkbox(label="Invert Image") canny_outputs = gr.outputs.Image(type="numpy") # Create the Gradio interface canny_interface = gr.Interface(fn=canny_process_image, inputs=[canny_input_image, canny_input_low_threshold, canny_input_high_threshold, canny_input_invert], outputs=canny_outputs, title='Canny Edge Tracing', allow_flagging='never') # ----- New ControlNet Canny Gradio Setup with Block ----------------------------------------------------------------- # !pip install -qq diffusers==0.14.0 transformers xformers git+https://github.com/huggingface/accelerate.git # !pip install -qq opencv-contrib-python # !pip install -qq controlnet_aux # !pip install -qq opencv-python # !pip install -qq gradio # !pip install -qq Pillow # !pip install -qq numpy from diffusers import StableDiffusionControlNetPipeline, ControlNetModel from diffusers import UniPCMultistepScheduler from PIL import Image import gradio as gr import numpy as np import torch import cv2 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) controlnet_pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 ) controlnet_pipe.scheduler = UniPCMultistepScheduler.from_config(controlnet_pipe.scheduler.config) controlnet_pipe.enable_model_cpu_offload() def controlnet_edges(canny_input_prompt, input_image, input_low_threshold, input_high_threshold, input_invert, canny_input_seed, canny_input_rotate, canny_negative_prompt): np_image = np.array(input_image) output_image = input_image numpy_image = np.array(output_image) low_threshold = 80 high_threshold = 100 canny_1 = cv2.Canny(numpy_image, input_low_threshold, input_high_threshold) canny_1 = canny_1[:, :, None] canny_1 = np.concatenate([canny_1, canny_1, canny_1], axis=2) if input_invert: canny_1 = 255 - canny_1 canny_2 = Image.fromarray(canny_1) canny_1 = Image.fromarray(canny_1) if canny_input_rotate and int(canny_input_rotate) > 0: canny_rotation = 360 - int(canny_input_rotate) canny_2 = canny_2.rotate(canny_rotation, resample=Image.BICUBIC) canny_1 = canny_1.rotate(canny_rotation, resample=Image.BICUBIC) input_width, input_height = canny_2.size limit_size = 768 # limit_size = 32 # resize image if input_width > input_height: new_width = min(input_width, limit_size) new_height = int(new_width * input_height / input_width) else: new_height = min(input_height, limit_size) new_width = int(new_height * input_width / input_height) canny_2 = canny_2.resize((new_width, new_height)) canny_1 = canny_1.resize((new_width, new_height)) # resize original input image input_resize = np.array(input_image) input_resize = Image.fromarray(input_resize) input_resize = input_resize.resize((new_width, new_height)) # make canny image now, after resize canny_resize = np.array(input_resize) canny_resize = cv2.Canny(canny_resize, input_low_threshold, input_high_threshold) canny_resize = canny_resize[:, :, None] canny_resize = np.concatenate([canny_resize, canny_resize, canny_resize], axis=2) if input_invert: canny_resize = 255 - canny_resize canny_resize = Image.fromarray(canny_resize) # rotate new resized canny image if canny_input_rotate and int(canny_input_rotate) > 0: canny_rotation = 360 - int(canny_input_rotate) canny_resize = canny_resize.rotate(canny_rotation, resample=Image.BICUBIC, expand=True) prompt = canny_input_prompt generator = torch.Generator(device="cpu").manual_seed(canny_input_seed) output_image = controlnet_pipe( prompt, canny_resize, negative_prompt=canny_negative_prompt, generator=generator, num_inference_steps=20, ) return [canny_resize, output_image[0][0]] # return output_image[0][0] import random def random_seed(): return random.randint(0, 99999999999999) with gr.Blocks() as canny_blocks_interface: gr.Markdown(''' # ControlNet + Canny Edge-Tracing This tool allows you to apply a Stable Diffusion text prompt to an existing image composition using an edge-tracing tool called Canny Edge Detector. Note that you cannot currently apply trained artist concepts from the other tabs in this application to this process currently as they were trained using a more recent version of Stable Diffusion.

