Create app_wrong_deepseek.py

app_wrong_deepseek.py

@@ -0,0 +1,318 @@
+# не запускается, ошибка с кешем, отложил пока
+
+# это файл только с LoRA, без ControlNet и IpAdapter
+
+import gradio as gr
+import numpy as np
+import random
+
+# import spaces #[uncomment to use ZeroGPU]
+from diffusers import DiffusionPipeline
+import torch
+
+from peft import PeftModel, LoraConfig
+import os
+
+# Добавляем глобальный кэш для пайплайнов
+pipe_cache = {}
+
+def get_lora_sd_pipeline(
+    ckpt_dir='./lora_logos', 
+    base_model_name_or_path=None, 
+    dtype=torch.float16, 
+    adapter_name="default"
+    ):
+    
+    unet_sub_dir = os.path.join(ckpt_dir, "unet")
+    text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
+    
+    if os.path.exists(text_encoder_sub_dir) and base_model_name_or_path is None:
+        config = LoraConfig.from_pretrained(text_encoder_sub_dir)
+        base_model_name_or_path = config.base_model_name_or_path
+    
+    if base_model_name_or_path is None:
+        raise ValueError("Please specify the base model name or path")
+    
+    pipe = DiffusionPipeline.from_pretrained(base_model_name_or_path, torch_dtype=dtype)
+    before_params = pipe.unet.parameters()
+#    pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
+    # Исправляем загрузку конфигурации
+    config = LoraConfig.from_pretrained(unet_sub_dir)
+    
+    pipe.unet = PeftModel.from_pretrained(
+        pipe.unet, 
+        unet_sub_dir, 
+        adapter_name=adapter_name,
+        config=config  # Явно передаем конфигурацию
+    )
+    
+    pipe.unet.set_adapter(adapter_name)
+    after_params = pipe.unet.parameters()
+    print("UNet Parameters changed:", any(torch.any(b != a) for b, a in zip(before_params, after_params)))
+    
+    if os.path.exists(text_encoder_sub_dir):
+        pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)
+    
+    if dtype in (torch.float16, torch.bfloat16):
+        pipe.unet.half()
+        if pipe.text_encoder is not None:
+            pipe.text_encoder.half()
+    
+    return pipe
+
+def process_prompt(prompt, tokenizer, text_encoder, max_length=77):
+    tokens = tokenizer(prompt, truncation=False, return_tensors="pt")["input_ids"]
+    chunks = [tokens[:, i:i + max_length] for i in range(0, tokens.shape[1], max_length)]
+    
+    with torch.no_grad():
+        embeds = [text_encoder(chunk.to(text_encoder.device))[0] for chunk in chunks]
+    
+    return torch.cat(embeds, dim=1)
+
+def align_embeddings(prompt_embeds, negative_prompt_embeds):
+    max_length = max(prompt_embeds.shape[1], negative_prompt_embeds.shape[1])
+    return torch.nn.functional.pad(prompt_embeds, (0, 0, 0, max_length - prompt_embeds.shape[1])), \
+           torch.nn.functional.pad(negative_prompt_embeds, (0, 0, 0, max_length - negative_prompt_embeds.shape[1]))
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+#model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+model_id_default = "sd-legacy/stable-diffusion-v1-5"
+model_dropdown = ['stabilityai/sdxl-turbo', 'CompVis/stable-diffusion-v1-4', 'sd-legacy/stable-diffusion-v1-5'  ]
+
+model_lora_default = "lora_pussinboots_logos"
+model_lora_dropdown = ['lora_lady_and_cats_logos', 'lora_pussinboots_logos'  ]
+
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+# pipe = pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+
+# @spaces.GPU #[uncomment to use ZeroGPU]
+def infer(
+    prompt,
+    negative_prompt,
+    randomize_seed,
+    width=512,
+    height=512,
+    model_repo_id=model_id_default,
+    seed=42,
+    guidance_scale=7,
+    num_inference_steps=20,
+    model_lora_id=model_lora_default,
+    lora_scale=0.5,
+    progress=gr.Progress(track_tqdm=True),
+    ):
+        
+    global pipe_cache
+ 
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(seed)
+
+    # Кэширование пайплайнов
+    cache_key = f"{model_repo_id}_{model_lora_id}"
+    if cache_key not in pipe_cache:
+        if model_repo_id != model_id_default:
+            pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype).to(device)
+            prompt_embeds = process_prompt(prompt, pipe.tokenizer, pipe.text_encoder)
+            negative_prompt_embeds = process_prompt(negative_prompt, pipe.tokenizer, pipe.text_encoder)
+            prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
+        else:
+            pipe = get_lora_sd_pipeline(
+                ckpt_dir='./'+model_lora_id,
+                base_model_name_or_path=model_id_default,
+                dtype=torch_dtype
+            ).to(device)
+        
+        pipe_cache[cache_key] = pipe
+    else:
+        pipe = pipe_cache[cache_key]
+
+    # Динамическое применение масштаба LoRA
