From 81fedeca045bdc935ca94c65ed8c709aab086a1d Mon Sep 17 00:00:00 2001 From: Michael Date: Thu, 17 Oct 2024 21:54:29 +0300 Subject: [PATCH 1/2] FIXES2 --- Install.bat | 1 + Install.sh | 1 + LaunchFile/app.py | 252 ++++++++++++++++------------- RequirementsFiles/requirements.txt | 25 ++- Update.bat | 1 + Update.sh | 1 + 6 files changed, 171 insertions(+), 110 deletions(-) diff --git a/Install.bat b/Install.bat index 29357762..7e669b3a 100644 --- a/Install.bat +++ b/Install.bat @@ -36,6 +36,7 @@ pip install --no-build-isolation -e git+https://github.com/turboderp/exllamav2.g pip install git+https://github.com/tencent-ailab/IP-Adapter.git 2>> %ERROR_LOG% pip install git+https://github.com/vork/PyNanoInstantMeshes.git 2>> %ERROR_LOG% pip install git+https://github.com/openai/CLIP.git 2>> %ERROR_LOG% +pip install git+https://github.com/xhinker/sd_embed.git@main 2>> %ERROR_LOG% timeout /t 3 /nobreak >nul cls diff --git a/Install.sh b/Install.sh index 638a12d5..53d2503a 100644 --- a/Install.sh +++ b/Install.sh @@ -36,6 +36,7 @@ pip install --no-build-isolation -e git+https://github.com/turboderp/exllamav2.g pip install git+https://github.com/tencent-ailab/IP-Adapter.git 2>> "$ERROR_LOG" pip install git+https://github.com/vork/PyNanoInstantMeshes.git 2>> "$ERROR_LOG" pip install git+https://github.com/openai/CLIP.git 2>> "$ERROR_LOG" +pip install git+https://github.com/xhinker/sd_embed.git@main 2>> "$ERROR_LOG" sleep 3 clear diff --git a/LaunchFile/app.py b/LaunchFile/app.py index 336090c8..11875377 100644 --- a/LaunchFile/app.py +++ b/LaunchFile/app.py @@ -41,6 +41,7 @@ from datetime import datetime from diffusers.utils import load_image, load_video, export_to_video, export_to_gif, export_to_ply, pt_to_pil from compel import Compel, ReturnedEmbeddingsType +from sd_embed.embedding_funcs import get_weighted_text_embeddings_sd3, get_weighted_text_embeddings_flux1 import trimesh from git import Repo import numpy as np @@ -2529,11 +2530,11 @@ def generate_image_img2img(prompt, negative_prompt, init_image, strength, stable if not stable_diffusion_model_name: gr.Info("Please, select a StableDiffusion model!") - return None, None, None + return None, None if not init_image: gr.Info("Please, upload an initial image!") - return None, None, None + return None, None if enable_quantize: try: @@ -2563,7 +2564,7 @@ def generate_image_img2img(prompt, negative_prompt, init_image, strength, stable if result.returncode != 0: gr.Info(f"Error in sd-img2img-quantize.py: {result.stderr}") - return None, None, None + return None, None image_paths = [] output = result.stdout.strip() @@ -2573,11 +2574,11 @@ def generate_image_img2img(prompt, negative_prompt, init_image, strength, stable if os.path.exists(image_path): image_paths.append(image_path) - return image_paths, None, f"Images generated successfully using quantized model. Seed used: {seed}" + return image_paths, f"Images generated successfully using quantized model. Seed used: {seed}" except Exception as e: gr.Error(f"An error occurred: {str(e)}") - return None, None, None + return None, None else: try: @@ -2586,7 +2587,7 @@ def generate_image_img2img(prompt, negative_prompt, init_image, strength, stable if not os.path.exists(stable_diffusion_model_path): gr.Info(f"StableDiffusion model not found: {stable_diffusion_model_path}") - return None, None, None + return None, None if stable_diffusion_model_type == "SD": stable_diffusion_model = StableDiffusionImg2ImgPipeline().StableDiffusionImg2ImgPipeline.from_single_file( @@ -2601,10 +2602,10 @@ def generate_image_img2img(prompt, negative_prompt, init_image, strength, stable stable_diffusion_model_path, use_safetensors=True, device_map="auto", attention_slice=1, torch_dtype=torch.float16, variant="fp16") else: - return None, None, "Invalid StableDiffusion model type!" + return None, "Invalid StableDiffusion model type!" except (ValueError, KeyError): gr.Error("The selected model is not compatible with the chosen model type") - return