A sample prompt of what you can find in this page
Prompt by 9c36b583024

the background is a gradient of blue and gray prompts

very few results

4 months ago

digital painting, semi-realistic anime-influenced style, soft brush strokes, clean lineless art, ethereal atmosphere, dreamy aesthetic, minimalist details, simplified forms, smooth color transitions, krenz cushart inspired, kuvshinov ilya influence, wlop style, soft light diffusion, high contrast lighting, stylized semi-realism, polished render, painterly technique with clean edges, elegant simplification, detailed facial features with simplified body, commercial illustration quality, magazine editorial style, professional digital painting, subtle gradient shading, dreamy pastel color palette, minimalist color scheme, high-key lighting, gentle color gradients, selective saturation, bright luminous atmosphere, clear summer day colors, cerulean blue and white dominant palette, soft contrast, airy aesthetic, clean highlights, soft ethereal lighting, powder blue sky, azure ocean, fashion painting of a 24 year old Burmese-Nahua female supermodel with relatable charm. Her body is Firm, toned triceps with a sleek contour, toned muscles, abs, small breast, flat chest. She has a pale porcelain skin with subtle pink undertones Her Large diamond-cut eyes, Classic, grey-blue eyes, framed by long, elegant eyelashes. Her haircut is Wild, teased style with streaks of gray and dark blue. platinum blonde hair. Her eyebrows are Defined and sharp shaped, complementing her delicate facial features. She wears a crisp white, cropped mock neck top, midriff, faded Monogram print lowrise shorts. Her navel is pierced with Double Gem Navel Piercing. She is posed in the image frame on the left side by the rule of thirds. She poses in Mid-motion flipping collar of jacket, eyes locked on camera. Background scene is Tropical island beach with volcanic backdrop.

2 months ago

# Keeps 589 bright, boosts jewelry shine, replaces background, and maps to Casinofi duotone. import cv2, numpy as np from google.colab import files from PIL import Image # Upload your image when prompted up = files.upload() fn = list(up.keys())[0] img_bgr = cv2.imdecode(np.frombuffer(up[fn], np.uint8), cv2.IMREAD_COLOR) # --- Palette (BGR) --- HEX = lambda h: (int(h[5:7],16), int(h[3:5],16), int(h[1:3],16)) SHADOW = np.array(HEX("#0F1011"), np.float32) MID = np.array(HEX("#8E7A55"), np.float32) HILITE = np.array(HEX("#E6D2A1"), np.float32) HILITE_PLUS = np.array(HEX("#EBDDB7"), np.float32) # extra-bright cream for 589 # --- Helper: gradient map (shadow -> mid -> highlight) --- def gradient_map(gray01): g = gray01[...,None] t1 = np.clip(g/0.5, 0, 1) t2 = np.clip((g-0.5)/0.5, 0, 1) low = SHADOW*(1-t1) + MID*t1 high = MID*(1-t2) + HILITE*t2 return np.where(g<=0.5, low, high) hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV) # --- Masks --- # Background (yellow) range bg_mask = cv2.inRange(hsv, (15, 120, 120), (40, 255, 255)) # tune if needed # Shirt (blue) range – helpful for separate contrast if you want shirt_mask = cv2.inRange(hsv, (95, 80, 40), (130, 255, 255)) # Numbers “589” (white-ish areas on shirt) gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) num_mask = cv2.threshold(gray, 210, 255, cv2.THRESH_BINARY)[1] # bright white # Jewelry (gold/yellow highlights) jew_mask = cv2.inRange(hsv, (12, 60, 120), (30, 255, 255)) # gold tones # Clean masks a bit def clean(m, k=3): kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k,k)) m = cv2.morphologyEx(m, cv2.MORPH_OPEN, kernel, iterations=1) m = cv2.morphologyEx(m, cv2.MORPH_CLOSE, kernel, iterations=1) return m bg_mask = clean(bg_mask, 5) shirt_mask= clean(shirt_mask, 5) num_mask = clean(num_mask, 3) jew_mask = clean(jew_mask, 3) # --- Step 1: Replace background with deep charcoal --- out = img_bgr.copy() out[bg_mask>0] = SHADOW # --- Step 2: Convert subject to Casinofi duotone --- # Work on non-background regions subj = out.copy() subj_mask = (bg_mask==0).astype(np.uint8)*255 subj_gray = cv2.cvtColor(subj, cv2.COLOR_BGR2GRAY).astype(np.float32)/255.0 mapped = gradient_map(subj_gray).astype(np.uint8) mapped = cv2.bitwise_and(mapped, mapped, mask=subj_mask) bg_area = cv2.bitwise_and(out, out, mask=bg_mask) out = cv2.add(mapped, bg_area) # --- Step 3: Boost numbers “589” to brighter cream and keep edges crisp --- num_rgb = np.zeros_like(out, dtype=np.uint8) num_rgb[:] = HILITE_PLUS num_layer = cv2.bitwise_and(num_rgb, num_rgb, mask=num_mask) out = cv2.bitwise_and(out, out, mask=cv2.bitwise_not(num_mask)) out = cv2.add(out, num_layer) # Optional: thin dark stroke around numbers edges = cv2.Canny(num_mask, 50, 150) stroke = cv2.dilate(edges, np.ones((2,2), np.uint8), iterations=1) out[stroke>0] = (out[stroke>0]*0 + SHADOW*0.9).astype(np.uint8) # --- Step 4: Jewelry shine (Screen-like brighten in cream) --- # Create a cream layer and blend additively where jewelry mask is j_layer = np.zeros_like(out, dtype=np.float32) j_layer[:] = HILITE j_mask_f = (jew_mask.astype(np.float32)/255.0)[...,None] out_f = out.astype(np.float32) out = np.clip(out_f + j_layer*0.35*j_mask_f, 0, 255).astype(np.uint8) # --- Step 5: Gentle contrast pop on subject only --- subj_mask3 = cv2.merge([subj_mask, subj_mask, subj_mask]) subj_pix = np.where(subj_mask3>0) sub = out.astype(np.float32) sub[subj_pix] = np.clip((sub[subj_pix]-20)*1.08 + 20, 0, 255) out = sub.astype(np.uint8) # Save cv2.imwrite("output_casinofi.png", out) files.download("output_casinofi.png") print("Done. Download output_casinofi.png")

17 days ago

Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.