{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-lucidrains--big-sleep","slug":"lucidrains--big-sleep","name":"big-sleep","type":"cli","url":"https://github.com/lucidrains/big-sleep","page_url":"https://unfragile.ai/lucidrains--big-sleep","categories":["image-generation"],"tags":["artificial-intelligence","deep-learning","generative-adversarial-networks","multimodality","text-to-image"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-lucidrains--big-sleep__cap_0","uri":"capability://image.visual.clip.guided.iterative.latent.space.optimization.for.text.to.image.generation","name":"clip-guided iterative latent space optimization for text-to-image generation","description":"Generates images from text prompts by iteratively optimizing BigGAN latent vectors using CLIP embeddings as a guidance signal. The system encodes text prompts into CLIP embeddings, generates candidate images from BigGAN, computes cosine similarity between text and image embeddings, and backpropagates gradients through the latent space to maximize alignment. Uses exponential moving average (EMA) smoothing on BigGAN parameters to stabilize the optimization trajectory and prevent mode collapse.","intents":["Generate photorealistic or artistic images from natural language descriptions without fine-tuning","Explore the latent space of pre-trained generative models guided by semantic text similarity","Create variations of images by iteratively refining latent vectors based on CLIP guidance"],"best_for":["Researchers experimenting with vision-language model guidance techniques","Artists and creators prototyping text-to-image workflows without GPU-intensive training","Developers building local-first generative AI tools that don't require cloud API calls"],"limitations":["Optimization is slow (~minutes per image) compared to diffusion-based models; requires 50-300+ iterations depending on prompt complexity","Image quality is bounded by BigGAN's pre-trained architecture (max 512x512 resolution); cannot generate arbitrary object categories outside BigGAN's training distribution","CLIP similarity metric does not always correlate with human perceptual quality; can produce artifacts that maximize cosine similarity but lack semantic coherence","Requires significant GPU memory (8GB+ VRAM) for simultaneous CLIP and BigGAN inference; no built-in memory optimization for smaller devices"],"requires":["Python 3.7+","PyTorch 1.9+ with CUDA support (CPU inference is impractically slow)","8GB+ GPU VRAM (tested on NVIDIA GPUs; AMD/Apple Silicon support limited)","Pre-trained BigGAN weights (auto-downloaded on first run, ~350MB)","Pre-trained CLIP model weights (auto-downloaded, ~350MB for ViT-B/32)"],"input_types":["text (natural language prompt)","text (optional negative prompts via text_min parameter)","integer (class index for BigGAN conditioning, optional)"],"output_types":["PIL Image (RGB, 128x128/256x256/512x512 depending on model)","PNG file (saved to disk with configurable path)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--big-sleep__cap_1","uri":"capability://image.visual.multi.prompt.weighted.optimization.with.text.penalty.terms","name":"multi-prompt weighted optimization with text penalty terms","description":"Enables simultaneous optimization toward multiple text prompts with configurable weights and negative prompts. The system computes separate CLIP embeddings for each positive and negative prompt, combines them into a weighted loss function where positive prompts maximize similarity and negative prompts minimize it, and performs joint gradient descent on the combined objective. Supports both additive weighting and multiplicative scaling of individual prompt contributions.","intents":["Generate images matching multiple semantic concepts simultaneously (e.g., 'a red car AND a blue sky')","Steer generation away from unwanted visual elements using negative prompts (e.g., avoid 'blurry' or 'low quality')","Fine-tune image generation by adjusting the relative importance of different textual constraints"],"best_for":["Creative professionals needing fine-grained control over multi-concept image composition","Researchers studying how vision-language models combine multiple semantic constraints","Developers building interactive image generation tools with real-time prompt refinement"],"limitations":["Conflicting prompts can produce incoherent results; no automatic conflict detection or resolution","Negative prompts are less effective than positive ones due to asymmetric CLIP loss landscape; requires careful weight tuning","Computational cost scales linearly with number of prompts (each prompt requires separate CLIP encoding and gradient computation)","No built-in mechanism to balance