Upload scripts/calculate_timestep_weighing_flex.py with huggingface_hub

scripts/calculate_timestep_weighing_flex.py

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+import gc
+import os, sys
+from tqdm import tqdm
+import numpy as np
+import json
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# set visible devices to 0
+# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+# protect from formatting
+if True:
+    import torch
+    from optimum.quanto import freeze, qfloat8, QTensor, qint4
+    from diffusers import FluxTransformer2DModel, FluxPipeline, AutoencoderKL, FlowMatchEulerDiscreteScheduler
+    from toolkit.util.quantize import quantize, get_qtype
+    from transformers import T5EncoderModel, T5TokenizerFast, CLIPTextModel, CLIPTokenizer
+    from torchvision import transforms
+
+qtype = "qfloat8"
+dtype = torch.bfloat16
+# base_model_path = "black-forest-labs/FLUX.1-dev"
+base_model_path = "ostris/Flex.1-alpha"
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Loading Transformer...")
+prompt = "Photo of a man and a woman in a park, sunny day"
+
+output_root = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "output")
+output_path = os.path.join(output_root, "flex_timestep_weights.json")
+img_output_path = os.path.join(output_root, "flex_timestep_weights.png")
+
+quantization_type = get_qtype(qtype)
+
+def flush():
+    torch.cuda.empty_cache()
+    gc.collect()
+    
+pil_to_tensor = transforms.ToTensor()
+    
+with torch.no_grad():
+    transformer = FluxTransformer2DModel.from_pretrained(
+        base_model_path,
+        subfolder='transformer',
+        torch_dtype=dtype
+    )
+
+    transformer.to(device, dtype=dtype)
+
+    print("Quantizing Transformer...")
+    quantize(transformer, weights=quantization_type)
+    freeze(transformer)
+    flush()
+
+    print("Loading Scheduler...")
+    scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(base_model_path, subfolder="scheduler")
+
+    print("Loading Autoencoder...")
+    vae = AutoencoderKL.from_pretrained(base_model_path, subfolder="vae", torch_dtype=dtype)
+
+    vae.to(device, dtype=dtype)
+
+    flush()
+    print("Loading Text Encoder...")
+    tokenizer_2 = T5TokenizerFast.from_pretrained(base_model_path, subfolder="tokenizer_2", torch_dtype=dtype)
+    text_encoder_2 = T5EncoderModel.from_pretrained(base_model_path, subfolder="text_encoder_2", torch_dtype=dtype)
+    text_encoder_2.to(device, dtype=dtype)
+
+    print("Quantizing Text Encoder...")
+    quantize(text_encoder_2, weights=get_qtype(qtype))
+    freeze(text_encoder_2)
+    flush()
+
+    print("Loading CLIP")
+    text_encoder = CLIPTextModel.from_pretrained(base_model_path, subfolder="text_encoder", torch_dtype=dtype)
+    tokenizer = CLIPTokenizer.from_pretrained(base_model_path, subfolder="tokenizer", torch_dtype=dtype)
+    text_encoder.to(device, dtype=dtype)
+
+    print("Making pipe")
+                
+    pipe: FluxPipeline = FluxPipeline(
+        scheduler=scheduler,
+        text_encoder=text_encoder,
+        tokenizer=tokenizer,
+        text_encoder_2=None,
+        tokenizer_2=tokenizer_2,
+        vae=vae,
+        transformer=None,
+    )
+    pipe.text_encoder_2 = text_encoder_2
+    pipe.transformer = transformer
+    
+    pipe.to(device, dtype=dtype)
+    
+    print("Encoding prompt...")
+    
+    prompt_embeds, pooled_prompt_embeds, text_ids = pipe.encode_prompt(
+        prompt,
+        prompt_2=prompt,
+        device=device
+    )
+    
+    
+    generator = torch.manual_seed(42)
+    
+    height = 1024
+    width = 1024
+    
+    print("Generating image...")