None, None, None + return None, None device = "cuda" if torch.cuda.is_available() else "cpu" @@ -2766,35 +2767,12 @@ def process_prompt_with_ti(input_prompt, textual_inversion_model_names): processed_prompt = process_prompt_with_ti(prompt, textual_inversion_model_names) processed_negative_prompt = process_prompt_with_ti(negative_prompt, textual_inversion_model_names) - def latents_to_rgb(latents): - weights = ( - (60, -60, 25, -70), - (60, -5, 15, -50), - (60, 10, -5, -35) - ) - - weights_tensor = torch.t(torch.tensor(weights, dtype=latents.dtype).to(latents.device)) - biases_tensor = torch.tensor((150, 140, 130), dtype=latents.dtype).to(latents.device) - rgb_tensor = torch.einsum("...lxy,lr -> ...rxy", latents, weights_tensor) + biases_tensor.unsqueeze( - -1).unsqueeze(-1) - image_array = rgb_tensor.clamp(0, 255)[0].byte().cpu().numpy() - image_array = image_array.transpose(1, 2, 0) - - return Image.fromarray(image_array) - - def decode_tensors(stable_diffusion_model, i, t, callback_kwargs): - latents = callback_kwargs["latents"] - image = latents_to_rgb(latents) - image.save(f"temp/{i}.png") - return callback_kwargs - def combined_callback(stable_diffusion_model, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i if i == stop_idx: stable_diffusion_model._interrupt = True - callback_kwargs = decode_tensors(stable_diffusion_model, i, t, callback_kwargs) progress((i + 1) / stable_diffusion_steps, f"Step {i + 1}/{stable_diffusion_steps}") @@ -2836,7 +2814,6 @@ def combined_callback(stable_diffusion_model, i, t, callback_kwargs): callback_on_step_end_tensor_inputs=["latents"]).images image_paths = [] - gif_images = [] for i, image in enumerate(images): today = datetime.now().date() image_dir = os.path.join('outputs', f"StableDiffusion_{today.strftime('%Y%m%d')}") @@ -2865,26 +2842,11 @@ def combined_callback(stable_diffusion_model, i, t, callback_kwargs): add_metadata_to_file(image_path, metadata) image_paths.append(image_path) - for i in range(stable_diffusion_steps): - if os.path.exists(f"temp/{i}.png"): - gif_images.append(imageio.imread(f"temp/{i}.png")) - - if gif_images: - gif_filename = f"img2img_process_{datetime.now().strftime('%Y%m%d_%H%M%S')}.gif" - gif_path = os.path.join(image_dir, gif_filename) - imageio.mimsave(gif_path, gif_images, duration=0.1) - else: - gif_path = None - - for i in range(stable_diffusion_steps): - if os.path.exists(f"temp/{i}.png"): - os.remove(f"temp/{i}.png") - - return image_paths, [gif_path] if gif_path else [], f"Images generated successfully. Seed used: {seed}" + return image_paths, f"Images generated successfully. Seed used: {seed}" except Exception as e: gr.Error(f"An error occurred: {str(e)}") - return None, None, None + return None, None finally: if 'stable_diffusion_model' in locals(): @@ -3287,7 +3249,7 @@ def generate_image_controlnet(prompt, negative_prompt, init_image, sd_version, s prompt_embeds = compel_proc(prompt) negative_prompt_embeds = compel_proc(negative_prompt) - def combined_callback_sd(i, t, callback_kwargs): + def combined_callback_sd(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -3429,7 +3391,7 @@ def get_depth_map(image): ) prompt_embeds, pooled_prompt_embeds = compel(prompt) - def combined_callback_sdxl(i, t, callback_kwargs): + def combined_callback_sdxl(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -3858,7 +3820,7 @@ def generate_image_inpaint(prompt, negative_prompt, init_image, mask_image, blur seed = int(seed) generator = torch.Generator(device).manual_seed(seed) - def combined_callback(i, t, callback_kwargs): + def combined_callback(stable_diffusion_model, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -4837,7 +4799,7 @@ def generate_image_ldm3d(prompt, negative_prompt, seed, width, height, num_infer flush() -def generate_image_sd3_txt2img(prompt, negative_prompt, quantize_sd3_model_name, enable_quantize, seed, stop_button, vae_model_name, lora_model_names, lora_scales, num_inference_steps, guidance_scale, width, height, max_sequence_length, clip_skip, num_images_per_prompt, enable_taesd, output_format, progress=gr.Progress()): +def