+    if model_repo_id == model_id_default:
+        # Убираем fuse_lora()
+        # pipe.fuse_lora(lora_scale=lora_scale)  # Закомментировали проблемную строку
+        
+        # Вместо этого устанавливаем адаптеры динамически
+        pipe.unet.set_adapters(
+            [model_lora_id], 
+            adapter_weights=[lora_scale]
+        )
+        if hasattr(pipe, 'text_encoder') and pipe.text_encoder is not None:
+            pipe.text_encoder.set_adapters(
+                [model_lora_id], 
+                adapter_weights=[lora_scale]
+            )
+        
+        print(f"Active adapters - UNet: {pipe.unet.active_adapters}, Text Encoder: {pipe.text_encoder.active_adapters if hasattr(pipe, 'text_encoder') else None}")
+        print("UNet first layer weights:", pipe.unet.base_model.model[0].weight.data[0,0,:5])
+        print(f"LoRA scale applied: {lora_scale}")
+    
+    
+    # на вызов pipe с эмбеддингами
+    params = {
+        'prompt_embeds': prompt_embeds,
+        'negative_prompt_embeds': negative_prompt_embeds,
+        'guidance_scale': guidance_scale,
+        'num_inference_steps': num_inference_steps,
+        'width': width,
+        'height': height,
+        'generator': generator,
+    }
+    
+    return pipe(**params).images[0], seed    
+
+    # return image, seed
+
+
+examples = [
+    "Puss in Boots wearing a sombrero crosses the Grand Canyon on a tightrope with a guitar.",
+    "A cat is playing a song called ""About the Cat"" on an accordion by the sea at sunset. The sun is quickly setting behind the horizon, and the light is fading.",
+    "A cat walks through the grass on the streets of an abandoned city. The camera view is always focused on the cat's face.",
+    "A young lady in a Russian embroidered kaftan is sitting on a beautiful carved veranda, holding a cup to her mouth and drinking tea from the cup. With her other hand, the girl holds a saucer. The cup and saucer are painted with gzhel. Next to the girl on the table stands a samovar, and steam can be seen above it.",
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 640px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(" # Text-to-Image SemaSci Template")
+
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+
+            run_button = gr.Button("Run", scale=0, variant="primary")
+
+        result = gr.Image(label="Result", show_label=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+#            model_repo_id = gr.Text(
+#                label="Model Id",
+#                max_lines=1,
+#                placeholder="Choose model",
+#                visible=True,
+#                value=model_repo_id,
+#            )
+            model_repo_id = gr.Dropdown(
+                label="Model Id",
+                choices=model_dropdown,
+                info="Choose model",
+                visible=True,
+                allow_custom_value=True,
+#                value=model_repo_id,
+                value=model_id_default,
+            )            
+            
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=True,
+            )
+
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+
+            with gr.Row():
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=256,  # Replace with defaults that work for your model
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=256,  # Replace with defaults that work for your model
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=7.0,  # Replace with defaults that work for your model
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=20,  # Replace with defaults that work for your model
+                )
+                
+            with gr.Row():
+                model_lora_id = gr.Dropdown(
+                    label="Lora Id",
+                    choices=model_lora_dropdown,
+                    info="Choose LoRA model",
+                    visible=True,
+                    allow_custom_value=True,
+                    value=model_lora_default,
+                )            
+            
+                lora_scale = gr.Slider(
+                    label="LoRA scale",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.5,
+                )                
+
+        gr.Examples(examples=examples, inputs=[prompt])
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            prompt,
+            negative_prompt,
+            randomize_seed,
+            width,
+            height,
+            model_repo_id,
+            seed,
+            guidance_scale,
+            num_inference_steps,
+            model_lora_id,
+            lora_scale,
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()