Sample the text-to-image model

dewml/hybrid-dit-176m is a 176M-parameter text-to-image model at 256×256, trained with Dew. Edit the prompt, the seed, the steps or the sampler and press Run.

sample.py, editableRun

from dew.sampling import CFG, DPMSolverMultistep, TextToImage

pipe = TextToImage.from_pretrained("dewml/hybrid-dit-176m", revision="4b322022189373fd9086313241269aa5cea92b86")
prompt = "the northern lights over a frozen lake at night, vivid colors, dramatic lighting, highly detailed"
negative = ("letterbox, white border, black border, frame, text, watermark, collage, blurry, lowres, "
            "low quality, dull colors, washed out, low contrast, grainy")
inputs = pipe.prepare([prompt], key=3, steps=15, unconditional=negative)
result = pipe(
    inputs,
    key=3,
    steps=15,
    solver=DPMSolverMultistep(),
    guidance=CFG(6.0, interval=(0.15, 0.9)),
)
result.pil()[0]

from dew.sampling import CFG, DPMSolverMultistep, TextToImage pipe = TextToImage.from_pretrained("dewml/hybrid-dit-176m", revision="4b322022189373fd9086313241269aa5cea92b86") prompt = "the northern lights over a frozen lake at night, vivid colors, dramatic lighting, highly detailed" negative = ("letterbox, white border, black border, frame, text, watermark, collage, blurry, lowres, " "low quality, dull colors, washed out, low contrast, grainy") inputs = pipe.prepare([prompt], key=3, steps=15, unconditional=negative) result = pipe( inputs, key=3, steps=15, solver=DPMSolverMultistep(), guidance=CFG(6.0, interval=(0.15, 0.9)), ) result.pil()[0]

Run sends the cell's Dew calls to a process on the host that already holds the model. The same cell runs as is with Dew installed.

Run opens an isolated Python context backed by the shared Dew model on a 4-vCPU CPU host.

Recorded on a Colab A100-SXM4-40GB with Dew ff6c4dc on 2026-10-16, in 102 s.

Or open it in Colab to run it on a GPU and change anything.