generate_image_sd3_txt2img(prompt, negative_prompt, model_type, quantize_sd3_model_name, enable_quantize, seed, stop_button, vae_model_name, lora_model_names, lora_scales, num_inference_steps, guidance_scale, width, height, max_sequence_length, clip_skip, num_images_per_prompt, enable_taesd, output_format, progress=gr.Progress()): global stop_signal stop_signal = False stop_idx = None @@ -4899,9 +4861,31 @@ def generate_image_sd3_txt2img(prompt, negative_prompt, quantize_sd3_model_name, return None, None stable_diffusion_model_path = os.path.join("inputs", "image", "sd_models", quantize_sd3_model_name) - pipe = StableDiffusion3Pipeline().StableDiffusion3Pipeline.from_single_file(stable_diffusion_model_path, - device_map=device, - torch_dtype=torch.float16) + + sd3_model_path = os.path.join("inputs", "image", "sd_models", "sd3") + if not os.path.exists(sd3_model_path): + gr.Info("Downloading Stable Diffusion 3 model...") + os.makedirs(sd3_model_path, exist_ok=True) + Repo.clone_from("https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers", + sd3_model_path) + gr.Info("Stable Diffusion 3 model downloaded") + + if model_type == "Diffusers": + quantization_config = BitsAndBytesConfig().BitsAndBytesConfig(load_in_8bit=True) + + text_encoder = T5EncoderModel().T5EncoderModel.from_pretrained( + sd3_model_path, + subfolder="text_encoder_3", + quantization_config=quantization_config, + ) + pipe = StableDiffusion3Pipeline().StableDiffusion3Pipeline.from_pretrained(sd3_model_path, + device_map="balanced", + text_encoder_3=text_encoder, + torch_dtype=torch.float16) + else: + pipe = StableDiffusion3Pipeline().StableDiffusion3Pipeline.from_single_file(stable_diffusion_model_path, + device_map=device, + torch_dtype=torch.float16) if vae_model_name is not None: vae_model_path = os.path.join("inputs", "image", "sd_models", "vae", f"{vae_model_name}") @@ -4961,9 +4945,13 @@ def combined_callback(pipe, i, t, callback_kwargs): return callback_kwargs + prompt_embeds, prompt_neg_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = get_weighted_text_embeddings_sd3(pipe=pipe, prompt=prompt, neg_prompt=negative_prompt) + images = pipe( - prompt=prompt, - negative_prompt=negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=prompt_neg_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, width=width, @@ -5011,6 +4999,10 @@ def combined_callback(pipe, i, t, callback_kwargs): finally: if 'pipe' in locals(): del pipe + if 'text_encoder' in locals(): + del text_encoder + if 'get_weighted_text_embeddings_sd3' in locals(): + del get_weighted_text_embeddings_sd3 flush() @@ -5107,7 +5099,7 @@ def generate_image_sd3_img2img(prompt, negative_prompt, init_image, strength, qu seed = int(seed) generator = torch.Generator(device).manual_seed(seed) - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -5217,7 +5209,7 @@ def generate_image_sd3_controlnet(prompt, negative_prompt, init_image, controlne seed = int(seed) generator = torch.Generator(device).manual_seed(seed) - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -5317,7 +5309,7 @@ def generate_image_sd3_inpaint(prompt, negative_prompt, init_image, mask_image, seed = int(seed) generator = torch.Generator(device).manual_seed(seed) - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -5397,7 +5389,7 @@ def generate_image_cascade(prompt, negative_prompt, seed, stop_button, width, he seed = int(seed) generator = torch.Generator(device).manual_seed(seed) - def combined_callback_prior(i, t, callback_kwargs): + def combined_callback_prior(prior, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -5406,7 +5398,7 @@ def combined_callback_prior(i, t, callback_kwargs): progress((i + 1) / prior_steps, f"Step {i + 1}/{prior_steps}") return callback_kwargs - def combined_callback_decoder(i, t, callback_kwargs): + def combined_callback_decoder(decoder, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -5723,7 +5715,7 @@ def generate_riffusion_text2image(prompt, negative_prompt, seed, stop_button, nu pipe = StableDiffusionPipeline().StableDiffusionPipeline.from_pretrained(riffusion_model_path, torch_dtype=torch.float16) pipe = pipe.to(device) - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -5870,7 +5862,7 @@ def generate_image_kandinsky_txt2img(prompt, negative_prompt, version, seed, sto pipe = KandinskyPipeline().KandinskyPipeline.from_pretrained(os.path.join(kandinsky_model_path, "2-1")) pipe.to(device) - def combined_callback_2_1(i, t, callback_kwargs): + def combined_callback_2_1(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -5903,7 +5895,7 @@ def combined_callback_2_1(i, t, callback_kwargs): pipe = KandinskyV22Pipeline().KandinskyV22Pipeline.from_pretrained(os.path.join(kandinsky_model_path, "2-2-decoder")) pipe.to(device) - def combined_callback_2_2(i, t, callback_kwargs): + def combined_callback_2_2(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -5933,7 +5925,7 @@ def combined_callback_2_2(i, t, callback_kwargs): pipe.to(device) pipe.enable_model_cpu_offload() - def combined_callback_3_0(i, t, callback_kwargs): + def combined_callback_3_0(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -6042,7 +6034,7 @@ def generate_image_kandinsky_img2img(prompt, negative_prompt, init_image, versio init_image = Image.open(init_image).convert("RGB") init_image = init_image.resize((width, height)) - def combined_callback_2_1(i, t, callback_kwargs): + def combined_callback_2_1(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -6076,7 +6068,7 @@ def combined_callback_2_1(i, t, callback_kwargs): init_image = Image.open(init_image).convert("RGB") init_image = init_image.resize((width, height)) - def combined_callback_2_2(i, t, callback_kwargs): + def combined_callback_2_2(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -6107,7 +6099,7 @@ def combined_callback_2_2(i, t, callback_kwargs): init_image = Image.open(init_image).convert("RGB") init_image = init_image.resize((width, height)) - def combined_callback_3_0(i, t, callback_kwargs): + def combined_callback_3_0(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -6199,7 +6191,7 @@ def generate_image_kandinsky_inpaint(prompt, negative_prompt, init_image, mask_i mask_image = Image.open(mask_image).convert("L") mask_image = mask_image.resize((width, height)) - def combined_callback_2_1_and_2_2(i, t, callback_kwargs): + def combined_callback_2_1_and_2_2(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -6319,7 +6311,6 @@ def generate_image_flux_txt2img(prompt, model_name, quantize_model_name, enable_ pipe.vae = vae.to(device) pipe.enable_model_cpu_offload() - pipe.enable_sequential_cpu_offload() pipe.vae.enable_slicing() pipe.vae.enable_tiling() pipe.to(torch.float16) @@ -6363,8 +6354,11 @@ def combined_callback(pipe, i, t, callback_kwargs): return callback_kwargs + prompt_embeds, pooled_prompt_embeds = get_weighted_text_embeddings_flux1(pipe=pipe, prompt=prompt) + output = pipe( - prompt=prompt, + prompt_embeds=prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, guidance_scale=guidance_scale, height=height, width=width, @@ -6409,10 +6403,12 @@ def combined_callback(pipe, i, t, callback_kwargs): else: if 'pipe' in locals(): del pipe + if 'get_weighted_text_embeddings_flux1' in locals(): + del get_weighted_text_embeddings_flux1 flush() -def generate_image_flux_img2img(prompt, init_image, model_name, quantize_model_name, enable_quantize, seed, stop_button, num_inference_steps, strength, guidance_scale, width, height, max_sequence_length, output_format, progress=gr.Progress()): +def generate_image_flux_img2img(prompt, init_image, model_name, quantize_model_name, enable_quantize, seed, stop_button, vae_model_name, lora_model_names, lora_scales, num_inference_steps, strength, guidance_scale, width, height, max_sequence_length, output_format, progress=gr.Progress()): global stop_signal stop_signal = False stop_idx = None @@ -6432,11 +6428,11 @@ def generate_image_flux_img2img(prompt, init_image, model_name, quantize_model_n flux_model_path = os.path.join("inputs", "image", "flux", model_name) quantize_model_path = os.path.join("inputs", "image", "flux", "quantize-flux", quantize_model_name) - if enable_quantize: - if not quantize_model_name: - gr.Info("Please select a GGUF model!") - return None, None + if not quantize_model_name: + gr.Info("Please select a Flux safetensors/GGUF model!") + return None, None + if enable_quantize: params = { 'prompt': prompt, 'guidance_scale': guidance_scale, @@ -6452,7 +6448,7 @@ def generate_image_flux_img2img(prompt, init_image, model_name, quantize_model_n env = os.environ.copy() env['PYTHONPATH'] = os.pathsep.join(sys.path) - result = subprocess.run([sys.executable, 'inputs/image/quantize-flux/flux/flux-img2img-quantize.py', json.dumps(params)], + result = subprocess.run([sys.executable, 'inputs/image/flux/quantize-flux/flux-img2img-quantize.py', json.dumps(params)], capture_output=True, text=True) if result.returncode != 0: @@ -6481,14 +6477,46 @@ def generate_image_flux_img2img(prompt, init_image, model_name, quantize_model_n gr.Info(f"Flux {model_name} model downloaded") try: - pipe = FluxImg2ImgPipeline().FluxImg2ImgPipeline.from_pretrained(flux_model_path, - torch_dtype=torch.bfloat16) - pipe = pipe.to(device) + pipe = FluxImg2ImgPipeline().FluxImg2ImgPipeline.from_pretrained(flux_model_path, torch_dtype=torch.bfloat16) + + if vae_model_name is not None: + vae_model_path = os.path.join("inputs", "image", "flux", "flux-vae", f"{vae_model_name}") + if os.path.exists(vae_model_path): + vae = AutoencoderKL().AutoencoderKL.from_single_file(vae_model_path, torch_dtype=torch.bfloat16) + pipe.vae = vae.to(device) init_image = Image.open(init_image).convert("RGB") init_image = init_image.resize((width, height)) - def combined_callback(i, t, callback_kwargs): + if isinstance(lora_scales, str): + lora_scales = [float(scale.strip()) for scale in lora_scales.split(',') if scale.strip()] + elif isinstance(lora_scales, (int, float)): + lora_scales = [float(lora_scales)] + + lora_loaded = False + if lora_model_names and lora_scales: + if len(lora_model_names) != len(lora_scales): + gr.Warning( + f"Number of LoRA models ({len(lora_model_names)}) does not match number of scales ({len(lora_scales)}). Using available scales.", duration=5) + + for i, lora_model_name in enumerate(lora_model_names): + if i < len(lora_scales): + lora_scale = lora_scales[i] + else: + lora_scale = 1.0 + + lora_model_path = os.path.join("inputs", "image", "flux", "flux-lora", lora_model_name) + if os.path.exists(lora_model_path): + adapter_name = os.path.splitext(os.path.basename(lora_model_name))[0] + try: + pipe.load_lora_weights(lora_model_path, adapter_name=adapter_name) + pipe.fuse_lora(lora_scale=lora_scale) + lora_loaded = True + gr.Info(f"Loaded LoRA {lora_model_name} with scale {lora_scale}") + except Exception as e: + gr.Warning(f"Error loading LoRA {lora_model_name}: {str(e)}", duration=5) + + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -6573,7 +6601,7 @@ def generate_image_flux_inpaint(prompt, init_image, mask_image, model_name, seed init_image = Image.open(init_image).convert("RGB") mask_image = Image.open(mask_image).convert("L") - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -6667,7 +6695,7 @@ def generate_image_flux_controlnet(prompt, init_image, base_model_name, seed, st init_image = Image.open(init_image).convert("RGB") - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -6751,7 +6779,7 @@ def generate_image_hunyuandit_txt2img(prompt, negative_prompt, seed, stop_button pipe.to(device) pipe.transformer.enable_forward_chunking(chunk_size=1, dim=1) - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -6842,7 +6870,7 @@ def generate_image_hunyuandit_controlnet(prompt, negative_prompt, init_image, co init_image = Image.open(init_image).convert("RGB") init_image = init_image.resize((width, height)) - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -7328,7 +7356,7 @@ def generate_image_wurstchen(prompt, negative_prompt, seed, stop_button, width, prior_pipeline.prior = torch.compile(prior_pipeline.prior, mode="reduce-overhead", fullgraph=True) decoder_pipeline.decoder = torch.compile(decoder_pipeline.decoder, mode="reduce-overhead", fullgraph=True) - def combined_callback_prior(i, t, callback_kwargs): + def combined_callback_prior(prior_pipeline, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -7337,7 +7365,7 @@ def combined_callback_prior(i, t, callback_kwargs): progress((i + 1) / prior_steps, f"Step {i + 1}/{prior_steps}") return callback_kwargs - def combined_callback_decoder(i, t, callback_kwargs): + def combined_callback_decoder(decoder_pipeline, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -7451,7 +7479,7 @@ def generate_image_deepfloyd_txt2img(prompt, negative_prompt, seed, stop_button, pipe_i.text_encoder = torch.compile(pipe_i.text_encoder, mode="reduce-overhead", fullgraph=True) pipe_i.unet = torch.compile(pipe_i.unet, mode="reduce-overhead", fullgraph=True) - def combined_callback_pipe_i(i, t, callback_kwargs): + def combined_callback_pipe_i(pipe_i, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -7481,7 +7509,7 @@ def combined_callback_pipe_i(i, t, callback_kwargs): pipe_ii.enable_model_cpu_offload() pipe_ii.enable_sequential_cpu_offload() - def combined_callback_pipe_ii(i, t, callback_kwargs): + def combined_callback_pipe_ii(pipe_ii, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -7644,7 +7672,7 @@ def generate_image_deepfloyd_img2img(prompt, negative_prompt, init_image, seed, original_image = Image.open(init_image).convert("RGB") original_image = original_image.resize((width, height)) - def combined_callback_stage_1(i, t, callback_kwargs): + def combined_callback_stage_1(stage_1, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -7653,7 +7681,7 @@ def combined_callback_stage_1(i, t, callback_kwargs): progress((i + 1) / num_inference_steps, f"Step {i + 1}/{num_inference_steps}") return callback_kwargs - def combined_callback_stage_2(i, t, callback_kwargs): + def combined_callback_stage_2(stage_2, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -7807,7 +7835,7 @@ def generate_image_deepfloyd_inpaint(prompt, negative_prompt, init_image, mask_i original_image = Image.open(init_image).convert("RGB") mask_image = Image.open(mask_image).convert("RGB") - def combined_callback_stage_1(i, t, callback_kwargs): + def combined_callback_stage_1(stage_1, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -7816,7 +7844,7 @@ def combined_callback_stage_1(i, t, callback_kwargs): progress((i + 1) / num_inference_steps, f"Step {i + 1}/{num_inference_steps}") return callback_kwargs - def combined_callback_stage_2(i, t, callback_kwargs): + def combined_callback_stage_2(stage_2, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -7955,7 +7983,7 @@ def generate_image_pixart(prompt, negative_prompt, version, seed, stop_button, n pipe.enable_model_cpu_offload() - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -8212,7 +8240,7 @@ def generate_video_modelscope(prompt, negative_prompt, seed, stop_button, num_in pipe.enable_model_cpu_offload() pipe.enable_vae_slicing() - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -8322,7 +8350,7 @@ def generate_video_zeroscope2(prompt, video_to_enhance, seed, stop_button, stren frames.append(frame) cap.release() - def combined_callback_enhance(i, t, callback_kwargs): + def combined_callback_enhance(enhance_pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -8388,7 +8416,7 @@ def combined_callback_enhance(i, t, callback_kwargs): base_pipe.enable_vae_slicing() base_pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) - def combined_callback_base(i, t, callback_kwargs): + def combined_callback_base(base_pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -8457,7 +8485,7 @@ def generate_video_cogvideox_text2video(prompt, negative_prompt, cogvideox_versi