multiple objectives; requires manual weight tuning via trial-and-error"],"requires":["Python 3.7+","PyTorch 1.9+","8GB+ GPU VRAM","text parameter (string or list of strings)","text_min parameter for negative prompts (optional, default empty string)"],"input_types":["text (primary prompt as string)","text (list of prompts as strings, joined internally)","text (negative prompts via text_min parameter)","float (weight parameter for each prompt, optional)"],"output_types":["PIL Image (RGB, resolution depends on BigGAN model)","PNG file"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--big-sleep__cap_2","uri":"capability://image.visual.differentiable.top.k.class.embedding.selection.for.biggan.conditioning","name":"differentiable top-k class embedding selection for biggan conditioning","description":"Implements a learnable mechanism to select the most relevant BigGAN class embeddings from the full class vocabulary using differentiable top-k selection. The Latents class maintains trainable parameters for class logits, applies softmax to create a probability distribution over classes, and uses straight-through estimators or Gumbel-softmax tricks to enable gradient flow through discrete class selection. This allows the optimization process to discover which semantic classes best align with the text prompt without explicit class specification.","intents":["Automatically discover which BigGAN object classes best match a text description without manual class index specification","Enable end-to-end differentiable optimization over both latent vectors and class embeddings","Generate images that blend multiple semantic classes when appropriate for the text prompt"],"best_for":["Researchers studying how generative models select from discrete class vocabularies","Users who want fully automatic class discovery without knowing BigGAN's class taxonomy","Systems requiring end-to-end differentiable image generation pipelines"],"limitations":["Top-k selection is non-differentiable; implementation uses approximations (straight-through estimators) that may have gradient flow issues","BigGAN's class vocabulary is fixed at training time; cannot generate objects outside the 1000 ImageNet classes","Softmax over 1000 classes adds computational overhead (~5-10% per iteration) compared to fixed class conditioning","Learned class embeddings may converge to suboptimal local minima if text prompt is ambiguous across multiple classes"],"requires":["Python 3.7+","PyTorch 1.9+ (requires autograd support for straight-through estimators)","8GB+ GPU VRAM","BigGAN model loaded and initialized"],"input_types":["text (prompt used to guide class selection indirectly through CLIP loss)","integer (optional: fixed class index to override learned selection)"],"output_types":["tensor (learned class logits, shape [1, 1000])","tensor (normalized class probabilities after softmax)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--big-sleep__cap_3","uri":"capability://image.visual.exponential.moving.average.ema.parameter.smoothing.for.stable.optimization","name":"exponential moving average (ema) parameter smoothing for stable optimization","description":"Applies exponential moving average smoothing to BigGAN parameters during the optimization process to stabilize training and prevent divergence. The Model class maintains both the original BigGAN weights and an EMA-smoothed copy; during each optimization step, the EMA weights are updated as a weighted average of previous EMA weights and current weights (with decay factor typically 0.99). The forward pass uses EMA-smoothed weights instead of raw weights, reducing high-frequency noise in the gradient signal and enabling longer optimization runs without mode collapse.","intents":["Stabilize iterative optimization of frozen pre-trained BigGAN weights without fine-tuning","Reduce visual artifacts and flickering that occur when directly optimizing latent vectors against a frozen generator","Enable longer optimization runs (100+ iterations) without divergence or quality degradation"],"best_for":["Researchers studying optimization dynamics of frozen pre-trained generative models","Systems requiring stable, long-running image generation without manual intervention","Applications where visual consistency across iterations is critical"],"limitations":["EMA smoothing introduces lag between optimization steps and visual updates; may slow convergence to final image","Decay factor (default 0.99) is a hyperparameter that requires tuning for different prompt complexities","EMA smoothing adds ~5-10% computational overhead per iteration due to parameter copying and averaging","Does not prevent mode collapse entirely; only reduces its likelihood compared to unsmoothed