+    
+    # Fix a bug in diffusers/torch
+    def callback_on_step_end(pipe, i, t, callback_kwargs):
+        latents = callback_kwargs["latents"]
+        if latents.dtype != dtype:
+            latents = latents.to(dtype)
+        return {"latents": latents}
+    img = pipe(
+        prompt_embeds=prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        height=height,
+        width=height,
+        num_inference_steps=30,
+        guidance_scale=3.5,
+        generator=generator,
+        callback_on_step_end=callback_on_step_end,
+    ).images[0]
+    
+    img.save(img_output_path)
+    print(f"Image saved to {img_output_path}")
+    
+    print("Encoding image...")
+    # img is a PIL image. convert it to a -1 to 1 tensor
+    img = pil_to_tensor(img)
+    img = img.unsqueeze(0)  # add batch dimension
+    img = img * 2 - 1  # convert to -1 to 1 range
+    img = img.to(device, dtype=dtype)
+    latents = vae.encode(img).latent_dist.sample()
+    
+    shift = vae.config['shift_factor'] if vae.config['shift_factor'] is not None else 0
+    latents = vae.config['scaling_factor'] * (latents - shift)
+    
+    num_channels_latents = pipe.transformer.config.in_channels // 4
+    
+    l_height = 2 * (int(height) // (pipe.vae_scale_factor * 2))
+    l_width = 2 * (int(width) // (pipe.vae_scale_factor * 2))
+    packed_latents = pipe._pack_latents(latents, 1, num_channels_latents, l_height, l_width)
+    
+    packed_latents, latent_image_ids = pipe.prepare_latents(
+        1,
+        num_channels_latents,
+        height,
+        width,
+        prompt_embeds.dtype,
+        device,
+        generator,
+        packed_latents,
+    )
+    
+    print("Calculating timestep weights...")
+    
+    torch.manual_seed(8675309)
+    noise = torch.randn_like(packed_latents, device=device, dtype=dtype)
+    
+    # Create linear timesteps from 1000 to 0
+    num_train_timesteps = 1000
+    timesteps_torch = torch.linspace(1000, 1, num_train_timesteps, device='cpu')
+    timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
+    timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
+    
+    timestep_weights = torch.zeros(num_train_timesteps, dtype=torch.float32, device=device)
+    
+    guidance = torch.full([1], 1.0, device=device, dtype=torch.float32)
+    guidance = guidance.expand(latents.shape[0])
+    
+    pbar = tqdm(range(num_train_timesteps), desc="loss: 0.000000 scaler: 0.0000")
+    for i in pbar:
+        timestep = timesteps[i:i+1].to(device)
+        t_01 = (timestep / 1000).to(device)
+        t_01 = t_01.reshape(-1, 1, 1)
+        noisy_latents = (1.0 - t_01) * packed_latents + t_01 * noise
+        
+        noise_pred = pipe.transformer(
+            hidden_states=noisy_latents, # torch.Size([1, 4096, 64])
+            timestep=timestep / 1000,
+            guidance=guidance,
+            pooled_projections=pooled_prompt_embeds,
+            encoder_hidden_states=prompt_embeds,
+            txt_ids=text_ids,
+            img_ids=latent_image_ids,
+            return_dict=False,
+        )[0]
+        
+        target = noise - packed_latents
+        
+        loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float())
+        loss = loss
+        
+        # determine scaler to multiply loss by to make it 1
+        scaler = 1.0 / (loss + 1e-6)
+        
+        timestep_weights[i] = scaler
+        pbar.set_description(f"loss: {loss.item():.6f} scaler: {scaler.item():.4f}")
+        
+    print("normalizing timestep weights...")
+    # normalize the timestep weights so they are a mean of 1.0
+    timestep_weights = timestep_weights / timestep_weights.mean()
+    timestep_weights = timestep_weights.cpu().numpy().tolist()
+    
+    print("Saving timestep weights...")
+    
+    with open(output_path, 'w') as f:
+        json.dump(timestep_weights, f)
+        
+
+print(f"Timestep weights saved to {output_path}")
+print("Done!")
+flush()
+        
+        
+    
+    
+    
+    
+    
+    
+    
+    
+    
+