pipe.vae.enable_slicing() pipe.vae.enable_tiling() - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -8543,7 +8571,7 @@ def generate_video_cogvideox_image2video(prompt, negative_prompt, init_image, se image = load_image(init_image) - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -8607,7 +8635,7 @@ def generate_video_cogvideox_video2video(prompt, negative_prompt, init_video, co input_video = load_video(init_video) - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -8672,7 +8700,7 @@ def generate_video_latte(prompt, negative_prompt, seed, stop_button, num_inferen pipe = LattePipeline().LattePipeline.from_pretrained(latte_model_path, torch_dtype=torch.float16).to(device) pipe.enable_model_cpu_offload() - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -9004,7 +9032,7 @@ def generate_stableaudio(prompt, negative_prompt, seed, stop_button, num_inferen pipe = StableAudioPipeline().StableAudioPipeline.from_pretrained(sa_model_path, torch_dtype=torch.float16) pipe = pipe.to(device) - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -9240,7 +9268,7 @@ def generate_audio_audioldm2(prompt, negative_prompt, model_name, seed, stop_but pipe = AudioLDM2Pipeline().AudioLDM2Pipeline.from_pretrained(model_path, torch_dtype=torch.float16) pipe = pipe.to(device) - def combined_callback(i, t, callback_kwargs): + def combined_callback(pipe, i, t, callback_kwargs): nonlocal stop_idx if stop_signal and stop_idx is None: stop_idx = i @@ -10597,7 +10625,6 @@ def create_footer(): additional_inputs_accordion=gr.Accordion(label=_("StableDiffusion Settings", lang), open=False), outputs=[ gr.Gallery(label=_("Generated images", lang), elem_id="gallery", columns=[2], rows=[2], object_fit="contain", height="auto"), - gr.Gallery(label=_("Generation process", lang), elem_id="process_gallery", columns=[2], rows=[2], object_fit="contain", height="auto"), gr.Textbox(label=_("Message", lang), type="text") ], title=_("NeuroSandboxWebUI - StableDiffusion (img2img)", lang), @@ -11114,6 +11141,7 @@ def create_footer(): inputs=[ gr.Textbox(label=_("Enter your prompt", lang)), gr.Textbox(label=_("Enter your negative prompt", lang), value=""), + gr.Radio(choices=["Diffusers", "Safetensors"], label=_("Select model type", lang), value="Diffusers"), gr.Dropdown(choices=stable_diffusion_models_list, label=_("Select StableDiffusion model", lang), value=None), gr.Checkbox(label=_("Enable Quantize", lang), value=False), gr.Textbox(label=_("Seed (optional)", lang), value=""), @@ -11529,7 +11557,7 @@ def create_footer(): fn=generate_image_flux_txt2img, inputs=[ gr.Textbox(label=_("Enter your prompt", lang)), - gr.Dropdown(choices=["FLUX.1-schnell", "FLUX.1-dev"], label=_("Select Flux model", lang), value="FLUX.1-schnell"), + gr.Radio(choices=["FLUX.1-schnell", "FLUX.1-dev"], label=_("Select model type", lang), value="FLUX.1-schnell"), gr.Dropdown(choices=quantized_flux_models_list, label=_("Select safetensors Flux model (GGUF if enabled quantize)", lang), value=None), gr.Checkbox(label=_("Enable Quantize", lang), value=False), gr.Textbox(label=_("Seed (optional)", lang), value=""), @@ -11576,6 +11604,10 @@ def create_footer(): gr.Button(value=_("Stop generation", lang), interactive=True, variant="stop") ], additional_inputs=[ + gr.Dropdown(choices=vae_models_list, label=_("Select VAE model (optional)", lang), value=None), + gr.Dropdown(choices=flux_lora_models_list, label=_("Select LORA models (optional)", lang), value=None, + multiselect=True), + gr.Textbox(label=_("LoRA Scales", lang)), gr.Slider(minimum=1, maximum=100, value=4, step=1, label=_("Steps", lang)), gr.Slider(minimum=0.0, maximum=1.0, value=0.95, step=0.01, label=_("Strength", lang)), gr.Slider(minimum=0.0, maximum=10.0, value=0.0, step=0.1, label=_("Guidance Scale", lang)), @@ -12984,7 +13016,7 @@ def