optimization"],"requires":["Python 3.7+","PyTorch 1.9+","BigGAN model with EMA wrapper initialized","decay parameter (float, typically 0.99)"],"input_types":["tensor (BigGAN weights)","float (EMA decay factor, default 0.99)"],"output_types":["tensor (EMA-smoothed weights, same shape as input)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--big-sleep__cap_4","uri":"capability://image.visual.adaptive.image.resampling.and.augmentation.during.optimization","name":"adaptive image resampling and augmentation during optimization","description":"Applies differentiable image transformations (resizing, cropping, rotation, color jittering) to generated images during the optimization loop to improve CLIP alignment and reduce overfitting to specific image statistics. The system generates images at the native BigGAN resolution, applies random augmentations, encodes augmented images through CLIP, and backpropagates gradients through both the augmentation pipeline and the latent vectors. This encourages the optimization to find latent vectors that produce images robust to transformations, improving generalization.","intents":["Improve CLIP-image alignment by training on augmented image views rather than single fixed images","Reduce overfitting to specific image statistics and encourage more robust visual features","Enable multi-scale optimization by resampling images to different resolutions during training"],"best_for":["Researchers studying data augmentation effects on vision-language model guidance","Systems requiring robust image generation that generalizes across viewing conditions","Applications where image quality consistency across different scales is important"],"limitations":["Augmentation adds ~10-20% computational overhead per iteration due to additional image processing","Random augmentations introduce stochasticity; same prompt may produce slightly different results across runs","Aggressive augmentation (large crops, rotations) can degrade final image quality if augmentation distribution diverges too far from natural images","Augmentation parameters (crop size, rotation angle, color jitter magnitude) require manual tuning"],"requires":["Python 3.7+","PyTorch 1.9+","torchvision library for image augmentation transforms","8GB+ GPU VRAM"],"input_types":["tensor (generated image from BigGAN, shape [1, 3, H, W])","dict (augmentation parameters: crop_size, rotation_angle, color_jitter_magnitude)"],"output_types":["tensor (augmented image, shape [1, 3, H, W])","tensor (CLIP embedding of augmented image)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--big-sleep__cap_5","uri":"capability://automation.workflow.command.line.interface.with.real.time.progress.tracking.and.image.saving","name":"command-line interface with real-time progress tracking and image saving","description":"Provides a CLI entry point (dream command) that wraps the Imagine class with progress bars, iteration logging, and automatic image saving. The CLI parses command-line arguments (text prompt, output path, iteration count, learning rate, etc.), instantiates an Imagine object with the parsed configuration, runs the optimization loop with tqdm progress bars showing iteration count and loss values, and saves the final image to disk with optional intermediate checkpoints. Supports both single-image generation and batch processing of multiple prompts.","intents":["Generate images from text prompts without writing Python code","Monitor optimization progress in real-time with loss curves and iteration counts","Batch-generate multiple images with different prompts in a single command"],"best_for":["Non-technical users and artists who prefer command-line interfaces","Batch processing workflows that generate many images unattended","Integration with shell scripts and automation pipelines"],"limitations":["CLI argument parsing is basic; complex configurations require editing Python code or config files","No interactive prompt refinement; must restart generation for each new prompt","Progress bars and logging output can be verbose; no quiet mode for production deployments","Batch processing requires manual loop over prompts; no built-in parallelization across multiple GPUs"],"requires":["Python 3.7+","big-sleep package installed (pip install big-sleep)","CUDA-capable GPU with 8GB+ VRAM","tqdm library for progress bars"],"input_types":["string (command-line argument: text prompt)","string (command-line argument: output file path)","integer (command-line argument: number of iterations)","float (command-line argument: learning rate)"],"output_types":["PNG file (saved to disk)","console output (progress bars and loss values)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--big-sleep__cap_6","uri":"capability://image.visual.configurable.clip.model.selection.and.image.encoding","name":"configurable clip model selection and image encoding","description":"Supports multiple pre-trained CLIP model variants (ViT-B/32, ViT-L/14) with automatic model loading and caching. The CLIP wrapper loads the specified model from OpenAI's model zoo, caches weights locally to avoid re-downloading, encodes text prompts into embeddings using the text encoder, and encodes generated images using the image encoder. Both encoders output normalized embeddings in the same vector space, enabling cosine similarity computation. The system automatically selects the appropriate model based on available GPU memory and desired quality/speed tradeoff.","intents":["Choose between different CLIP models with different speed/quality tradeoffs (ViT-B/32 is faster, ViT-L/14 is higher quality)","Leverage different CLIP variants trained on different data distributions for domain-specific image generation","Customize the vision-language model used to guide image generation"],"best_for":["Researchers experimenting with different CLIP variants and their effects on image generation","Systems with limited GPU memory that need to use smaller CLIP models","Applications requiring high-quality CLIP embeddings for precise semantic alignment"],"limitations":["Only supports OpenAI CLIP models; no support for alternative vision-language models (BLIP, LLaVA, etc.)","ViT-L/14 requires 10GB+ VRAM; cannot run on smaller GPUs alongside BigGAN","CLIP model weights are large (~350MB each); first run requires downloading and caching","No fine-tuning support; must use pre-trained weights as-is"],"requires":["Python 3.7+","PyTorch 1.9+","clip library (pip install clip-by-openai or git clone from OpenAI)","8GB+ GPU VRAM for ViT-B/32, 10GB+ for ViT-L/14","Internet connection for first-time model download"],"input_types":["string (CLIP model name: 'ViT-B/32' or 'ViT-L/14')","string (text prompt)","tensor (image from BigGAN, shape [1, 3, H, W])"],"output_types":["tensor (text embedding, shape [1, 512] for ViT-B/32 or [1, 768] for ViT-L/14)","tensor (image embedding, same shape as text embedding)","float (cosine similarity between text and image embeddings)"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--big-sleep__cap_7","uri":"capability://image.visual.learnable.latent.vector.initialization.and.optimization.with.gradient.descent","name":"learnable latent vector initialization and optimization with gradient descent","description":"Maintains trainable latent vectors (z) and class embeddings that are optimized via gradient descent to maximize CLIP text-image similarity. The Latents class initializes latent vectors from a normal distribution, wraps them in nn.Parameter to make them trainable, and exposes them to PyTorch's autograd system. During each optimization step, the system computes the CLIP loss (negative cosine similarity), backpropagates gradients through CLIP and BigGAN to the latent vectors, and updates them using an optimizer (typically Adam) with a configurable learning rate. The optimization loop runs for a fixed number of iterations or until convergence.","intents":["Iteratively refine latent vectors to maximize alignment between generated images and text prompts","Explore the latent space of BigGAN by following gradients in the direction of increasing CLIP similarity","Generate multiple diverse images by running optimization from different random initializations"],"best_for":["Researchers studying latent space optimization and gradient-based image generation","Artists exploring the latent space of pre-trained generative models","Systems requiring fine-grained control over the optimization process"],"limitations":["Optimization is slow (minutes per image) compared to feed-forward models; requires 50-300+ iterations","Convergence depends on initialization; poor initializations may get stuck in local minima","Learning rate is a critical hyperparameter; too high causes instability, too low causes slow convergence","No built-in convergence detection; requires manual specification of iteration count"],"requires":["Python 3.7+","PyTorch 1.9+ with autograd support","8GB+ GPU VRAM","Adam optimizer or similar (built into PyTorch)","learning_rate parameter (float, typically 0.05-0.1)"],"input_types":["integer (latent dimension, typically 120 for BigGAN)","integer (number of optimization iterations)","float (learning rate for Adam optimizer)"],"output_types":["tensor (optimized latent vectors, shape [1, 120])","PIL Image (final generated image)","list of floats (loss values per iteration)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lucidrains--big-sleep__cap_8","uri":"capability://image.visual.normalized.biggan.output.with.configurable.image.resolution","name":"normalized biggan output with configurable image resolution","description":"Wraps BigGAN to normalize its output to [-1, 1] range and supports multiple output resolutions (128x128, 256x256, 512x512). The Model class loads the appropriate pre-trained BigGAN checkpoint based on the desired resolution, applies normalization to the raw BigGAN output (which is typically in [-1, 1] or [0, 1] range depending on the model), and optionally applies post-processing (e.g., clipping, scaling) to ensure valid image ranges. The system automatically selects the correct BigGAN variant based on the resolution parameter.","intents":["Generate images at different resolutions depending on quality/speed requirements","Ensure consistent image normalization across different BigGAN model variants","Support both low-resolution fast generation (128x128) and high-resolution quality generation (512x512)"],"best_for":["Applications requiring flexible output resolutions","Systems with varying GPU memory constraints that need to trade off resolution for speed","Researchers studying how resolution affects CLIP-guided generation quality"],"limitations":["BigGAN is limited to 512x512 maximum resolution; cannot generate higher resolutions","Higher resolutions require more GPU memory (512x512 requires 10GB+ VRAM)","BigGAN was trained on ImageNet; quality degrades for out-of-distribution concepts","No super-resolution or upsampling; output is limited to native BigGAN resolution"],"requires":["Python 3.7+","PyTorch 1.9+","Pre-trained BigGAN weights (auto-downloaded, ~350MB per resolution)","GPU VRAM: 8GB for 128x128, 10GB+ for 256x256/512x512"],"input_types":["integer (resolution: 128, 256, or 512)","tensor (latent vectors, shape [1, 120])","tensor (class embeddings, shape [1, 1000])"],"output_types":["tensor (normalized image, shape [1, 3, H, W] where H=W=resolution, values in [-1, 1])","PIL Image (RGB image, values in [0, 255])"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ with CUDA support (CPU inference is impractically slow)","8GB+ GPU VRAM (tested on NVIDIA GPUs; AMD/Apple Silicon support limited)","Pre-trained BigGAN weights (auto-downloaded on first run, ~350MB)","Pre-trained CLIP model weights (auto-downloaded, ~350MB for ViT-B/32)","PyTorch 1.9+","8GB+ GPU VRAM","text parameter (string or list of strings)","text_min parameter for negative prompts (optional, default empty string)","PyTorch 1.9+ (requires autograd support for straight-through estimators)"],"failure_modes":["Optimization is slow (~minutes per image) compared to diffusion-based models; requires 50-300+ iterations depending on prompt complexity","Image quality is bounded by BigGAN's pre-trained architecture (max 512x512 resolution); cannot generate arbitrary object categories outside BigGAN's training distribution","CLIP similarity metric does not always correlate with human perceptual quality; can produce artifacts that maximize cosine similarity but lack semantic coherence","Requires significant GPU memory (8GB+ VRAM) for simultaneous CLIP and BigGAN inference; no built-in memory optimization for smaller devices","Conflicting prompts can produce incoherent results; no automatic conflict detection or resolution","Negative prompts are less effective than positive ones due to asymmetric CLIP loss landscape; requires careful weight tuning","Computational cost scales linearly with number of prompts (each prompt requires separate CLIP encoding and gradient computation)","No built-in mechanism to balance multiple objectives; requires manual weight tuning via trial-and-error","Top-k selection is non-differentiable; implementation uses approximations (straight-through estimators) that may have gradient flow issues","BigGAN's class vocabulary is fixed at training time; cannot generate objects outside the 1000 ImageNet classes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5256354541906955,"quality":0.43,"ecosystem":0.55,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.061Z","last_scraped_at":"2026-05-03T13:58:44.860Z","last_commit":"2022-02-06T18:04:34Z"},"community":{"stars":2568,"forks":301,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lucidrains--big-sleep","compare_url":"https://unfragile.ai/compare?artifact=lucidrains--big-sleep"}},"signature":"8cVJ3O2cGfljD4wyMmB8DfUlhAm3/ybQK4wI0tvoUFXeXAa4civJrWs3MeBSTPkxy8BwH2bipMdZ6O8YDnH5AA==","signedAt":"2026-06-22T01:21:32.684Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lucidrains--big-sleep","artifact":"https://unfragile.ai/lucidrains--big-sleep","verify":"https://unfragile.ai/api/v1/verify?slug=lucidrains--big-sleep","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}