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--- |
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library_name: diffusers |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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SAVETENSORS VERSION >>> https://civitai.com/models/130664 |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Abdallah Alswaiti |
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- **Model type:** SDXL |
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## Uses |
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## Using with Diffusers for low GPU |
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### 🧨 Diffusers |
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> [!IMPORTANT] |
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> Make sure to upgrade diffusers to >= 0.28.0: |
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> ```bash |
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> pip install -U diffusers --upgrade |
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> ``` |
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> In addition make sure to install `transformers`, `safetensors`, `sentencepiece`,`gc` and `accelerate`: |
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> ``` |
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> pip install transformers accelerate safetensors sentencepiece gc |
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```python |
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import torch |
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from diffusers import StableDiffusionXLPipeline |
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import gc |
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from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection |
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# from onediffx import compile_pipe, save_pipe, load_pipe |
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from PIL import Image |
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def encode_prompt(prompts, tokenizers, text_encoders): |
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embeddings_list = [] |
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for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): |
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cond_input = tokenizer( |
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prompt, |
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max_length=tokenizer.model_max_length, |
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padding='max_length', |
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truncation=True, |
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return_tensors='pt', |
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) |
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prompt_embeds = text_encoder(cond_input.input_ids.to('cuda'), output_hidden_states=True) |
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pooled_prompt_embeds = prompt_embeds[0] |
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embeddings_list.append(prompt_embeds.hidden_states[-2]) |
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prompt_embeds = torch.concat(embeddings_list, dim=-1) |
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negative_prompt_embeds = torch.zeros_like(prompt_embeds) |
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negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) |
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bs_embed, seq_len, _ = prompt_embeds.shape |
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prompt_embeds = prompt_embeds.repeat(1, 1, 1).view(bs_embed * 1, seq_len, -1) |
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seq_len = negative_prompt_embeds.shape[1] |
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negative_prompt_embeds = negative_prompt_embeds.repeat(1, 1, 1).view(1 * 1, seq_len, -1) |
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pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, 1).view(bs_embed * 1, -1) |
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negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, 1).view(bs_embed * 1, -1) |
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return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds |
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def get_user_input(): |
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prompt = input("Enter prompt: ") or '3/4 shot, candid photograph of a beautiful 30 year old redhead woman with messy dark hair, peacefully sleeping in her bed, night, dark, light from window, dark shadows, masterpiece, uhd, moody' |
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negative_prompt = input("Enter negative prompt: ") or "" |
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cfg_scale = float(input("Enter CFG scale (default 7.5): ") or 7.5) |
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steps = int(input("Enter number of steps (default 50): ") or 50) |
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width = int(input("Enter image width (default 512): ") or 512) |
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height = int(input("Enter image height (default 512): ") or 512) |
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seed = int(input("Enter seed (default 42): ") or 42) |
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return prompt, negative_prompt, cfg_scale, steps, width, height, seed |
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pipe = StableDiffusionXLPipeline.from_pretrained('ABDALLALSWAITI/DAVINCI-DIFF', use_safetensors=True, torch_dtype=torch.float16, tokenizer=None, text_encoder=None, tokenizer_2=None, text_encoder_2=None).to('cuda') |
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# pipe = compile_pipe(pipe) |
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# load_pipe(pipe, dir="cached_pipe") |
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while True: |
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queue = [] |
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prompt, negative_prompt, cfg_scale, steps, width, height, seed = get_user_input() |
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queue.append({ |
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'prompt': prompt, |
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'negative_prompt': negative_prompt, |
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'cfg_scale': cfg_scale, |
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'steps': steps, |
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'width': width, |
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'height': height, |
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'seed': seed, |
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}) |
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tokenizer = CLIPTokenizer.from_pretrained('ABDALLALSWAITI/DAVINCI-DIFF', subfolder='tokenizer') |
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text_encoder = CLIPTextModel.from_pretrained('ABDALLALSWAITI/DAVINCI-DIFF', subfolder='text_encoder', use_safetensors=True, torch_dtype=torch.float16).to('cuda') |
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tokenizer_2 = CLIPTokenizer.from_pretrained('ABDALLALSWAITI/DAVINCI-DIFF', subfolder='tokenizer_2') |
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text_encoder_2 = CLIPTextModelWithProjection.from_pretrained('ABDALLALSWAITI/DAVINCI-DIFF', subfolder='text_encoder_2', use_safetensors=True, torch_dtype=torch.float16).to('cuda') |
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with torch.no_grad(): |
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for generation in queue: |
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generation['embeddings'] = encode_prompt( |
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[generation['prompt'], generation['prompt']], |
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[tokenizer, tokenizer_2], |
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[text_encoder, text_encoder_2], |
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) |
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del tokenizer, text_encoder, tokenizer_2, text_encoder_2 |
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gc.collect() |
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torch.cuda.empty_cache() |
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generator = torch.Generator(device='cuda') |
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for i, generation in enumerate(queue, start=1): |
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generator.manual_seed(generation['seed']) |
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generation['latents'] = pipe( |
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prompt_embeds=generation['embeddings'][0], |
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negative_prompt_embeds=generation['embeddings'][1], |
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pooled_prompt_embeds=generation['embeddings'][2], |
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negative_pooled_prompt_embeds=generation['embeddings'][3], |
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generator=generator, |
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output_type='latent', |
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guidance_scale=generation['cfg_scale'], |
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num_inference_steps=generation['steps'], |
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height=generation['height'], |
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width=generation['width'] |
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).images |
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del pipe.unet |
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gc.collect() |
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torch.cuda.empty_cache() |
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# load_pipe(pipe, dir="cached_pipe") |
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pipe.upcast_vae() |
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with torch.no_grad(): |
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for i, generation in enumerate(queue, start=1): |
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generation['latents'] = generation['latents'].to(next(iter(pipe.vae.post_quant_conv.parameters())).dtype) |
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image = pipe.vae.decode(generation['latents'] / pipe.vae.config.scaling_factor, return_dict=False)[0] |
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image = pipe.image_processor.postprocess(image, output_type='pil')[0] |
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image_path = f'image_{i}.png' |
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image.save(image_path) |
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print(f"Image saved at: {image_path}") |
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# save_pipe(pipe, dir="cached_pipe") |
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if input("Do you want to create another image? (y/n): ").lower() != 'y': |
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break |
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``` |