https://wikipedia.org/wiki/canny_edge_detector
http://www.astronaut.horse
https://discord.com

''') with gr.Row(): with gr.Column(): canny_input_prompt = gr.inputs.Textbox(label="enter your text prompt here") with gr.Accordion(label='negative prompt (optional)', open=False): canny_negative_prompt = gr.inputs.Textbox() canny_input_low_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Lower Threshold:", default=100) canny_input_high_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Upper Threshold:", default=120) canny_input_seed = gr.Slider(0, 99999999999999, label="seed", dtype=int, value=random_seed, interactive=True, step=1) canny_input_invert = gr.inputs.Checkbox(label="invert edge tracing image") canny_input_rotate = gr.Dropdown([0, 90, 180, 270], label="rotate image (for smartphones)") with gr.Column(): canny_input_image = gr.inputs.Image(label="input image") go_button = gr.Button('generate image') # with gr.Row(): with gr.Accordion(label='traced edge image', open=False): canny_output_1 = gr.outputs.Image(type="pil", label="traced edges") with gr.Row(): canny_output_2 = gr.outputs.Image(type="pil", label="final image") go_button.click(fn=controlnet_edges, inputs=[canny_input_prompt, canny_input_image, canny_input_low_threshold, canny_input_high_threshold, canny_input_invert, canny_input_seed, canny_input_rotate, canny_negative_prompt], outputs=[canny_output_1, canny_output_2]) # canny_blocks_interface.launch(debug=False) # ----- Old ControlNet Canny Gradio Setup without Block (working) ----------------------------------------------------------------- # import gradio as gr # from PIL import Image # import numpy as np # import cv2 # from diffusers import StableDiffusionControlNetPipeline, ControlNetModel # from diffusers import UniPCMultistepScheduler # import torch # controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) # controlnet_pipe = StableDiffusionControlNetPipeline.from_pretrained( # "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 # ) # controlnet_pipe.scheduler = UniPCMultistepScheduler.from_config(controlnet_pipe.scheduler.config) # controlnet_pipe.enable_model_cpu_offload() # controlnet_pipe.enable_xformers_memory_efficient_attention() # def controlnet_edges(canny_input_prompt, input_image, input_low_threshold, input_high_threshold, input_invert): # np_image = np.array(input_image) # output_image = input_image # numpy_image = np.array(output_image) # low_threshold = 80 # high_threshold = 100 # canny_1 = cv2.Canny(numpy_image, input_low_threshold, input_high_threshold) # canny_1 = canny_1[:, :, None] # canny_1 = np.concatenate([canny_1, canny_1, canny_1], axis=2) # if input_invert: # canny_1 = 255 - canny_1 # canny_2 = Image.fromarray(canny_1) # prompt = canny_input_prompt # generator = torch.Generator(device="cpu").manual_seed(2) # # output_image = controlnet_pipe( # # prompt, # # canny_2, # # negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality", # # generator=generator, # # num_inference_steps=20, # # ) # output_image = controlnet_pipe( # prompt, # canny_2, # negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality", # num_inference_steps=20, # ) # return output_image[0][0] # canny_input_prompt = gr.inputs.Textbox(label="Enter a single word or phrase") # canny_input_image = gr.inputs.Image() # canny_input_low_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Lower Threshold:", default=100) # canny_input_high_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Upper Threshold:", default=200) # canny_input_invert = gr.inputs.Checkbox(label="Invert Image") # canny_outputs = gr.outputs.Image(type="pil") # make and launch the gradio app... # controlnet_canny_interface = gr.Interface(fn=controlnet_edges, inputs=[canny_input_prompt, canny_input_image, canny_input_low_threshold, canny_input_high_threshold, canny_input_invert], outputs=canny_outputs, title='Canny Edge Tracing', allow_flagging='never') # controlnet_canny_interface.launch() # ----- Depth Map Tab ----------------------------------------------------------------- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler from controlnet_aux import CannyDetector, ContentShuffleDetector, HEDdetector, LineartAnimeDetector, LineartDetector, MidasDetector, MLSDdetector, NormalBaeDetector, OpenposeDetector, PidiNetDetector from PIL import Image, ImageChops, ImageOps from diffusers.utils import load_image from transformers import pipeline import numpy as np import requests import torch import cv2 def resize_image(image, max_dimension, multiplier=16): original_width, original_height = image.size aspect_ratio = original_width / original_height if original_width > original_height: new_width = min(max_dimension, original_width) new_height = round(new_width / aspect_ratio) else: new_height = min(max_dimension, original_height) new_width = round(new_height * aspect_ratio) new_width = round(new_width / multiplier) * multiplier new_height = round(new_height / multiplier) * multiplier resized_image = image.resize((new_width, new_height), Image.LANCZOS) return resized_image def depth_map_prompt(prompt, image_url, controlnet_pipe, controlnet_model, negative_prompt): image = load_image(image_url) max_dimension = 768 resized_image = resize_image(image, max_dimension) depth_map = controlnet_model(resized_image) output = controlnet_pipe( prompt, depth_map, negative_prompt=negative_prompt, generator=torch.Generator(device="cpu").manual_seed(2), num_inference_steps=20, ) return {"output": output.images[0], "depth_map": depth_map} controlnet_depth = ControlNetModel.from_pretrained( "fusing/stable-diffusion-v1-5-controlnet-depth", torch_dtype=torch.float16 ) model_id = "runwayml/stable-diffusion-v1-5" depth_pipe = StableDiffusionControlNetPipeline.from_pretrained( model_id, controlnet=controlnet_depth, torch_dtype=torch.float16, ) depth_pipe.scheduler = UniPCMultistepScheduler.from_config(depth_pipe.scheduler.config) depth_pipe.enable_model_cpu_offload() depth_pipe.enable_xformers_memory_efficient_attention() loaded_model = MidasDetector.from_pretrained("lllyasviel/ControlNet") # works def rotate_image(image, rotation): rotation = 360 - int(rotation) image = image.rotate(rotation, resample=Image.BICUBIC, expand=True) return image def controlnet_function(input_prompt, input_image, input_negative_prompt, input_seed, input_rotate, input_invert): pil_image = Image.fromarray(input_image) max_dimension = 768 processed_image = resize_image(pil_image, max_dimension, 32) # rotate image if input_rotate and int(input_rotate) > 0: processed_image = rotate_image(processed_image, int(input_rotate)) depth_map = loaded_model(processed_image) if input_invert: depth_map = np.array(depth_map) depth_map = 255 - depth_map depth_map = Image.fromarray(depth_map) generator = torch.Generator(device="cpu").manual_seed(input_seed) output = depth_pipe( input_prompt, depth_map, negative_prompt=input_negative_prompt, generator=generator, num_inference_steps=20, ) return_text = f''' prompt: "{input_prompt}" seed: {input_seed} negative-prompt: "{input_negative_prompt}" controlnet: "fusing/stable-diffusion-v1-5-controlnet-depth" stable-diffusion: "runwayml/stable-diffusion-v1-5" inverted: {input_invert} ''' return [return_text, output.images[0], depth_map] # import random def random_seed(): return random.randint(0, 99999999999999) with gr.Blocks() as depth_controlnet_gradio: gr.Markdown(''' # ControlNet + Depthmap --- ''') with gr.Row(): with gr.Column(): gr.Markdown(''' ## Inputs... ''') input_prompt = gr.inputs.Textbox(label="text prompt") input_image = gr.inputs.Image(label="input image") with gr.Accordion(label="options", open=False): with gr.Row(): with gr.Column(): input_negative_prompt = gr.inputs.Textbox(label="negative prompt") with gr.Column(): input_seed = gr.Slider(0, 99999999999999, label="seed", dtype=int, value=random_seed, interactive=True, step=1) with gr.Row(): with gr.Column(): input_rotate = gr.Dropdown([0, 90, 180, 270], label="rotate image (for smartphones)") with gr.Column(): input_invert = gr.inputs.Checkbox(label="invert depthmap") submit = gr.Button('generate image') with gr.Column(): gr.Markdown(''' ## Outputs... ''') output_image = gr.Image(label="output image") with gr.Accordion(label="depth map image", open=False): depth_map = gr.Image(label="depth map") output_text = gr.Textbox(label="output details") submit.click(fn=controlnet_function, inputs=[input_prompt, input_image, input_negative_prompt, input_seed, input_rotate, input_invert], outputs=[output_text, output_image, depth_map]) # depth_controlnet_gradio.launch(debug=False) # ----- Launch Tabs ----------------------------------------------------------------- tabbed_interface = gr.TabbedInterface([new_welcome, artbot_1, advanced_tab, beta, canny_blocks_interface, depth_controlnet_gradio], ["Welcome", "ArtBot", "Advanced", "Beta", "EdgeTrace", "DepthMap"]) # tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta], ["Artbots", "Advanced", "Beta"]) tabbed_interface.launch()