create_footer(): inpaint_interface.input_components[10].click(stop_generation, [], [], queue=False) cascade_interface.input_components[3].click(stop_generation, [], [], queue=False) riffusion_text2image_interface.input_components[3].click(stop_generation, [], [], queue=False) - sd3_txt2img_interface.input_components[5].click(stop_generation, [], [], queue=False) + sd3_txt2img_interface.input_components[6].click(stop_generation, [], [], queue=False) sd3_img2img_interface.input_components[7].click(stop_generation, [], [], queue=False) sd3_controlnet_interface.input_components[5].click(stop_generation, [], [], queue=False) sd3_inpaint_interface.input_components[5].click(stop_generation, [], [], queue=False) @@ -13039,12 +13071,14 @@ def create_footer(): sd3_txt2img_interface.input_components[7], sd3_txt2img_interface.input_components[8], sd3_img2img_interface.input_components[5], - flux_img2img_interface.input_components[3], t2i_ip_adapter_interface.input_components[4], ip_adapter_faceid_interface.input_components[5], flux_txt2img_interface.input_components[2], flux_txt2img_interface.input_components[6], flux_txt2img_interface.input_components[7], + flux_img2img_interface.input_components[3], + flux_img2img_interface.input_components[7], + flux_img2img_interface.input_components[8], auraflow_interface.input_components[3], kolors_txt2img_interface.input_components[3], rvc_interface.input_components[1], diff --git a/RequirementsFiles/requirements.txt b/RequirementsFiles/requirements.txt index 6edfc44d..fdf9d500 100644 --- a/RequirementsFiles/requirements.txt +++ b/RequirementsFiles/requirements.txt @@ -389,4 +389,27 @@ gekko==1.2.1 rouge==1.0.1 cramjam==2.8.4 fastparquet==2024.5.0 -optimum==1.22.0 \ No newline at end of file +optimum==1.22.0 +asttokens==2.4.1 +comm==0.2.2 +debugpy==1.8.7 +executing==2.1.0 +ipykernel==6.29.5 +ipython==8.28.0 +ipywidgets==8.1.5 +jedi==0.19.1 +jupyter-client==8.6.3 +jupyter-core==5.7.2 +jupyterlab-widgets==3.0.13 +lark==1.2.2 +matplotlib-inline==0.1.7 +nest-asyncio==1.6.0 +optimum-quanto==0.2.5 +parso==0.8.4 +prompt-toolkit==3.0.48 +pure-eval==0.2.3 +pyzmq==26.2.0 +stack-data==0.6.3 +tornado==6.4.1 +traitlets==5.14.3 +widgetsnbextension==4.0.13 \ No newline at end of file diff --git a/Update.bat b/Update.bat index 292871e2..7e133d6d 100644 --- a/Update.bat +++ b/Update.bat @@ -34,6 +34,7 @@ pip install --no-build-isolation -e git+https://github.com/turboderp/exllamav2.g pip install git+https://github.com/tencent-ailab/IP-Adapter.git 2>> %ERROR_LOG% pip install git+https://github.com/vork/PyNanoInstantMeshes.git 2>> %ERROR_LOG% pip install git+https://github.com/openai/CLIP.git 2>> %ERROR_LOG% +pip install git+https://github.com/xhinker/sd_embed.git@main 2>> %ERROR_LOG% timeout /t 3 /nobreak >nul cls diff --git a/Update.sh b/Update.sh index 0fa00bd9..def4e0d0 100644 --- a/Update.sh +++ b/Update.sh @@ -33,6 +33,7 @@ pip install --no-build-isolation -e git+https://github.com/turboderp/exllamav2.g pip install git+https://github.com/tencent-ailab/IP-Adapter.git 2>> "$ERROR_LOG" pip install git+https://github.com/vork/PyNanoInstantMeshes.git 2>> "$ERROR_LOG" pip install git+https://github.com/openai/CLIP.git 2>> "$ERROR_LOG" +pip install git+https://github.com/xhinker/sd_embed.git@main 2>> "$ERROR_LOG" sleep 3 clear From 5022dedbbac9c04980700edbade1ee4eaafb4a10 Mon Sep 17 00:00:00 2001 From: Michael Date: Thu, 17 Oct 2024 21:59:12 +0300 Subject: [PATCH 2/2] Update app.py --- LaunchFile/app.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LaunchFile/app.py b/LaunchFile/app.py index 11875377..1aa32165 100644 --- a/LaunchFile/app.py +++ b/LaunchFile/app.py @@ -1351,7 +1351,7 @@ def image_to_base64_data_uri(image_path): else: if llm_model_type == "Llama": - tokenizer, llm_model = load_model(llm_model_name, llm_model_type, n_ctx, n_batch, n_ubatch, freq_base, freq_scale) + tokenizer, llm_model, * _ = load_model(llm_model_name, llm_model_type, n_ctx, n_batch, n_ubatch, freq_base, freq_scale) elif llm_model_type == "ExLlamaV2": cache, tokenizer, llm_model, * _ = load_model(llm_model_name, llm_model_type, n_ctx=None, n_batch=None, n_ubatch=None, freq_base=None, freq_scale=None) else: